path: root/reports/stats/unknown_papers.csv

610a4451423ad7f82916c736cd8adb86a5a64c59,A Survey on Search Based Face Annotation Using Weakly Labelled Facial Images,"Volume 4, Issue 11, November 2014                                  ISSN: 2277 128X
International Journal of Advanced Research in
Computer Science and Software Engineering
Research Paper
Available online at: www.ijarcsse.com
A Survey on Search Based Face Annotation Using Weakly
Labelled Facial Images
Shital A. Shinde*, Prof. Archana Chaugule
Department of Computer Engg, DYPIET Pimpri,
Savitri Bai Phule Pune University, Maharashtra India"
6180bc0816b1776ca4b32ced8ea45c3c9ce56b47,Fast Randomized Algorithms for Convex Optimization and Statistical Estimation,"Fast Randomized Algorithms for Convex Optimization and
Statistical Estimation
Mert Pilanci
Electrical Engineering and Computer Sciences
University of California at Berkeley
Technical Report No. UCB/EECS-2016-147
http://www.eecs.berkeley.edu/Pubs/TechRpts/2016/EECS-2016-147.html
August 14, 2016"
61f04606528ecf4a42b49e8ac2add2e9f92c0def,Deep Deformation Network for Object Landmark Localization,"Deep Deformation Network for Object Landmark
Localization
Xiang Yu, Feng Zhou and Manmohan Chandraker
NEC Laboratories America, Department of Media Analytics"
614a7c42aae8946c7ad4c36b53290860f6256441,Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks,"Joint Face Detection and Alignment using
Multi-task Cascaded Convolutional Networks
Kaipeng Zhang, Zhanpeng Zhang, Zhifeng Li, Senior Member, IEEE, and Yu Qiao, Senior Member, IEEE"
0d88ab0250748410a1bc990b67ab2efb370ade5d,Error handling in multimodal biometric systems using reliability measures,"Author(s) :
ERROR HANDLING IN MULTIMODAL BIOMETRIC SYSTEMS USING
RELIABILITY MEASURES  (ThuPmOR6)
(EPFL, Switzerland)
(EPFL, Switzerland)
(EPFL, Switzerland)
(EPFL, Switzerland)
Krzysztof Kryszczuk
Jonas Richiardi
Plamen Prodanov
Andrzej Drygajlo"
0d538084f664b4b7c0e11899d08da31aead87c32,Deformable Part Descriptors for Fine-Grained Recognition and Attribute Prediction,"Deformable Part Descriptors for
Fine-grained Recognition and Attribute Prediction
Ning Zhang1
Ryan Farrell1,2
Forrest Iandola1
ICSI / UC Berkeley 2Brigham Young University
Trevor Darrell1"
0dccc881cb9b474186a01fd60eb3a3e061fa6546,Effective face frontalization in unconstrained images,"Effective Face Frontalization in Unconstrained Images
Tal Hassner1, Shai Harel1 †, Eran Paz1 † and Roee Enbar2
The open University of Israel. 2Adience.
Figure 1: Frontalized faces. Top: Input photos; bottom: our frontalizations,
obtained without estimating 3D facial shapes.
“Frontalization” is the process of synthesizing frontal facing views of faces
ppearing in single unconstrained photos. Recent reports have suggested
that this process may substantially boost the performance of face recogni-
tion systems. This, by transforming the challenging problem of recognizing
faces viewed from unconstrained viewpoints to the easier problem of rec-
ognizing faces in constrained, forward facing poses. Previous frontalization
methods did this by attempting to approximate 3D facial shapes for each
query image. We observe that 3D face shape estimation from unconstrained
photos may be a harder problem than frontalization and can potentially in-
troduce facial misalignments. Instead, we explore the simpler approach of
using a single, unmodified, 3D surface as an approximation to the shape of
ll input faces. We show that this leads to a straightforward, efficient and
easy to implement method for frontalization. More importantly, it produces
esthetic new frontal views and is surprisingly effective when used for face
recognition and gender estimation."
0d6b28691e1aa2a17ffaa98b9b38ac3140fb3306,Review of Perceptual Resemblance of Local Plastic Surgery Facial Images using Near Sets,"Review of Perceptual Resemblance of Local
Plastic Surgery Facial Images using Near Sets
Prachi V. Wagde1, Roshni Khedgaonkar2
,2 Department of Computer Technology,
YCCE Nagpur, India"
0d3882b22da23497e5de8b7750b71f3a4b0aac6b,Context is routinely encoded during emotion perception.,"Research Article
Context Is Routinely Encoded
During Emotion Perception
1(4) 595 –599
© The Author(s) 2010
Reprints and permission:
sagepub.com/journalsPermissions.nav
DOI: 10.1177/0956797610363547
http://pss.sagepub.com
Lisa Feldman Barrett1,2,3 and Elizabeth A. Kensinger1,3
Boston College; 2Psychiatric Neuroimaging Program, Massachusetts General Hospital, Harvard Medical School; and 3Athinoula A. Martinos
Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School"
0d760e7d762fa449737ad51431f3ff938d6803fe,LCDet: Low-Complexity Fully-Convolutional Neural Networks for Object Detection in Embedded Systems,"LCDet: Low-Complexity Fully-Convolutional Neural Networks for
Object Detection in Embedded Systems
Subarna Tripathi
UC San Diego ∗
Gokce Dane
Qualcomm Inc.
Byeongkeun Kang
UC San Diego
Vasudev Bhaskaran
Qualcomm Inc.
Truong Nguyen
UC San Diego"
0dd72887465046b0f8fc655793c6eaaac9c03a3d,Real-Time Head Orientation from a Monocular Camera Using Deep Neural Network,"Real-time Head Orientation from a Monocular
Camera using Deep Neural Network
Byungtae Ahn, Jaesik Park, and In So Kweon
KAIST, Republic of Korea"
0d33b6c8b4d1a3cb6d669b4b8c11c2a54c203d1a,Detection and Tracking of Faces in Videos: A Review of Related Work,"Detection and Tracking of Faces in Videos: A Review
© 2016 IJEDR | Volume 4, Issue 2 | ISSN: 2321-9939
of Related Work
Seema Saini, 2 Parminder Sandal
Student, 2Assistant Professor
, 2Dept. of Electronics & Comm., S S I E T, Punjab, India
________________________________________________________________________________________________________"
0da4c3d898ca2fff9e549d18f513f4898e960aca,The Headscarf Effect Revisited: Further Evidence for a Culture-Based Internal Face Processing Advantage.,"Wang, Y., Thomas, J., Weissgerber, S. C., Kazemini, S., Ul-Haq, I., &
Quadflieg, S. (2015). The Headscarf Effect Revisited: Further Evidence for a
36. 10.1068/p7940
Peer reviewed version
Link to published version (if available):
0.1068/p7940
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956317de62bd3024d4ea5a62effe8d6623a64e53,Lighting Analysis and Texture Modification of 3D Human Face Scans,"Lighting Analysis and Texture Modification of 3D Human
Face Scans
Author
Zhang, Paul, Zhao, Sanqiang, Gao, Yongsheng
Published
Conference Title
Digital Image Computing Techniques and Applications
https://doi.org/10.1109/DICTA.2007.4426825
Copyright Statement
© 2007 IEEE. Personal use of this material is permitted. However, permission to reprint/
republish this material for advertising or promotional purposes or for creating new collective
works for resale or redistribution to servers or lists, or to reuse any copyrighted component of
this work in other works must be obtained from the IEEE.
Downloaded from
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Link to published version
http://www.ieee.org/
Griffith Research Online
https://research-repository.griffith.edu.au"
959bcb16afdf303c34a8bfc11e9fcc9d40d76b1c,Temporal Coherency based Criteria for Predicting Video Frames using Deep Multi-stage Generative Adversarial Networks,"Temporal Coherency based Criteria for Predicting
Video Frames using Deep Multi-stage Generative
Adversarial Networks
Prateep Bhattacharjee1, Sukhendu Das2
Visualization and Perception Laboratory
Department of Computer Science and Engineering
Indian Institute of Technology Madras, Chennai, India"
951f21a5671a4cd14b1ef1728dfe305bda72366f,Use of l2/3-norm Sparse Representation for Facial Expression Recognition,"International Journal of Science and Research (IJSR)
ISSN (Online): 2319-7064
Impact Factor (2012): 3.358
Use of ℓ2/3-norm Sparse Representation for Facial
Expression Recognition
Sandeep Rangari1, Sandeep Gonnade2
MATS University, MATS School of Engineering and Technology, Arang, Raipur, India
MATS University, MATS School of Engineering and Technology, Arang, Raipur, India
three
to  discriminate
represents  emotion,"
9547a7bce2b85ef159b2d7c1b73dea82827a449f,Facial expression recognition using Gabor motion energy filters,"Facial Expression Recognition Using Gabor Motion Energy Filters
Tingfan Wu
Dept. Computer Science Engineering
UC San Diego
Marian S. Bartlett
Javier R. Movellan
Institute for Neural Computation
UC San Diego"
9513503867b29b10223f17c86e47034371b6eb4f,Comparison of Optimisation Algorithms for Deformable Template Matching,"Comparison of optimisation algorithms for
deformable template matching
Vasileios Zografos
Link¨oping University, Computer Vision Laboratory
ISY, SE-581 83 Link¨oping, SWEDEN"
956c634343e49319a5e3cba4f2bd2360bdcbc075,A novel incremental principal component analysis and its application for face recognition,"IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART B: CYBERNETICS, VOL. 36, NO. 4, AUGUST 2006
A Novel Incremental Principal Component Analysis
nd Its Application for Face Recognition
Haitao Zhao, Pong Chi Yuen, Member, IEEE, and James T. Kwok, Member, IEEE"
95ea564bd983129ddb5535a6741e72bb1162c779,Multi-Task Learning by Deep Collaboration and Application in Facial Landmark Detection,"Multi-Task Learning by Deep Collaboration and
Application in Facial Landmark Detection
Ludovic Trottier
Philippe Giguère
Brahim Chaib-draa
Laval University, Québec, Canada"
958c599a6f01678513849637bec5dc5dba592394,Generalized Zero-Shot Learning for Action Recognition with Web-Scale Video Data,"Noname manuscript No.
(will be inserted by the editor)
Generalized Zero-Shot Learning for Action
Recognition with Web-Scale Video Data
Kun Liu · Wu Liu · Huadong Ma ·
Wenbing Huang · Xiongxiong Dong
Received: date / Accepted: date"
59fc69b3bc4759eef1347161e1248e886702f8f7,Final Report of Final Year Project HKU-Face : A Large Scale Dataset for Deep Face Recognition,"Final Report of Final Year Project
HKU-Face: A Large Scale Dataset for
Deep Face Recognition
Haoyu Li
035141841
COMP4801 Final Year Project
Project Code: 17007"
59bfeac0635d3f1f4891106ae0262b81841b06e4,Face Verification Using the LARK Face Representation,"Face Verification Using the LARK Face
Representation
Hae Jong Seo, Student Member, IEEE, Peyman Milanfar, Fellow, IEEE,"
59efb1ac77c59abc8613830787d767100387c680,DIF : Dataset of Intoxicated Faces for Drunk Person Identification,"DIF : Dataset of Intoxicated Faces for Drunk Person
Identification
Devendra Pratap Yadav
Indian Institute of Technology Ropar
Abhinav Dhall
Indian Institute of Technology Ropar"
59eefa01c067a33a0b9bad31c882e2710748ea24,Fast Landmark Localization with 3D Component Reconstruction and CNN for Cross-Pose Recognition,"IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
Fast Landmark Localization
with 3D Component Reconstruction and CNN for
Cross-Pose Recognition
Gee-Sern (Jison) Hsu, Hung-Cheng Shie, Cheng-Hua Hsieh"
59d225486161b43b7bf6919b4a4b4113eb50f039,Complex Event Recognition from Images with Few Training Examples,"Complex Event Recognition from Images with Few Training Examples
Unaiza Ahsan∗
Chen Sun∗∗
James Hays∗
Irfan Essa∗
*Georgia Institute of Technology
**University of Southern California1"
5945464d47549e8dcaec37ad41471aa70001907f,Every Moment Counts: Dense Detailed Labeling of Actions in Complex Videos,"Noname manuscript No.
(will be inserted by the editor)
Every Moment Counts: Dense Detailed Labeling of Actions in Complex
Videos
Serena Yeung · Olga Russakovsky · Ning Jin · Mykhaylo Andriluka · Greg Mori ·
Li Fei-Fei
Received: date / Accepted: date"
59c9d416f7b3d33141cc94567925a447d0662d80,Matrix factorization over max-times algebra for data mining,"Universität des Saarlandes
Max-Planck-Institut für Informatik
Matrix factorization over max-times
lgebra for data mining
Masterarbeit im Fach Informatik
Master’s Thesis in Computer Science
von / by
Sanjar Karaev
ngefertigt unter der Leitung von / supervised by
Dr. Pauli Miettinen
egutachtet von / reviewers
Dr. Pauli Miettinen
Prof. Gerhard Weikum
November 2013
UNIVERSITASSARAVIENSIS"
59a35b63cf845ebf0ba31c290423e24eb822d245,The FaceSketchID System: Matching Facial Composites to Mugshots,"The FaceSketchID System: Matching Facial
Composites to Mugshots
Scott J. Klum, Student Member, IEEE, Hu Han, Member, IEEE, Brendan F. Klare, Member, IEEE,
nd Anil K. Jain, Fellow, IEEE
tedious, and may not"
59f325e63f21b95d2b4e2700c461f0136aecc171,Kernel sparse representation with local patterns for face recognition,"978-1-4577-1302-6/11/$26.00 ©2011 IEEE
FOR FACE RECOGNITION
. INTRODUCTION"
59031a35b0727925f8c47c3b2194224323489d68,Sparse Variation Dictionary Learning for Face Recognition with a Single Training Sample per Person,"Sparse Variation Dictionary Learning for Face Recognition with A Single
Training Sample Per Person
Meng Yang, Luc Van Gool
ETH Zurich
Switzerland"
926c67a611824bc5ba67db11db9c05626e79de96,Enhancing Bilinear Subspace Learning by Element Rearrangement,"Enhancing Bilinear Subspace Learning
y Element Rearrangement
Dong Xu, Shuicheng Yan, Stephen Lin,
Thomas S. Huang, and
Shih-Fu Chang"
923ede53b0842619831e94c7150e0fc4104e62f7,Masked correlation filters for partially occluded face recognition,"978-1-4799-9988-0/16/$31.00 ©2016 IEEE
ICASSP 2016"
92b61b09d2eed4937058d0f9494d9efeddc39002,BoxCars: Improving Vehicle Fine-Grained Recognition using 3D Bounding Boxes in Traffic Surveillance,"Under review in IJCV manuscript No.
(will be inserted by the editor)
BoxCars: Improving Vehicle Fine-Grained Recognition using
D Bounding Boxes in Traf‌f‌ic Surveillance
Jakub Sochor · Jakub ˇSpaˇnhel · Adam Herout
Received: date / Accepted: date"
920a92900fbff22fdaaef4b128ca3ca8e8d54c3e,Learning Pattern Transformation Manifolds with Parametric Atom Selection,"LEARNING PATTERN TRANSFORMATION MANIFOLDS WITH PARAMETRIC ATOM
SELECTION
Elif Vural and Pascal Frossard
Ecole Polytechnique F´ed´erale de Lausanne (EPFL)
Signal Processing Laboratory (LTS4)
Switzerland-1015 Lausanne"
9207671d9e2b668c065e06d9f58f597601039e5e,Face Detection Using a 3D Model on Face Keypoints,"Face Detection Using a 3D Model on
Face Keypoints
Adrian Barbu, Gary Gramajo"
9282239846d79a29392aa71fc24880651826af72,Classification of extreme facial events in sign language videos,"Antonakos et al. EURASIP Journal on Image and Video Processing 2014, 2014:14
http://jivp.eurasipjournals.com/content/2014/1/14
RESEARCH
Open Access
Classification of extreme facial events in sign
language videos
Epameinondas Antonakos1,2*, Vassilis Pitsikalis1 and Petros Maragos1"
92115b620c7f653c847f43b6c4ff0470c8e55dab,Training Deformable Object Models for Human Detection Based on Alignment and Clustering,"Training Deformable Object Models for Human
Detection Based on Alignment and Clustering
Benjamin Drayer and Thomas Brox
Department of Computer Science,
Centre of Biological Signalling Studies (BIOSS),
University of Freiburg, Germany"
92c2dd6b3ac9227fce0a960093ca30678bceb364,On Color Texture Normalization for Active Appearance Models,"Provided by the author(s) and NUI Galway in accordance with publisher policies. Please cite the published
version when available.
Title
On color texture normalization for active appearance models
Author(s)
Ionita, Mircea C.; Corcoran, Peter M.; Buzuloiu, Vasile
Publication
009-05-12
Publication
Information
Ionita, M. C., Corcoran, P., & Buzuloiu, V. (2009). On Color
Texture Normalization for Active Appearance Models. Image
Processing, IEEE Transactions on, 18(6), 1372-1378.
Publisher
Link to
publisher's
version
http://dx.doi.org/10.1109/TIP.2009.2017163
Item record
http://hdl.handle.net/10379/1350"
927ba64123bd4a8a31163956b3d1765eb61e4426,Customer satisfaction measuring based on the most significant facial emotion,"Customer satisfaction measuring based on the most
significant facial emotion
Mariem Slim, Rostom Kachouri, Ahmed Atitallah
To cite this version:
Mariem Slim, Rostom Kachouri, Ahmed Atitallah. Customer satisfaction measuring based on the
most significant facial emotion. 15th IEEE International Multi-Conference on Systems, Signals
Devices (SSD 2018), Mar 2018, Hammamet, Tunisia. <hal-01790317>
HAL Id: hal-01790317
https://hal-upec-upem.archives-ouvertes.fr/hal-01790317
Submitted on 11 May 2018
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
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broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d’enseignement et de"
927ad0dceacce2bb482b96f42f2fe2ad1873f37a,Interest-Point based Face Recognition System,"Interest-Point based Face Recognition System
Interest-Point based Face Recognition System
Cesar Fernandez and Maria Asuncion Vicente
Miguel Hernandez University
Spain
. Introduction
Among  all  applications  of  face  recognition  systems,  surveillance  is  one  of  the  most
hallenging ones. In such an application, the goal is to detect known criminals in crowded
environments, like airports or train stations. Some attempts have been made, like those of
Tokio (Engadget, 2006) or Mainz (Deutsche Welle, 2006), with limited success.
The first task to be carried out in an automatic surveillance system involves the detection of
ll the faces in the images taken by the video cameras. Current face detection algorithms are
highly reliable and thus, they will not be the focus of our work. Some of the best performing
examples are the Viola-Jones algorithm (Viola & Jones, 2004) or the Schneiderman-Kanade
lgorithm (Schneiderman & Kanade, 2000).
The second task to be carried out involves the comparison of all detected faces among the
database of known criminals. The ideal behaviour of an automatic system performing this
task  would  be  to  get  a  100%  correct  identification  rate,  but  this  behaviour  is  far  from  the
apabilities  of  current  face  recognition  algorithms.  Assuming  that  there  will  be  false
identifications,  supervised  surveillance  systems  seem  to  be  the  most  realistic  option:  the"
929bd1d11d4f9cbc638779fbaf958f0efb82e603,"Improving the Performance of Facial Expression Recognition Using Dynamic, Subtle and Regional Features","This is the author’s version of a work that was submitted/accepted for pub-
lication in the following source:
Zhang, Ligang & Tjondronegoro, Dian W. (2010) Improving the perfor-
mance of facial expression recognition using dynamic, subtle and regional
features.
In Kok, WaiWong, B. Sumudu, U. Mendis, & Abdesselam ,
Bouzerdoum (Eds.) Neural Information Processing. Models and Applica-
tions, Lecture Notes in Computer Science, Sydney, N.S.W, pp. 582-589.
This file was downloaded from: http://eprints.qut.edu.au/43788/
(cid:13) Copyright 2010 Springer-Verlag
Conference proceedings published, by Springer Verlag, will be available
via Lecture Notes in Computer Science http://www.springer.de/comp/lncs/
Notice: Changes introduced as a result of publishing processes such as
opy-editing and formatting may not be reflected in this document. For a
definitive version of this work, please refer to the published source:
http://dx.doi.org/10.1007/978-3-642-17534-3_72"
0cb7e4c2f6355c73bfc8e6d5cdfad26f3fde0baf,F Acial E Xpression R Ecognition Based on Wapa and Oepa F Ast Ica,"International Journal of Artificial Intelligence & Applications (IJAIA), Vol. 5, No. 3, May 2014
FACIAL EXPRESSION RECOGNITION BASED ON
WAPA AND OEPA FASTICA
Humayra Binte Ali1 and David M W Powers2
Computer Science, Engineering and Mathematics School, Flinders University, Australia
Computer Science, Engineering and Mathematics School, Flinders University, Australia"
0c8a0a81481ceb304bd7796e12f5d5fa869ee448,A Spatial Regularization of LDA for Face Recognition,"International Journal of Fuzzy Logic and Intelligent Systems, vol. 10, no. 2, June 2010, pp. 95-100
A Spatial Regularization of LDA for Face Recognition
Lae-Jeong Park
Department of Electronics Engineering, Gangnung-Wonju National University
23 Chibyun-Dong, Kangnung, 210-702, Korea
Tel : +82-33-640-2389, Fax : +82-33-646-0740, E-mail :"
0c36c988acc9ec239953ff1b3931799af388ef70,Face Detection Using Improved Faster RCNN,"Face Detection Using Improved Faster RCNN
Changzheng Zhang, Xiang Xu, Dandan Tu*
Huawei Cloud BU, China
{zhangzhangzheng, xuxiang12,
Figure1.Face detection results of FDNet1.0"
0c5ddfa02982dcad47704888b271997c4de0674b,Model-driven and Data-driven Approaches for some Object Recognition Problems,
0c069a870367b54dd06d0da63b1e3a900a257298,Weakly Supervised Learning of Foreground-Background Segmentation Using Masked RBMs,"Author manuscript, published in ""ICANN 2011 - International Conference on Artificial Neural Networks (2011)"""
0c75c7c54eec85e962b1720755381cdca3f57dfb,Face Landmark Fitting via Optimized Part Mixtures and Cascaded Deformable Model,"Face Landmark Fitting via Optimized Part
Mixtures and Cascaded Deformable Model
Xiang Yu, Member, IEEE, Junzhou Huang, Member, IEEE,
Shaoting Zhang, Senior Member, IEEE, and Dimitris N. Metaxas, Fellow, IEEE"
0ca36ecaf4015ca4095e07f0302d28a5d9424254,Improving Bag-of-Visual-Words Towards Effective Facial Expressive Image Classification,"Improving Bag-of-Visual-Words Towards Effective Facial Expressive
Image Classification
Dawood Al Chanti1 and Alice Caplier1
Univ. Grenoble Alpes, CNRS, Grenoble INP∗ , GIPSA-lab, 38000 Grenoble, France
Keywords:
BoVW, k-means++, Relative Conjunction Matrix, SIFT, Spatial Pyramids, TF.IDF."
0cfca73806f443188632266513bac6aaf6923fa8,Predictive Uncertainty in Large Scale Classification using Dropout - Stochastic Gradient Hamiltonian Monte Carlo,"Predictive Uncertainty in Large Scale Classification
using Dropout - Stochastic Gradient Hamiltonian
Monte Carlo.
Vergara, Diego∗1, Hern´andez, Sergio∗2, Valdenegro-Toro, Mat´ıas∗∗3 and Jorquera, Felipe∗4.
Laboratorio de Procesamiento de Informaci´on Geoespacial, Universidad Cat´olica del Maule, Chile.
German Research Centre for Artificial Intelligence, Bremen, Germany.
Email:"
0c3f7272a68c8e0aa6b92d132d1bf8541c062141,Kruskal-Wallis-Based Computationally Efficient Feature Selection for Face Recognition,"Hindawi Publishing Corporation
e Scientific World Journal
Volume 2014, Article ID 672630, 6 pages
http://dx.doi.org/10.1155/2014/672630
Research Article
Kruskal-Wallis-Based Computationally Efficient Feature
Selection for Face Recognition
Sajid Ali Khan,1,2 Ayyaz Hussain,3 Abdul Basit,1 and Sheeraz Akram1
Department of Software Engineering, Foundation University, Rawalpindi 46000, Pakistan
Department of Computer Science, Shaheed Zulfikar Ali Bhutto Institute of Science and Technology Islamabad,
Islamabad 44000, Pakistan
Department of Computer Science and Software Engineering, International Islamic University, Islamabad 44000, Pakistan
Correspondence should be addressed to Sajid Ali Khan;
Received 5 December 2013; Accepted 10 February 2014; Published 21 May 2014
Academic Editors: S. Balochian, V. Bhatnagar, and Y. Zhang
Copyright © 2014 Sajid Ali Khan et al. This is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Face recognition in today’s technological world, and face recognition applications attain much more importance. Most of the
existing work used frontal face images to classify face image. However these techniques fail when applied on real world face images.
The proposed technique effectively extracts the prominent facial features. Most of the features are redundant and do not contribute"
0c5afb209b647456e99ce42a6d9d177764f9a0dd,Recognizing Action Units for Facial Expression Analysis,"Recognizing Action Units for
Facial Expression Analysis
Ying-li Tian, Member, IEEE, Takeo Kanade, Fellow, IEEE, and Jeffrey F. Cohn, Member, IEEE"
0c377fcbc3bbd35386b6ed4768beda7b5111eec6,A Unified Probabilistic Framework for Spontaneous Facial Action Modeling and Understanding,"A Unified Probabilistic Framework
for Spontaneous Facial Action Modeling
nd Understanding
Yan Tong, Member, IEEE, Jixu Chen, Student Member, IEEE, and Qiang Ji, Senior Member, IEEE"
0cb2dd5f178e3a297a0c33068961018659d0f443,IARPA Janus Benchmark-B Face Dataset,"© 2017 Noblis, Inc.  IARPA Janus Benchmark-B Face Dataset Cameron Whitelam, Emma Taborsky*, Austin Blanton, Brianna Maze*, Jocelyn Adams*, Tim Miller*, Nathan Kalka*, Anil K. Jain**, James A. Duncan*, Kristen Allen, Jordan Cheney*, Patrick Grother***  Noblis* Michigan State University** NIST*** 21 July 2017"
0cd8895b4a8f16618686f622522726991ca2a324,Discrete Choice Models for Static Facial Expression Recognition,"Discrete Choice Models for Static Facial Expression
Recognition
Gianluca Antonini1, Matteo Sorci1, Michel Bierlaire2, and Jean-Philippe Thiran1
Ecole Polytechnique Federale de Lausanne, Signal Processing Institute
Ecole Polytechnique Federale de Lausanne, Operation Research Group
Ecublens, 1015 Lausanne, Switzerland
Ecublens, 1015 Lausanne, Switzerland"
0cf7da0df64557a4774100f6fde898bc4a3c4840,Shape matching and object recognition using low distortion correspondences,"Shape Matching and Object Recognition using Low Distortion Correspondences
Alexander C. Berg Tamara L. Berg
Jitendra Malik
Department of Electrical Engineering and Computer Science
U.C. Berkeley"
0cbe059c181278a373292a6af1667c54911e7925,'Owl' and 'Lizard': patterns of head pose and eye pose in driver gaze classification,"Owl and Lizard: Patterns of Head Pose and Eye
Pose in Driver Gaze Classification
Lex Fridman1, Joonbum Lee1, Bryan Reimer1, and Trent Victor2
Massachusetts Institute of Technology (MIT)
Chalmers University of Technology, SAFER"
0c4659b35ec2518914da924e692deb37e96d6206,Registering a MultiSensor Ensemble of Images,"Registering a MultiSensor Ensemble of Images
Jeff Orchard, Member, IEEE, and Richard Mann"
0ced7b814ec3bb9aebe0fcf0cac3d78f36361eae,Central Local Directional Pattern Value Flooding Co-occurrence Matrix based Features for Face Recognition,"Dr. P Chandra Sekhar Reddy, International Journal of Computer Science and Mobile Computing, Vol.6 Issue.1, January- 2017, pg. 221-227
Available Online at www.ijcsmc.com
International Journal of Computer Science and Mobile Computing
A Monthly Journal of Computer Science and Information Technology
ISSN 2320–088X
IMPACT FACTOR: 6.017
IJCSMC, Vol. 6, Issue. 1, January 2017, pg.221 – 227
Central Local Directional Pattern Value
Flooding Co-occurrence Matrix based
Features for Face Recognition
Dr. P Chandra Sekhar Reddy
Professor, CSE Department, Gokaraju Rangaraju Institute of Engineering and Technology, Hyderabad"
0c53ef79bb8e5ba4e6a8ebad6d453ecf3672926d,Weakly Supervised PatchNets: Describing and Aggregating Local Patches for Scene Recognition,"SUBMITTED TO JOURNAL
Weakly Supervised PatchNets: Describing and
Aggregating Local Patches for Scene Recognition
Zhe Wang, Limin Wang, Yali Wang, Bowen Zhang, and Yu Qiao, Senior Member, IEEE"
6601a0906e503a6221d2e0f2ca8c3f544a4adab7,Detection of Ancient Settlement Mounds : Archaeological Survey Based on the SRTM Terrain Model,"SRTM-2  2/9/06  3:27 PM  Page 321
Detection of Ancient Settlement Mounds:
Archaeological Survey Based on the
SRTM Terrain Model
B.H. Menze, J.A. Ur, and A.G. Sherratt"
660b73b0f39d4e644bf13a1745d6ee74424d4a16,Constructing Kernel Machines in the Empirical Kernel Feature Space,",250+OPEN ACCESS BOOKS106,000+INTERNATIONALAUTHORS AND EDITORS113+ MILLIONDOWNLOADSBOOKSDELIVERED TO151 COUNTRIESAUTHORS AMONGTOP 1%MOST CITED SCIENTIST12.2%AUTHORS AND EDITORSFROM TOP 500 UNIVERSITIESSelection of our books indexed in theBook Citation Index in Web of Science™Core Collection (BKCI)Chapter from the book Reviews, Refinements and New Ideas in Face RecognitionDownloaded from: http://www.intechopen.com/books/reviews-refinements-and-new-ideas-in-face-recognitionPUBLISHED BYWorld's largest Science,Technology & Medicine Open Access book publisherInterested in publishing with InTechOpen?Contact us at"
66d512342355fb77a4450decc89977efe7e55fa2,Learning Non-linear Transform with Discrim- Inative and Minimum Information Loss Priors,"Under review as a conference paper at ICLR 2018
LEARNING NON-LINEAR TRANSFORM WITH DISCRIM-
INATIVE AND MINIMUM INFORMATION LOSS PRIORS
Anonymous authors
Paper under double-blind review"
6643a7feebd0479916d94fb9186e403a4e5f7cbf,Chapter 8 3 D Face Recognition,"Chapter 8
D Face Recognition
Ajmal Mian and Nick Pears"
661ca4bbb49bb496f56311e9d4263dfac8eb96e9,Datasheets for Datasets,"Datasheets for Datasets
Timnit Gebru 1 Jamie Morgenstern 2 Briana Vecchione 3 Jennifer Wortman Vaughan 1 Hanna Wallach 1
Hal Daumé III 1 4 Kate Crawford 1 5"
66d087f3dd2e19ffe340c26ef17efe0062a59290,Dog Breed Identification,"Dog Breed Identification
Whitney LaRow
Brian Mittl
Vijay Singh"
6618cff7f2ed440a0d2fa9e74ad5469df5cdbe4c,Ordinal Regression with Multiple Output CNN for Age Estimation,"Ordinal Regression with Multiple Output CNN for Age Estimation
Zhenxing Niu1
Gang Hua3
Xidian University 2Xi’an Jiaotong University 3Microsoft Research Asia
Xinbo Gao1
Mo Zhou1
Le Wang2"
66a2c229ac82e38f1b7c77a786d8cf0d7e369598,A Probabilistic Adaptive Search System for Exploring the Face Space,"Proceedings of the 2016 Industrial and Systems Engineering Research Conference
H. Yang, Z. Kong, and MD Sarder, eds.
A Probabilistic Adaptive Search System
for Exploring the Face Space
Andres G. Abad and Luis I. Reyes Castro
Escuela Superior Politecnica del Litoral (ESPOL)
Guayaquil-Ecuador"
66a9935e958a779a3a2267c85ecb69fbbb75b8dc,Fast and Robust Fixed-Rank Matrix Recovery,"FAST AND ROBUST FIXED-RANK MATRIX RECOVERY
Fast and Robust Fixed-Rank Matrix
Recovery
German Ros*, Julio Guerrero, Angel Sappa, Daniel Ponsa and
Antonio Lopez"
66533107f9abdc7d1cb8f8795025fc7e78eb1122,Visual Servoing for a User's Mouth with Effective Intention Reading in a Wheelchair-based Robotic Arm,"Vi	a Sevig f a Ue 	h wih E(cid:11)ecive 
ei Readig
i a Wheechai	baed Rbic A

W	y	g Sgy Dae	i i
y g	S	g i
z ad Ze	ga
 Biey
y EECS A
ST 373	1 	g	Dg Y	g	G	 Taej 305	701 REA
z VR Cee ETR
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Abac
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a eae ehabiiai b i he a
e evi

e ad ha he bee(cid:12) f ehabiiai b
	ch a 
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bie f	ci. 
ei
eadig i e f he eeia f	ci f h	
a
fiedy ehabiiai b i de  
ie he

f ad afey f a wh eed he
. Fi f
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bic a
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 ARES 

 ad i h	
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g he
echgie we cceae  vi	a evig ha
w hi bic a
  eae a	
	y via
vi	a feedback. E(cid:11)ecive iei eadig 	ch a"
66810438bfb52367e3f6f62c24f5bc127cf92e56,Face Recognition of Illumination Tolerance in 2D Subspace Based on the Optimum Correlation Filter,"Face Recognition of Illumination Tolerance in 2D
Subspace Based on the Optimum Correlation
Filter
Xu Yi
Department of Information Engineering, Hunan Industry Polytechnic, Changsha, China
images  will  be  tested  to  project"
66af2afd4c598c2841dbfd1053bf0c386579234e,Context-assisted face clustering framework with human-in-the-loop,"Noname manuscript No.
(will be inserted by the editor)
Context Assisted Face Clustering Framework with
Human-in-the-Loop
Liyan Zhang · Dmitri V. Kalashnikov ·
Sharad Mehrotra
Received: date / Accepted: date"
66e6f08873325d37e0ec20a4769ce881e04e964e,The SUN Attribute Database: Beyond Categories for Deeper Scene Understanding,"Int J Comput Vis (2014) 108:59–81
DOI 10.1007/s11263-013-0695-z
The SUN Attribute Database: Beyond Categories for Deeper Scene
Understanding
Genevieve Patterson · Chen Xu · Hang Su ·
James Hays
Received: 27 February 2013 / Accepted: 28 December 2013 / Published online: 18 January 2014
© Springer Science+Business Media New York 2014"
661da40b838806a7effcb42d63a9624fcd684976,An Illumination Invariant Accurate Face Recognition with Down Scaling of DCT Coefficients,"An Illumination Invariant Accurate
Face Recognition with Down Scaling
of DCT Coefficients
Virendra P. Vishwakarma, Sujata Pandey and M. N. Gupta
Department of Computer Science and Engineering, Amity School of Engineering and Technology, New Delhi, India
In this paper, a novel approach for illumination normal-
ization under varying lighting conditions is presented.
Our approach utilizes the fact that discrete cosine trans-
form (DCT) low-frequency coefficients correspond to
illumination variations in a digital image. Under varying
illuminations, the images captured may have low con-
trast; initially we apply histogram equalization on these
for contrast stretching. Then the low-frequency DCT
oefficients are scaled down to compensate the illumi-
nation variations. The value of scaling down factor and
the number of low-frequency DCT coefficients, which
re to be rescaled, are obtained experimentally. The
lassification is done using k−nearest neighbor classi-
fication and nearest mean classification on the images
obtained by inverse DCT on the processed coefficients."
66886f5af67b22d14177119520bd9c9f39cdd2e6,Learning Additive Kernel For Feature Transformation and Its Application to CNN Features,"T. KOBAYASHI: LEARNING ADDITIVE KERNEL
Learning Additive Kernel For Feature
Transformation and Its Application to CNN
Features
Takumi Kobayashi
National Institute of Advanced Industrial
Science and Technology
Tsukuba, Japan"
3edb0fa2d6b0f1984e8e2c523c558cb026b2a983,Automatic Age Estimation Based on Facial Aging Patterns,"Automatic Age Estimation Based on
Facial Aging Patterns
Xin Geng, Zhi-Hua Zhou, Senior Member, IEEE,
Kate Smith-Miles, Senior Member, IEEE"
3e4b38b0574e740dcbd8f8c5dfe05dbfb2a92c07,Facial Expression Recognition with Local Binary Patterns and Linear Programming,"FACIAL EXPRESSION RECOGNITION WITH LOCAL BINARY PATTERNS
AND LINEAR PROGRAMMING
Xiaoyi Feng1, 2, Matti Pietikäinen1, Abdenour Hadid1
Machine Vision Group, Infotech Oulu and Dept. of Electrical and Information Engineering
P. O. Box 4500 Fin-90014 University of Oulu, Finland
2 College of Electronics and Information, Northwestern Polytechnic University
710072 Xi’an, China
In  this  work,  we  propose  a  novel  approach  to  recognize  facial  expressions  from  static
images. First, the Local Binary Patterns (LBP) are used to efficiently represent the facial
images and then the Linear Programming (LP) technique is adopted to classify the seven
facial  expressions  anger,  disgust,  fear,  happiness,  sadness,  surprise  and  neutral.
Experimental results demonstrate an average recognition accuracy of 93.8% on the JAFFE
database, which outperforms the rates of all other reported methods on the same database.
Introduction
Facial  expression  recognition  from  static
images  is  a  more  challenging  problem
than  from  image  sequences  because  less
information  for  expression  actions
vailable.  However,  information  in  a
single  image  is  sometimes  enough  for"
3e4acf3f2d112fc6516abcdddbe9e17d839f5d9b,Deep Value Networks Learn to Evaluate and Iteratively Refine Structured Outputs,"Deep Value Networks Learn to
Evaluate and Iteratively Refine Structured Outputs
Michael Gygli 1 * Mohammad Norouzi 2 Anelia Angelova 2"
3e3f305dac4fbb813e60ac778d6929012b4b745a,Feature sampling and partitioning for visual vocabulary generation on large action classification datasets,"Feature sampling and partitioning for visual vocabulary
generation on large action classification datasets.
Michael Sapienza1, Fabio Cuzzolin1, and Philip H.S. Torr2
Department of Computing and Communications Technology, Oxford Brookes University.
Department of Engineering Science, University of Oxford."
3ea8a6dc79d79319f7ad90d663558c664cf298d4,Automatic Facial Expression Recognition from Video Sequences,"(cid:13) Copyright by Ira Cohen, 2000"
3e4f84ce00027723bdfdb21156c9003168bc1c80,A co-training approach to automatic face recognition,"© EURASIP, 2011  -  ISSN 2076-1465
9th European Signal Processing Conference (EUSIPCO 2011)
INTRODUCTION"
3e04feb0b6392f94554f6d18e24fadba1a28b65f,Subspace Image Representation for Facial Expression Analysis and Face Recognition and its Relation to the Human Visual System,"Subspace Image Representation for Facial
Expression Analysis and Face Recognition
nd its Relation to the Human Visual System
Ioan Buciu1,2 and Ioannis Pitas1
Department of Informatics, Aristotle University of Thessaloniki GR-541 24,
Thessaloniki, Box 451, Greece.
Electronics Department, Faculty of Electrical Engineering and Information
Technology, University of Oradea 410087, Universitatii 1, Romania.
Summary. Two main theories exist with respect to face encoding and representa-
tion in the human visual system (HVS). The first one refers to the dense (holistic)
representation of the face, where faces have “holon”-like appearance. The second one
laims that a more appropriate face representation is given by a sparse code, where
only a small fraction of the neural cells corresponding to face encoding is activated.
Theoretical and experimental evidence suggest that the HVS performs face analysis
(encoding, storing, face recognition, facial expression recognition) in a structured
nd hierarchical way, where both representations have their own contribution and
goal. According to neuropsychological experiments, it seems that encoding for face
recognition, relies on holistic image representation, while a sparse image represen-
tation is used for facial expression analysis and classification. From the computer
vision perspective, the techniques developed for automatic face and facial expres-"
3e685704b140180d48142d1727080d2fb9e52163,Single Image Action Recognition by Predicting Space-Time Saliency,"Single Image Action Recognition by Predicting
Space-Time Saliency
Marjaneh Safaei and Hassan Foroosh"
3e687d5ace90c407186602de1a7727167461194a,Photo Tagging by Collection-Aware People Recognition,"Photo Tagging by Collection-Aware People Recognition
Cristina Nader Vasconcelos
Vinicius Jardim
Asla S´a
Paulo Cezar Carvalho"
50f0c495a214b8d57892d43110728e54e413d47d,Pairwise support vector machines and their application to large scale problems,"Submitted 8/11; Revised 3/12; Published 8/12
Pairwise Support Vector Machines and their Application to Large
Scale Problems
Carl Brunner
Andreas Fischer
Institute for Numerical Mathematics
Technische Universit¨at Dresden
01062 Dresden, Germany
Klaus Luig
Thorsten Thies
Cognitec Systems GmbH
Grossenhainer Str. 101
01127 Dresden, Germany
Editor: Corinna Cortes"
501096cca4d0b3d1ef407844642e39cd2ff86b37,Illumination Invariant Face Image Representation Using Quaternions,"Illumination Invariant Face Image
Representation using Quaternions
Dayron Rizo-Rodr´ıguez, Heydi M´endez-V´azquez, and Edel Garc´ıa-Reyes
Advanced Technologies Application Center. 7a # 21812 b/ 218 and 222,
Rpto. Siboney, Playa, P.C. 12200, La Habana, Cuba."
501eda2d04b1db717b7834800d74dacb7df58f91,Discriminative Sparse Representation for Expression Recognition,"Pedro Miguel Neves Marques    Discriminative Sparse Representation for Expression Recognition     Master Thesis in Electrical and Computer Engineering September, 2014"
5083c6be0f8c85815ead5368882b584e4dfab4d1,Automated Face Analysis for Affective Computing Jeffrey,"Please do not quote.  In press, Handbook of affective computing. New York, NY: Oxford
Automated Face Analysis for Affective Computing
Jeffrey F. Cohn & Fernando De la Torre"
5058a7ec68c32984c33f357ebaee96c59e269425,A Comparative Evaluation of Regression Learning Algorithms for Facial Age Estimation,"A Comparative Evaluation of Regression Learning
Algorithms for Facial Age Estimation
Carles Fern´andez1, Ivan Huerta2, and Andrea Prati2
Herta Security
Pau Claris 165 4-B, 08037 Barcelona, Spain
DPDCE, University IUAV
Santa Croce 1957, 30135 Venice, Italy"
50ff21e595e0ebe51ae808a2da3b7940549f4035,Age Group and Gender Estimation in the Wild With Deep RoR Architecture,"IEEE TRANSACTIONS ON LATEX CLASS FILES, VOL. XX, NO. X, AUGUST 2017
Age Group and Gender Estimation in the Wild with
Deep RoR Architecture
Ke Zhang, Member, IEEE, Ce Gao, Liru Guo, Miao Sun, Student Member, IEEE, Xingfang Yuan, Student
Member, IEEE, Tony X. Han, Member, IEEE, Zhenbing Zhao, Member, IEEE and Baogang Li"
5042b358705e8d8e8b0655d07f751be6a1565482,Review on Emotion Detection in Image,"International Journal of
Emerging Research in Management &Technology
ISSN: 2278-9359 (Volume-4, Issue-8)
Research  Article
August
Review  on Emotion Detection  in Image
Aswinder Kaur*                                                                                                        Kapil Dewan
CSE & PCET, PTU                                                                                             HOD, CSE & PCET, PTU
Punjab, India                                                                                                            Punj ab, India"
50e47857b11bfd3d420f6eafb155199f4b41f6d7,3D Human Face Reconstruction Using a Hybrid of Photometric Stereo and Independent Component Analysis,"International Journal of Computer, Consumer and Control (IJ3C), Vol. 2, No.1 (2013)
D Human Face Reconstruction Using a Hybrid of Photometric
Stereo and Independent Component Analysis
*Cheng-Jian Lin, 2Shyi-Shiun Kuo, 1Hsueh-Yi Lin, 2Shye-Chorng Kuo and 1Cheng-Yi Yu"
50eb75dfece76ed9119ec543e04386dfc95dfd13,Learning Visual Entities and Their Visual Attributes from Text Corpora,"Learning Visual Entities and their Visual Attributes from Text Corpora
Erik Boiy
Dept. of Computer Science
K.U.Leuven, Belgium
Koen Deschacht
Dept. of Computer Science
K.U.Leuven, Belgium
Marie-Francine Moens
Dept. of Computer Science
K.U.Leuven, Belgium"
50a0930cb8cc353e15a5cb4d2f41b365675b5ebf,Robust Facial Landmark Detection and Face Tracking in Thermal Infrared Images using Active Appearance Models,
50eb2ee977f0f53ab4b39edc4be6b760a2b05f96,Emotion recognition based on texture analysis of facial expression,"Australian Journal of Basic and Applied Sciences, 11(5) April 2017, Pages: 1-11
AUSTRALIAN JOURNAL OF BASIC AND
APPLIED SCIENCES
ISSN:1991-8178        EISSN: 2309-8414
Journal home page: www.ajbasweb.com
Emotion  Recognition  Based  on  Texture  Analysis  of  Facial  Expressions
Using Wavelets Transform
Suhaila N. Mohammed and 2Loay E. George
Assistant Lecturer, Computer Science Department, College of Science, Baghdad University, Baghdad, Iraq,
Assistant Professor, Computer Science Department, College of Science, Baghdad University, Baghdad, Iraq,
Address For Correspondence:
Suhaila N. Mohammed, Baghdad University, Computer Science Department, College of Science, Baghdad, Iraq.
A R T I C L E   I N F O
Article history:
Received 18 January 2017
Accepted 28 March 2017
Available online 15 April 2017
Keywords:
Facial  Emotion,  Face  Detection,
Template  Based  Methods,  Texture"
50d15cb17144344bb1879c0a5de7207471b9ff74,"Divide, Share, and Conquer: Multi-task Attribute Learning with Selective Sharing","Divide, Share, and Conquer: Multi-task
Attribute Learning with Selective Sharing
Chao-Yeh Chen*, Dinesh Jayaraman*, Fei Sha, and Kristen Grauman"
50d961508ec192197f78b898ff5d44dc004ef26d,A Low Indexed Content Based Neural Network Approach for Natural Objects Recognition,"International Journal of Computer science & Information Technology (IJCSIT), Vol 1, No 2, November 2009
A LOW INDEXED CONTENT BASED
NEURAL NETWORK APPROACH FOR
NATURAL OBJECTS RECOGNITION
G.Shyama Chandra Prasad1 and Dr. A.Govardhan 2  Dr. T.V.Rao 3
Research Scholar, JNTUH, Hyderabad, AP. India
Principal, JNTUH College of Engineering, jagitial, Karimnagar, AP, India
Principal, Chaithanya Institute of Engineering and Technology, Kakinada, AP, India"
50ccc98d9ce06160cdf92aaf470b8f4edbd8b899,Towards robust cascaded regression for face alignment in the wild,"Towards Robust Cascaded Regression for Face Alignment in the Wild
Chengchao Qu1,2 Hua Gao3
Eduardo Monari2
J¨urgen Beyerer2,1
Jean-Philippe Thiran3
Vision and Fusion Laboratory (IES), Karlsruhe Institute of Technology (KIT)
Fraunhofer Institute of Optronics, System Technologies and Image Exploitation (Fraunhofer IOSB)
Signal Processing Laboratory (LTS5), ´Ecole Polytechnique F´ed´erale de Lausanne (EPFL)"
5028c0decfc8dd623c50b102424b93a8e9f2e390,Revisiting Classifier Two-sample Tests,"Published as a conference paper at ICLR 2017
REVISITING CLASSIFIER TWO-SAMPLE TESTS
David Lopez-Paz1, Maxime Oquab1,2
Facebook AI Research, 2WILLOW project team, Inria / ENS / CNRS"
505e55d0be8e48b30067fb132f05a91650666c41,A Model of Illumination Variation for Robust Face Recognition,"A Model of Illumination Variation for Robust Face Recognition
Florent Perronnin and Jean-Luc Dugelay
Institut Eur´ecom
Multimedia Communications Department
BP 193, 06904 Sophia Antipolis Cedex, France
fflorent.perronnin,"
507c9672e3673ed419075848b4b85899623ea4b0,Multi-View Facial Expression Classification,"Faculty of Informatics
Institute for Anthropomatics
Chair Prof. Dr.-Ing. R. Stiefelhagen
Facial Image Processing and Analysis Group
Multi-View Facial Expression
Classification
DIPLOMA THESIS OF
Nikolas Hesse
ADVISORS
Dr.-Ing. Hazım Kemal Ekenel
Dipl.-Inform. Hua Gao
Dipl.-Inform. Tobias Gehrig
MARCH 2011
KIT – University of the State of Baden-Württemberg and National Laboratory of the Helmholtz Association
www.kit.edu"
680d662c30739521f5c4b76845cb341dce010735,Part and Attribute Discovery from Relative Annotations,"Int J Comput Vis (2014) 108:82–96
DOI 10.1007/s11263-014-0716-6
Part and Attribute Discovery from Relative Annotations
Subhransu Maji · Gregory Shakhnarovich
Received: 25 February 2013 / Accepted: 14 March 2014 / Published online: 26 April 2014
© Springer Science+Business Media New York 2014"
68a2ee5c5b76b6feeb3170aaff09b1566ec2cdf5,Age Classification Based on Simple Lbp Transitions,"AGE CLASSIFICATION BASED ON
SIMPLE LBP TRANSITIONS
Research Scholar & Assoc Professor, Aditya institute of Technology and Management, Tekkalli-532 201, A.P.,
Gorti Satyanarayana Murty
India,
Dr. V.Vijaya Kumar
A. Obulesu
Dean-Computer Sciences (CSE & IT), Anurag Group of Institutions, Hyderabad – 500088, A.P., India.,
3Asst. Professor, Dept. Of CSE, Anurag Group of Institutions, Hyderabad – 500088, A.P., India."
68d2afd8c5c1c3a9bbda3dd209184e368e4376b9,Representation Learning by Rotating Your Faces,"Representation Learning by Rotating Your Faces
Luan Tran, Xi Yin, and Xiaoming Liu, Member, IEEE"
6859b891a079a30ef16f01ba8b85dc45bd22c352,"2D Face Recognition Based on PCA & Comparison of Manhattan Distance, Euclidean Distance & Chebychev Distance","International Journal of Emerging Technology and Advanced Engineering
Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 4, Issue 10, October 2014)
D Face Recognition Based on PCA & Comparison of
Manhattan Distance, Euclidean Distance & Chebychev
Distance
Rajib Saha1, Sayan Barman2
RCC Institute of Information Technology, Kolkata, India"
68d08ed9470d973a54ef7806318d8894d87ba610,Drive Video Analysis for the Detection of Traffic Near-Miss Incidents,"Drive Video Analysis for the Detection of Traffic Near-Miss Incidents
Hirokatsu Kataoka1, Teppei Suzuki1
, Shoko Oikawa3, Yasuhiro Matsui4 and Yutaka Satoh1"
68caf5d8ef325d7ea669f3fb76eac58e0170fff0,Long-term face tracking in the wild using deep learning,
68003e92a41d12647806d477dd7d20e4dcde1354,Fuzzy Based Image Dimensionality Reduction Using Shape Primitives for Efficient Face Recognition,"ISSN: 0976-9102 (ONLINE)
DOI: 10.21917/ijivp.2013.0101
ICTACT JOURNAL ON IMAGE AND VIDEO PROCESSING, NOVEMBER 2013, VOLUME: 04, ISSUE: 02
FUZZY BASED IMAGE DIMENSIONALITY REDUCTION USING SHAPE
PRIMITIVES FOR EFFICIENT FACE RECOGNITION
P. Chandra Sekhar Reddy1, B. Eswara Reddy2 and V. Vijaya Kumar3
Deprtment of Computer Science and Engineering, Nalla Narasimha Reddy Education Society’s Group of Institutions, India
E-Mail:
Deprtment of Computer Science and Engineering, JNTUA College of Engineering, India
Deprtment of Computer Science and Engineering, Anurag Group of Institutions, India
E-mail:
E-mail:"
68d4056765c27fbcac233794857b7f5b8a6a82bf,Example-Based Face Shape Recovery Using the Zenith Angle of the Surface Normal,"Example-Based Face Shape Recovery Using the
Zenith Angle of the Surface Normal
Mario Castel´an1, Ana J. Almaz´an-Delf´ın2, Marco I. Ram´ırez-Sosa-Mor´an3,
nd Luz A. Torres-M´endez1
CINVESTAV Campus Saltillo, Ramos Arizpe 25900, Coahuila, M´exico
Universidad Veracruzana, Facultad de F´ısica e Inteligencia Artificial, Xalapa 91000,
ITESM, Campus Saltillo, Saltillo 25270, Coahuila, M´exico
Veracruz, M´exico"
684f5166d8147b59d9e0938d627beff8c9d208dd,Discriminative Block-Diagonal Representation Learning for Image Recognition,"IEEE TRANS. NNLS, JUNE 2017
Discriminative Block-Diagonal Representation
Learning for Image Recognition
Zheng Zhang, Yong Xu, Senior Member, IEEE, Ling Shao, Senior Member, IEEE, Jian Yang, Member, IEEE"
68e9c837431f2ba59741b55004df60235e50994d,Detecting Faces Using Region-based Fully Convolutional Networks,"Detecting Faces Using Region-based Fully
Convolutional Networks
Yitong Wang Xing Ji Zheng Zhou Hao Wang Zhifeng Li∗
Tencent AI Lab, China"
685f8df14776457c1c324b0619c39b3872df617b,Face Recognition with Preprocessing and Neural Networks,"Master of Science Thesis in Electrical Engineering
Department of Electrical Engineering, Linköping University, 2016
Face Recognition with
Preprocessing and Neural
Networks
David Habrman"
68484ae8a042904a95a8d284a7f85a4e28e37513,Spoofing Deep Face Recognition with Custom Silicone Masks,"Spoofing Deep Face Recognition with Custom Silicone Masks
Sushil Bhattacharjee Amir Mohammadi
S´ebastien Marcel
Idiap Research Institute. Centre du Parc, Rue Marconi 19, Martigny (VS), Switzerland
{sushil.bhattacharjee; amir.mohammadi;"
687e17db5043661f8921fb86f215e9ca2264d4d2,A robust elastic and partial matching metric for face recognition,"A Robust Elastic and Partial Matching Metric for Face Recognition
Gang Hua
Amir Akbarzadeh
Microsoft Corporate
One Microsoft Way, Redmond, WA 98052
{ganghua,"
688754568623f62032820546ae3b9ca458ed0870,Resting high frequency heart rate variability is not associated with the recognition of emotional facial expressions in healthy human adults,"ioRxiv preprint first posted online Sep. 27, 2016;
http://dx.doi.org/10.1101/077784
The copyright holder for this preprint (which was not
peer-reviewed) is the author/funder. It is made available under a
CC-BY-NC-ND 4.0 International license
Resting high frequency heart rate variability is not associated with the
recognition of emotional facial expressions in healthy human adults.
Brice Beffara1,2,3, Nicolas Vermeulen3,4, Martial Mermillod1,2
Univ. Grenoble Alpes, LPNC, F-38040, Grenoble, France
CNRS, LPNC UMR 5105, F-38040, Grenoble, France
IPSY, Université Catholique de Louvain, Louvain-la-Neuve, Belgium
Fund for Scientific Research (FRS-FNRS), Brussels, Belgium
Correspondence concerning this article should be addressed to Brice Beffara, Of‌f‌ice E250, Institut
de Recherches en Sciences Psychologiques, IPSY - Place du Cardinal Mercier, 10 bte L3.05.01 B-1348
Louvain-la-Neuve, Belgium. E-mail:
Author note
This study explores whether the myelinated vagal connection between the heart and the brain
is involved in emotion recognition. The Polyvagal theory postulates that the activity of the
myelinated vagus nerve underlies socio-emotional skills. It has been proposed that the perception
of emotions could be one of this skills dependent on heart-brain interactions. However, this"
68f9cb5ee129e2b9477faf01181cd7e3099d1824,ALDA Algorithms for Online Feature Extraction,"ALDA Algorithms for Online Feature Extraction
Youness Aliyari Ghassabeh, Hamid Abrishami Moghaddam"
68d40176e878ebffbc01ffb0556e8cb2756dd9e9,Locality Repulsion Projection and Minutia Extraction Based Similarity Measure for Face Recognition,"International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622
International Conference on Humming Bird ( 01st March 2014)
RESEARCH ARTICLE
OPEN ACCESS
Locality Repulsion Projection and Minutia Extraction Based
Similarity Measure for Face Recognition
Agnel AnushyaP.1,RamyaP.2
AgnelAnushya P. is currently pursuing M.E (Computer Science and engineering) at Vins Christian college of
Ramya P. is currently working as an Asst. Professor in the dept. of Information Technology at Vins Christian
Engineering.
ollege of Engineering."
6889d649c6bbd9c0042fadec6c813f8e894ac6cc,Analysis of Robust Soft Learning Vector Quantization and an application to Facial Expression Recognition,"Analysis of Robust Soft Learning Vector
Quantization and an application to Facial
Expression Recognition"
68c17aa1ecbff0787709be74d1d98d9efd78f410,Gender Classification from Face Images Using Mutual Information and Feature Fusion,"International Journal of Optomechatronics, 6: 92–119, 2012
Copyright # Taylor & Francis Group, LLC
ISSN: 1559-9612 print=1559-9620 online
DOI: 10.1080/15599612.2012.663463
GENDER CLASSIFICATION FROM FACE IMAGES
USING MUTUAL INFORMATION AND FEATURE
FUSION
Claudio Perez, Juan Tapia, Pablo Este´vez, and Claudio Held
Department of Electrical Engineering and Advanced Mining Technology
Center, Universidad de Chile, Santiago, Chile
In this article we report a new method for gender classification from frontal face images
using feature selection based on mutual information and fusion of features extracted from
intensity, shape, texture, and from three different spatial scales. We compare the results of
three different mutual information measures: minimum redundancy and maximal relevance
(mRMR), normalized mutual information feature selection (NMIFS), and conditional
mutual information feature selection (CMIFS). We also show that by fusing features
extracted from six different methods we significantly improve the gender classification
results relative to those previously published, yielding 99.13% of the gender classification
rate on the FERET database.
Keywords: Feature fusion, feature selection, gender classification, mutual information, real-time gender"
68f61154a0080c4aae9322110c8827978f01ac2e,"Recognizing blurred , non-frontal , illumination and expression variant partially occluded faces","Research Article
Journal of the Optical Society of America A
Recognizing blurred, non-frontal, illumination and
expression variant partially occluded faces
ABHIJITH PUNNAPPURATH1* AND AMBASAMUDRAM NARAYANAN RAJAGOPALAN1
Department of Electrical Engineering, Indian Institute of Technology Madras, Chennai 600036, India.
*Corresponding author:
Compiled June 26, 2016
The focus of this paper is on the problem of recognizing faces across space-varying motion blur, changes
in pose, illumination, and expression, as well as partial occlusion, when only a single image per subject
is available in the gallery. We show how the blur incurred due to relative motion between the camera and
the subject during exposure can be estimated from the alpha matte of pixels that straddle the boundary
etween the face and the background. We also devise a strategy to automatically generate the trimap re-
quired for matte estimation. Having computed the motion via the matte of the probe, we account for pose
variations by synthesizing from the intensity image of the frontal gallery, a face image that matches the
pose of the probe. To handle illumination and expression variations, and partial occlusion, we model the
probe as a linear combination of nine blurred illumination basis images in the synthesized non-frontal
pose, plus a sparse occlusion. We also advocate a recognition metric that capitalizes on the sparsity of the
occluded pixels. The performance of our method is extensively validated on synthetic as well as real face
data. © 2016 Optical Society of America"
6888f3402039a36028d0a7e2c3df6db94f5cb9bb,Classifier-to-generator Attack: Estimation,"Under review as a conference paper at ICLR 2018
CLASSIFIER-TO-GENERATOR ATTACK: ESTIMATION
OF TRAINING DATA DISTRIBUTION FROM CLASSIFIER
Anonymous authors
Paper under double-blind review"
57fd229097e4822292d19329a17ceb013b2cb648,Fast Structural Binary Coding,"Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI-16)
Fast Structural Binary Coding
⇤Department of Electrical and Computer Engineering,University of California, San Diego
Dongjin Song⇤, Wei Liu], and David A. Meyer†
La Jolla, USA, 92093-0409. Email:
] Didi Research, Didi Kuaidi, Beijing, China. Email:
Department of Mathematics,University of California, San Diego
La Jolla, USA, 92093-0112. Email:"
57c59011614c43f51a509e10717e47505c776389,Unsupervised Human Action Detection by Action Matching,"Unsupervised Human Action Detection by Action Matching
Basura Fernando∗ Sareh Shirazi† Stephen Gould∗
The Australian National University †Queensland University of Technology"
57f8e1f461ab25614f5fe51a83601710142f8e88,Region Selection for Robust Face Verification using UMACE Filters,"Region Selection for Robust Face Verification using UMACE Filters
Salina Abdul Samad*, Dzati Athiar Ramli, Aini Hussain
Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering,
Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia.
In  this  paper,  we  investigate  the  verification  performances  of  four  subdivided  face  images  with  varying  expressions.  The
objective of this study is to evaluate which part of the face image is more tolerant to facial expression and still retains its personal
haracteristics due to the variations of the image. The Unconstrained Minimum Average Correlation Energy (UMACE) filter is
implemented to perform the verification process because of its advantages such as shift–invariance, ability to trade-off between
discrimination and distortion tolerance, e.g. variations in pose, illumination and facial expression. The database obtained from the
facial expression database of Advanced Multimedia Processing (AMP) Lab at CMU is used in this study. Four equal
sizes of face regions i.e. bottom, top, left and right halves are used for the purpose of this study. The results show that the bottom
half of the face region gives the best performance in terms of the PSR values with zero false accepted rate (FAR) and zero false
rejection rate (FRR) compared to the other three regions.
. Introduction
Face  recognition  is  a  well  established  field  of  research,
nd a large number of algorithms have been proposed in the
literature. Various classifiers have been explored to improve
the accuracy of face classification. The basic approach is to
use distance-base methods which measure Euclidean distance
etween any two vectors and then compare it with the preset"
57a1466c5985fe7594a91d46588d969007210581,A taxonomy of face-models for system evaluation,"A Taxonomy of Face-models for System Evaluation
Vijay N. Iyer, Shane. R. Kirkbride, Brian C. Parks, Walter J. Scheirer and Terrance. E. Boult
Motivation and Data Types
Synthetic Data Types
Unverified – Have no underlying physical or
statistical basis
Physics -Based – Based on structure and
materials combined with the properties
formally modeled in physics.
Statistical  – Use statistics from real
data/experiments to estimate/learn model
parameters. Generally have measurements
of accuracy
Guided Synthetic – Individual models based
on individual people. No attempt to capture
properties of large groups, a unique model
per person. For faces, guided models are
omposed of 3D structure models and skin
textures,  capturing many artifacts  not
easily  parameterized. Can be combined with"
57246142814d7010d3592e3a39a1ed819dd01f3b,Verification of Very Low-Resolution Faces Using An Identity-Preserving Deep Face Super-resolution Network,"MITSUBISHI ELECTRIC RESEARCH LABORATORIES
http://www.merl.com
Verification of Very Low-Resolution Faces Using An
Identity-Preserving Deep Face Super-resolution Network
Ataer-Cansizoglu, E.; Jones, M.J.; Zhang, Z.; Sullivan, A.
TR2018-116 August 24, 2018"
574705812f7c0e776ad5006ae5e61d9b071eebdb,A Novel Approach for Face Recognition Using PCA and Artificial Neural Network,"Karthik G et al, International Journal of Computer Science and Mobile Computing, Vol.3 Issue.5, May- 2014, pg. 780-787
Available Online at www.ijcsmc.com
International Journal of Computer Science and Mobile Computing
A Monthly Journal of Computer Science and Information Technology
ISSN 2320–088X
IJCSMC, Vol. 3, Issue. 5, May 2014, pg.780 – 787
RESEARCH ARTICLE
A Novel Approach for Face Recognition
Using PCA and Artificial Neural Network
Karthik G1, Sateesh Kumar H C2
¹Deptartment of Telecommunication Engg., Dayananda Sagar College of Engg., India
²Department of Telecommunication Engg., Dayananda Sagar College of Engg., India
email : 2 email :"
571b83f7fc01163383e6ca6a9791aea79cafa7dd,SeqFace: Make full use of sequence information for face recognition,"SeqFace: Make full use of sequence information for face recognition
Wei Hu1 ∗
Yangyu Huang2
Guodong Yuan2
Fan Zhang1
Ruirui Li1
Wei Li1
College of Information Science and Technology,
Beijing University of Chemical Technology, China
YUNSHITU Corp., China"
57a14a65e8ae15176c9afae874854e8b0f23dca7,Seeing Mixed Emotions: The Specificity of Emotion Perception From Static and Dynamic Facial Expressions Across Cultures,"UvA-DARE (Digital Academic Repository)
Seeing mixed emotions: The specificity of emotion perception from static and dynamic
facial expressions across cultures
Fang, X.; Sauter, D.A.; van Kleef, G.A.
Published in:
Journal of Cross-Cultural Psychology
0.1177/0022022117736270
Link to publication
Citation for published version (APA):
Fang, X., Sauter, D. A., & van Kleef, G. A. (2018). Seeing mixed emotions: The specificity of emotion perception
from static and dynamic facial expressions across cultures. Journal of Cross-Cultural Psychology, 49(1), 130-
48. DOI: 10.1177/0022022117736270
General rights
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the Library: http://uba.uva.nl/en/contact, or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam,
The Netherlands. You will be contacted as soon as possible."
57d37ad025b5796457eee7392d2038910988655a,Aeaeêêìáîî Áåèääååaeììáçae Çç Àááêêêàáááä Aeçîîäìì Ììììçê,"GEERAT
VE
EETAT
F

ERARC
CAVETYDETECTR
DagaEha
UdeheS	eviif
f.DahaWeiha
ATheiS	b
iediaiaF	(cid:28)
efhe
Re	ie
efheDegeef
aefSciece
TheSchfC
	eScieceadEgieeig
ebewUiveiyfe	ae

ae91904
Dece
be2009"
3b1aaac41fc7847dd8a6a66d29d8881f75c91ad5,Sparse Representation-Based Open Set Recognition,"Sparse Representation-based Open Set Recognition
He Zhang, Student Member, IEEE and Vishal M. Patel, Senior Member, IEEE"
3bc776eb1f4e2776f98189e17f0d5a78bb755ef4,View Synthesis from Image and Video for Object Recognition Applications,
3b15a48ffe3c6b3f2518a7c395280a11a5f58ab0,On knowledge transfer in object class recognition,"On Knowledge Transfer in
Object Class Recognition
A dissertation approved by
TECHNISCHE UNIVERSITÄT DARMSTADT
Fachbereich Informatik
for the degree of
Doktor-Ingenieur (Dr.-Ing.)
presented by
MICHAEL STARK
Dipl.-Inform.
orn in Mainz, Germany
Prof. Dr.-Ing. Michael Goesele, examiner
Prof. Martial Hebert, Ph.D., co-examiner
Prof. Dr. Bernt Schiele, co-examiner
Date of Submission: 12th of August, 2010
Date of Defense: 23rd of September, 2010
Darmstadt, 2010"
3baa3d5325f00c7edc1f1427fcd5bdc6a420a63f,Enhancing Convolutional Neural Networks for Face Recognition with Occlusion Maps and Batch Triplet Loss,"Enhancing Convolutional Neural Networks for Face Recognition with
Occlusion Maps and Batch Triplet Loss
Daniel S´aez Triguerosa,b, Li Menga,∗, Margaret Hartnettb
School of Engineering and Technology, University of Hertfordshire, Hatfield AL10 9AB, UK
IDscan Biometrics (a GBG company), London E14 9QD, UK"
3ba8f8b6bfb36465018430ffaef10d2caf3cfa7e,Local Directional Number Pattern for Face Analysis: Face and Expression Recognition,"Local Directional Number Pattern for Face
Analysis: Face and Expression Recognition
Adin Ramirez Rivera, Student Member, IEEE, Jorge Rojas Castillo, Student Member, IEEE,
nd Oksam Chae, Member, IEEE"
3b9d94752f8488106b2c007e11c193f35d941e92,"Appearance, Visual and Social Ensembles for Face Recognition in Personal Photo Collections","#2052
CVPR 2013 Submission #2052. CONFIDENTIAL REVIEW COPY. DO NOT DISTRIBUTE.
#2052
Appearance, Visual and Social Ensembles for
Face Recognition in Personal Photo Collections
Anonymous CVPR submission
Paper ID 2052"
3b557c4fd6775afc80c2cf7c8b16edde125b270e,Face recognition: Perspectives from the real world,"Face Recognition: Perspectives from the
Real-World
Bappaditya Mandal
Institute for Infocomm Research, A*STAR,
Fusionopolis Way, #21-01 Connexis (South Tower), Singapore 138632.
Phone: +65 6408 2071; Fax: +65 6776 1378;
E-mail:"
3b410ae97e4564bc19d6c37bc44ada2dcd608552,Scalability Analysis of Audio-Visual Person Identity Verification,"Scalability Analysis of Audio-Visual Person
Identity Verification
Jacek Czyz1, Samy Bengio2, Christine Marcel2, and Luc Vandendorpe1
Communications Laboratory,
Universit´e catholique de Louvain, B-1348 Belgium,
IDIAP, CH-1920 Martigny,
Switzerland"
6f5ce5570dc2960b8b0e4a0a50eab84b7f6af5cb,Low Resolution Face Recognition Using a Two-Branch Deep Convolutional Neural Network Architecture,"Low Resolution Face Recognition Using a
Two-Branch Deep Convolutional Neural Network
Architecture
Erfan Zangeneh, Mohammad Rahmati, and Yalda Mohsenzadeh"
6f288a12033fa895fb0e9ec3219f3115904f24de,Learning Expressionlets via Universal Manifold Model for Dynamic Facial Expression Recognition,"Learning Expressionlets via Universal Manifold
Model for Dynamic Facial Expression Recognition
Mengyi Liu, Student Member, IEEE, Shiguang Shan, Senior Member, IEEE, Ruiping Wang, Member, IEEE,
Xilin Chen, Senior Member, IEEE"
6f957df9a7d3fc4eeba53086d3d154fc61ae88df,Modélisation et suivi des déformations faciales : applications à la description des expressions du visage dans le contexte de la langue des signes,"Mod´elisation et suivi des d´eformations faciales :
pplications `a la description des expressions du visage
dans le contexte de la langue des signes
Hugo Mercier
To cite this version:
Hugo Mercier. Mod´elisation et suivi des d´eformations faciales : applications `a la description
des expressions du visage dans le contexte de la langue des signes. Interface homme-machine
[cs.HC]. Universit´e Paul Sabatier - Toulouse III, 2007. Fran¸cais. <tel-00185084>
HAL Id: tel-00185084
https://tel.archives-ouvertes.fr/tel-00185084
Submitted on 5 Nov 2007
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destin´ee au d´epˆot et `a la diffusion de documents
scientifiques de niveau recherche, publi´es ou non,"
6f7d06ced04ead3b9a5da86b37e7c27bfcedbbdd,Multi-Scale Fully Convolutional Network for Fast Face Detection,"Pages 51.1-51.12
DOI: https://dx.doi.org/10.5244/C.30.51"
6f6b4e2885ea1d9bea1bb2ed388b099a5a6d9b81,"Structured Output SVM Prediction of Apparent Age, Gender and Smile from Deep Features","Structured Output SVM Prediction of Apparent Age,
Gender and Smile From Deep Features
Michal Uˇriˇc´aˇr
CMP, Dept. of Cybernetics
FEE, CTU in Prague
Radu Timofte
Computer Vision Lab
D-ITET, ETH Zurich
Rasmus Rothe
Computer Vision Lab
D-ITET, ETH Zurich
Luc Van Gool
PSI, ESAT, KU Leuven
CVL, D-ITET, ETH Zurich
Jiˇr´ı Matas
CMP, Dept. of Cybernetics
FEE, CTU in Prague"
6f08885b980049be95a991f6213ee49bbf05c48d,Author's Personal Copy Multi-kernel Appearance Model ☆,"This article appeared in a journal published by Elsevier. The attached
opy is furnished to the author for internal non-commercial research
nd education use, including for instruction at the authors institution
nd sharing with colleagues.
Other uses, including reproduction and distribution, or selling or
licensing copies, or posting to personal, institutional or third party
websites are prohibited.
In most cases authors are permitted to post their version of the
rticle (e.g. in Word or Tex form) to their personal website or
institutional repository. Authors requiring further information
regarding Elsevier’s archiving and manuscript policies are
encouraged to visit:
http://www.elsevier.com/authorsrights"
6f35b6e2fa54a3e7aaff8eaf37019244a2d39ed3,Learning probabilistic classifiers for human–computer interaction applications,"DOI 10.1007/s00530-005-0177-4
R E G U L A R PA P E R
Nicu Sebe · Ira Cohen · Fabio G. Cozman ·
Theo Gevers · Thomas S. Huang
Learning probabilistic classifiers for human–computer
interaction applications
Published online: 10 May 2005
(cid:1) Springer-Verlag 2005
intelligent
interaction,"
6f3054f182c34ace890a32fdf1656b583fbc7445,Age Estimation Robust to Optical and Motion Blurring by Deep Residual CNN,"Article
Age Estimation Robust to Optical and Motion
Blurring by Deep Residual CNN
Jeon Seong Kang, Chan Sik Kim, Young Won Lee, Se Woon Cho and Kang Ryoung Park *
Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro, 1-gil, Jung-gu,
Seoul 100-715, Korea; (J.S.K.); (C.S.K.);
(Y.W.L.); (S.W.C.)
* Correspondence: Tel.: +82-10-3111-7022; Fax: +82-2-2277-8735
Received: 9 March 2018; Accepted: 10 April 2018; Published: 13 April 2018"
6fa3857faba887ed048a9e355b3b8642c6aab1d8,Face Recognition in Challenging Environments: An Experimental and Reproducible Research Survey,"Face Recognition in Challenging Environments:
An Experimental and Reproducible Research
Survey
Manuel G¨unther and Laurent El Shafey and S´ebastien Marcel"
6f7ce89aa3e01045fcd7f1c1635af7a09811a1fe,A novel rank order LoG filter for interest point detection,"978-1-4673-0046-9/12/$26.00 ©2012 IEEE
ICASSP 2012"
6fe2efbcb860767f6bb271edbb48640adbd806c3,Soft Biometrics; Human Identification Using Comparative Descriptions,"SOFT BIOMETRICS: HUMAN IDENTIFICATION USING COMPARATIVE DESCRIPTIONS
Soft Biometrics; Human Identification using
Comparative Descriptions
Daniel A. Reid, Mark S. Nixon, Sarah V. Stevenage"
6fdc0bc13f2517061eaa1364dcf853f36e1ea5ae,DAISEE: Dataset for Affective States in E-Learning Environments,"DAISEE: Dataset for Affective States in
E-Learning Environments
Abhay Gupta1, Richik Jaiswal2, Sagar Adhikari2, Vineeth Balasubramanian2
Microsoft India R&D Pvt. Ltd.
Department of Computer Science, IIT Hyderabad
{cs12b1032, cs12b1034,"
6f5151c7446552fd6a611bf6263f14e729805ec7,Facial Action Unit Recognition using Filtered Local Binary Pattern Features with Bootstrapped and Weighted ECOC Classi ers,".=?E= )?JE 7EJ 4A?CEJE KIEC
?= *E=HO 2=JJAH .A=JKHAI MEJD
-++ +=IIE
AHI
55EJD
+AJHA BH 8EIE 5FAA?D 5EC= 2H?AIIEC 7ELAHIEJO B 5KHHAO
5KHHAO /7  %:0 7
)>IJH=?J 9EJDE JDA ?JANJ B=?A ANFHAIIE ?=IIE
?=JE KIEC JDA
B=?E= =?JE IOIJA .)+5 MA JDA FH>A B
EC B=?E= =?JE KEJI )7I 6DA EI J JH=E = IECA
AHHH?HHA?JEC KJFKJ -++ KJE?=II ?=IIE
AH J AIJE=JA JDA
FH>=>EEJEAI JD=J A=?D A B IALAH= ?O ??KHHEC )7 CHKFI EI
FHAIAJ E JDA FH>A E=CA 2=JJ I?=EC EI J ?=E>H=JA JDA -++
KJFKJI J FH>=>EEJEAI =FFHFHE=JA IKI B JDAIA FH>=>EEJEAI =HA
J=A J >J=E = IAF=H=JA FH>=>EEJO BH A=?D )7 .A=JKHA
ANJH=?JE EI >O CAAH=JEC = =HCA K>AH B ?= >E=HO F=J
JAH *2 BA=JKHAI JDA IAA?JEC BH JDAIA KIEC B=IJ ?HHA=JE

JAHEC .+*. 6DA >E=I L=HE=?A FHFAHJEAI B JDA ?=IIE
AH
=HA MA IDM JD=J >JD JDAIA IKH?AI B AHHH ?= >A HA
>O AD=?EC -++ JDHKCD JDA =FFE?=JE B >JIJH=FFEC
?=IIIAF=H=>EEJO MAECDJEC"
03c56c176ec6377dddb6a96c7b2e95408db65a7a,A Novel Geometric Framework on Gram Matrix Trajectories for Human Behavior Understanding,"A Novel Geometric Framework on Gram Matrix
Trajectories for Human Behavior Understanding
Anis Kacem, Mohamed Daoudi, Boulbaba Ben Amor, Stefano Berretti, and Juan Carlos Alvarez-Paiva"
0322e69172f54b95ae6a90eb3af91d3daa5e36ea,Face Classification using Adjusted Histogram in Grayscale,"Face Classification using Adjusted Histogram in
Grayscale
Weenakorn Ieosanurak, and Watcharin Klongdee"
03f7041515d8a6dcb9170763d4f6debd50202c2b,Clustering Millions of Faces by Identity,"Clustering Millions of Faces by Identity
Charles Otto, Student Member, IEEE, Dayong Wang, Member, IEEE, and Anil K. Jain, Fellow, IEEE"
038ce930a02d38fb30d15aac654ec95640fe5cb0,Approximate structured output learning for Constrained Local Models with application to real-time facial feature detection and tracking on low-power devices,"Approximate Structured Output Learning for Constrained Local
Models with Application to Real-time Facial Feature Detection and
Tracking on Low-power Devices
Shuai Zheng, Paul Sturgess and Philip H. S. Torr"
03c1fc9c3339813ed81ad0de540132f9f695a0f8,Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification,"Proceedings of Machine Learning Research 81:1–15, 2018
Conference on Fairness, Accountability, and Transparency
Gender Shades: Intersectional Accuracy Disparities in
Commercial Gender Classification∗
Joy Buolamwini
MIT Media Lab 75 Amherst St. Cambridge, MA 02139
Timnit Gebru
Microsoft Research 641 Avenue of the Americas, New York, NY 10011
Editors: Sorelle A. Friedler and Christo Wilson"
0339459a5b5439d38acd9c40a0c5fea178ba52fb,Multimodal recognition of emotions in car environments,"D|C|I&I 2009 Prague
Multimodal recognition of emotions in car
environments
Dragoş DatcuA and Léon J.M. RothkrantzB"
032825000c03b8ab4c207e1af4daeb1f225eb025,A Novel Approach for Human Face Detection in Color Images Using Skin Color and Golden Ratio,"J. Appl. Environ. Biol. Sci., 7(10)159-164, 2017
ISSN: 2090-4274
© 2017, TextRoad Publication
Journal of Applied Environmental
nd Biological Sciences
www.textroad.com
A Novel Approach for Human Face Detection in Color Images Using Skin
Color and Golden Ratio
Faizan Ullah*1, Dilawar Shah1, Sabir Shah1, Abdus Salam2, Shujaat Ali1
Department of Computer Science, Bacha Khan University, Charsadda, KPK, Pakistan1
Department of Computer Science, Abdul WaliKhan University, Mardan, KPK, Pakistan2
Received: May 9, 2017
Accepted: August 2, 2017"
03a8f53058127798bc2bc0245d21e78354f6c93b,Max-margin additive classifiers for detection,"Max-Margin Additive Classifiers for Detection
Subhransu Maji and Alexander C. Berg
Sam Hare
VGG Reading Group
October 30, 2009"
03b98b4a2c0b7cc7dae7724b5fe623a43eaf877b,Acume: A Novel Visualization Tool for Understanding Facial Expression and Gesture Data,"Acume: A Novel Visualization Tool for Understanding Facial
Expression and Gesture Data"
03adcf58d947a412f3904a79f2ab51cfdf0e838a,Video-based face recognition: a survey,"World Journal of Science and Technology 2012, 2(4):136-139
ISSN: 2231 – 2587
Available Online: www.worldjournalofscience.com
_________________________________________________________________
Proceedings of ""Conference on Advances in Communication and Computing (NCACC'12)”
Held at R.C.Patel Institute of Technology, Shirpur, Dist. Dhule,Maharastra,India.
April 21, 2012
Video-based face recognition: a survey
Shailaja A Patil1 and Pramod J Deore2
Department of Electronics and Telecommunication, R.C.Patel Institute of Technology,Shirpur,Dist.Dhule.Maharashtra,India."
03f14159718cb495ca50786f278f8518c0d8c8c9,Performance evaluation of HOG and Gabor features for vision-based vehicle detection,"015 IEEE International Conference on Control System, Computing and Engineering, Nov 27 – Nov 29, 2015 Penang, Malaysia
015 IEEE International Conference on Control System,
Computing and Engineering (ICCSCE2015)
Technical Session 1A – DAY 1 – 27th Nov 2015
Time: 3.00 pm – 4.30 pm
Venue: Jintan
Topic: Signal and Image Processing
.00 pm – 3.15pm
.15 pm – 3.30pm
.30 pm – 3.45pm
.45 pm – 4.00pm
.00 pm – 4.15pm
.15 pm – 4.30pm
.30 pm – 4.45pm
A 01 ID3
Can  Subspace  Based  Learning  Approach  Perform  on  Makeup  Face
Recognition?
Khor Ean Yee, Pang Ying Han, Ooi Shih Yin and Wee Kuok Kwee
A 02 ID35
Performance  Evaluation  of  HOG  and  Gabor  Features  for  Vision-based"
0394040749195937e535af4dda134206aa830258,Geodesic entropic graphs for dimension and entropy estimation in manifold learning,"Geodesic Entropic Graphs for Dimension and
Entropy Estimation in Manifold Learning
Jose A. Costa and Alfred O. Hero III
December 16, 2003"
03ac1c694bc84a27621da6bfe73ea9f7210c6d45,Chapter 1 Introduction to information security foundations and applications,"Chapter 1
Introduction to information security
foundations and applications
Ali Ismail Awad1,2
.1 Background
Information security has extended to include several research directions like user
uthentication and authorization, network security, hardware security, software secu-
rity, and data cryptography. Information security has become a crucial need for
protecting almost all information transaction applications. Security is considered as
n important science discipline whose many multifaceted complexities deserve the
synergy of the computer science and engineering communities.
Recently, due to the proliferation of Information and Communication Tech-
nologies, information security has started to cover emerging topics such as cloud
omputing security, smart cities’ security and privacy, healthcare and telemedicine,
the Internet-of-Things (IoT) security [1], the Internet-of-Vehicles security, and sev-
eral types of wireless sensor networks security [2,3]. In addition, information security
has extended further to cover not only technical security problems but also social and
organizational security challenges [4,5].
Traditional systems’ development approaches were focusing on the system’s
usability where security was left to the last stage with less priority. However, the"
0394e684bd0a94fc2ff09d2baef8059c2652ffb0,Median Robust Extended Local Binary Pattern for Texture Classification,"Median Robust Extended Local Binary Pattern
for Texture Classification
Li Liu, Songyang Lao, Paul W. Fieguth, Member, IEEE, Yulan Guo,
Xiaogang Wang, and Matti Pietikäinen, Fellow, IEEE
Index Terms— Texture descriptors, rotation invariance, local
inary pattern (LBP), feature extraction, texture analysis.
how the texture recognition process works in humans as
well as in the important role it plays in the wide variety of
pplications of computer vision and image analysis [1], [2].
The many applications of texture classification include medical
image analysis and understanding, object recognition, biomet-
rics, content-based image retrieval, remote sensing, industrial
inspection, and document classification.
As a classical pattern recognition problem, texture classifi-
ation primarily consists of two critical subproblems: feature
extraction and classifier designation [1], [2]. It is generally
greed that the extraction of powerful texture features plays a
relatively more important role, since if poor features are used
even the best classifier will fail to achieve good recognition
results. Consequently, most research in texture classification"
03f4c0fe190e5e451d51310bca61c704b39dcac8,CHEAVD: a Chinese natural emotional audio-visual database,"J Ambient Intell Human Comput
DOI 10.1007/s12652-016-0406-z
O R I G I N A L R E S E A R C H
CHEAVD: a Chinese natural emotional audio–visual database
Ya Li1
• Jianhua Tao1,2,3
• Linlin Chao1
• Wei Bao1,4
• Yazhu Liu1,4
Received: 30 March 2016 / Accepted: 22 August 2016
Ó Springer-Verlag Berlin Heidelberg 2016"
031055c241b92d66b6984643eb9e05fd605f24e2,Multi-fold MIL Training for Weakly Supervised Object Localization,"Multi-fold MIL Training for Weakly Supervised Object Localization
Ramazan Gokberk Cinbis
Jakob Verbeek Cordelia Schmid
Inria∗"
0332ae32aeaf8fdd8cae59a608dc8ea14c6e3136,Large Scale 3D Morphable Models,"Int J Comput Vis
DOI 10.1007/s11263-017-1009-7
Large Scale 3D Morphable Models
James Booth1
Stefanos Zafeiriou1
· Anastasios Roussos1,3 · Allan Ponniah2 · David Dunaway2 ·
Received: 15 March 2016 / Accepted: 24 March 2017
© The Author(s) 2017. This article is an open access publication"
034addac4637121e953511301ef3a3226a9e75fd,Implied Feedback: Learning Nuances of User Behavior in Image Search,"Implied Feedback: Learning Nuances of User Behavior in Image Search
Devi Parikh
Virginia Tech"
03701e66eda54d5ab1dc36a3a6d165389be0ce79,Improved Principal Component Regression for Face Recognition Under Illumination Variations,"Improved Principal Component Regression for Face
Recognition Under Illumination Variations
Shih-Ming Huang and Jar-Ferr Yang, Fellow, IEEE"
9b318098f3660b453fbdb7a579778ab5e9118c4c,Joint Patch and Multi-label Learning for Facial Action Unit and Holistic Expression Recognition,"Joint Patch and Multi-label Learning for Facial
Action Unit and Holistic Expression Recognition
Kaili Zhao, Wen-Sheng Chu, Student Member, IEEE, Fernando De la Torre,
Jeffrey F. Cohn, and Honggang Zhang, Senior Member, IEEE
lassifiers without"
9b474d6e81e3b94e0c7881210e249689139b3e04,VG-RAM Weightless Neural Networks for Face Recognition,"VG-RAM Weightless Neural Networks for
Face Recognition
Alberto F. De Souza, Claudine Badue, Felipe Pedroni, Stiven Schwanz Dias,
Hallysson Oliveira and Soterio Ferreira de Souza
Departamento de Inform´atica
Universidade Federal do Esp´ırito Santo
Av. Fernando Ferrari, 514, 29075-910 - Vit´oria-ES
Brazil
. Introduction
Computerized human face recognition has many practical applications, such as access control,
security monitoring, and surveillance systems, and has been one of the most challenging and
ctive research areas in computer vision for many decades (Zhao et al.; 2003). Even though
urrent machine recognition systems have reached a certain level of maturity, the recognition
of faces with different facial expressions, occlusions, and changes in illumination and/or pose
is still a hard problem.
A general statement of the problem of machine recognition of faces can be formulated as fol-
lows: given an image of a scene, (i) identify or (ii) verify one or more persons in the scene
using a database of faces. In identification problems, given a face as input, the system reports
ack the identity of an individual based on a database of known individuals; whereas in veri-
fication problems, the system confirms or rejects the claimed identity of the input face. In both"
9bcfadd22b2c84a717c56a2725971b6d49d3a804,How to Detect a Loss of Attention in a Tutoring System using Facial Expressions and Gaze Direction,"How to Detect a Loss of Attention in a Tutoring System
using Facial Expressions and Gaze Direction
Mark ter Maat"
9b164cef4b4ad93e89f7c1aada81ae7af802f3a4,A Fully Automatic and Haar like Feature Extraction-Based Method for Lip Contour Detection,"Research Journal of Recent Sciences _________________________________________________ ISSN 2277-2502
Vol. 2(1), 17-20, January (2013)
Res.J.Recent Sci.
A Fully Automatic and Haar like Feature Extraction-Based Method for Lip
Contour Detection
Zahedi Morteza and Mohamadian Zahra
School of Computer Engineering, Shahrood University of Technology, Shahrood, IRAN
Received 26th September 2012, revised 27th October 2012, accepted 6th November 2012
Available online at: www.isca.in"
9bac481dc4171aa2d847feac546c9f7299cc5aa0,Matrix Product State for Higher-Order Tensor Compression and Classification,"Matrix Product State for Higher-Order Tensor
Compression and Classification
Johann A. Bengua1, Ho N. Phien1, Hoang D. Tuan1 and Minh N. Do2"
9b7974d9ad19bb4ba1ea147c55e629ad7927c5d7,Faical Expression Recognition by Combining Texture and Geometrical Features,"Faical Expression Recognition by Combining
Texture and Geometrical Features
Renjie Liu, Ruofei Du, Bao-Liang Lu*"
9b6d0b3fbf7d07a7bb0d86290f97058aa6153179,"NII , Japan at the first THUMOS Workshop 2013","NII, Japan at the first THUMOS Workshop 2013
Sang Phan, Duy-Dinh Le, Shin’ichi Satoh
National Institute of Informatics
-1-2 Hitotsubashi, Chiyoda-ku, Tokyo, Japan 101-8430"
9e8637a5419fec97f162153569ec4fc53579c21e,Segmentation and Normalization of Human Ears Using Cascaded Pose Regression,"Segmentation and Normalization of Human Ears
using Cascaded Pose Regression
Anika Pflug and Christoph Busch
University of Applied Sciences Darmstadt - CASED,
Haardtring 100,
64295 Darmstadt, Germany
http://www.h-da.de"
9e4b052844d154c3431120ec27e78813b637b4fc,Local gradient pattern - A novel feature representation for facial expression recognition,"Journal of AI and Data Mining
Vol. 2, No .1, 2014, 33-38.
Local gradient pattern - A novel feature representation for facial
expression recognition
M. Shahidul Islam
Department of Computer Science, School of Applied Statistics, National Institute of Development Administration, Bangkok, Thailand.
Received 23 April 2013; accepted 16 June 2013
*Corresponding author: (M.Shahidul Islam)"
9ea73660fccc4da51c7bc6eb6eedabcce7b5cead,Talking head detection by likelihood-ratio test,"Talking Head Detection by Likelihood-Ratio Test†
Carl Quillen, Kara Greenfield, and William Campbell
MIT Lincoln Laboratory,
Lexington MA 02420, USA"
9e9052256442f4e254663ea55c87303c85310df9,Review On Attribute - assisted Reranking for Image Search,"International Journal of Advanced Research in Computer Engineering & Technology (IJARCET)
Volume 4 Issue 10, October 2015
Review On Attribute-assisted Reranking for
Image Search
Waghmare Supriya, Wavhal Archana, Patil Nital, Tapkir Yogita, Prof. Yogesh Thorat"
9eeada49fc2cba846b4dad1012ba8a7ee78a8bb7,A New Facial Expression Recognition Method Based on Local Gabor Filter Bank and PCA plus LDA,"Hong-Bo Deng, Lian-Wen Jin, Li-Xin Zhen, Jian-Cheng Huang
A New Facial Expression Recognition Method Based on Local Gabor Filter Bank and PCA plus LDA
A New Facial Expression Recognition Method Based on
Local Gabor Filter Bank and PCA plus LDA
Hong-Bo Deng1, Lian-Wen Jin1, Li-Xin Zhen2, Jian-Cheng Huang2
School of Electronic and Information Engineering, South China
University of Technology, Guangzhou, 510640, P.R.China
Motorola China Research Center, Shanghai, 210000, P.R.China
{hbdeng,
{Li-Xin.Zhen,"
9ef2b2db11ed117521424c275c3ce1b5c696b9b3,Robust Face Alignment Using a Mixture of Invariant Experts,"Robust Face Alignment Using a Mixture of Invariant Experts
Oncel Tuzel†
Salil Tambe‡∗
Tim K. Marks†
Intel Corporation
Mitsubishi Electric Research Labs (MERL)
{oncel,"
9e5acdda54481104aaf19974dca6382ed5ff21ed,Automatic localization of facial landmarks from expressive images of high complexity,"Yulia Gizatdinova and Veikko Surakka
Automatic localization of facial
landmarks from expressive images
of high complexity
DEPARTMENT OF COMPUTER SCIENCES
UNIVERSITY OF TAMPERE
D‐2008‐9
TAMPERE 2008"
9e0285debd4b0ba7769b389181bd3e0fd7a02af6,From Face Images and Attributes to Attributes,"From face images and attributes to attributes
Robert Torfason, Eirikur Agustsson, Rasmus Rothe, Radu Timofte
Computer Vision Laboratory, ETH Zurich, Switzerland"
040dc119d5ca9ea3d5fc39953a91ec507ed8cc5d,Large-scale Bisample Learning on ID vs. Spot Face Recognition,"Noname manuscript No.
(will be inserted by the editor)
Large-scale Bisample Learning on ID vs. Spot Face Recognition
Xiangyu Zhu∗ · Hao Liu∗ · Zhen Lei · Hailin Shi · Fan Yang · Dong
Yi · Stan Z. Li
Received: date / Accepted: date"
047f6afa87f48de7e32e14229844d1587185ce45,An Improvement of Energy-Transfer Features Using DCT for Face Detection,"An Improvement of Energy-Transfer Features
Using DCT for Face Detection
Radovan Fusek, Eduard Sojka, Karel Mozdˇreˇn, and Milan ˇSurkala
Technical University of Ostrava, FEECS, Department of Computer Science,
7. listopadu 15, 708 33 Ostrava-Poruba, Czech Republic"
04b851f25d6d49e61a528606953e11cfac7df2b2,Optical Flow Guided Feature: A Fast and Robust Motion Representation for Video Action Recognition,"Optical Flow Guided Feature: A Fast and Robust Motion Representation for
Video Action Recognition
Shuyang Sun1,2, Zhanghui Kuang2, Lu Sheng3, Wanli Ouyang1, Wei Zhang2
The University of Sydney 2SenseTime Research 3The Chinese University of Hong Kong
{shuyang.sun
{wayne.zhang"
0447bdb71490c24dd9c865e187824dee5813a676,Manifold Estimation in View-based Feature Space for Face Synthesis Across Pose,"Manifold Estimation in View-based Feature
Space for Face Synthesis Across Pose
Paper 27"
0435a34e93b8dda459de49b499dd71dbb478dc18,"VEGAC: Visual Saliency-based Age, Gender, and Facial Expression Classification Using Convolutional Neural Networks","VEGAC: Visual Saliency-based Age, Gender, and Facial Expression Classification
Using Convolutional Neural Networks
Ayesha Gurnani£1, Vandit Gajjar£1, Viraj Mavani£1, Yash Khandhediya£1
Department of Electronics and Communication Engineering and
Computer Vision Group, L. D. College of Engineering, Ahmedabad, India
{gurnani.ayesha.52, gajjar.vandit.381, mavani.viraj.604,
the  need  for  handcrafted  facial  descriptors  and  data
preprocessing.  D-CNN  models  have  been  not  only
successfully  applied  to  human  face  analysis,  but  also  for
the visual saliency detection [21, 22, 23]. Visual Saliency
is  fundamentally  an  intensity  map  where  higher  intensity
signifies  regions,  where  a  general  human  being  would
look, and lower intensities mean decreasing level of visual
ttention.  It’s  a  measure  of  visual  attention  of  humans
ased  on  the  content  of  the  image.  It  has  numerous
pplications  in  computer  vision  and  image  processing
tasks. It is still an open problem when considering the MIT
Saliency Benchmark [24].
In  previous  five  years,  considering  age  estimation,
gender  classification  and  facial  expression  classification"
044ba70e6744e80c6a09fa63ed6822ae241386f2,Early Prediction for Physical Human Robot Collaboration in the Operating Room,"TO APPEAR IN AUTONOMOUS ROBOTS, SPECIAL ISSUE IN LEARNING FOR HUMAN-ROBOT COLLABORATION
Early Prediction for Physical Human Robot
Collaboration in the Operating Room
Tian Zhou, Student Member, IEEE, and Juan Wachs, Member, IEEE"
04dcdb7cb0d3c462bdefdd05508edfcff5a6d315,Assisting the training of deep neural networks with applications to computer vision,"Assisting the training of deep neural networks
with applications to computer vision
Adriana Romero
tesi  doctoral  està  subjecta  a
Aquesta
CompartirIgual  4.0. Espanya de Creative Commons.
Esta tesis doctoral está sujeta a la licencia  Reconocimiento - NoComercial – CompartirIgual
.0.  España de Creative Commons.
This  doctoral  thesis  is  licensed  under  the Creative  Commons  Attribution-NonCommercial-
ShareAlike 4.0. Spain License.
llicència Reconeixement-  NoComercial  –"
044fdb693a8d96a61a9b2622dd1737ce8e5ff4fa,Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions,"Dynamic Texture Recognition Using Local Binary
Patterns with an Application to Facial Expressions
Guoying Zhao and Matti Pietik¨ainen, Senior Member, IEEE"
04f55f81bbd879773e2b8df9c6b7c1d324bc72d8,Multi-view Face Analysis Based on Gabor Features,"Multi-view Face Analysis Based on Gabor Features
Hongli Liu，  Weifeng Liu，  Yanjiang Wang
College of Information and Control Engineering in China University of Petroleum,
Qingdao 266580, China"
0431e8a01bae556c0d8b2b431e334f7395dd803a,Learning Localized Perceptual Similarity Metrics for Interactive Categorization,"Learning Localized Perceptual Similarity Metrics for Interactive Categorization
Catherine Wah ∗
Google Inc.
google.com"
04b4c779b43b830220bf938223f685d1057368e9,Video retrieval based on deep convolutional neural network,"Video retrieval based on deep convolutional
neural network
Yajiao Dong
School of Information and Electronics,
Beijing Institution of Technology, Beijing, China
Jianguo Li
School of Information and Electronics,
Beijing Institution of Technology, Beijing, China"
04616814f1aabe3799f8ab67101fbaf9fd115ae4,UNIVERSITÉ DE CAEN BASSE NORMANDIE U . F . R . de Sciences,"UNIVERSIT´EDECAENBASSENORMANDIEU.F.R.deSciences´ECOLEDOCTORALESIMEMTH`ESEPr´esent´eeparM.GauravSHARMAsoutenuele17D´ecembre2012envuedel’obtentionduDOCTORATdel’UNIVERSIT´EdeCAENSp´ecialit´e:InformatiqueetapplicationsArrˆet´edu07aoˆut2006Titre:DescriptionS´emantiquedesHumainsPr´esentsdansdesImagesVid´eo(SemanticDescriptionofHumansinImages)TheworkpresentedinthisthesiswascarriedoutatGREYC-UniversityofCaenandLEAR–INRIAGrenobleJuryM.PatrickPEREZDirecteurdeRechercheINRIA/Technicolor,RennesRapporteurM.FlorentPERRONNINPrincipalScientistXeroxRCE,GrenobleRapporteurM.JeanPONCEProfesseurdesUniversit´esENS,ParisExaminateurMme.CordeliaSCHMIDDirectricedeRechercheINRIA,GrenobleDirectricedeth`eseM.Fr´ed´ericJURIEProfesseurdesUniversit´esUniversit´edeCaenDirecteurdeth`ese"
047d7cf4301cae3d318468fe03a1c4ce43b086ed,Co-Localization of Audio Sources in Images Using Binaural Features and Locally-Linear Regression,"Co-Localization of Audio Sources in Images Using
Binaural Features and Locally-Linear Regression
Antoine Deleforge, Radu Horaud, Yoav Y. Schechner, Laurent Girin
To cite this version:
Antoine Deleforge, Radu Horaud, Yoav Y. Schechner, Laurent Girin. Co-Localization of Audio
Sources in Images Using Binaural Features and Locally-Linear Regression. IEEE Transactions
on Audio Speech and Language Processing, 2015, 15p. <hal-01112834>
HAL Id: hal-01112834
https://hal.inria.fr/hal-01112834
Submitted on 3 Feb 2015
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destin´ee au d´epˆot et `a la diffusion de documents
scientifiques de niveau recherche, publi´es ou non,
´emanant des ´etablissements d’enseignement et de"
04317e63c08e7888cef480fe79f12d3c255c5b00,Face Recognition Using a Unified 3D Morphable Model,"Face Recognition Using a Unified 3D Morphable Model
Hu, G., Yan, F., Chan, C-H., Deng, W., Christmas, W., Kittler, J., & Robertson, N. M. (2016). Face Recognition
Using a Unified 3D Morphable Model. In Computer Vision – ECCV 2016: 14th European Conference,
Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part VIII (pp. 73-89). (Lecture Notes in
Computer Science; Vol. 9912). Springer Verlag. DOI: 10.1007/978-3-319-46484-8_5
Published in:
Computer Vision – ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14,
016, Proceedings, Part VIII
Document Version:
Peer reviewed version
Queen's University Belfast - Research Portal:
Link to publication record in Queen's University Belfast Research Portal
Publisher rights
The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-46484-8_5
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Copyright for the publications made accessible via the Queen's University Belfast Research Portal is retained by the author(s) and / or other
opyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated
with these rights.
Take down policy
The Research Portal is Queen's institutional repository that provides access to Queen's research output. Every effort has been made to"
0470b0ab569fac5bbe385fa5565036739d4c37f8,Automatic face naming with caption-based supervision,"Automatic Face Naming with Caption-based Supervision
Matthieu Guillaumin, Thomas Mensink, Jakob Verbeek, Cordelia Schmid
To cite this version:
Matthieu Guillaumin, Thomas Mensink, Jakob Verbeek, Cordelia Schmid. Automatic Face Naming
with Caption-based Supervision. CVPR 2008 - IEEE Conference on Computer Vision
Pattern Recognition,
iety,
<10.1109/CVPR.2008.4587603>. <inria-00321048v2>
008,
pp.1-8,
008, Anchorage, United
<http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4587603>.
IEEE Computer
States.
HAL Id: inria-00321048
https://hal.inria.fr/inria-00321048v2
Submitted on 11 Apr 2011
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-"
6a3a07deadcaaab42a0689fbe5879b5dfc3ede52,Learning to Estimate Pose by Watching Videos,"Learning to Estimate Pose by Watching Videos
Prabuddha Chakraborty and Vinay P. Namboodiri
Department of Computer Science and Engineering
IIT Kanpur
{prabudc, vinaypn}"
6afed8dc29bc568b58778f066dc44146cad5366c,Kernel Hebbian Algorithm for Single-Frame Super-Resolution,"Kernel Hebbian Algorithm for Single-Frame
Super-Resolution
Kwang In Kim1, Matthias O. Franz1, and Bernhard Sch¨olkopf1
Max Planck Institute f¨ur biologische Kybernetik
Spemannstr. 38, D-72076 T¨ubingen, Germany
{kimki, mof,
http://www.kyb.tuebingen.mpg.de/"
6a16b91b2db0a3164f62bfd956530a4206b23fea,A Method for Real-Time Eye Blink Detection and Its Application,"A Method for Real-Time Eye Blink Detection and Its Application
Chinnawat Devahasdin Na Ayudhya
Mahidol Wittayanusorn School
Puttamonton, Nakornpatom 73170, Thailand"
6a806978ca5cd593d0ccd8b3711b6ef2a163d810,Facial Feature Tracking for Emotional Dynamic Analysis,"Facial feature tracking for Emotional Dynamic
Analysis
Thibaud Senechal1, Vincent Rapp1, and Lionel Prevost2
ISIR, CNRS UMR 7222
Univ. Pierre et Marie Curie, Paris
{rapp,
LAMIA, EA 4540
Univ. of Fr. West Indies & Guyana"
6a8a3c604591e7dd4346611c14dbef0c8ce9ba54,An Affect-Responsive Interactive Photo Frame,"ENTERFACE’10, JULY 12TH - AUGUST 6TH, AMSTERDAM, THE NETHERLANDS.
An Affect-Responsive Interactive Photo Frame
Hamdi Dibeklio˘glu, Ilkka Kosunen, Marcos Ortega Hortas, Albert Ali Salah, Petr Zuz´anek"
6a52e6fce541126ff429f3c6d573bc774f5b8d89,Role of Facial Emotion in Social Correlation,"Role of Facial Emotion in Social Correlation
Pankaj Mishra, Rafik Hadfi, and Takayuki Ito
Department of Computer Science and Engineering
Nagoya Institute of Technology, Gokiso, Showa-ku, Nagoya, 466-8555 Japan
{pankaj.mishra,"
6aefe7460e1540438ffa63f7757c4750c844764d,Non-rigid Segmentation Using Sparse Low Dimensional Manifolds and Deep Belief Networks,"Non-rigid Segmentation using Sparse Low Dimensional Manifolds and
Deep Belief Networks ∗
Jacinto C. Nascimento
Instituto de Sistemas e Rob´otica
Instituto Superior T´ecnico, Portugal"
6a7e464464f70afea78552c8386f4d2763ea1d9c,Facial Landmark Localization – A Literature Survey,"Review Article
International Journal of Current Engineering and Technology
E-ISSN 2277 – 4106, P-ISSN 2347 - 5161
©2014 INPRESSCO
, All Rights Reserved
Available at http://inpressco.com/category/ijcet
Facial Landmark Localization – A Literature Survey
Dhananjay RathodȦ*, Vinay A, Shylaja SSȦ and S NatarajanȦ
ȦDepartment of Information Science and Engineering, PES Institute of Technology, Bangalore, Karnataka, India
Accepted 25 May 2014, Available online 01 June2014, Vol.4, No.3 (June 2014)"
32925200665a1bbb4fc8131cd192cb34c2d7d9e3,An Active Appearance Model with a Derivative-Free Optimization,"MVA2009 IAPR Conference on Machine Vision Applications, May 20-22, 2009, Yokohama, JAPAN
An Active Appearance Model with a Derivative-Free
Optimization
Jixia ZHANG‡, Franck DAVOINE†, Chunhong PAN‡
CNRS†, Institute of Automation of the Chinese Academy of Sciences‡
95, Zhongguancun Dong Lu, PO Box 2728 − Beijing 100190 − PR China
LIAMA Sino-French IT Lab."
322c063e97cd26f75191ae908f09a41c534eba90,Improving Image Classification Using Semantic Attributes,"Noname manuscript No.
(will be inserted by the editor)
Improving Image Classification using Semantic Attributes
Yu Su · Fr´ed´eric Jurie
Received: date / Accepted: date"
325b048ecd5b4d14dce32f92bff093cd744aa7f8,Multi-Image Graph Cut Clothing Segmentation for Recognizing People,"#2670
CVPR 2008 Submission #2670. CONFIDENTIAL REVIEW COPY. DO NOT DISTRIBUTE.
#2670
Multi-Image Graph Cut Clothing Segmentation for Recognizing People
Anonymous CVPR submission
Paper ID 2670"
32f7e1d7fa62b48bedc3fcfc9d18fccc4074d347,Hierarchical Sparse and Collaborative Low-Rank representation for emotion recognition,"HIERARCHICAL SPARSE AND COLLABORATIVE LOW-RANK REPRESENTATION FOR
EMOTION RECOGNITION
Xiang Xiang, Minh Dao, Gregory D. Hager, Trac D. Tran
Johns Hopkins University, 3400 N. Charles Street, Baltimore, MD 21218, USA
{xxiang, minh.dao, ghager1,"
324f39fb5673ec2296d90142cf9a909e595d82cf,Relationship Matrix Nonnegative Decomposition for Clustering,"Hindawi Publishing Corporation
Mathematical Problems in Engineering
Volume 2011, Article ID 864540, 15 pages
doi:10.1155/2011/864540
Research Article
Relationship Matrix Nonnegative
Decomposition for Clustering
Ji-Yuan Pan and Jiang-She Zhang
Faculty of Science and State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong
University, Xi’an Shaanxi Province, Xi’an 710049, China
Correspondence should be addressed to Ji-Yuan Pan,
Received 18 January 2011; Revised 28 February 2011; Accepted 9 March 2011
Academic Editor: Angelo Luongo
Copyright q 2011 J.-Y. Pan and J.-S. Zhang. This is an open access article distributed under
the Creative Commons Attribution License, which permits unrestricted use, distribution, and
reproduction in any medium, provided the original work is properly cited.
Nonnegative matrix factorization (cid:2)NMF(cid:3) is a popular tool for analyzing the latent structure of non-
negative data. For a positive pairwise similarity matrix, symmetric NMF (cid:2)SNMF(cid:3) and weighted
NMF (cid:2)WNMF(cid:3) can be used to cluster the data. However, both of them are not very ef‌f‌icient
for the ill-structured pairwise similarity matrix. In this paper, a novel model, called relationship"
321bd4d5d80abb1bae675a48583f872af3919172,Entropy-weighted feature-fusion method for head-pose estimation,"Wang et al. EURASIP Journal on Image and Video Processing  (2016) 2016:44
DOI 10.1186/s13640-016-0152-3
EURASIP Journal on Image
nd Video Processing
R EV I E W
Entropy-weighted feature-fusion method
for head-pose estimation
Xiao-Meng Wang*, Kang Liu and Xu Qian
Open Access"
32575ffa69d85bbc6aef5b21d73e809b37bf376d,Measuring Biometric Sample Quality in Terms of Biometric Information,"-)5741/ *1-641+ 5)2- 37)16; 1 6-45 . *1-641+ 1.4)61
;K=H=
5?D B 1BH=JE 6A?DCO -CEAAHEC
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IKHA L=HE=JEI E >EAJHE? I=FA GK=EJO 9A >ACE MEJD
JDA EJKEJE JD=J J = >EAJHE? I=FA ME HA
JDA =KJ B EBH=JE =L=E=>A 1 H
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>EAJHE? EBH=JE =I JDA E K?AHJ=EJO
=>KJ JDA B = FAHI J = IAJ B >EAJHE? A=
IKHAAJI 9A JDA IDM JD=J JDA >EAJHE? EBH=JE BH
= FAHI =O >A >O JDA HA=JELA AJHFO D(p(cid:107)q)
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FAI MA = =CHEJD MDE?D HACK=HEAI = /=KIIE="
32728e1eb1da13686b69cc0bd7cce55a5c963cdd,Automatic Facial Emotion Recognition Method Based on Eye Region Changes,"Automatic Facial Emotion Recognition Method Based on Eye
Region Changes
Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran
Mina Navraan
Nasrollah Moghadam Charkari*
Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran
Muharram Mansoorizadeh
Faculty of Electrical and Computer Engineering, Bu-Ali Sina University, Hamadan, Iran
Received: 19/Apr/2015            Revised: 19/Mar/2016            Accepted: 19/Apr/2016"
324b9369a1457213ec7a5a12fe77c0ee9aef1ad4,Dynamic Facial Analysis: From Bayesian Filtering to Recurrent Neural Network,"Dynamic Facial Analysis: From Bayesian Filtering to Recurrent Neural Network
Jinwei Gu Xiaodong Yang Shalini De Mello Jan Kautz
NVIDIA"
32df63d395b5462a8a4a3c3574ae7916b0cd4d1d,Facial expression recognition using ensemble of classifiers,"978-1-4577-0539-7/11/$26.00 ©2011 IEEE
ICASSP 2011"
35308a3fd49d4f33bdbd35fefee39e39fe6b30b7,Efficient and effective human action recognition in video through motion boundary description with a compact set of trajectories,"biblio.ugent.be   The UGent Institutional Repository is the electronic archiving and dissemination platform for allUGent research publications. Ghent University has implemented a mandate stipulating that allacademic publications of UGent researchers should be deposited and archived in this repository.Except for items where current copyright restrictions apply, these papers are available in OpenAccess.   This item is the archived peer-reviewed author-version of:   Efficient and effective human action recognition in video through motion boundary description witha compact set of trajectories   Jeong-Jik Seo, Jisoo Son, Hyung-Il Kim, Wesley De Neve, and Yong Man Ro   In: 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition,1, 1-6, 2015.     To refer to or to cite this work, please use the citation to the published version:   Seo, J., Son, J., Kim, H., De Neve, W., and Ro, Y. M. (2015). Efficient and effective human actionrecognition in video through motion boundary description with a compact set of trajectories. 11thIEEE International Conference and Workshops on Automatic Face and Gesture Recognition 1 1-6.http://dx.doi.org/10.1109/FG.2015.7163123"
352d61eb66b053ae5689bd194840fd5d33f0e9c0,Analysis Dictionary Learning based Classification: Structure for Robustness,"Analysis Dictionary Learning based
Classification: Structure for Robustness
Wen Tang, Ashkan Panahi, Hamid Krim, and Liyi Dai"
3538d2b5f7ab393387ce138611ffa325b6400774,A DSP-based approach for the implementation of face recognition algorithms,"A DSP-BASED APPROACH FOR THE IMPLEMENTATION OF FACE RECOGNITION
ALGORITHMS
A. U. Batur
B. E. Flinchbaugh
M. H. Hayes IIl
Center for Signal and Image Proc.
Georgia Inst. Of Technology
Atlanta, GA
Imaging and Audio Lab.
Texas Instruments
Dallas, TX
Center for Signal and Image Proc.
Georgia Inst. Of Technology
Atlanta, CA"
3504907a2e3c81d78e9dfe71c93ac145b1318f9c,Unconstrained Still/Video-Based Face Verification with Deep Convolutional Neural Networks,"Noname manuscript No.
(will be inserted by the editor)
Unconstrained Still/Video-Based Face Verification with Deep
Convolutional Neural Networks
Jun-Cheng Chen∗
Kumar∗ · Ching-Hui Chen∗ · Vishal M. Patel · Carlos D. Castillo ·
Rama Chellappa
· Rajeev Ranjan∗ · Swami Sankaranarayanan∗ · Amit
Received: date / Accepted: date"
35b1c1f2851e9ac4381ef41b4d980f398f1aad68,Geometry Guided Convolutional Neural Networks for Self-Supervised Video Representation Learning,"Geometry Guided Convolutional Neural Networks for
Self-Supervised Video Representation Learning
Chuang Gan1, Boqing Gong2, Kun Liu3, Hao Su 4, Leonidas J. Guibas 5
MIT-IBM Watson AI Lab , 2 Tencent AI Lab, 3 BUPT, 4 UCSD, 5 Stanford University"
351c02d4775ae95e04ab1e5dd0c758d2d80c3ddd,ActionSnapping: Motion-Based Video Synchronization,"ActionSnapping: Motion-based Video
Synchronization
Jean-Charles Bazin and Alexander Sorkine-Hornung
Disney Research"
35e4b6c20756cd6388a3c0012b58acee14ffa604,Gender Classification in Large Databases,"Gender Classification in Large Databases
E. Ram´on-Balmaseda, J. Lorenzo-Navarro, and M. Castrill´on-Santana (cid:63)
Universidad de Las Palmas de Gran Canaria
SIANI
Spain"
35f084ddee49072fdb6e0e2e6344ce50c02457ef,A bilinear illumination model for robust face recognition,"A Bilinear Illumination Model
for Robust Face Recognition
The Harvard community has made this
rticle openly available.  Please share  how
this access benefits you. Your story matters
Citation
Lee, Jinho, Baback Moghaddam, Hanspeter Pfister, and Raghu
Machiraju. 2005. A bilinear illumination model for robust face
recognition. Proceedings of the Tenth IEEE International Conference
on Computer Vision: October 17-21, 2005, Beijing, China. 1177-1184.
Los Almamitos, C.A.: IEEE Computer Society.
Published Version
doi:10.1109/ICCV.2005.5
Citable link
http://nrs.harvard.edu/urn-3:HUL.InstRepos:4238979
Terms of Use
This article was downloaded from Harvard University’s DASH
repository, and is made available under the terms and conditions
pplicable to Other Posted Material, as set forth at http://
nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-"
353a89c277cca3e3e4e8c6a199ae3442cdad59b5,Learning from Multiple Views of Data,
35e0256b33212ddad2db548484c595334f15b4da,Attentive Fashion Grammar Network for Fashion Landmark Detection and Clothing Category Classification,"Attentive Fashion Grammar Network for
Fashion Landmark Detection and Clothing Category Classification
Wenguan Wang∗1,2, Yuanlu Xu∗2, Jianbing Shen†1, and Song-Chun Zhu2
Beijing Lab of Intelligent Information Technology, School of Computer Science, Beijing Institute of Technology, China
Department of Computer Science and Statistics, University of California, Los Angeles, USA"
35e6f6e5f4f780508e5f58e87f9efe2b07d8a864,Summarization of User-Generated Sports Video by Using Deep Action Recognition Features,"This paper is a preprint (IEEE accepted status). IEEE copyright notice. 2018 IEEE.
Personal use of this material is permitted. Permission from IEEE must be obtained for all
other uses, in any current or future media, including reprinting/republishing this material for
dvertising or promotional purposes, creating new collective works, for resale or redistribu-
tion to servers or lists, or reuse of any copyrighted.
A. Tejero-de-Pablos, Y. Nakashima, T. Sato, N. Yokoya, M. Linna and E. Rahtu, ”Sum-
marization of User-Generated Sports Video by Using Deep Action Recognition Features,” in
doi: 10.1109/TMM.2018.2794265
keywords: Cameras; Feature extraction; Games; Hidden Markov models; Semantics;
Three-dimensional displays; 3D convolutional neural networks; Sports video summarization;
ction recognition; deep learning; long short-term memory; user-generated video,
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8259321&isnumber=4456689"
35e87e06cf19908855a16ede8c79a0d3d7687b5c,Strategies for Multi-View Face Recognition for Identification of Human Faces: A Review,"Strategies for Multi-View Face Recognition for
Identification of Human Faces: A Review
Pritesh G. Shah
Department of Computer Science
Mahatma Gandhi Shikshan Mandal’s,
Arts, Science and Commerce College, Chopda
Dist: Jalgaon (M.S)
Dr. R.R.Manza
Department of Computer Science and IT
Dr. Babasaheb Ambedkar Marathwada University
Aurangabad."
352110778d2cc2e7110f0bf773398812fd905eb1,Matrix Completion for Weakly-Supervised Multi-Label Image Classification,"TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. X, NO. X, JUNE 2014
Matrix Completion for Weakly-supervised
Multi-label Image Classification
Ricardo Cabral, Fernando De la Torre, João P. Costeira, Alexandre Bernardino"
69ff40fd5ce7c3e6db95a2b63d763edd8db3a102,Human Age Estimation via Geometric and Textural Features,"HUMAN AGE ESTIMATION VIA GEOMETRIC AND TEXTURAL
FEATURES
Merve KILINC1 and Yusuf Sinan AKGUL2
TUBITAK BILGEM UEKAE, Anibal Street, 41470, Gebze, Kocaeli, Turkey
GIT Vision Lab, http://vision.gyte.edu.tr/, Department of Computer Engineering, Gebze Institute of Technology, 41400,
Kocaeli, Turkey
Keywords:
Age estimation:age classification:geometric features:LBP:Gabor:LGBP:cross ratio:FGNET:MORPH"
69a55c30c085ad1b72dd2789b3f699b2f4d3169f,Automatic Happiness Strength Analysis of a Group of People using Facial Expressions,"International Journal of Computer Trends and Technology (IJCTT) – Volume 34 Number 3 - April 2016
Automatic Happiness Strength Analysis of a
Group of People using Facial Expressions
Sagiri Prasanthi#1, Maddali M.V.M. Kumar*2,
#1PG Student, #2Assistant Professor
#1, #2Department of MCA, St. Ann’s College of Engineering & Technology, Andhra Pradesh, India
is  a  collective  concern"
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690d669115ad6fabd53e0562de95e35f1078dfbb,"Progressive versus Random Projections for Compressive Capture of Images, Lightfields and Higher Dimensional Visual Signals","Progressive versus Random Projections for Compressive Capture of Images,
Lightfields and Higher Dimensional Visual Signals
Rohit Pandharkar
MIT Media Lab
75 Amherst St, Cambridge, MA
Ashok Veeraraghavan
01 Broadway, Cambridge MA
Ramesh Raskar
MIT Media Lab
75 Amherst St, Cambridge, MA"
69063f7e0a60ad6ce16a877bc8f11b59e5f7348e,Class-Specific Image Deblurring,"Class-Specific Image Deblurring
Saeed Anwar1, Cong Phuoc Huynh1
, Fatih Porikli1
The Australian National University∗ Canberra ACT 2601, Australia
NICTA, Locked Bag 8001, Canberra ACT 2601, Australia"
3cb2841302af1fb9656f144abc79d4f3d0b27380,When 3 D-Aided 2 D Face Recognition Meets Deep Learning : An extended UR 2 D for Pose-Invariant Face Recognition,"See	discussions,	stats,	and	author	profiles	for	this	publication	at:	https://www.researchgate.net/publication/319928941
When	3D-Aided	2D	Face	Recognition	Meets	Deep
Learning:	An	extended	UR2D	for	Pose-Invariant
Face	Recognition
Article	·	September	2017
CITATIONS
authors:
READS
Xiang	Xu
University	of	Houston
Pengfei	Dou
University	of	Houston
8	PUBLICATIONS			10	CITATIONS
9	PUBLICATIONS			29	CITATIONS
SEE	PROFILE
SEE	PROFILE
Ha	Le
University	of	Houston
7	PUBLICATIONS			2	CITATIONS
Ioannis	A	Kakadiaris"
3cc3cf57326eceb5f20a02aefae17108e8c8ab57,Benchmark for Evaluating Biological Image Analysis Tools,"BENCHMARK FOR EVALUATING BIOLOGICAL IMAGE ANALYSIS TOOLS
Elisa Drelie Gelasca, Jiyun Byun, Boguslaw Obara, B.S. Manjunath
Center for Bio-Image Informatics, Electrical and Computer Engineering Department,
University of California, Santa Barbara 93106-9560,
http://www.bioimage.ucsb.edu
Biological images are critical components for a detailed understanding of the structure and functioning of cells and proteins.
Image processing and analysis tools increasingly play a significant role in better harvesting this vast amount of data, most of
which is currently analyzed manually and qualitatively. A number of image analysis tools have been proposed to automatically
extract the image information. As the studies relying on image analysis tools have become widespread, the validation of
these methods, in particular, segmentation methods, has become more critical. There have been very few efforts at creating
enchmark datasets in the context of cell and tissue imaging, while, there have been successful benchmarks in other fields, such
s the Berkeley segmentation dataset [1], the handwritten digit recognition dataset MNIST [2] and face recognition dataset [3, 4].
In the field of biomedical image processing, most of standardized benchmark data sets concentrates on macrobiological images
such as mammograms and magnet resonance imaging (MRI) images [5], however, there is still a lack of a standardized dataset
for microbiological structures (e.g. cells and tissues) and it is well known in biomedical imaging [5].
We propose a benchmark for biological images to: 1) provide image collections with well defined ground truth; 2) provide
image analysis tools and evaluation methods to compare and validate analysis tools. We include a representative dataset of
microbiological structures whose scales range from a subcellular level (nm) to a tissue level (µm), inheriting intrinsic challenges
in the domain of biomedical image analysis (Fig. 1). The dataset is acquired through two of the main microscopic imaging
techniques: transmitted light microscopy and confocal laser scanning microscopy. The analysis tools1in the benchmark are"
3cfbe1f100619a932ba7e2f068cd4c41505c9f58,A Realistic Simulation Tool for Testing Face Recognition Systems under Real-World Conditions,"A Realistic Simulation Tool for Testing Face Recognition
Systems under Real-World Conditions∗
M. Correa, J. Ruiz-del-Solar, S. Parra-Tsunekawa, R. Verschae
Department of Electrical Engineering, Universidad de Chile
Advanced Mining Technology Center, Universidad de Chile"
3cd7b15f5647e650db66fbe2ce1852e00c05b2e4,"ACTIVE, an Extensible Cataloging Platform for Automatic Indexing of Audiovisual Content",
3c374cb8e730b64dacb9fbf6eb67f5987c7de3c8,Measuring Gaze Orientation for Human-Robot Interaction,"Measuring Gaze Orientation for Human-Robot
Interaction
R. Brochard∗, B. Burger∗, A. Herbulot∗†, F. Lerasle∗†
CNRS; LAAS; 7 avenue du Colonel Roche, 31077 Toulouse Cedex, France
Universit´e de Toulouse; UPS; LAAS-CNRS : F-31077 Toulouse, France
Introduction
In the context of Human-Robot interaction estimating gaze orientation brings
useful information about human focus of attention. This is a contextual infor-
mation : when you point something you usually look at it. Estimating gaze
orientation requires head pose estimation. There are several techniques to esti-
mate head pose from images, they are mainly based on training [3, 4] or on local
face features tracking [6]. The approach described here is based on local face
features tracking in image space using online learning, it is a mixed approach
since we track face features using some learning at feature level. It uses SURF
features [2] to guide detection and tracking. Such key features can be matched
etween images, used for object detection or object tracking [10]. Several ap-
proaches work on fixed size images like training techniques which mainly work
on low resolution images because of computation costs whereas approaches based
on local features tracking work on high resolution images. Tracking face features
such as eyes, nose and mouth is a common problem in many applications such as"
3c0bbfe664fb083644301c67c04a7f1331d9515f,The Role of Color and Contrast in Facial Age Estimation,"The Role of Color and Contrast in Facial Age Estimation
Paper ID: 7
No Institute Given"
3c4f6d24b55b1fd3c5b85c70308d544faef3f69a,A Hybrid Deep Learning Architecture for Privacy-Preserving Mobile Analytics,"A Hybrid Deep Learning Architecture for
Privacy-Preserving Mobile Analytics
Seyed Ali Ossia(cid:63), Ali Shahin Shamsabadi(cid:63), Ali Taheri(cid:63), Hamid R. Rabiee(cid:63),
Nic Lane‡, Hamed Haddadi†
(cid:63)Sharif University of Technology, ‡University College London, †Queen Mary University of London"
3cb0ef5aabc7eb4dd8d32a129cb12b3081ef264f,Absolute Head Pose Estimation From Overhead Wide-Angle Cameras,"Absolute Head Pose Estimation From Overhead Wide-Angle Cameras
Ying-Li Tian, Lisa Brown, Jonathan Connell,
Sharat Pankanti, Arun Hampapur, Andrew Senior, Ruud Bolle
IBM T.J. Watson Research Center
9 Skyline Drive, Hawthorne, NY 10532 USA
{ yltian,lisabr,jconnell,sharat,arunh,aws,bolle"
3c56acaa819f4e2263638b67cea1ec37a226691d,Body Joint Guided 3-D Deep Convolutional Descriptors for Action Recognition,"Body Joint guided 3D Deep Convolutional
Descriptors for Action Recognition
Congqi Cao, Yifan Zhang, Member, IEEE, Chunjie Zhang, Member, IEEE, and Hanqing Lu, Senior Member, IEEE"
3c8da376576938160cbed956ece838682fa50e9f,Aiding face recognition with social context association rule based re-ranking,"Chapter 4
Aiding Face Recognition with
Social Context Association Rule
ased Re-Ranking
Humans are very ef‌f‌icient at recognizing familiar face images even in challenging condi-
tions. One reason for such capabilities is the ability to understand social context between
individuals. Sometimes the identity of the person in a photo can be inferred based on the
identity of other persons in the same photo, when some social context between them is
known. This chapter presents an algorithm to utilize the co-occurrence of individuals as
the social context to improve face recognition. Association rule mining is utilized to infer
multi-level social context among subjects from a large repository of social transactions.
The results are demonstrated on the G-album and on the SN-collection pertaining to 4675
identities prepared by the authors from a social networking website. The results show that
ssociation rules extracted from social context can be used to augment face recognition and
improve the identification performance.
Introduction
Face recognition capabilities of humans have inspired several researchers to understand
the science behind it and use it in developing automated algorithms. Recently, it is also
rgued that encoding social context among individuals can be leveraged for improved
utomatic face recognition [175]. As shown in Figure 4.1, often times a person’s identity"
56e4dead93a63490e6c8402a3c7adc493c230da5,Face Recognition Techniques: A Survey,"World Journal of Computer Application and Technology 1(2): 41-50, 2013
DOI: 10.13189/wjcat.2013.010204
http://www.hrpub.org
Face Recognition Techniques: A Survey
V.Vijayakumari
Department of Electronics and Communication, Sri krishna College of Technology, Coimbatore, India
*Corresponding Author:
Copyright © 2013 Horizon Research Publishing All rights reserved."
56e885b9094391f7d55023a71a09822b38b26447,Face Retrieval using Frequency Decoded Local Descriptor,"FREQUENCY DECODED LOCAL BINARY PATTERN
Face Retrieval using Frequency Decoded Local
Descriptor
Shiv Ram Dubey, Member, IEEE"
56a653fea5c2a7e45246613049fb16b1d204fc96,Quaternion Collaborative and Sparse Representation With Application to Color Face Recognition,"Quaternion Collaborative and Sparse Representation
With Application to Color Face Recognition
Cuiming Zou, Kit Ian Kou, Member, IEEE, and Yulong Wang, Student Member, IEEE
representation-based"
5666ed763698295e41564efda627767ee55cc943,Relatively-Paired Space Analysis: Learning a Latent Common Space From Relatively-Paired Observations,"Manuscript
Click here to download Manuscript: template.tex
Click here to view linked References
Noname manuscript No.
(will be inserted by the editor)
Relatively-Paired Space Analysis: Learning a Latent Common
Space from Relatively-Paired Observations
Zhanghui Kuang · Kwan-Yee K. Wong
Received: date / Accepted: date"
5615d6045301ecbc5be35e46cab711f676aadf3a,Discriminatively Learned Hierarchical Rank Pooling Networks,"Discriminatively Learned Hierarchical Rank Pooling Networks
Basura Fernando · Stephen Gould
Received: date / Accepted: date"
566038a3c2867894a08125efe41ef0a40824a090,Face recognition and gender classification in personal memories,"978-1-4244-2354-5/09/$25.00 ©2009 IEEE
ICASSP 2009"
56dca23481de9119aa21f9044efd7db09f618704,Riemannian Dictionary Learning and Sparse Coding for Positive Definite Matrices,"Riemannian Dictionary Learning and Sparse
Coding for Positive Definite Matrices
Anoop Cherian
Suvrit Sra"
516a27d5dd06622f872f5ef334313350745eadc3,Fine-Grained Facial Expression Analysis Using Dimensional Emotion Model,"> REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE-CLICK HERE TO EDIT) <
Fine-Grained Facial Expression Analysis Us-
ing Dimensional Emotion Model
ǂFeng Zhou, ǂShu Kong, Charless C. Fowlkes, Tao Chen, *Baiying Lei, Member, IEEE"
51c3050fb509ca685de3d9ac2e965f0de1fb21cc,Fantope Regularization in Metric Learning,"Fantope Regularization in Metric Learning
Marc T. Law
Nicolas Thome
Matthieu Cord
Sorbonne Universit´es, UPMC Univ Paris 06, UMR 7606, LIP6, F-75005, Paris, France"
51c7c5dfda47647aef2797ac3103cf0e108fdfb4,Cs 395t: Celebrity Look-alikes *,"CS 395T: Celebrity Look-Alikes ∗
Adrian Quark"
519f4eb5fe15a25a46f1a49e2632b12a3b18c94d,Non-Lambertian Reflectance Modeling and Shape Recovery of Faces Using Tensor Splines,"Non-Lambertian Reflectance Modeling and
Shape Recovery of Faces using Tensor Splines
Ritwik Kumar, Student Member, IEEE, Angelos Barmpoutis, Member, IEEE,
Arunava Banerjee, Member, IEEE, and Baba C. Vemuri, Fellow, IEEE"
51cc78bc719d7ff2956b645e2fb61bab59843d2b,Face and Facial Expression Recognition with an Embedded System for Human-Robot Interaction,"Face and Facial Expression Recognition with an
Embedded System for Human-Robot Interaction
Yang-Bok Lee1, Seung-Bin Moon1, and Yong-Guk Kim 1*
School of Computer Engineering, Sejong University, Seoul, Korea"
511b06c26b0628175c66ab70dd4c1a4c0c19aee9,Face Recognition using Laplace Beltrami Operator by Optimal Linear Approximations,"International Journal of Engineering Research and General ScienceVolume 2, Issue 5, August – September 2014
ISSN 2091-2730
Face Recognition using Laplace Beltrami Operator by Optimal Linear
Approximations
Tapasya Sinsinwar1, P.K.Dwivedi2
Professor and Director Academics, Institute of Engineering and Technology, Alwar, Rajasthan Technical University, Kota(Raj.)
Research Scholar (M.Tech, IT), Institute of Engineering and Technology"
5161e38e4ea716dcfb554ccb88901b3d97778f64,SSPP-DAN: Deep domain adaptation network for face recognition with single sample per person,"SSPP-DAN: DEEP DOMAIN ADAPTATION NETWORK FOR
FACE RECOGNITION WITH SINGLE SAMPLE PER PERSON
Sungeun Hong, Woobin Im, Jongbin Ryu, Hyun S. Yang
School of Computing, KAIST, Republic of Korea"
5121f42de7cb9e41f93646e087df82b573b23311,Classifying Online Dating Profiles on Tinder using FaceNet Facial Embeddings,"CLASSIFYING ONLINE DATING PROFILES ON TINDER USING FACENET FACIAL
EMBEDDINGS
Charles F. Jekel and Raphael T. Haftka
Department of Mechanical & Aerospace Engineering - University of Florida - Gainesville, FL 32611"
51d1a6e15936727e8dd487ac7b7fd39bd2baf5ee,"A Fast and Accurate System for Face Detection, Identification, and Verification","JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015
A Fast and Accurate System for Face Detection,
Identification, and Verification
Rajeev Ranjan, Ankan Bansal, Jingxiao Zheng, Hongyu Xu, Joshua Gleason, Boyu Lu, Anirudh Nanduri,
Jun-Cheng Chen, Carlos D. Castillo, Rama Chellappa"
5157dde17a69f12c51186ffc20a0a6c6847f1a29,Evolutionary Cost-Sensitive Extreme Learning Machine,"Evolutionary Cost-sensitive Extreme Learning
Machine
Lei Zhang, Member, IEEE, and David Zhang, Fellow, IEEE"
3daafe6389d877fe15d8823cdf5ac15fd919676f,Human Action Localization with Sparse Spatial Supervision,"Human Action Localization
with Sparse Spatial Supervision
Philippe Weinzaepfel, Xavier Martin, and Cordelia Schmid, Fellow, IEEE"
3daf1191d43e21a8302d98567630b0e2025913b0,Can Autism be Catered with Artificial Intelligence-Assisted Intervention Technology? A Literature Review,"Can Autism be Catered with Artificial Intelligence-Assisted Intervention
Technology? A Literature Review
Muhammad Shoaib Jaliawala∗, Rizwan Ahmed Khan∗†
Faculty of Information Technology, Barrett Hodgson University, Karachi, Pakistan
Universit´e Claude Bernard Lyon 1, France"
3d36f941d8ec613bb25e80fb8f4c160c1a2848df,Out-of-Sample Generalizations for Supervised Manifold Learning for Classification,"Out-of-sample generalizations for supervised
manifold learning for classification
Elif Vural and Christine Guillemot"
3d5a1be4c1595b4805a35414dfb55716e3bf80d8,Hidden Two-Stream Convolutional Networks for Action Recognition,"Hidden Two-Stream Convolutional Networks for
Action Recognition
Yi Zhu, Zhenzhong Lan, Shawn Newsam, Alexander G. Hauptmann"
3d62b2f9cef997fc37099305dabff356d39ed477,Joint Face Alignment and 3D Face Reconstruction with Application to Face Recognition,"Joint Face Alignment and 3D Face
Reconstruction with Application to Face
Recognition
Feng Liu, Qijun Zhao, Member, IEEE, Xiaoming Liu, Member, IEEE and Dan Zeng"
3dd4d719b2185f7c7f92cc97f3b5a65990fcd5dd,Ensemble of Hankel Matrices for Face Emotion Recognition,"Ensemble of Hankel Matrices for
Face Emotion Recognition
Liliana Lo Presti and Marco La Cascia
DICGIM, Universit´a degli Studi di Palermo,
V.le delle Scienze, Ed. 6, 90128 Palermo, Italy,
DRAFT
To appear in ICIAP 2015"
3dcebd4a1d66313dcd043f71162d677761b07a0d,Local binary pattern domain local appearance face recognition,"Yerel Đkili Örüntü Ortamında Yerel Görünüme Dayalı Yüz Tanıma
Local Binary Pattern Domain Local Appearance Face Recognition
Hazım K. Ekenel1, Mika Fischer1, Erkin Tekeli2, Rainer Stiefelhagen1, Aytül Erçil2
Institut für Theorestische Informatik, Universität Karlsruhe (TH), Karlsruhe, Germany
Faculty of Engineering and Natural Sciences, Sabancı University, Đstanbul, Turkey
Özetçe
Bu bildiride, ayrık kosinüs dönüşümü tabanlı yerel görünüme
dayalı  yüz  tanıma  algoritması  ile  yüz  imgelerinin  yerel  ikili
örüntüye  (YĐÖ)  dayalı  betimlemesini  birleştiren  hızlı  bir  yüz
tanıma  algoritması  sunulmuştur.  Bu  tümleştirmedeki  amaç,
yerel  ikili  örüntünün  dayanıklı  imge  betimleme  yeteneği  ile
yrık  kosinüs  dönüşümünün  derli-toplu  veri  betimleme
yeteneğinden  yararlanmaktır.  Önerilen  yaklaşımda,  yerel
görünümün  modellenmesinden  önce  girdi  yüz  imgesi  yerel
ikili  örüntü  ile  betimlenmiştir.  Elde  edilen  YĐÖ  betimlemesi,
irbirleri  ile  örtüşmeyen  bloklara  ayrılmış  ve  her  blok
üzerinde  yerel  özniteliklerin  çıkartımı  için  ayrık  kosinüs
dönüşümü uygulanmıştır.  Çıkartımı  yapılan  yerel  öznitelikler
daha  sonra  arka  arkaya  eklenerek  global  öznitelik  vektörü
oluşturulmuştur.  Önerilen  algoritma,  CMU  PIE  ve  FRGC"
3d42e17266475e5d34a32103d879b13de2366561,The Global Dimensionality of Face Space,"Proc.4thIEEEInt’lConf.AutomaticFace&GestureRecognition,Grenoble,France,pp264–270
The Global Dimensionality of Face Space
(cid:3)
http://venezia.rockefeller.edu/
The Rockefeller University
Penio S. Penev
Laboratory of Computational Neuroscience
Lawrence Sirovich
Laboratory for Applied Mathematics
Mount Sinai School of Medicine
(cid:13) IEEE2000
230 York Avenue, New York, NY 10021
One Gustave L. Levy Place, New York, NY 10029"
3df7401906ae315e6aef3b4f13126de64b894a54,Robust learning of discriminative projection for multicategory classification on the Stiefel manifold,"Robust Learning of Discriminative Projection for Multicategory Classification on
the Stiefel Manifold
Duc-Son Pham and Svetha Venkatesh
Dept. of Computing, Curtin University of Technology
GPO Box U1987, Perth, WA 6845, Australia"
3d1af6c531ebcb4321607bcef8d9dc6aa9f0dc5a,Random Multispace Quantization as an Analytic Mechanism for BioHashing of Biometric and Random Identity Inputs,"Random Multispace Quantization as
n Analytic Mechanism for BioHashing
of Biometric and Random Identity Inputs
Andrew B.J. Teoh, Member, IEEE, Alwyn Goh, and David C.L. Ngo, Member, IEEE"
3d94f81cf4c3a7307e1a976dc6cb7bf38068a381,Data-Dependent Label Distribution Learning for Age Estimation,"Data-Dependent Label Distribution Learning
for Age Estimation
Zhouzhou He, Xi Li, Zhongfei Zhang, Fei Wu, Xin Geng, Yaqing Zhang, Ming-Hsuan Yang, and Yueting Zhuang"
5892f8367639e9c1e3cf27fdf6c09bb3247651ed,Estimating Missing Features to Improve Multimedia Information Retrieval,"Estimating Missing Features to Improve Multimedia Information Retrieval
Abraham Bagherjeiran
Nicole S. Love
Chandrika Kamath (cid:3)"
587f81ae87b42c18c565694c694439c65557d6d5,DeepFace: Face Generation using Deep Learning,"DeepFace: Face Generation using Deep Learning
Hardie Cate
Fahim Dalvi
Zeshan Hussain"
580054294ca761500ada71f7d5a78acb0e622f19,A Subspace Model-Based Approach to Face Relighting Under Unknown Lighting and Poses,"A Subspace Model-Based Approach to Face
Relighting Under Unknown Lighting and Poses
Hyunjung Shim, Student Member, IEEE, Jiebo Luo, Senior Member, IEEE, and Tsuhan Chen, Fellow, IEEE"
587c48ec417be8b0334fa39075b3bfd66cc29dbe,Serial dependence in the perception of attractiveness,"Journal of Vision (2016) 16(15):28, 1–8
Serial dependence in the perception of attractiveness
Ye Xia
Department of Psychology, University of California,
Berkeley, CA, USA
Allison Yamanashi Leib
Department of Psychology, University of California,
Berkeley, CA, USA
David Whitney
Department of Psychology, University of California,
Berkeley, CA, USA
Helen Wills Neuroscience Institute, University of
California, Berkeley, CA, USA
Vision Science Group, University of California,
Berkeley, CA, USA
The perception of attractiveness is essential for choices
of food, object, and mate preference. Like perception of
other visual features, perception of attractiveness is
stable despite constant changes of image properties due
to factors like occlusion, visual noise, and eye"
58081cb20d397ce80f638d38ed80b3384af76869,Embedded Real-Time Fall Detection Using Deep Learning For Elderly Care,"Embedded Real-Time Fall Detection Using Deep
Learning For Elderly Care
Hyunwoo Lee∗
Jooyoung Kim
Dojun Yang
Joon-Ho Kim
Samsung Research, Samsung Electronics
{hyun0772.lee, joody.kim, dojun.yang,"
581e920ddb6ecfc2a313a3aa6fed3d933b917ab0,Automatic Mapping of Remote Crowd Gaze to Stimuli in the Classroom,"Automatic Mapping of Remote Crowd Gaze to
Stimuli in the Classroom
Thiago Santini1, Thomas K¨ubler1, Lucas Draghetti1, Peter Gerjets2, Wolfgang
Wagner3, Ulrich Trautwein3, and Enkelejda Kasneci1
University of T¨ubingen, T¨ubingen, Germany
Leibniz-Institut f¨ur Wissensmedien, T¨ubingen, Germany
Hector Research Institute of Education Sciences and Psychology, T¨ubingen,
Germany"
58fa85ed57e661df93ca4cdb27d210afe5d2cdcd,Facial expression recognition by re-ranking with global and local generic features,"Cancún Center, Cancún, México, December 4-8, 2016
978-1-5090-4847-2/16/$31.00 ©2016 IEEE"
5860cf0f24f2ec3f8cbc39292976eed52ba2eafd,COMPUTATION EvaBio: A TOOL FOR PERFORMANCE EVALUATION IN BIOMETRICS,"International Journal of Automated Identification Technology, 3(2), July-December 2011, pp. 51-60
COMPUTATION EvaBio: A TOOL FOR PERFORMANCE
EVALUATION IN BIOMETRICS
Julien Mahier, Baptiste Hemery, Mohamad El-Abed*, Mohamed T. El-Allam, Mohamed Y.
Bouhaddaoui and Christophe Rosenberger
GREYC Laboratory, ENSICAEN - University of Caen Basse Normandie - CNRS,
6 Boulevard Maréchal Juin, 14000 Caen Cedex - France"
58bf72750a8f5100e0c01e55fd1b959b31e7dbce,PyramidBox: A Context-assisted Single Shot Face Detector,"PyramidBox: A Context-assisted Single Shot
Face Detector.
Xu Tang∗, Daniel K. Du∗, Zeqiang He, and Jingtuo Liu†
Baidu Inc."
58542eeef9317ffab9b155579256d11efb4610f2,"Face Recognition Revisited On Pose , Alignment , Color , Illumination And Expression-Pyten","International Journal of Science and Research (IJSR)
ISSN (Online): 2319-7064
Index Copernicus Value (2013): 6.14 | Impact Factor (2014): 5.611
Face Recognition Revisited on Pose, Alignment,
Color, Illumination and Expression-PyTen
Mugdha Tripathi
Computer Science, BIT Noida, India"
58823377757e7dc92f3b70a973be697651089756,Automatic facial expression analysis,"Technical Report
UCAM-CL-TR-861
ISSN 1476-2986
Number 861
Computer Laboratory
Automatic facial expression analysis
Tadas Baltrusaitis
October 2014
5 JJ Thomson Avenue
Cambridge CB3 0FD
United Kingdom
phone +44 1223 763500
http://www.cl.cam.ac.uk/"
5865e824e3d8560e07840dd5f75cfe9bf68f9d96,Embodied conversational agents for multimodal automated social skills training in people with autism spectrum disorders,"RESEARCH ARTICLE
Embodied conversational agents for
multimodal automated social skills training in
people with autism spectrum disorders
Hiroki Tanaka1*, Hideki Negoro2, Hidemi Iwasaka3, Satoshi Nakamura1
Graduate School of Information Science, Nara Institute of Science and Technology, Ikoma-shi, Nara, 630-
0101, Japan, 2 Center for Special Needs Education, Nara University of Education, Nara-shi, Nara, 630-8538,
Japan, 3 Developmental Center for Child and Adult, Shigisan Hospital, Ikoma-gun, Nara, 636-0815, Japan"
58bb77dff5f6ee0fb5ab7f5079a5e788276184cc,Facial expression recognition with PCA and LBP features extracting from active facial patches,"Facial Expression Recognition with PCA and LBP
Features Extracting from Active Facial Patches
Yanpeng Liua, Yuwen Caoa, Yibin Lia, Ming Liu, Rui Songa
Yafang Wang, Zhigang Xu , Xin Maa†"
58db008b204d0c3c6744f280e8367b4057173259,Facial Expression Recognition,"International  Journal  of Current Engineering  and Technology
ISSN 2277 - 4106
© 2012  INPRESSCO.  All  Rights Reserved.
Available at http://inpressco.com/category/ijcet
Research Article
Facial Expression  Recognition
Riti Kushwahaa and  Neeta Naina*
Department of Computer  Engineering Malaviya National Institute of Technology, Jaipur, Rajasthan, India
Accepted 3June  2012,  Available online 8 June 2012"
677585ccf8619ec2330b7f2d2b589a37146ffad7,A flexible model for training action localization with varying levels of supervision,"A flexible model for training action localization
with varying levels of supervision
Guilhem Chéron∗ 1 2
Jean-Baptiste Alayrac∗ 1
Ivan Laptev1
Cordelia Schmid2"
6789bddbabf234f31df992a3356b36a47451efc7,Unsupervised Generation of Free-Form and Parameterized Avatars.,"Unsupervised Generation of Free-Form and
Parameterized Avatars
Adam Polyak, Yaniv Taigman, and Lior Wolf, Member, IEEE"
675b2caee111cb6aa7404b4d6aa371314bf0e647,AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions,"AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions
Chunhui Gu∗
Yeqing Li∗
Chen Sun∗
David A. Ross∗
Sudheendra Vijayanarasimhan∗
Carl Vondrick∗
George Toderici∗
Caroline Pantofaru∗
Susanna Ricco∗
Rahul Sukthankar∗
Cordelia Schmid† ∗
Jitendra Malik‡ ∗"
67484723e0c2cbeb936b2e863710385bdc7d5368,Anchor Cascade for Efficient Face Detection,"Anchor Cascade for Efficient Face Detection
Baosheng Yu and Dacheng Tao, Fellow, IEEE"
670637d0303a863c1548d5b19f705860a23e285c,Face swapping: automatically replacing faces in photographs,"Face Swapping: Automatically Replacing Faces in Photographs
Dmitri Bitouk
Neeraj Kumar
Samreen Dhillon∗
Columbia University†
Peter Belhumeur
Shree K. Nayar
Figure 1: We have developed a system that automatically replaces faces in an input image with ones selected from a large collection of
face images, obtained by applying face detection to publicly available photographs on the internet. In this example, the faces of (a) two
people are shown after (b) automatic replacement with the top three ranked candidates. Our system for face replacement can be used for face
de-identification, personalized face replacement, and creating an appealing group photograph from a set of “burst” mode images. Original
images in (a) used with permission from Retna Ltd. (top) and Getty Images Inc. (bottom).
Rendering, Computational Photography
Introduction
Advances in digital photography have made it possible to cap-
ture large collections of high-resolution images and share them
on the internet. While the size and availability of these col-
lections is leading to many exciting new applications,
lso creating new problems. One of the most
important of"
6742c0a26315d7354ab6b1fa62a5fffaea06da14,What does 2D geometric information really tell us about 3D face shape?,"BAS AND SMITH: WHAT DOES 2D GEOMETRIC INFORMATION REALLY TELL US ABOUT 3D FACE SHAPE?
What does 2D geometric information
really tell us about 3D face shape?
Anil Bas and William A. P. Smith, Member, IEEE"
67c703a864aab47eba80b94d1935e6d244e00bcb,Face Retrieval Based On Local Binary Pattern and Its Variants: A Comprehensive Study,"(IJACSA) International Journal of Advanced Computer Science and Applications
Vol. 7, No. 6, 2016
Face Retrieval Based On Local Binary Pattern and Its
Variants: A Comprehensive Study
Department of Computer Vision and Robotics, University of Science, VNU-HCM, Viet Nam
Phan Khoi, Lam Huu Thien, Vo Hoai Viet
face  searching,"
67ba3524e135c1375c74fe53ebb03684754aae56,A compact pairwise trajectory representation for action recognition,"978-1-5090-4117-6/17/$31.00 ©2017 IEEE
ICASSP 2017"
6769cfbd85329e4815bb1332b118b01119975a95,Tied factor analysis for face recognition across large pose changes,"Tied factor analysis for face recognition across
large pose changes"
0be43cf4299ce2067a0435798ef4ca2fbd255901,Title A temporal latent topic model for facial expression recognition,"Title
A temporal latent topic model for facial expression recognition
Author(s)
Shang, L; Chan, KP
Citation
The 10th Asian Conference on Computer Vision (ACCV 2010),
Queenstown, New Zealand, 8-12 November 2010. In Lecture
Notes in Computer Science, 2010, v. 6495, p. 51-63
Issued Date
http://hdl.handle.net/10722/142604
Rights
Creative Commons: Attribution 3.0 Hong Kong License"
0b2277a0609565c30a8ee3e7e193ce7f79ab48b0,Cost-Sensitive Semi-Supervised Discriminant Analysis for Face Recognition,"Cost-Sensitive Semi-Supervised Discriminant
Analysis for Face Recognition
Jiwen Lu, Member, IEEE, Xiuzhuang Zhou, Member, IEEE, Yap-Peng Tan, Senior Member, IEEE,
Yuanyuan Shang, Member, IEEE, and Jie Zhou, Senior Member, IEEE"
0b9ce839b3c77762fff947e60a0eb7ebbf261e84,Logarithmic Fourier Pca: a New Approach to Face Recognition,"Proceedings of the IASTED International Conference
Computer Vision (CV 2011)
June 1 - 3, 2011  Vancouver, BC, Canada
LOGARITHMIC FOURIER PCA: A NEW APPROACH TO FACE
RECOGNITION
Lakshmiprabha  Nattamai  Sekar,
Jhilik Bhattacharya,
omjyoti
Majumder
Surface Robotics Lab
Central Mechanical Engineering Research Institute
Mahatma Gandhi Avenue,
Durgapur - 713209, West Bengal, India.
email: 1 n prabha 2 3"
0b6a5200c33434cbfa9bf24ba482f6e06bf5fff7,"The use of deep learning in image segmentation, classification and detection","The Use of Deep Learning in Image
Segmentation, Classification and Detection
Mihai-Sorin Badea, Iulian-Ionuț Felea, Laura Maria Florea, Constantin Vertan
The Image Processing and Analysis Lab (LAPI), Politehnica University of Bucharest, Romania"
0b605b40d4fef23baa5d21ead11f522d7af1df06,Label-Embedding for Attribute-Based Classification,"Label-Embedding for Attribute-Based Classification
Zeynep Akataa,b, Florent Perronnina, Zaid Harchaouib and Cordelia Schmidb
Computer Vision Group∗, XRCE, France
LEAR†, INRIA, France"
0b0eb562d7341231c3f82a65cf51943194add0bb,Line with Your Paper Identification Number ( Double - Click Here to Edit,"> REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE-CLICK HERE TO EDIT) <
Facial Image Analysis Based on Local Binary
Patterns: A Survey
Di Huang, Caifeng Shan, Mohsen Ardebilian, Liming Chen"
0b3a146c474166bba71e645452b3a8276ac05998,Whos In the Picture,"Who’s in the Picture?
Tamara L. Berg, Alexander C. Berg, Jaety Edwards and D.A. Forsyth
Berkeley, CA 94720
Computer Science Division
U.C. Berkeley"
0b0958493e43ca9c131315bcfb9a171d52ecbb8a,A Unified Neural Based Model for Structured Output Problems,"A Unified Neural Based Model for Structured Output Problems
Soufiane Belharbi∗1, Cl´ement Chatelain∗1, Romain H´erault∗1, and S´ebastien Adam∗2
LITIS EA 4108, INSA de Rouen, Saint ´Etienne du Rouvray 76800, France
LITIS EA 4108, UFR des Sciences, Universit´e de Rouen, France.
April 13, 2015"
0b20f75dbb0823766d8c7b04030670ef7147ccdd,Feature selection using nearest attributes,"Feature selection using nearest attributes
Alex Pappachen James, Member, IEEE, and Sima Dimitrijev, Senior Member, IEEE"
0b5a82f8c0ee3640503ba24ef73e672d93aeebbf,On Learning 3D Face Morphable Model from In-the-wild Images,"On Learning 3D Face Morphable Model
from In-the-wild Images
Luan Tran, and Xiaoming Liu, Member, IEEE"
0b174d4a67805b8796bfe86cd69a967d357ba9b6,A Survey on Face Detection and Recognition Approaches,"Research Journal of Recent Sciences _________________________________________________ ISSN 2277-2502
Vol. 3(4), 56-62, April (2014)
Res.J.Recent Sci."
0b87d91fbda61cdea79a4b4dcdcb6d579f063884,Research on Theory and Method for Facial Expression Recognition Sys- tem Based on Dynamic Image Sequence,"The Open Automation and Control Systems Journal, 2015, 7, 569-579
Open Access
Research  on  Theory  and  Method  for  Facial  Expression  Recognition  Sys-
tem Based on Dynamic Image Sequence
Send Orders for Reprints to
Yang Xinfeng1,* and Jiang Shan2
School  of  Computer  &  Information  Engineering,  Nanyang  Institute  of  Technology,  Henan,  Nanyang,  473000,  P.R.
China
Henan University of Traditional Chinese Medicine, Henan, Zhengzhou, 450000, P.R. China"
0b79356e58a0df1d0efcf428d0c7c4651afa140d,Bayesian Modeling of Facial Similarity,"Appears In: Advances in Neural Information Processing Systems 

, MIT Press, 
			.
Bayesian Modeling of Facial Similarity
Baback Moghaddam
Mitsubishi Electric Research Laboratory

 Broadway
Cambridge, MA 
	, USA
Tony Jebara and Alex Pentland
Massachusettes Institute of Technology
 Ames St.
Cambridge, MA 
	, USA"
0b572a2b7052b15c8599dbb17d59ff4f02838ff7,Automatic Subspace Learning via Principal Coefficients Embedding,"Automatic Subspace Learning via Principal
Coefficients Embedding
Xi Peng, Jiwen Lu, Senior Member, IEEE, Zhang Yi, Fellow, IEEE and Rui Yan, Member, IEEE,"
0b02bfa5f3a238716a83aebceb0e75d22c549975,Learning Probabilistic Models for Recognizing Faces under Pose Variations,"Learning Probabilistic Models for Recognizing Faces
under Pose Variations
M. Saquib Sarfraz and Olaf Hellwich
Computer vision and Remote Sensing, Berlin university of Technology
Sekr. FR-3-1, Franklinstr. 28/29, Berlin, Germany"
0bce54bfbd8119c73eb431559fc6ffbba741e6aa,Recurrent Neural Networks,"Published as a conference paper at ICLR 2018
SKIP RNN: LEARNING TO SKIP STATE UPDATES IN
RECURRENT NEURAL NETWORKS
V´ıctor Campos∗†, Brendan Jou‡, Xavier Gir´o-i-Nieto§, Jordi Torres†, Shih-Fu ChangΓ
Barcelona Supercomputing Center, ‡Google Inc,
§Universitat Polit`ecnica de Catalunya, ΓColumbia University
{victor.campos,"
0b4c4ea4a133b9eab46b217e22bda4d9d13559e6,MORF: Multi-Objective Random Forests for face characteristic estimation,"MORF: Multi-Objective Random Forests for Face Characteristic Estimation
Dario Di Fina1
MICC - University of Florence
Svebor Karaman1,3
Andrew D. Bagdanov2
{dario.difina,
CVC - Universitat Autonoma de Barcelona
Alberto Del Bimbo1
DVMM Lab - Columbia University"
0b8c92463f8f5087696681fb62dad003c308ebe2,On matching sketches with digital face images,"On Matching Sketches with Digital Face Images
Himanshu S. Bhatt, Samarth Bharadwaj, Richa Singh, and Mayank Vatsa
in local"
0bc0f9178999e5c2f23a45325fa50300961e0226,Recognizing facial expressions from videos using Deep Belief Networks,"Recognizing facial expressions from videos using Deep
Belief Networks
CS 229 Project
Advisor: Prof. Andrew Ng
Adithya Rao Narendran Thiagarajan"
9391618c09a51f72a1c30b2e890f4fac1f595ebd,Globally Tuned Cascade Pose Regression via Back Propagation with Application in 2D Face Pose Estimation and Heart Segmentation in 3D CT Images,"Globally Tuned Cascade Pose Regression via
Back Propagation with Application in 2D Face
Pose Estimation and Heart Segmentation in 3D
CT Images
Peng Sun
James K Min
Guanglei Xiong
Dalio Institute of Cardiovascular Imaging, Weill Cornell Medical College
April 1, 2015
This work was submitted to ICML 2015 but got rejected. We put the initial
submission ”as is” in Page 2 - 11 and add updated contents at the tail. The
ode of this work is available at https://github.com/pengsun/bpcpr5."
93675f86d03256f9a010033d3c4c842a732bf661,Localized Growth and Characterization of Silicon Nanowires,Universit´edesSciencesetTechnologiesdeLilleEcoleDoctoraleSciencesPourl’ing´enieurUniversit´eLilleNord-de-FranceTHESEPr´esent´ee`al’Universit´edesSciencesetTechnologiesdeLillePourobtenirletitredeDOCTEURDEL’UNIVERSIT´ESp´ecialit´e:MicroetNanotechnologieParTaoXULocalizedgrowthandcharacterizationofsiliconnanowiresSoutenuele25Septembre2009Compositiondujury:Pr´esident:TuamiLASRIRapporteurs:ThierryBARONHenriMARIETTEExaminateurs:EricBAKKERSXavierWALLARTDirecteurdeth`ese:BrunoGRANDIDIER
936c7406de1dfdd22493785fc5d1e5614c6c2882,Detecting Visual Text,"012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 762–772,
Montr´eal, Canada, June 3-8, 2012. c(cid:13)2012 Association for Computational Linguistics"
93721023dd6423ab06ff7a491d01bdfe83db7754,Robust Face Alignment Using Convolutional Neural Networks,"ROBUST FACE ALIGNMENT USING CONVOLUTIONAL NEURAL
NETWORKS
Stefan Duffner and Christophe Garcia
Orange Labs, 4, Rue du Clos Courtel, 35512 Cesson-S´evign´e, France
{stefan.duffner,
Keywords:
Face alignment, Face registration, Convolutional Neural Networks."
93cbb3b3e40321c4990c36f89a63534b506b6daf,Learning from examples in the small sample case: face expression recognition,"IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART B: CYBERNETICS, VOL. 35, NO. 3, JUNE 2005
Learning From Examples in the Small Sample Case:
Face Expression Recognition
Guodong Guo and Charles R. Dyer, Fellow, IEEE"
94b9c0a6515913bad345f0940ee233cdf82fffe1,Face Recognition using Local Ternary Pattern for Low Resolution Image,"International Journal of Science and Research (IJSR)
ISSN (Online): 2319-7064
Impact Factor (2012): 3.358
Face Recognition using Local Ternary Pattern for
Low Resolution Image
Vikas1, Amanpreet Kaur2
Research Scholar, CGC Group of Colleges, Gharuan, Punjab, India
Assistant Professor, Department of Computer Science Engineering, Chandigarh University, Gharuan, Punjab, India"
94eeae23786e128c0635f305ba7eebbb89af0023,On the Emergence of Invariance and Disentangling in Deep Representations,"Journal of Machine Learning Research 18 (2018) 1-34
Submitted 01/17; Revised 4/18; Published 6/18
Emergence of Invariance and Disentanglement
in Deep Representations∗
Alessandro Achille
Department of Computer Science
University of California
Los Angeles, CA 90095, USA
Stefano Soatto
Department of Computer Science
University of California
Los Angeles, CA 90095, USA
Editor: Yoshua Bengio"
944faf7f14f1bead911aeec30cc80c861442b610,Action Tubelet Detector for Spatio-Temporal Action Localization,"Action Tubelet Detector for Spatio-Temporal Action Localization
Vicky Kalogeiton1,2
Philippe Weinzaepfel3
Vittorio Ferrari2
Cordelia Schmid1"
9458c518a6e2d40fb1d6ca1066d6a0c73e1d6b73,A Benchmark and Comparative Study of Video-Based Face Recognition on COX Face Database,"A Benchmark and Comparative Study of
Video-Based Face Recognition
on COX Face Database
Zhiwu Huang, Student Member, IEEE, Shiguang Shan, Senior Member, IEEE,
Ruiping Wang, Member, IEEE, Haihong Zhang, Member, IEEE,
Shihong Lao, Member, IEEE, Alifu Kuerban,
nd Xilin Chen, Senior Member, IEEE"
948af4b04b4a9ae4bff2777ffbcb29d5bfeeb494,Face Recognition From Single Sample Per Person by Learning of Generic Discriminant Vectors,"Available online at www.sciencedirect.com
Procedia Engineering   41  ( 2012 )  465 – 472
International Symposium on Robotics and Intelligent Sensors 2012 (IRIS 2012)
Face Recognition From Single Sample Per Person by Learning of
Generic Discriminant Vectors
Fadhlan Hafiza*, Amir A. Shafieb, Yasir Mohd Mustafahb
Faculty of Electrical Engineering, University of Technology MARA, Shah Alam, 40450 Selangor, Malaysia
Faculty of Engineering, International Islamic University, Jalan Gombak, 53100 Kuala Lumpur, Malaysia"
9441253b638373a0027a5b4324b4ee5f0dffd670,A Novel Scheme for Generating Secure Face Templates Using BDA,"A Novel Scheme for Generating Secure Face
Templates Using BDA
Shraddha S. Shinde
Prof. Anagha P. Khedkar
P.G. Student, Department of Computer Engineering,
Associate Professor, Department of Computer
MCERC,
Nashik (M.S.), India
e-mail:"
94a11b601af77f0ad46338afd0fa4ccbab909e82,"Title of dissertation : EFFICIENT SENSING , SUMMARIZATION AND CLASSIFICATION OF VIDEOS",
0efdd82a4753a8309ff0a3c22106c570d8a84c20,Lda with Subgroup Pca Method for Facial Image Retrieval,"LDA WITH SUBGROUP PCA METHOD FOR FACIAL IMAGE RETRIEVAL
Wonjun Hwang, Tae-Kyun Kim, Seokcheol Kee
Human Computer Interaction Lab., Samsung Advanced Institute of Technology, Korea."
0eac652139f7ab44ff1051584b59f2dc1757f53b,Efficient Branching Cascaded Regression for Face Alignment under Significant Head Rotation,"Efficient Branching Cascaded Regression
for Face Alignment under Significant Head Rotation
Brandon M. Smith
Charles R. Dyer
University of Wisconsin–Madison"
0e50fe28229fea45527000b876eb4068abd6ed8c,Angle Principal Component Analysis,Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17)
0eff410cd6a93d0e37048e236f62e209bc4383d1,Learning discriminative MspLBP features based on Ada-LDA for multi-class pattern classification,"Anchorage Convention District
May 3-8, 2010, Anchorage, Alaska, USA
978-1-4244-5040-4/10/$26.00 ©2010 IEEE"
0ee737085af468f264f57f052ea9b9b1f58d7222,SiGAN: Siamese Generative Adversarial Network for Identity-Preserving Face Hallucination,"SiGAN: Siamese Generative Adversarial Network
for Identity-Preserving Face Hallucination
Chih-Chung Hsu, Member, IEEE, Chia-Wen Lin, Fellow, IEEE, Weng-Tai Su, Student Member, IEEE,
nd Gene Cheung, Senior Member, IEEE,"
0ee661a1b6bbfadb5a482ec643573de53a9adf5e,On the Use of Discriminative Cohort Score Normalization for Unconstrained Face Recognition,"JOURNAL OF LATEX CLASS FILES, VOL. X, NO. X, MONTH YEAR
On the Use of Discriminative Cohort Score
Normalization for Unconstrained Face Recognition
Massimo Tistarelli, Senior Member, IEEE, Yunlian Sun, and Norman Poh, Member, IEEE"
0e986f51fe45b00633de9fd0c94d082d2be51406,"Face detection, pose estimation, and landmark localization in the wild","Face Detection, Pose Estimation, and Landmark Localization in the Wild
Xiangxin Zhu Deva Ramanan
Dept. of Computer Science, University of California, Irvine"
0e49a23fafa4b2e2ac097292acf00298458932b4,Unsupervised Detection of Outlier Images Using Multi-Order Image Transforms,"Theory and Applications of Mathematics & Computer Science 3 (1) (2013) 13–31
Unsupervised Detection of Outlier Images Using Multi-Order
Image Transforms
Lior Shamira,∗
Lawrence Technological University, 21000 W Ten Mile Rd., Southfield, MI 48075, United States."
0e78af9bd0f9a0ce4ceb5f09f24bc4e4823bd698,Spontaneous Subtle Expression Recognition: Imbalanced Databases & Solutions,"Spontaneous Subtle Expression Recognition:
Imbalanced Databases & Solutions (cid:63)
Anh Cat Le Ngo1, Raphael Chung-Wei Phan1, John See2
Faculty of Engineering,
Multimedia University (MMU), Cyberjaya, Malaysia
Faculty of Computing & Informatics,
Multimedia University (MMU), Cyberjaya, Malaysia"
0e2ea7af369dbcaeb5e334b02dd9ba5271b10265,Multi-Level Feature Abstraction from Convolutional Neural Networks for Multimodal Biometric Identification,
0e7c70321462694757511a1776f53d629a1b38f3,2012 Proceedings of the Performance Metrics for Intelligent Systems (PerMI'12) Workshop,"NIST Special Publication 1136
012 Proceedings of the
Performance Metrics for Intelligent
Systems (PerMI ‘12) Workshop
Rajmohan Madhavan
Elena R. Messina
Brian A. Weiss
http://dx.doi.org/10.6028/NIST.SP.1136"
600025c9a13ff09c6d8b606a286a79c823d89db8,A Review on Linear and Non-linear Dimensionality Reduction Techniques,"Machine Learning and Applications: An International Journal (MLAIJ) Vol.1, No.1, September 2014
A REVIEW ON LINEAR AND NON-LINEAR
DIMENSIONALITY REDUCTION
TECHNIQUES
Arunasakthi. K, 2KamatchiPriya. L
Assistant Professor
Department of Computer Science and Engineering
Ultra College of Engineering and Technology for Women,India.
Assistant Professor
Department of Computer Science and Engineering
Vickram College of Engineering, Enathi, Tamil Nadu, India."
60e2b9b2e0db3089237d0208f57b22a3aac932c1,Frankenstein: Learning Deep Face Representations Using Small Data,"Frankenstein: Learning Deep Face Representations
using Small Data
Guosheng Hu, Member, IEEE, Xiaojiang Peng, Yongxin Yang, Timothy M. Hospedales, and Jakob Verbeek"
60ce4a9602c27ad17a1366165033fe5e0cf68078,Combination of Face Regions in Forensic Scenarios.,"TECHNICAL NOTE
DIGITAL & MULTIMEDIA SCIENCES
J Forensic Sci, 2015
doi: 10.1111/1556-4029.12800
Available online at: onlinelibrary.wiley.com
Pedro Tome,1 Ph.D.; Julian Fierrez,1 Ph.D.; Ruben Vera-Rodriguez,1 Ph.D.; and Javier Ortega-Garcia,1
Ph.D.
Combination of Face Regions in Forensic
Scenarios*"
60efdb2e204b2be6701a8e168983fa666feac1be,Transferring Deep Object and Scene Representations for Event Recognition in Still Images,"Int J Comput Vis
DOI 10.1007/s11263-017-1043-5
Transferring Deep Object and Scene Representations for Event
Recognition in Still Images
Limin Wang1
· Zhe Wang2 · Yu Qiao3 · Luc Van Gool1
Received: 31 March 2016 / Accepted: 1 September 2017
© Springer Science+Business Media, LLC 2017"
60824ee635777b4ee30fcc2485ef1e103b8e7af9,Cascaded Collaborative Regression for Robust Facial Landmark Detection Trained Using a Mixture of Synthetic and Real Images With Dynamic Weighting,"Cascaded Collaborative Regression for Robust Facial
Landmark Detection Trained using a Mixture of Synthetic and
Real Images with Dynamic Weighting
Zhen-Hua Feng, Student Member, IEEE, Guosheng Hu, Student Member, IEEE, Josef Kittler,
Life Member, IEEE, William Christmas, and Xiao-Jun Wu"
60cdcf75e97e88638ec973f468598ae7f75c59b4,Face Annotation Using Transductive Kernel Fisher Discriminant,"Face Annotation Using Transductive
Kernel Fisher Discriminant
Jianke Zhu, Steven C.H. Hoi, and Michael R. Lyu"
60040e4eae81ab6974ce12f1c789e0c05be00303,Graphical Facial Expression Analysis and Design Method: An Approach to Determine Humanoid Skin Deformation,"Yonas Tadesse1,2
e-mail:
Shashank Priya
e-mail:
Center for Energy Harvesting
Materials and Systems (CEHMS),
Bio-Inspired Materials and
Devices Laboratory (BMDL),
Center for Intelligent Material
Systems and Structure (CIMSS),
Department of Mechanical Engineering,
Virginia Tech,
Blacksburg, VA 24061
Graphical Facial Expression
Analysis and Design Method:
An Approach to Determine
Humanoid Skin Deformation
The architecture of human face is complex consisting of 268 voluntary muscles that perform
oordinated action to create real-time facial expression. In order to replicate facial expres-
sion on humanoid face by utilizing discrete actuators, the first and foremost step is the identi-"
60b3601d70f5cdcfef9934b24bcb3cc4dde663e7,Binary Gradient Correlation Patterns for Robust Face Recognition,"SUBMITTED TO IEEE TRANS. ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
Binary Gradient Correlation Patterns
for Robust Face Recognition
Weilin Huang, Student Member, IEEE, and Hujun Yin, Senior Member, IEEE"
60bffecd79193d05742e5ab8550a5f89accd8488,Proposal Classification using sparse representation and applications to skin lesion diagnosis,"PhD Thesis Proposal
Classification using sparse representation and applications to skin
lesion diagnosis
I. Description
In only a few decades, sparse representation modeling has undergone a tremendous expansion with
successful applications in many fields including signal and image processing, computer science,
machine  learning,  statistics.  Mathematically,  it  can  be  considered  as  the  problem  of  finding  the
sparsest solution (the one with the fewest non-zeros entries) to an underdetermined linear system
of equations [1]. Based on the observation for natural images (or images rich in textures) that small
scale  structures  tend  to  repeat  themselves  in  an  image  or  in  a  group  of  similar  images,  a  signal
source can be sparsely represented over some well-chosen redundant basis (a dictionary). In other
words, it can be approximately representable by a linear combination of a few elements (also called
toms or basis vectors) of a redundant/over-complete dictionary.
Such models have been proven successful in many tasks including denoising [2]-[5], compression
[6],[7], super-resolution [8],[9], classification and pattern recognition [10]-[16]. In the context of
lassification, the objective is to find the class to which a test signal belongs, given training data
from multiple classes. Sparse representation has become a powerful technique in classification and
pplications, including texture classification [16], face recognition [12], object detection [10], and
segmentation of medical images [17], [18]. In conventional Sparse Representation Classification
(SRC) schemes, learned dictionaries and sparse representation are involved to classify image pixels"
601834a4150e9af028df90535ab61d812c45082c,A short review and primer on using video for psychophysiological observations in human-computer interaction applications,"A short review and primer on using video for
psychophysiological observations in
human-computer interaction applications
Teppo Valtonen1
Quantified Employee unit, Finnish Institute of Occupational Health,
teppo. valtonen fi,
POBox 40, 00250, Helsinki, Finland"
346dbc7484a1d930e7cc44276c29d134ad76dc3f,Artists portray human faces with the Fourier statistics of complex natural scenes.,"This article was downloaded by:[University of Toronto]
On: 21 November 2007
Access Details: [subscription number 785020433]
Publisher: Informa Healthcare
Informa Ltd Registered in England and Wales Registered Number: 1072954
Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK
Systems
Publication details, including instructions for authors and subscription information:
http://www.informaworld.com/smpp/title~content=t713663148
Artists portray human faces with the Fourier statistics of
omplex natural scenes
Christoph Redies a; Jan Hänisch b; Marko Blickhan a; Joachim Denzler b
Institute of Anatomy I, School of Medicine, Friedrich Schiller University, Germany
Department of Computer Science, Friedrich Schiller University, D-07740 Jena,
Germany
First Published on: 28 August 2007
To cite this Article: Redies, Christoph, Hänisch, Jan, Blickhan, Marko and Denzler,
Joachim (2007) 'Artists portray human faces with the Fourier statistics of complex
To link to this article: DOI: 10.1080/09548980701574496
URL: http://dx.doi.org/10.1080/09548980701574496"
34b3b14b4b7bfd149a0bd63749f416e1f2fc0c4c,The AXES submissions at TrecVid 2013,"The AXES submissions at TrecVid 2013
Robin Aly1, Relja Arandjelovi´c3, Ken Chatfield3, Matthijs Douze6, Basura Fernando4, Zaid Harchaoui6,
Kevin McGuinness2, Noel E. O’Conner2, Dan Oneata6, Omkar M. Parkhi3, Danila Potapov6, Jérôme Revaud6,
Cordelia Schmid6, Jochen Schwenninger5, David Scott2, Tinne Tuytelaars4, Jakob Verbeek6, Heng Wang6,
Andrew Zisserman3
University of Twente 2Dublin City University 3Oxford University
KU Leuven 5Fraunhofer Sankt Augustin 6INRIA Grenoble"
34d484b47af705e303fc6987413dc0180f5f04a9,RI:Medium: Unsupervised and Weakly-Supervised Discovery of Facial Events,"RI:Medium: Unsupervised and Weakly-Supervised
Discovery of Facial Events
Introduction
The face is one of the most powerful channels of nonverbal communication. Facial expression has been a
focus of emotion research for over a hundred years [11]. It is central to several leading theories of emotion
[16, 28, 44] and has been the focus of at times heated debate about issues in emotion science [17, 23, 40].
Facial expression figures prominently in research on almost every aspect of emotion, including psychophys-
iology [30], neural correlates [18], development [31], perception [4], addiction [24], social processes [26],
depression [39] and other emotion disorders [46], to name a few. In general, facial expression provides cues
bout emotional response, regulates interpersonal behavior, and communicates aspects of psychopathology.
While people have believed for centuries that facial expressions can reveal what people are thinking and
feeling, it is relatively recently that the face has been studied scientifically for what it can tell us about
internal states, social behavior, and psychopathology.
Faces possess their own language. Beginning with Darwin and his contemporaries, extensive efforts
have been made to manually describe this language. A leading approach, the Facial Action Coding System
(FACS) [19] , segments the visible effects of facial muscle activation into ”action units.” Because of its
descriptive power, FACS has become the state of the art in manual measurement of facial expression and is
widely used in studies of spontaneous facial behavior. The FACS taxonomy was develop by manually ob-
serving graylevel variation between expressions in images and to a lesser extent by recording the electrical
ctivity of underlying facial muscles [9]. Because of its importance to human social dynamics, person per-"
341002fac5ae6c193b78018a164d3c7295a495e4,von Mises-Fisher Mixture Model-based Deep learning: Application to Face Verification,"von Mises-Fisher Mixture Model-based Deep
learning: Application to Face Verification
Md. Abul Hasnat, Julien Bohn´e, Jonathan Milgram, St´ephane Gentric and Liming Chen"
34ec83c8ff214128e7a4a4763059eebac59268a6,Action Anticipation By Predicting Future Dynamic Images,"Action Anticipation By Predicting Future
Dynamic Images
Cristian Rodriguez, Basura Fernando and Hongdong Li
Australian Centre for Robotic Vision, ANU, Canberra, Australia
{cristian.rodriguez, basura.fernando,"
34c594abba9bb7e5813cfae830e2c4db78cf138c,Transport-based single frame super resolution of very low resolution face images,"Transport-Based Single Frame Super Resolution of Very Low Resolution Face Images
Soheil Kolouri1, Gustavo K. Rohde1,2
Department of Biomedical Engineering, Carnegie Mellon University. 2Department of Electrical and Computer Engineering, Carnegie Mellon University.
We describe a single-frame super-resolution method for reconstructing high-
resolution (abbr. high-res) faces from very low-resolution (abbr. low-res)
face images (e.g. smaller than 16× 16 pixels) by learning a nonlinear La-
grangian model for the high-res face images. Our technique is based on the
mathematics of optimal transport, and hence we denote it as transport-based
SFSR (TB-SFSR). In the training phase, a nonlinear model of high-res fa-
ial images is constructed based on transport maps that morph a reference
image into the training face images. In the testing phase, the resolution of
degraded image is enhanced by finding the model parameters that best fit
the given low resolution data.
Generally speaking, most SFSR methods [2, 3, 4, 5] are based on a
linear model for the high-res images. Hence, ultimately, the majority of
SFSR models in the literature can be written as, Ih(x) = ∑i wiψi(x), where
Ih is a high-res image or a high-res image patch, w’s are weight coefficients,
nd ψ’s are high-res images (or image patches), which are learned from the
training images using a specific model. Here we propose a fundamentally
different approach toward modeling high-res images. In our approach the"
341ed69a6e5d7a89ff897c72c1456f50cfb23c96,"DAGER: Deep Age, Gender and Emotion Recognition Using Convolutional Neural Network","DAGER: Deep Age, Gender and Emotion
Recognition Using Convolutional Neural
Networks
Afshin Dehghan
Enrique G. Ortiz
Guang Shu
Syed Zain Masood
{afshindehghan, egortiz, guangshu,
Computer Vision Lab, Sighthound Inc., Winter Park, FL"
340d1a9852747b03061e5358a8d12055136599b0,Audio-Visual Recognition System Insusceptible to Illumination Variation over Internet Protocol _ICIE_28_,"Audio-Visual Recognition System Insusceptible
to Illumination Variation over Internet Protocol
Yee Wan Wong, Kah Phooi Seng, and Li-Minn Ang"
5a3da29970d0c3c75ef4cb372b336fc8b10381d7,CNN-based Real-time Dense Face Reconstruction with Inverse-rendered Photo-realistic Face Images.,"CNN-based Real-time Dense Face Reconstruction
with Inverse-rendered Photo-realistic Face Images
Yudong Guo, Juyong Zhang†, Jianfei Cai, Boyi Jiang and Jianmin Zheng"
5a93f9084e59cb9730a498ff602a8c8703e5d8a5,Face Recognition using Local Quantized Patterns,"HUSSAIN ET. AL: FACE RECOGNITION USING LOCAL QUANTIZED PATTERNS
Face Recognition using Local Quantized
Patterns
Sibt ul Hussain
Thibault Napoléon
Fréderic Jurie
GREYC — CNRS UMR 6072,
University of Caen Basse-Normandie,
Caen, France"
5a34a9bb264a2594c02b5f46b038aa1ec3389072,Label-Embedding for Image Classification,"Label-Embedding for Image Classification
Zeynep Akata, Member, IEEE, Florent Perronnin, Member, IEEE, Zaid Harchaoui, Member, IEEE,
nd Cordelia Schmid, Fellow, IEEE"
5a4c6246758c522f68e75491eb65eafda375b701,Contourlet structural similarity for facial expression recognition,"978-1-4244-4296-6/10/$25.00 ©2010 IEEE
ICASSP 2010"
5aad5e7390211267f3511ffa75c69febe3b84cc7,Driver Gaze Region Estimation Without Using Eye Movement,"Driver Gaze Estimation
Without Using Eye Movement
Lex Fridman, Philipp Langhans, Joonbum Lee, Bryan Reimer
MIT AgeLab"
5a86842ab586de9d62d5badb2ad8f4f01eada885,Facial Emotion Recognition and Classification Using Hybridization Method,"International Journal of Engineering Research and General Science Volume 3, Issue 3, May-June, 2015
ISSN 2091-2730
Facial Emotion Recognition and Classification Using Hybridization
Method
Anchal Garg , Dr. Rohit Bajaj
Deptt. of CSE, Chandigarh Engg. College,  Mohali, Punjab, India.
07696449500"
5a4ec5c79f3699ba037a5f06d8ad309fb4ee682c,Automatic age and gender classification using supervised appearance model,"Downloaded From: https://www.spiedigitallibrary.org/journals/Journal-of-Electronic-Imaging on 12/17/2017 Terms of Use: https://www.spiedigitallibrary.org/terms-of-use
AutomaticageandgenderclassificationusingsupervisedappearancemodelAliMainaBukarHassanUgailDavidConnahAliMainaBukar,HassanUgail,DavidConnah,“Automaticageandgenderclassificationusingsupervisedappearancemodel,”J.Electron.Imaging25(6),061605(2016),doi:10.1117/1.JEI.25.6.061605."
5aed0f26549c6e64c5199048c4fd5fdb3c5e69d6,Human Expression Recognition using Facial Features,"International Journal of Computer Applications® (IJCA) (0975 – 8887)
International Conference on Knowledge Collaboration in Engineering, ICKCE-2014
Human Expression Recognition using Facial Features
G.Saranya
Post graduate student, Dept. of ECE
Parisutham Institute of Technology & Science
Thanjavur.
Affiliated to Anna university, Chennai
recognition  can  be  used"
5a7520380d9960ff3b4f5f0fe526a00f63791e99,The Indian Spontaneous Expression Database for Emotion Recognition,"The Indian Spontaneous Expression
Database for Emotion Recognition
S L Happy, Student Member, IEEE, Priyadarshi Patnaik, Aurobinda Routray, Member, IEEE,
nd Rajlakshmi Guha"
5fff61302adc65d554d5db3722b8a604e62a8377,Additive Margin Softmax for Face Verification,"Additive Margin Softmax for Face Verification
Feng Wang
UESTC
Weiyang Liu
Georgia Tech
Haijun Liu
UESTC
Jian Cheng
UESTC
haijun"
5fa6e4a23da0b39e4b35ac73a15d55cee8608736,RED-Net: A Recurrent Encoder–Decoder Network for Video-Based Face Alignment,"IJCV special issue (Best papers of ECCV 2016) manuscript No.
(will be inserted by the editor)
RED-Net:
A Recurrent Encoder-Decoder Network for Video-based Face Alignment
Xi Peng · Rogerio S. Feris · Xiaoyu Wang · Dimitris N. Metaxas
Submitted: April 19 2017 / Revised: December 12 2017"
5f871838710a6b408cf647aacb3b198983719c31,Locally Linear Regression for Pose-Invariant Face Recognition,"Locally Linear Regression for Pose-Invariant
Face Recognition
Xiujuan Chai, Shiguang Shan, Member, IEEE, Xilin Chen, Member, IEEE, and Wen Gao, Senior Member, IEEE"
5f344a4ef7edfd87c5c4bc531833774c3ed23542,Semisupervised Learning of Classifiers with Application to Human-computer Interaction," Copyright by Ira Cohen, 2003"
5f5906168235613c81ad2129e2431a0e5ef2b6e4,A Unified Framework for Compositional Fitting of Active Appearance Models,"Noname manuscript No.
(will be inserted by the editor)
A Unified Framework for Compositional Fitting of
Active Appearance Models
Joan Alabort-i-Medina · Stefanos Zafeiriou
Received: date / Accepted: date"
5fb5d9389e2a2a4302c81bcfc068a4c8d4efe70c,Multiple Facial Attributes Estimation Based on Weighted Heterogeneous Learning,"Multiple Facial Attributes Estimation based on
Weighted Heterogeneous Learning
H.Fukui* T.Yamashita* Y.Kato* R.Matsui*
T. Ogata** Y.Yamauchi* H.Fujiyoshi*
*Chubu University
**Abeja Inc.
200, Matuoto-cho, Kasugai,
-1-20, Toranomon, Minato-ku,
Aichi, Japan
Tokyo, Japan"
5fc664202208aaf01c9b62da5dfdcd71fdadab29,Automatic Face Recognition from Video,rXiv:1504.05308v1  [cs.CV]  21 Apr 2015
5fa1724a79a9f7090c54925f6ac52f1697d6b570,The Development of Multimodal Lexical Resources,"Proceedings of the Workshop on Grammar and Lexicon: Interactions and Interfaces,
pages 41–47, Osaka, Japan, December 11 2016."
33a1a049d15e22befc7ddefdd3ae719ced8394bf,An Efficient Approach to Facial Feature Detection for Expression Recognition,"FULL PAPER
International Journal of Recent Trends in Engineering, Vol 2, No. 1, November 2009
An Efficient Approach to Facial Feature Detection
for Expression Recognition
S.P. Khandait1, P.D. Khandait2 and Dr.R.C.Thool2
Deptt. of Info.Tech., K.D.K.C.E., Nagpur, India
2Deptt.of Electronics Engg., K.D.K.C.E., Nagpur, India, 2Deptt. of Info.Tech., SGGSIET, Nanded"
3399f8f0dff8fcf001b711174d29c9d4fde89379,Face R-CNN,"Face R-CNN
Hao Wang Zhifeng Li∗ Xing Ji Yitong Wang
Tencent AI Lab, China"
333aa36e80f1a7fa29cf069d81d4d2e12679bc67,Suggesting Sounds for Images from Video Collections,"Suggesting Sounds for Images
from Video Collections
Matthias Sol`er1, Jean-Charles Bazin2, Oliver Wang2, Andreas Krause1 and
Alexander Sorkine-Hornung2
Computer Science Department, ETH Z¨urich, Switzerland
Disney Research, Switzerland"
33792bb27ef392973e951ca5a5a3be4a22a0d0c6,Two-Dimensional Whitening Reconstruction for Enhancing Robustness of Principal Component Analysis,"Two-dimensional Whitening Reconstruction for
Enhancing Robustness of Principal Component
Analysis
Xiaoshuang Shi, Zhenhua Guo, Feiping Nie, Lin Yang, Jane You, and Dacheng Tao"
3328674d71a18ed649e828963a0edb54348ee598,A face and palmprint recognition approach based on discriminant DCT feature extraction,"IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART B: CYBERNETICS, VOL. 34, NO. 6, DECEMBER 2004
A Face and Palmprint Recognition Approach Based
on Discriminant DCT Feature Extraction
Xiao-Yuan Jing and David Zhang"
339937141ffb547af8e746718fbf2365cc1570c8,Facial Emotion Recognition in Real Time,"Facial Emotion Recognition in Real Time
Dan Duncan
Gautam Shine
Chris English"
33ae696546eed070717192d393f75a1583cd8e2c,Subspace selection to suppress confounding source domain information in AAM transfer learning,
3393459600368be2c4c9878a3f65a57dcc0c2cfa,Eigen-PEP for Video Face Recognition,"Eigen-PEP for Video Face Recognition
Haoxiang Li†, Gang Hua†, Xiaohui Shen‡, Zhe Lin‡, Jonathan Brandt‡
Stevens Institute of Technology ‡Adobe Systems Inc."
3352426a67eabe3516812cb66a77aeb8b4df4d1b,Joint Multi-view Face Alignment in the Wild,"JOURNAL OF LATEX CLASS FILES, VOL. 4, NO. 5, APRIL 2015
Joint Multi-view Face Alignment in the Wild
Jiankang Deng, Student Member, IEEE, George Trigeorgis, Yuxiang Zhou, and Stefanos Zafeiriou, Member, IEEE"
334d6c71b6bce8dfbd376c4203004bd4464c2099,Biconvex Relaxation for Semidefinite Programming in Computer Vision,"BICONVEX RELAXATION FOR SEMIDEFINITE PROGRAMMING IN
COMPUTER VISION
SOHIL SHAH*, ABHAY KUMAR*, DAVID JACOBS,
CHRISTOPH STUDER, AND TOM GOLDSTEIN"
33695e0779e67c7722449e9a3e2e55fde64cfd99,Riemannian coding and dictionary learning: Kernels to the rescue,"Riemannian Coding and Dictionary Learning: Kernels to the Rescue
Mehrtash Harandi, Mathieu Salzmann
Australian National University & NICTA
While sparse coding on non-flat Riemannian manifolds has recently become
increasingly popular, existing solutions either are dedicated to specific man-
ifolds, or rely on optimization problems that are difficult to solve, especially
when it comes to dictionary learning. In this paper, we propose to make use
of kernels to perform coding and dictionary learning on Riemannian man-
ifolds. To this end, we introduce a general Riemannian coding framework
with its kernel-based counterpart. This lets us (i) generalize beyond the spe-
ial case of sparse coding; (ii) introduce efficient solutions to two coding
schemes; (iii) learn the kernel parameters; (iv) perform unsupervised and
supervised dictionary learning in a much simpler manner than previous Rie-
mannian coding approaches.
i=1, di ∈ M, be a dictionary on a Rie-
mannian manifold M, and x ∈ M be a query point on the manifold. We
(cid:17)
define a general Riemannian coding formulation as
More specifically, let D = {di}N
(cid:93)N"
33e20449aa40488c6d4b430a48edf5c4b43afdab,The Faces of Engagement: Automatic Recognition of Student Engagementfrom Facial Expressions,"TRANSACTIONS ON AFFECTIVE COMPUTING
The Faces of Engagement: Automatic
Recognition of Student Engagement from Facial
Expressions
Jacob Whitehill, Zewelanji Serpell, Yi-Ching Lin, Aysha Foster, and Javier R. Movellan"
333e7ad7f915d8ee3bb43a93ea167d6026aa3c22,3D Assisted Face Recognition: Dealing With Expression Variations,"This is the author's version of an article that has been published in this journal. Changes were made to this version by the publisher prior to publication.
The final version of record is available at http://dx.doi.org/10.1109/TIFS.2014.2309851
DRAFT
D Assisted Face Recognition: Dealing With
Expression Variations
Nesli Erdogmus, Member, IEEE, Jean-Luc Dugelay, Fellow Member, IEEE"
334166a942acb15ccc4517cefde751a381512605,Facial Expression Analysis using Deep Learning,"International Research Journal of Engineering and Technology (IRJET)       e-ISSN: 2395-0056
Volume: 04 Issue: 10 | Oct -2017                     www.irjet.net                                                                 p-ISSN: 2395-0072
Facial Expression Analysis using Deep Learning
Hemanth Singh1, Raman Patel2
,2 M.Tech Student, SSG Engineering College, Odisha, India
---------------------------------------------------------------------***---------------------------------------------------------------------
examination structures need to analyse the facial exercises"
05b8673d810fadf888c62b7e6c7185355ffa4121,A Comprehensive Survey to Face Hallucination,"(will be inserted by the editor)
A Comprehensive Survey to Face Hallucination
Nannan Wang · Dacheng Tao · Xinbo Gao · Xuelong Li · Jie Li
Received: date / Accepted: date"
05e658fed4a1ce877199a4ce1a8f8cf6f449a890,Domain Transfer Learning for Object and Action Recognition,
05ad478ca69b935c1bba755ac1a2a90be6679129,Attribute Dominance: What Pops Out?,"Attribute Dominance: What Pops Out?
Naman Turakhia
Georgia Tech"
054738ce39920975b8dcc97e01b3b6cc0d0bdf32,Towards the design of an end-to-end automated system for image and video-based recognition,"Towards the Design of an End-to-End Automated
System for Image and Video-based Recognition
Rama Chellappa1, Jun-Cheng Chen3, Rajeev Ranjan1, Swami Sankaranarayanan1, Amit Kumar1,
Vishal M. Patel2 and Carlos D. Castillo4"
05e03c48f32bd89c8a15ba82891f40f1cfdc7562,Scalable Robust Principal Component Analysis Using Grassmann Averages,"Scalable Robust Principal Component
Analysis using Grassmann Averages
Søren Hauberg, Aasa Feragen, Raffi Enficiaud, and Michael J. Black"
056ba488898a1a1b32daec7a45e0d550e0c51ae4,Cascaded Continuous Regression for Real-Time Incremental Face Tracking,"Cascaded Continuous Regression for Real-time
Incremental Face Tracking
Enrique S´anchez-Lozano, Brais Martinez,
Georgios Tzimiropoulos, and Michel Valstar
Computer Vision Laboratory. University of Nottingham"
050fdbd2e1aa8b1a09ed42b2e5cc24d4fe8c7371,Spatio-Temporal Scale Selection in Video Data,"Contents
Scale Space and PDE Methods
Spatio-Temporal Scale Selection in Video Data . . . . . . . . . . . . . . . . . . . . .
Tony Lindeberg
Dynamic Texture Recognition Using Time-Causal Spatio-Temporal
Scale-Space Filters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Ylva Jansson and Tony Lindeberg
Corner Detection Using the Affine Morphological Scale Space . . . . . . . . . . .
Luis Alvarez
Nonlinear Spectral Image Fusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Martin Benning, Michael Möller, Raz Z. Nossek, Martin Burger,
Daniel Cremers, Guy Gilboa, and Carola-Bibiane Schönlieb
Tubular Structure Segmentation Based on Heat Diffusion. . . . . . . . . . . . . . .
Fang Yang and Laurent D. Cohen
Analytic Existence and Uniqueness Results for PDE-Based Image
Reconstruction with the Laplacian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Laurent Hoeltgen, Isaac Harris, Michael Breuß, and Andreas Kleefeld
Combining Contrast Invariant L1 Data Fidelities with Nonlinear
Spectral Image Decomposition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Leonie Zeune, Stephan A. van Gils, Leon W.M.M. Terstappen,"
052880031be0a760a5b606b2ad3d22f237e8af70,Datasets on object manipulation and interaction: a survey,"Datasets on object manipulation and interaction: a survey
Yongqiang Huang and Yu Sun"
053c2f592a7f153e5f3746aa5ab58b62f2cf1d21,Performance Evaluation of Illumination Normalization Techniques for Face Recognition,"International Journal of Research in
Engineering & Technology (IJRET)
ISSN 2321-8843
Vol. 1, Issue 2, July 2013, 11-20
© Impact Journals
PERFORMANCE EVALUATION OF ILLUMINATION NORMALIZATION TECHNIQUES
FOR FACE RECOGNITION
A. P. C. SARATHA DEVI & V. MAHESH
Department of Information Technology, PSG College of Technology, Coimbatore, Tamil Nadu, India"
05ea7930ae26165e7e51ff11b91c7aa8d7722002,Learning And-Or Model to Represent Context and Occlusion for Car Detection and Viewpoint Estimation,"Learning And-Or Model to Represent Context and
Occlusion for Car Detection and Viewpoint Estimation
Tianfu Wu∗, Bo Li∗ and Song-Chun Zhu"
051a84f0e39126c1ebeeb379a405816d5d06604d,Biometric Recognition Performing in a Bioinspired System,"Cogn Comput (2009) 1:257–267
DOI 10.1007/s12559-009-9018-7
Biometric Recognition Performing in a Bioinspired System
Joan Fa`bregas Æ Marcos Faundez-Zanuy
Published online: 20 May 2009
Ó Springer Science+Business Media, LLC 2009"
0559fb9f5e8627fecc026c8ee6f7ad30e54ee929,Facial Expression Recognition,"Facial Expression Recognition
Bogdan J. Matuszewski, Wei Quan and Lik-Kwan Shark
ADSIP Research Centre, University of Central Lancashire
. Introduction
Facial  expressions  are  visible  signs  of  a  person’s  affective  state,  cognitive  activity  and
personality.  Humans  can  perform  expression  recognition  with  a  remarkable  robustness
without  conscious  effort  even  under  a  variety  of  adverse  conditions  such  as  partially
occluded faces, different appearances and poor illumination. Over the last two decades, the
dvances in imaging technology and ever increasing computing power have opened up a
possibility of automatic facial expression recognition and this has led to significant research
efforts from the computer vision and pattern recognition communities. One reason for this
growing interest is due to a wide spectrum of possible applications in diverse areas, such as
more engaging human-computer interaction (HCI) systems, video conferencing, augmented
reality.  Additionally  from  the  biometric  perspective,  automatic  recognition  of  facial
expressions has been investigated in the context of monitoring patients in the intensive care
nd neonatal units for signs of pain and anxiety, behavioural research, identifying level of
oncentration, and improving face recognition.
Automatic  facial  expression  recognition  is  a  difficult  task  due  to  its  inherent  subjective
nature,  which  is  additionally  hampered  by  usual  difficulties  encountered  in  pattern
recognition and computer vision research. The vast majority of the current state-of-the-art"
05a7be10fa9af8fb33ae2b5b72d108415519a698,Multilayer and Multimodal Fusion of Deep Neural Networks for Video Classification,"Multilayer and Multimodal Fusion of Deep Neural Networks
for Video Classification
Xiaodong Yang Pavlo Molchanov Jan Kautz
{xiaodongy, pmolchanov,
NVIDIA"
050a149051a5d268fcc5539e8b654c2240070c82,Magisterské a doktorské studijnı́ programy,MAGISTERSKÉ A DOKTORSKÉSTUDIJNÍ PROGRAMY31. 5. 2018SBORNÍKSTUDENTSKÁ VĚDECKÁ KONFERENCE
0580edbd7865414c62a36da9504d1169dea78d6f,Baseline CNN structure analysis for facial expression recognition,"Baseline CNN structure analysis for facial expression recognition
Minchul Shin1, Munsang Kim2 and Dong-Soo Kwon1"
9d58e8ab656772d2c8a99a9fb876d5611fe2fe20,Beyond Temporal Pooling: Recurrence and Temporal Convolutions for Gesture Recognition in Video,"Beyond Temporal Pooling: Recurrence and Temporal
Convolutions for Gesture Recognition in Video
Lionel Pigou, A¨aron van den Oord∗ , Sander Dieleman∗ ,
{lionel.pigou,aaron.vandenoord,sander.dieleman,
Mieke Van Herreweghe & Joni Dambre
mieke.vanherreweghe,
Ghent University
February 11, 2016"
9d42df42132c3d76e3447ea61e900d3a6271f5fe,AutoCAP: An Automatic Caption Generation System based on the Text Knowledge Power Series Representation Model,"International Journal of Computer Applications (0975 – 8887)
Advanced Computing and Communication Techniques for High Performance Applications (ICACCTHPA-2014)
AutoCAP: An Automatic Caption Generation System
ased on the Text Knowledge Power Series
Representation Model
Krishnapriya P S
M.Tech Dept of CSE
NSS College of Engineering
Palakkad, Kerala"
9d8fd639a7aeab0dd1bc6eef9d11540199fd6fe2,L Earning to C Luster,"Workshop track - ICLR 2018
LEARNING TO CLUSTER
Benjamin B. Meier, Thilo Stadelmann & Oliver D¨urr
ZHAW Datalab, Zurich University of Applied Sciences
Winterthur, Switzerland"
9d357bbf014289fb5f64183c32aa64dc0bd9f454,Face Identification by Fitting a 3D Morphable Model Using Linear Shape and Texture Error Functions,"Face Identification by Fitting a 3D Morphable Model
using Linear Shape and Texture Error Functions
Sami Romdhani, Volker Blanz, and Thomas Vetter
University of Freiburg, Instit¨ut f¨ur Informatik,
Georges-K¨ohler-Allee 52, 79110 Freiburg, Germany,
fromdhani, volker,"
9d839dfc9b6a274e7c193039dfa7166d3c07040b,Augmented faces,"Augmented Faces
Matthias Dantone1
Lukas Bossard1
Till Quack1,2
Luc van Gool1,3
ETH Z¨urich
Kooaba AG
K.U. Leuven"
9d36c81b27e67c515df661913a54a797cd1260bb,3d Face Recognition Techniques - a Review,"Preeti.B.Sharma, Mahesh M. Goyani / International Journal of Engineering Research and
Applications (IJERA)      ISSN: 2248-9622                           www.ijera.com
Vol. 2, Issue 1,Jan-Feb 2012, pp.787-793
3D FACE RECOGNITION TECHNIQUES - A REVIEW
Preeti B. Sharma*, Mahesh M. Goyani**
*(Department of Information Technology, Gujarat Technological University, India)
**( Department of Computer Engineering, Gujarat Technological University, India)
security  at  many  places"
9d757c0fede931b1c6ac344f67767533043cba14,Search Based Face Annotation Using PCA and Unsupervised Label Refinement Algorithms,"Search Based Face Annotation Using PCA and
Unsupervised Label Refinement Algorithms
Shital Shinde1, Archana Chaugule2
Computer Department, Savitribai Phule Pune University
D.Y.Patil Institute of Engineering and Technology, Pimpri, Pune-18
Mahatma Phulenagar, 120/2 Mahaganpati soc, Chinchwad, Pune-19, MH, India
D.Y.Patil Institute of Engineering and Technology, Pimpri, Pune-18
Computer Department, D.Y.PIET, Pimpri, Pune-18, MH, India
presents"
9d60ad72bde7b62be3be0c30c09b7d03f9710c5f,A Survey: Face Recognition Techniques,"A Survey: Face Recognition Techniques
Arun Agrawal
Assistant Professor, ITM GOI
Ranjana Sikarwar
M Tech, ITM GOI
video
(Eigen
passport-verification,"
9d24179aa33a94c8c61f314203bf9e906d6b64de,Searching for People through Textual and Visual Attributes,"Searching for People through
Textual and Visual Attributes
Junior Fabian, Ramon Pires, Anderson Rocha
Institute of Computing
University of Campinas (Unicamp)
Campinas-SP, Brazil
Fig. 1. The proposed approach aims at searching for people using textual and visual attributes. Given an image database of faces, we extract the points of
interest (PoIs) to construct a visual dictionary that allow us to obtain the feature vectors by a quantization process (top). Then we train attribute classifiers to
generate a score for each image (middle). Finally, given a textual query (e.g., male), we fusion obtained scores to return a unique final rank (bottom)."
9c1305383ce2c108421e9f5e75f092eaa4a5aa3c,Speaker Retrieval for Tv Show Videos by Associating Audio Speaker Recognition Result to Visual Faces∗,"SPEAKER RETRIEVAL FOR TV SHOW VIDEOS BY ASSOCIATING AUDIO SPEAKER
RECOGNITION RESULT TO VISUAL FACES∗
Yina Han*’, Joseph Razik’, Gerard Chollet’, and Guizhong Liu*
*School of Electrical and Information Engineering, Xi’an Jiaotong University, Xi’an, China
’CNRS-LTCI, TELECOM-ParisTech, Paris, France"
9c1860de6d6e991a45325c997bf9651c8a9d716f,3D reconstruction and face recognition using kernel-based ICA and neural networks,"D Reconstruction and Face Recognition Using Kernel-Based
ICA and Neural Networks
Cheng-Jian Lin                  Ya-Tzu Huang
Chi-Yung Lee
Dept. of Electrical                Dept. of CSIE                    Dept. of CSIE
Engineering              Chaoyang University              Nankai Institute of
National University            of Technology                        Technology
of Kaohsiung"
9ca7899338129f4ba6744f801e722d53a44e4622,Deep neural networks regularization for structured output prediction,"Deep Neural Networks Regularization for Structured
Output Prediction
Soufiane Belharbi∗
INSA Rouen, LITIS
76000 Rouen, France
Clément Chatelain
INSA Rouen, LITIS
76000 Rouen, France
Romain Hérault
INSA Rouen, LITIS
76000 Rouen, France
Sébastien Adam
INSA Rouen, LITIS
76000 Rouen, France
Normandie Univ, UNIROUEN, UNIHAVRE,
Normandie Univ, UNIROUEN, UNIHAVRE,
Normandie Univ, UNIROUEN, UNIHAVRE,
Normandie Univ, UNIROUEN, UNIHAVRE,"
9c1664f69d0d832e05759e8f2f001774fad354d6,Action Representations in Robotics: A Taxonomy and Systematic Classification,"Action representations in robotics: A
taxonomy and systematic classification
Journal Title
XX(X):1–32
(cid:13)The Author(s) 2016
Reprints and permission:
sagepub.co.uk/journalsPermissions.nav
DOI: 10.1177/ToBeAssigned
www.sagepub.com/
Philipp Zech, Erwan Renaudo, Simon Haller, Xiang Zhang and Justus Piater"
9c065dfb26ce280610a492c887b7f6beccf27319,Learning from Video and Text via Large-Scale Discriminative Clustering,"Learning from Video and Text via Large-Scale Discriminative Clustering
Antoine Miech1,2
Jean-Baptiste Alayrac1,2
Piotr Bojanowski2
Ivan Laptev 1,2
Josef Sivic1,2,3
´Ecole Normale Sup´erieure
Inria
CIIRC"
9c781f7fd5d8168ddae1ce5bb4a77e3ca12b40b6,Attribute Based Face Classification Using Support Vector Machine,"International Research Journal of Engineering and Technology (IRJET)      e-ISSN: 2395 -0056
Volume: 03 Issue: 07 | July-2016                       www.irjet.net                                                               p-ISSN: 2395-0072
Attribute Based Face Classification Using Support Vector Machine
Brindha.M1, Amsaveni.R2
Research Scholar, Dept. of Computer Science, PSGR Krishnammal College for Women, Coimbatore
Assistant Professor, Dept. of Information Technology, PSGR Krishnammal College for Women, Coimbatore."
9ce0d64125fbaf625c466d86221505ad2aced7b1,Recognizing expressions of children in real life scenarios View project PhD ( Doctor of Philosophy ) View project,"Saliency Based Framework for Facial Expression
Recognition
Rizwan Ahmed Khan, Alexandre Meyer, Hubert Konik, Saïda Bouakaz
To cite this version:
Rizwan Ahmed Khan, Alexandre Meyer, Hubert Konik, Saïda Bouakaz. Saliency Based Framework for
Facial Expression Recognition. Frontiers of Computer Science, 2017, <10.1007/s11704-017-6114-9>.
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02601d184d79742c7cd0c0ed80e846d95def052e,Graphical Representation for Heterogeneous Face Recognition,"Graphical Representation for Heterogeneous
Face Recognition
Chunlei Peng, Xinbo Gao, Senior Member, IEEE, Nannan Wang, Member, IEEE, and Jie Li"
02e43d9ca736802d72824892c864e8cfde13718e,Transferring a semantic representation for person re-identification and search,"Transferring a Semantic Representation for Person Re-Identification and
Search
Shi, Z; Yang, Y; Hospedales, T; XIANG, T; IEEE Conference on Computer Vision and
Pattern Recognition
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02fda07735bdf84554c193811ba4267c24fe2e4a,Illumination Invariant Face Recognition Using Near-Infrared Images,"Illumination Invariant Face Recognition
Using Near-Infrared Images
Stan Z. Li, Senior Member, IEEE, RuFeng Chu, ShengCai Liao, and Lun Zhang"
0241513eeb4320d7848364e9a7ef134a69cbfd55,Supervised translation-invariant sparse coding,"Supervised Translation-Invariant Sparse
Coding
¹Jianchao Yang, ²Kai Yu, and ¹Thomas Huang
¹University of Illinois at Urbana Champaign
²NEC Laboratories America at Cupertino"
02dd0af998c3473d85bdd1f77254ebd71e6158c6,PPP: Joint Pointwise and Pairwise Image Label Prediction,"PPP: Joint Pointwise and Pairwise Image Label Prediction
Yilin Wang1 Suhang Wang1
Jiliang Tang2 Huan Liu1 Baoxin Li1
Department of Computer Science, Arizona State Univerity
Yahoo Research"
029317f260b3303c20dd58e8404a665c7c5e7339,Character Identification in Feature-Length Films Using Global Face-Name Matching,"Character Identification in Feature-Length Films
Using Global Face-Name Matching
Yi-Fan Zhang, Student Member, IEEE, Changsheng Xu, Senior Member, IEEE, Hanqing Lu, Senior Member, IEEE,
nd Yeh-Min Huang, Member, IEEE"
0273414ba7d56ab9ff894959b9d46e4b2fef7fd0,Photographic home styles in Congress: a computer vision approach,"Photographic home styles in Congress: a
omputer vision approach∗
L. Jason Anastasopoulos†.
Dhruvil Badani‡
Crystal Lee§
Shiry Ginosar¶
Jake Williams(cid:107)
December 1, 2016"
02e133aacde6d0977bca01ffe971c79097097b7f,Convolutional Neural Fabrics,
02567fd428a675ca91a0c6786f47f3e35881bcbd,Deep Label Distribution Learning With Label Ambiguity,"ACCEPTED BY IEEE TIP
Deep Label Distribution Learning
With Label Ambiguity
Bin-Bin Gao, Chao Xing, Chen-Wei Xie, Jianxin Wu, Member, IEEE, and Xin Geng, Member, IEEE"
0278acdc8632f463232e961563e177aa8c6d6833,Selective Transfer Machine for Personalized Facial Expression Analysis,"Selective Transfer Machine for Personalized
Facial Expression Analysis
Wen-Sheng Chu, Fernando De la Torre, and Jeffrey F. Cohn
INTRODUCTION
Index Terms—Facial expression analysis, personalization, domain adaptation, transfer learning, support vector machine (SVM)
A UTOMATIC facial AU detection confronts a number of"
a4a5ad6f1cc489427ac1021da7d7b70fa9a770f2,Gated spatio and temporal convolutional neural network for activity recognition: towards gated multimodal deep learning,"Yudistira and Kurita EURASIP Journal on Image and Video
Processing  (2017) 2017:85
DOI 10.1186/s13640-017-0235-9
EURASIP Journal on Image
nd Video Processing
RESEARCH
Open Access
Gated spatio and temporal convolutional
neural network for activity recognition:
towards gated multimodal deep learning
Novanto Yudistira1* and Takio Kurita2"
a40f8881a36bc01f3ae356b3e57eac84e989eef0,"End-to-end semantic face segmentation with conditional random fields as convolutional, recurrent and adversarial networks","End-to-end semantic face segmentation with conditional
random fields as convolutional, recurrent and adversarial
networks
Umut Güçlü*, 1, Yağmur Güçlütürk*, 1,
Meysam Madadi2, Sergio Escalera3, Xavier Baró4, Jordi González2,
Rob van Lier1, Marcel van Gerven1"
a4a0b5f08198f6d7ea2d1e81bd97fea21afe3fc3,Efficient Recurrent Residual Networks Improved by Feature Transfer,"Ecient Recurrent Residual Networks Improved by
Feature Transfer
MSc Thesis
written by
Yue Liu
under the supervision of Dr. Silvia-Laura Pintea, Dr. Jan van Gemert,
nd Dr. Ildiko Suveg and submitted to the Board of Examiners for the
degree of
Master of Science
t the Delft University of Technology.
Date of the public defense: Members of the Thesis Committee:
August 31, 2017
Prof. Marcel Reinders
Dr. Jan van Gemert
Dr. Julian Urbano Merino
Dr. Silvia-Laura Pintea
Dr. Ildiko Suveg (Bosch)
Dr. Gonzalez Adrlana (Bosch)"
a44590528b18059b00d24ece4670668e86378a79,Learning the Hierarchical Parts of Objects by Deep Non-Smooth Nonnegative Matrix Factorization,"Learning the Hierarchical Parts of Objects by Deep
Non-Smooth Nonnegative Matrix Factorization
Jinshi Yu, Guoxu Zhou, Andrzej Cichocki
IEEE Fellow, and Shengli Xie IEEE Senior Member"
a472d59cff9d822f15f326a874e666be09b70cfd,Visual Learning with Weakly Labeled Video a Dissertation Submitted to the Department of Computer Science and the Committee on Graduate Studies of Stanford University in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy,"VISUAL LEARNING WITH WEAKLY LABELED VIDEO
A DISSERTATION
SUBMITTED TO THE DEPARTMENT OF COMPUTER SCIENCE
AND THE COMMITTEE ON GRADUATE STUDIES
OF STANFORD UNIVERSITY
IN PARTIAL FULFILLMENT OF THE REQUIREMENTS
FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY
Kevin Tang
May 2015"
a4c430b7d849a8f23713dc283794d8c1782198b2,Video Concept Embedding,"Video Concept Embedding
Anirudh Vemula
Rahul Nallamothu
Syed Zahir Bokhari
. Introduction
In the area of natural language processing, there has been
much success in learning distributed representations for
words as vectors. Doing so has an advantage over using
simple labels, or a one-hot coding scheme for representing
individual words. In learning distributed vector representa-
tions for words, we manage to capture semantic relatedness
of words in vector distance. For example, the word vector
for ”car” and ”road” should end up being closer together in
the vector space representation than ”car” and ”penguin”.
This has been very useful in NLP areas of machine transla-
tion and semantic understanding.
In the computer vision domain, video understanding is a
very important topic.
It is made hard due to the large
mount of high dimensional data in videos. One strategy"
a4f37cfdde3af723336205b361aefc9eca688f5c,Recent Advances in Face Recognition,"Recent Advances
in Face Recognition"
a30869c5d4052ed1da8675128651e17f97b87918,Fine-Grained Comparisons with Attributes,"Fine-Grained Comparisons with Attributes
Aron Yu and Kristen Grauman"
a3ebacd8bcbc7ddbd5753935496e22a0f74dcf7b,"First International Workshop on Adaptive Shot Learning for Gesture Understanding and Production ASL4GUP 2017 Held in conjunction with IEEE FG 2017, in May 30, 2017, Washington DC, USA","First International Workshop on Adaptive Shot Learning
for Gesture Understanding and Production
ASL4GUP 2017
Held in conjunction with IEEE FG 2017, in May 30, 2017,
Washington DC, USA"
a3d8b5622c4b9af1f753aade57e4774730787a00,Pose-Aware Person Recognition,"Pose-Aware Person Recognition
Vijay Kumar (cid:63)
Anoop Namboodiri (cid:63)
(cid:63) CVIT, IIIT Hyderabad, India
Manohar Paluri †
Facebook AI Research
C. V. Jawahar (cid:63)"
a3017bb14a507abcf8446b56243cfddd6cdb542b,Face Localization and Recognition in Varied Expressions and Illumination,"Face Localization and Recognition in Varied
Expressions and Illumination
Hui-Yu Huang, Shih-Hang Hsu"
a3c8c7da177cd08978b2ad613c1d5cb89e0de741,A Spatio-temporal Approach for Multiple Object Detection in Videos Using Graphs and Probability Maps,"A Spatio-temporal Approach for Multiple
Object Detection in Videos Using Graphs
nd Probability Maps
Henrique Morimitsu1(B), Roberto M. Cesar Jr.1, and Isabelle Bloch2
University of S˜ao Paulo, S˜ao Paulo, Brazil
Institut Mines T´el´ecom, T´el´ecom ParisTech, CNRS LTCI, Paris, France"
a378fc39128107815a9a68b0b07cffaa1ed32d1f,Determining a Suitable Metric when Using Non-Negative Matrix Factorization,"Determining a Suitable Metric When using Non-negative Matrix Factorization∗
David Guillamet and Jordi Vitri`a
Computer Vision Center, Dept. Inform`atica
Universitat Aut`onoma de Barcelona
08193 Bellaterra, Barcelona, Spain"
a34d75da87525d1192bda240b7675349ee85c123,Naive-Deep Face Recognition: Touching the Limit of LFW Benchmark or Not?,"Naive-Deep Face Recognition: Touching the Limit of LFW Benchmark or Not?
Erjin Zhou
Face++, Megvii Inc.
Zhimin Cao
Face++, Megvii Inc.
Qi Yin
Face++, Megvii Inc."
a3dc109b1dff3846f5a2cc1fe2448230a76ad83f,Active Appearance Model and Pca Based Face Recognition System,"J.Savitha et al, International Journal of Computer Science and Mobile Computing, Vol.4 Issue.4, April- 2015, pg. 722-731
Available Online at www.ijcsmc.com
International Journal of Computer Science and Mobile Computing
A Monthly Journal of Computer Science and Information Technology
ISSN 2320–088X
IJCSMC, Vol. 4, Issue. 4, April 2015, pg.722 – 731
RESEARCH ARTICLE
ACTIVE APPEARANCE MODEL AND PCA
BASED FACE RECOGNITION SYSTEM
Mrs. J.Savitha M.Sc., M.Phil.
Ph.D Research Scholar, Karpagam University, Coimbatore, Tamil Nadu, India
Email:
Dr. A.V.Senthil Kumar
Director, Hindustan College of Arts and Science, Coimbatore, Tamil Nadu, India
Email:"
a3f69a073dcfb6da8038607a9f14eb28b5dab2db,3D-Aided Deep Pose-Invariant Face Recognition,Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI-18)
a38045ed82d6800cbc7a4feb498e694740568258,African American and Caucasian males ' evaluation of racialized female facial averages,"UNLV Theses, Dissertations, Professional Papers, and Capstones
5-2010
African American and Caucasian males' evaluation
of racialized female facial averages
Rhea M. Watson
University of Nevada Las Vegas
Follow this and additional works at: http://digitalscholarship.unlv.edu/thesesdissertations
Part of the Cognition and Perception Commons, Race and Ethnicity Commons, and the Social
Psychology Commons
Repository Citation
Watson, Rhea M., ""African American and Caucasian males' evaluation of racialized female facial averages"" (2010). UNLV Theses,
Dissertations, Professional Papers, and Capstones. 366.
http://digitalscholarship.unlv.edu/thesesdissertations/366
This Thesis is brought to you for free and open access by Digital It has been accepted for inclusion in UNLV Theses, Dissertations,
Professional Papers, and Capstones by an authorized administrator of Digital For more information, please contact"
a3f78cc944ac189632f25925ba807a0e0678c4d5,Action Recognition in Realistic Sports Videos,"Action Recognition in Realistic Sports Videos
Khurram Soomro and Amir Roshan Zamir"
a3a6a6a2eb1d32b4dead9e702824375ee76e3ce7,Multiple Local Curvature Gabor Binary Patterns for Facial Action Recognition,"Multiple Local Curvature Gabor Binary
Patterns for Facial Action Recognition
Anıl Y¨uce, Nuri Murat Arar and Jean-Philippe Thiran
Signal Processing Laboratory (LTS5),
´Ecole Polytechnique F´ed´erale de Lausanne, Switzerland"
a32c5138c6a0b3d3aff69bcab1015d8b043c91fb,Video redaction: a survey and comparison of enabling technologies,"Downloaded From: https://www.spiedigitallibrary.org/journals/Journal-of-Electronic-Imaging on 9/19/2018
Terms of Use: https://www.spiedigitallibrary.org/terms-of-use
Videoredaction:asurveyandcomparisonofenablingtechnologiesShaganSahAmeyaShringiRaymondPtuchaAaronBurryRobertLoceShaganSah,AmeyaShringi,RaymondPtucha,AaronBurry,RobertLoce,“Videoredaction:asurveyandcomparisonofenablingtechnologies,”J.Electron.Imaging26(5),051406(2017),doi:10.1117/1.JEI.26.5.051406."
a3eab933e1b3db1a7377a119573ff38e780ea6a3,Sparse Representation for accurate classification of corrupted and occluded facial expressions,"978-1-4244-4296-6/10/$25.00 ©2010 IEEE
ICASSP 2010"
a3a34c1b876002e0393038fcf2bcb00821737105,Face Identification across Different Poses and Illuminations with a 3D Morphable Model,"Face Identification across Different Poses and Illuminations
with a 3D Morphable Model
V. Blanz, S. Romdhani, and T. Vetter
University of Freiburg
Georges-K¨ohler-Allee 52, 79110 Freiburg, Germany
fvolker, romdhani,"
a3f1db123ce1818971a57330d82901683d7c2b67,Poselets and Their Applications in High-Level Computer Vision,"Poselets and Their Applications in High-Level
Computer Vision
Lubomir Bourdev
Electrical Engineering and Computer Sciences
University of California at Berkeley
Technical Report No. UCB/EECS-2012-52
http://www.eecs.berkeley.edu/Pubs/TechRpts/2012/EECS-2012-52.html
May 1, 2012"
a3a97bb5131e7e67316b649bbc2432aaa1a6556e,Role of the hippocampus and orbitofrontal cortex during the disambiguation of social cues in working memory.,"Cogn Affect Behav Neurosci
DOI 10.3758/s13415-013-0170-x
Role of the hippocampus and orbitofrontal cortex
during the disambiguation of social cues in working memory
Robert S. Ross & Matthew L. LoPresti & Karin Schon &
Chantal E. Stern
# Psychonomic Society, Inc. 2013"
a35d3ba191137224576f312353e1e0267e6699a1,Increasing security in DRM systems through biometric authentication,"Javier Ortega-Garcia, Josef Bigun, Douglas Reynolds,
nd Joaquin Gonzalez-Rodriguez
Increasing security in DRM systems
through biometric authentication.
ecuring  the  exchange
of  intellectual  property
nd  providing  protection
to  multimedia  contents  in
distribution systems have enabled the
dvent  of  digital  rights  management
(DRM)  systems  [5],  [14],  [21],  [47],
[51], [53]. Rights holders should be able to
license, monitor, and track the usage of rights
in  a  dynamic  digital  trading  environment,  espe-
ially in the near future when universal multimedia
ccess (UMA) becomes a reality, and any multimedia
ontent  will  be  available  anytime,  anywhere.  In  such
DRM  systems,  encryption  algorithms,  access  control,
key  management  strategies,  identification  and  tracing
of contents, or copy control will play a prominent role"
b558be7e182809f5404ea0fcf8a1d1d9498dc01a,Bottom-up and top-down reasoning with convolutional latent-variable models,"Bottom-up and top-down reasoning with convolutional latent-variable models
Peiyun Hu
UC Irvine
Deva Ramanan
UC Irvine"
b5cd8151f9354ee38b73be1d1457d28e39d3c2c6,Finding Celebrities in Video,"Finding Celebrities in Video
Nazli Ikizler
Jai Vasanth
Linus Wong
David Forsyth
Electrical Engineering and Computer Sciences
University of California at Berkeley
Technical Report No. UCB/EECS-2006-77
http://www.eecs.berkeley.edu/Pubs/TechRpts/2006/EECS-2006-77.html
May 23, 2006"
b5fc4f9ad751c3784eaf740880a1db14843a85ba,Significance of image representation for face verification,"SIViP (2007) 1:225–237
DOI 10.1007/s11760-007-0016-5
ORIGINAL PAPER
Significance of image representation for face verification
Anil Kumar Sao · B. Yegnanarayana ·
B. V. K. Vijaya Kumar
Received: 29 August 2006 / Revised: 28 March 2007 / Accepted: 28 March 2007 / Published online: 1 May 2007
© Springer-Verlag London Limited 2007"
b506aa23949b6d1f0c868ad03aaaeb5e5f7f6b57,Modeling Social and Temporal Context for Video Analysis,"UNIVERSITY OF CALIFORNIA
RIVERSIDE
Modeling Social and Temporal Context for Video Analysis
A Dissertation submitted in partial satisfaction
of the requirements for the degree of
Doctor of Philosophy
Computer Science
Zhen Qin
June 2015
Dissertation Committee:
Dr. Christian R. Shelton, Chairperson
Dr. Tao Jiang
Dr. Stefano Lonardi
Dr. Amit Roy-Chowdhury"
b599f323ee17f12bf251aba928b19a09bfbb13bb,Autonomous Quadcopter Videographer,"AUTONOMOUS QUADCOPTER VIDEOGRAPHER
REY R. COAGUILA
B.S. Universidad Peruana de Ciencias Aplicadas, 2009
A thesis submitted in partial fulfillment of the requirements
for the degree of Master of Science in Computer Science
in the Department of Electrical Engineering and Computer Science
in the College of Engineering and Computer Science
t the University of Central Florida
Orlando, Florida
Spring Term
Major Professor: Gita R. Sukthankar"
b5160e95192340c848370f5092602cad8a4050cd,Video Classification With CNNs: Using The Codec As A Spatio-Temporal Activity Sensor,"IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, TO APPEAR
Video Classification With CNNs: Using The Codec
As A Spatio-Temporal Activity Sensor
Aaron Chadha, Alhabib Abbas and Yiannis Andreopoulos, Senior Member, IEEE"
b52886610eda6265a2c1aaf04ce209c047432b6d,Microexpression Identification and Categorization Using a Facial Dynamics Map,"Microexpression Identification and Categorization
using a Facial Dynamics Map
Feng Xu, Junping Zhang, James Z. Wang"
b5857b5bd6cb72508a166304f909ddc94afe53e3,SSIG and IRISA at Multimodal Person Discovery,"SSIG and IRISA at Multimodal Person Discovery
Cassio E. dos Santos Jr1, Guillaume Gravier2, William Robson Schwartz1
Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
IRISA & Inria Rennes , CNRS, Rennes, France"
b51e3d59d1bcbc023f39cec233f38510819a2cf9,"Can a biologically-plausible hierarchy effectively replace face detection, alignment, and recognition pipelines?","CBMM Memo No. 003
March 27, 2014
Can a biologically-plausible hierarchy effectively
replace face detection, alignment, and
recognition pipelines?
Qianli Liao1, Joel Z Leibo1, Youssef Mroueh1, Tomaso Poggio1"
b54c477885d53a27039c81f028e710ca54c83f11,Semi-Supervised Kernel Mean Shift Clustering,"Semi-Supervised Kernel Mean Shift Clustering
Saket Anand, Member, IEEE, Sushil Mittal, Member, IEEE, Oncel Tuzel, Member, IEEE,
nd Peter Meer, Fellow, IEEE"
b503f481120e69b62e076dcccf334ee50559451e,Recognition of Facial Action Units with Action Unit Classifiers and an Association Network,"Recognition of Facial Action Units with Action
Unit Classifiers and An Association Network
Junkai Chen1, Zenghai Chen1, Zheru Chi1 and Hong Fu1,2
Department of Electronic and Information Engineering, The Hong Kong Polytechnic
University, Hong Kong
Department of Computer Science, Chu Hai College of Higher Education, Hong Kong"
b55d0c9a022874fb78653a0004998a66f8242cad,Hybrid Facial Representations for Emotion Recognition Woo,"Hybrid Facial Representations
for Emotion Recognition
Woo-han Yun, DoHyung Kim, Chankyu Park, and Jaehong Kim
Automatic  facial  expression  recognition  is  a  widely
studied  problem  in  computer  vision  and  human-robot
interaction.  There  has  been  a  range  of  studies  for
representing  facial  descriptors  for  facial  expression
recognition. Some prominent descriptors were presented
in  the  first  facial  expression  recognition  and  analysis
hallenge  (FERA2011).  In  that  competition,  the  Local
Gabor  Binary  Pattern  Histogram  Sequence  descriptor
showed the most powerful description capability. In this
paper, we introduce hybrid facial representations for facial
expression  recognition,  which  have  more  powerful
description  capability  with  lower  dimensionality.  Our
descriptors consist of a block-based descriptor and a pixel-
ased  descriptor.  The  block-based  descriptor  represents
the  micro-orientation  and  micro-geometric  structure
information. The pixel-based descriptor represents texture
information.  We  validate  our  descriptors  on  two  public"
b216040f110d2549f61e3f5a7261cab128cab361,Weighted Voting of Discriminative Regions for Face Recognition,"IEICE TRANS. INF. & SYST., VOL.E100–D, NO.11 NOVEMBER 2017
LETTER
Weighted Voting of Discriminative Regions for Face Recognition∗
Wenming YANG†, Member, Riqiang GAO†a), and Qingmin LIAO†, Nonmembers
SUMMARY
This paper presents a strategy, Weighted Voting of Dis-
riminative Regions (WVDR), to improve the face recognition perfor-
mance, especially in Small Sample Size (SSS) and occlusion situations.
In WVDR, we extract the discriminative regions according to facial key
points and abandon the rest parts. Considering different regions of face
make different contributions to recognition, we assign weights to regions
for weighted voting. We construct a decision dictionary according to the
recognition results of selected regions in the training phase, and this dic-
tionary is used in a self-defined loss function to obtain weights. The final
identity of test sample is the weighted voting of selected regions. In this
paper, we combine the WVDR strategy with CRC and SRC separately, and
extensive experiments show that our method outperforms the baseline and
some representative algorithms.
key words: discriminative regions, small sample size, occlusion, weighted
strategy, face recognition"
b261439b5cde39ec52d932a222450df085eb5a91,Facial Expression Recognition using Analytical Hierarchy Process,"International Journal of Computer Trends and Technology (IJCTT) – volume 24 Number 2 – June 2015
Facial Expression Recognition using Analytical Hierarchy
Process
MTech Student 1 , Assistant Professor 2  , Department of Computer Science and Engineeringt1, 2, Disha Institute of
Management and Technology, Raipur Chhattisgarh, India1, 2
Vinita Phatnani1, Akash Wanjari2,
its  significant  contribution"
b2b535118c5c4dfcc96f547274cdc05dde629976,Automatic Recognition of Facial Displays of Unfelt Emotions,"JOURNAL OF IEEE TRANSACTIONS ON AFFECTIVE COMPUTING, VOL. XX, NO. X, XXX 2017
Automatic Recognition of Facial Displays of
Unfelt Emotions
Kaustubh Kulkarni*, Ciprian Adrian Corneanu*, Ikechukwu Ofodile*, Student Member, IEEE, Sergio
Escalera, Xavier Bar´o, Sylwia Hyniewska, Member, IEEE, J¨uri Allik,
nd Gholamreza Anbarjafari, Senior Member, IEEE"
b235b4ccd01a204b95f7408bed7a10e080623d2e,Regularizing Flat Latent Variables with Hierarchical Structures,"Regularizing Flat Latent Variables with Hierarchical Structures
Rongcheng Lin(cid:117) , Huayu Li(cid:117) , Xiaojun Quan† , Richang Hong(cid:63) , Zhiang Wu∓ , Yong Ge(cid:117)
(cid:117)UNC Charlotte. Email: {rlin4, hli38,
(cid:63) Hefei University of Technology. Email:
Institute for Infocomm Research. Email:
∓ Nanjing University of Finance and Economics. Email:"
b2c25af8a8e191c000f6a55d5f85cf60794c2709,A novel dimensionality reduction technique based on kernel optimization through graph embedding,"Noname manuscript No.
(will be inserted by the editor)
A Novel Dimensionality Reduction Technique based on
Kernel Optimization Through Graph Embedding
N. Vretos, A. Tefas and I. Pitas
the date of receipt and acceptance should be inserted later"
d904f945c1506e7b51b19c99c632ef13f340ef4c,0 ° 15 ° 30 ° 45 ° 60 ° 75 ° 90 °,"A scalable 3D HOG model for fast object detection and viewpoint estimation
Marco Pedersoli
Tinne Tuytelaars
KU Leuven, ESAT/PSI - iMinds
Kasteelpark Arenberg 10 B-3001 Leuven, Belgium"
d9810786fccee5f5affaef59bc58d2282718af9b,Adaptive Frame Selection for Enhanced Face Recognition in Low-Resolution Videos,"Adaptive Frame Selection for
Enhanced Face Recognition in
Low-Resolution Videos
Raghavender Reddy Jillela
Thesis submitted to the
College of Engineering and Mineral Resources
t West Virginia University
in partial fulfillment of the requirements
for the degree of
Master of Science
Electrical Engineering
Arun Ross, PhD., Chair
Xin Li, PhD.
Donald Adjeroh, PhD.
Lane Department of Computer Science and Electrical Engineering
Morgantown, West Virginia
Keywords: Face Biometrics, Super-Resolution, Optical Flow, Super-Resolution using
Optical Flow, Adaptive Frame Selection, Inter-Frame Motion Parameter, Image Quality,
Image-Level Fusion, Score-Level Fusion
Copyright 2008 Raghavender Reddy Jillela"
d94d7ff6f46ad5cab5c20e6ac14c1de333711a0c,Face Album: Towards automatic photo management based on person identity on mobile phones,"978-1-5090-4117-6/17/$31.00 ©2017 IEEE
ICASSP 2017"
d930ec59b87004fd172721f6684963e00137745f,Face Pose Estimation using a Tree of Boosted Classifiers,"Face Pose Estimation using a
Tree of Boosted Classifiers
Javier Cruz Mota
Project Assistant: Julien Meynet
Professor: Jean-Philippe Thiran
Signal Processing Institute,
´Ecole Polytechnique F´ed´erale de Lausanne (EPFL)
September 11, 2006"
d9318c7259e394b3060b424eb6feca0f71219179,Face Matching and Retrieval Using Soft Biometrics,"Face Matching and Retrieval Using Soft Biometrics
Unsang Park, Member, IEEE, and Anil K. Jain, Fellow, IEEE"
d9ef1a80738bbdd35655c320761f95ee609b8f49,A Research - Face Recognition by Using Near Set Theory,"Volume 5, Issue 4, 2015                                     ISSN: 2277 128X
International Journal of Advanced Research in
Computer Science and Software Engineering
Research Paper
Available online at: www.ijarcsse.com
A Research - Face Recognition by Using Near Set Theory
Manisha V. Borkar, Bhakti Kurhade
Department of Computer Science and  Engineering
Abha Gaikwad -Patil College of  Engineering, Nagpur, Maharashtra, India"
d9c4b1ca997583047a8721b7dfd9f0ea2efdc42c,Learning Inference Models for Computer Vision,Learning Inference Models for Computer Vision
d9bad7c3c874169e3e0b66a031c8199ec0bc2c1f,"It All Matters: Reporting Accuracy, Inference Time and Power Consumption for Face Emotion Recognition on Embedded Systems","It All Matters:
Reporting Accuracy, Inference Time and Power Consumption
for Face Emotion Recognition on Embedded Systems
Jelena Milosevic
Institute of Telecommunications, TU Wien
Andrew Forembsky
Movidius an Intel Company
Dexmont Pe˜na
Movidius an Intel Company
David Moloney
Movidius an Intel Company
Miroslaw Malek
ALaRI, Faculty of Informatics, USI"
d9327b9621a97244d351b5b93e057f159f24a21e,Laplacian smoothing transform for face recognition,"SCIENCE CHINA
Information Sciences
. RESEARCH PAPERS .
December 2010 Vol. 53 No. 12: 2415–2428
doi: 10.1007/s11432-010-4099-1
Laplacian smoothing transform for face recognition
GU SuiCheng, TAN Ying
& HE XinGui
Key Laboratory of Machine Perception (MOE); Department of Machine Intelligence,
School of Electronics Engineering and Computer Science; Peking University, Beijing 100871, China
Received March 16, 2009; accepted April 1, 2010"
aca232de87c4c61537c730ee59a8f7ebf5ecb14f,Ebgm Vs Subspace Projection for Face Recognition,"EBGM VS SUBSPACE PROJECTION FOR FACE RECOGNITION
Andreas Stergiou, Aristodemos Pnevmatikakis, Lazaros Polymenakos
9.5 Km Markopoulou Avenue, P.O. Box 68, Peania, Athens, Greece
Athens Information Technology
Keywords:
Human-Machine Interfaces, Computer Vision, Face Recognition."
ac6a9f80d850b544a2cbfdde7002ad5e25c05ac6,Privacy-Protected Facial Biometric Verification Using Fuzzy Forest Learning,"Privacy-Protected Facial Biometric Verification
Using Fuzzy Forest Learning
Richard Jiang, Ahmed Bouridane, Senior Member, IEEE, Danny Crookes, Senior Member, IEEE,
M. Emre Celebi, Senior Member, IEEE, and Hua-Liang Wei"
accbd6cd5dd649137a7c57ad6ef99232759f7544,Facial Expression Recognition with Local Binary Patterns and Linear Programming,"FACIAL EXPRESSION RECOGNITION WITH LOCAL BINARY PATTERNS
AND LINEAR PROGRAMMING
Xiaoyi Feng1, 2, Matti Pietikäinen1, Abdenour Hadid1
Machine Vision Group, Infotech Oulu and Dept. of Electrical and Information Engineering
P. O. Box 4500 Fin-90014 University of Oulu, Finland
2 College of Electronics and Information, Northwestern Polytechnic University
710072 Xi’an, China
In  this  work,  we  propose  a  novel  approach  to  recognize  facial  expressions  from  static
images. First, the Local Binary Patterns (LBP) are used to efficiently represent the facial
images and then the Linear Programming (LP) technique is adopted to classify the seven
facial  expressions  anger,  disgust,  fear,  happiness,  sadness,  surprise  and  neutral.
Experimental results demonstrate an average recognition accuracy of 93.8% on the JAFFE
database, which outperforms the rates of all other reported methods on the same database.
Introduction
Facial  expression  recognition  from  static
images  is  a  more  challenging  problem
than  from  image  sequences  because  less
information  for  expression  actions
vailable.  However,  information  in  a
single  image  is  sometimes  enough  for"
ac26166857e55fd5c64ae7194a169ff4e473eb8b,Personalized Age Progression with Bi-Level Aging Dictionary Learning,"Personalized Age Progression with Bi-level
Aging Dictionary Learning
Xiangbo Shu, Jinhui Tang, Senior Member, IEEE, Zechao Li, Hanjiang Lai, Liyan Zhang
nd Shuicheng Yan, Fellow, IEEE"
ac559873b288f3ac28ee8a38c0f3710ea3f986d9,Team DEEP-HRI Moments in Time Challenge 2018 Technical Report,"Team DEEP-HRI Moments in Time Challenge 2018 Technical Report
Chao Li, Zhi Hou, Jiaxu Chen, Yingjia Bu, Jiqiang Zhou, Qiaoyong Zhong, Di Xie and Shiliang Pu
Hikvision Research Institute"
ac8e09128e1e48a2eae5fa90f252ada689f6eae7,Leolani: A Reference Machine with a Theory of Mind for Social Communication,"Leolani: a reference machine with a theory of
mind for social communication
Piek Vossen, Selene Baez, Lenka Baj˘ceti´c, and Bram Kraaijeveld
VU University Amsterdam, Computational Lexicology and Terminology Lab, De
Boelelaan 1105, 1081HV Amsterdam, The Netherlands
www.cltl.nl"
ac8441e30833a8e2a96a57c5e6fede5df81794af,Hierarchical Representation Learning for Kinship Verification,"IEEE TRANSACTIONS ON IMAGE PROCESSING
Hierarchical Representation Learning for Kinship
Verification
Naman Kohli, Student Member, IEEE, Mayank Vatsa, Senior Member, IEEE, Richa Singh, Senior Member, IEEE,
Afzel Noore, Senior Member, IEEE, and Angshul Majumdar, Senior Member, IEEE"
ac12ba5bf81de83991210b4cd95b4ad048317681,Combining Deep Facial and Ambient Features for First Impression Estimation,"Combining Deep Facial and Ambient Features
for First Impression Estimation
Furkan G¨urpınar1, Heysem Kaya2, Albert Ali Salah3
Program of Computational Science and Engineering, Bo˘gazi¸ci University,
Bebek, Istanbul, Turkey
Department of Computer Engineering, Namık Kemal University,
C¸ orlu, Tekirda˘g, Turkey
Department of Computer Engineering, Bo˘gazi¸ci University,
Bebek, Istanbul, Turkey"
acb83d68345fe9a6eb9840c6e1ff0e41fa373229,"Kernel methods in computer vision: object localization, clustering, and taxonomy discovery","Kernel Methods in Computer Vision:
Object Localization, Clustering,
nd Taxonomy Discovery
vorgelegt von
Matthew Brian Blaschko, M.S.
us La Jolla
Von der Fakult¨at IV - Elektrotechnik und Informatik
der Technischen Universit¨at Berlin
zur Erlangung des akademischen Grades
Doktor der Naturwissenschaften
Dr. rer. nat.
genehmigte Dissertation
Promotionsausschuß:
Vorsitzender: Prof. Dr. O. Hellwich
Berichter: Prof. Dr. T. Hofmann
Berichter: Prof. Dr. K.-R. M¨uller
Berichter: Prof. Dr. B. Sch¨olkopf
Tag der wissenschaftlichen Aussprache: 23.03.2009
Berlin 2009"
ade1034d5daec9e3eba1d39ae3f33ebbe3e8e9a7,Multimodal Caricatural Mirror,"Multimodal Caricatural Mirror
Martin O.(1), Adell J.(2), Huerta A.(3), Kotsia I.(4), Savran A.(5), Sebbe R.(6)
(1)  : Université catholique de Louvain, Belgium
(2)  Universitat Polytecnica de Barcelona, Spain
(3)  Universidad Polytècnica de Madrid, Spain
(4)  Aristotle University of Thessaloniki, Greece
(5)  Bogazici University, Turkey
(6)  Faculté Polytechnique de Mons, Belgium"
adf7ccb81b8515a2d05fd3b4c7ce5adf5377d9be,Apprentissage de métrique appliqué à la détection de changement de page Web et aux attributs relatifs,"Apprentissage de métrique appliqué à la
détection de changement de page Web et
ux attributs relatifs
Marc T. Law* — Nicolas Thome* — Stéphane Gançarski* — Mat-
thieu Cord*
* Sorbonne Universités, UPMC Univ Paris 06, UMR 7606, LIP6, F-75005, Paris,
France
RÉSUMÉ. Nous proposons dans cet article un nouveau schéma d’apprentissage de métrique.
Basé sur l’exploitation de contraintes qui impliquent des quadruplets d’images, notre approche
vise à modéliser des relations sémantiques de similarités riches ou complexes. Nous étudions
omment ce schéma peut être utilisé dans des contextes tels que la détection de régions impor-
tantes dans des pages Web ou la reconnaissance à partir d’attributs relatifs."
ada73060c0813d957576be471756fa7190d1e72d,VRPBench: A Vehicle Routing Benchmark Tool,"VRPBench: A Vehicle Routing Benchmark Tool
October 19, 2016
Guilherme A. Zeni1 , Mauro Menzori1, P. S. Martins1, Luis A. A. Meira1"
adfaf01773c8af859faa5a9f40fb3aa9770a8aa7,Large Scale Visual Recognition,"LARGE SCALE VISUAL RECOGNITION
JIA DENG
A DISSERTATION
PRESENTED TO THE FACULTY
OF PRINCETON UNIVERSITY
IN CANDIDACY FOR THE DEGREE
OF DOCTOR OF PHILOSOPHY
RECOMMENDED FOR ACCEPTANCE
BY THE DEPARTMENT OF
COMPUTER SCIENCE
ADVISER: FEI-FEI LI
JUNE 2012"
adf5caca605e07ee40a3b3408f7c7c92a09b0f70,Line-Based PCA and LDA Approaches for Face Recognition,"Line-based PCA and LDA approaches for Face Recognition
Vo Dinh Minh Nhat, and Sungyoung Lee
Kyung Hee University – South of Korea
{vdmnhat,"
adaf2b138094981edd615dbfc4b7787693dbc396,Statistical methods for facial shape-from-shading and recognition,"Statistical Methods For Facial
Shape-from-shading and Recognition
William A. P. Smith
Submitted for the degree of Doctor of Philosophy
Department of Computer Science
0th February 2007"
adf62dfa00748381ac21634ae97710bb80fc2922,ViFaI : A trained video face indexing scheme Harsh,"ViFaI: A trained video face indexing scheme
Harsh Nayyar
Audrey Wei
. Introduction
With the increasing prominence of inexpensive
video recording devices (e.g., digital camcorders and
video recording smartphones),
the average user’s
video collection today is increasing rapidly. With this
development, there arises a natural desire to rapidly
ccess a subset of one’s collection of videos. The solu-
tion to this problem requires an effective video index-
ing scheme. In particular, we must be able to easily
process a video to extract such indexes.
Today, there also exist large sets of labeled (tagged)
face images. One important example is an individual’s
Facebook profile. Such a set of of tagged images of
one’s self, family, friends, and colleagues represents
n extremely valuable potential training set.
In this work, we explore how to leverage the afore-"
bb489e4de6f9b835d70ab46217f11e32887931a2,Everything You Wanted to Know about Deep Learning for Computer Vision but Were Afraid to Ask,"Everything you wanted to know about Deep Learning for Computer Vision but were
fraid to ask
Moacir A. Ponti, Leonardo S. F. Ribeiro, Tiago S. Nazare
ICMC – University of S˜ao Paulo
S˜ao Carlos/SP, 13566-590, Brazil
Tu Bui, John Collomosse
CVSSP – University of Surrey
Guildford, GU2 7XH, UK
Email: [ponti, leonardo.sampaio.ribeiro,
Email: [t.bui,
tools,"
bba281fe9c309afe4e5cc7d61d7cff1413b29558,An unpleasant emotional state reduces working memory capacity: electrophysiological evidence,"Social Cognitive and Affective Neuroscience, 2017, 984–992
doi: 10.1093/scan/nsx030
Advance Access Publication Date: 11 April 2017
Original article
An unpleasant emotional state reduces working
memory capacity: electrophysiological evidence
Jessica S. B. Figueira,1 Leticia Oliveira,1 Mirtes G. Pereira,1 Luiza B. Pacheco,1
Isabela Lobo,1,2 Gabriel C. Motta-Ribeiro,3 and Isabel A. David1
Laboratorio de Neurofisiologia do Comportamento, Departamento de Fisiologia e Farmacologia, Instituto
Biome´dico, Universidade Federal Fluminense, Niteroi, Brazil, 2MograbiLab, Departamento de Psicologia,
Pontifıcia Universidade Catolica do Rio de Janeiro, Rio de Janeiro, Brazil, and 3Laboratorio de Engenharia
Pulmonar, Programa de Engenharia Biome´dica, COPPE, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
Correspondence should be addressed to Isabel A. David, Departamento de Fisiologia e Farmacologia, Instituto Biome´dico, Universidade Federal
Fluminense, Rua Hernani Pires de Mello, 101, Niteroi, RJ 24210-130, Brazil. E-mail:"
bb22104d2128e323051fb58a6fe1b3d24a9e9a46,Analyzing Facial Expression by Fusing Manifolds,")=OEC .=?E= -NFHAIIE >O .KIEC
9A;= +D=C1,2 +DK5C +DA1,3 ;E2EC 0KC1,2,3
1IJEJKJA B 1BH=JE 5?EA?A 5EE?= 6=EM=
,AFJ B +FKJAH 5?EA?A 1BH=JE -CEAAHEC =JE= 6=EM= 7ELAHIEJO
IJEJKJA B AJMHEC =JE= 6=EM= 7ELAHIEJO
{wychang,
)>IJH=?J .A=JKHA HAFHAIAJ=JE ?=IIE?=JE =HA JM =H EIIKAI E B=?E=
ANFHAIIE ==OIEI 1 JDA F=IJ IJ AEJDAH DEIJE? H ?= HAFHA
IAJ=JE BH ==OIEI 1 AIIA?A ?= EBH=JE =EO B?KIAI  JDA IK>JA
L=HE=JEI B ANFHAIIEI DEIJE? HAFHAIAJ=JE IJHAIIAI  C>=
JEAI 6 J=A JDA B >JD = HAFHAIAJ=JE EI E JDEI
F=FAH A=HEC EI J ?D=H=?JAHEA C>= ?= EBH=
JE 7EA IA KIEC A=H
EC =FFH=?DAI B JDA HAFHAIAJ=JE =HA >O
= A=HEC JA?DEGKA 6 EJACH=JA JDAIA
ABBA?JELAO = BKIE ?=IIEAH EI MDE?D ?= DAF J AFO IKEJ=>A
?>E=JE MAECDJI B B=?E= ?FAJI J = ANFHAIIE +FHADA
IELA ?F=HEII  B=?E= ANFHAIIE HA?CEJE =HA J JDA
ABBA?JELAAII B KH =CHEJD
A=EEC DK= AJEI F=OI = EFHJ=J HA E DK= ?KE?=JE 6"
bb7f2c5d84797742f1d819ea34d1f4b4f8d7c197,From Images to 3D Shape Attributes.,"TO APPEAR IN TPAMI
From Images to 3D Shape Attributes
David F. Fouhey, Abhinav Gupta, Andrew Zisserman"
bb451dc2420e1a090c4796c19716f93a9ef867c9,A Review on: Automatic Movie Character Annotation by Robust Face-Name Graph Matching,"International Journal of Computer Applications (0975 – 8887)
Volume 104 – No.5, October 2014
A Review on: Automatic Movie Character Annotation
y Robust Face-Name Graph Matching
Bhandare P.S.
Research Scholar
Sinhgad College of
Engineering, korti, Pandharpur,
Solapur University, INDIA
Gadekar P.R.
Assistant Professor
Sinhgad College of
Engineering, korti, Pandharpur,
Solapur University, INDIA
Bandgar Vishal V.
Assistant Professor
College of Engineering (Poly),
Pandharpur, Solapur, INDIA
Bhise Avdhut S.
HOD, Department of"
bbd1eb87c0686fddb838421050007e934b2d74ab,Look at Boundary: A Boundary-Aware Face Alignment Algorithm,"(68	points)	COFW	(29	points)	AFLW	(19	points)	Figure1:Thefirstcolumnshowsthefaceimagesfromdifferentdatasetswithdifferentnumberoflandmarks.Thesecondcolumnillustratestheuniversallydefinedfacialboundariesestimatedbyourmethods.Withthehelpofboundaryinformation,ourapproachachieveshighaccuracylocalisationresultsacrossmultipledatasetsandannotationprotocols,asshowninthethirdcolumn.Differenttofacedetection[45]andrecognition[75],facealignmentidentifiesgeometrystructureofhumanfacewhichcanbeviewedasmodelinghighlystructuredout-put.Eachfaciallandmarkisstronglyassociatedwithawell-definedfacialboundary,e.g.,eyelidandnosebridge.However,comparedtoboundaries,faciallandmarksarenotsowell-defined.Faciallandmarksotherthancornerscanhardlyremainthesamesemanticallocationswithlargeposevariationandocclusion.Besides,differentannotationschemesofexistingdatasetsleadtoadifferentnumberoflandmarks[28,5,66,30](19/29/68/194points)andanno-tationschemeoffuturefacealignmentdatasetscanhardlybedetermined.Webelievethereasoningofauniquefacial"
d7fe2a52d0ad915b78330340a8111e0b5a66513a,Photo-to-Caricature Translation on Faces in the Wild,"Unpaired Photo-to-Caricature Translation on Faces in
the Wild
Ziqiang Zhenga, Chao Wanga, Zhibin Yua, Nan Wanga, Haiyong Zhenga,∗,
Bing Zhenga
No. 238 Songling Road, Department of Electronic Engineering, Ocean University of
China, Qingdao, China"
d708ce7103a992634b1b4e87612815f03ba3ab24,FCVID: Fudan-Columbia Video Dataset,"FCVID: Fudan-Columbia Video Dataset
Yu-Gang Jiang, Zuxuan Wu, Jun Wang, Xiangyang Xue, Shih-Fu Chang
Available at: http://bigvid.fudan.edu.cn/FCVID/
OVERVIEW
Recognizing visual contents in unconstrained videos
has become a very important problem for many ap-
plications, such as Web video search and recommen-
dation, smart content-aware advertising, robotics, etc.
Existing datasets for video content recognition are
either small or do not have reliable manual labels.
In this work, we construct and release a new Inter-
net video dataset called Fudan-Columbia Video Dataset
(FCVID), containing 91,223 Web videos (total duration
,232 hours) annotated manually according to 239
ategories. We believe that the release of FCVID can
stimulate innovative research on this challenging and
important problem.
COLLECTION AND ANNOTATION
The categories in FCVID cover a wide range of topics
like social events (e.g., “tailgate party”), procedural"
d7b6bbb94ac20f5e75893f140ef7e207db7cd483,griffith . edu . au Face Recognition across Pose : A Review,"Griffith Research Online
https://research-repository.griffith.edu.au
Face Recognition across Pose: A
Review
Author
Zhang, Paul, Gao, Yongsheng
Published
Journal Title
Pattern Recognition
https://doi.org/10.1016/j.patcog.2009.04.017
Copyright Statement
Copyright 2009 Elsevier. This is the author-manuscript version of this paper. Reproduced in accordance
with the copyright policy of the publisher. Please refer to the journal's website for access to the
definitive, published version.
Downloaded from
http://hdl.handle.net/10072/30193"
d7d166aee5369b79ea2d71a6edd73b7599597aaa,Fast Subspace Clustering Based on the Kronecker Product,"Fast Subspace Clustering Based on the
Kronecker Product
Lei Zhou1, Xiao Bai1, Xianglong Liu1, Jun Zhou2, and Hancock Edwin3
Beihang University 2Grif‌f‌ith University 3University of York, UK"
d79f9ada35e4410cd255db39d7cc557017f8111a,Evaluation of accurate eye corner detection methods for gaze estimation,"Journal of Eye Movement Research
7(3):3, 1-8
Evaluation of accurate eye corner detection methods for gaze
estimation
Jose Javier Bengoechea
Public University of Navarra, Spain
Juan J. Cerrolaza
Childrens National Medical Center, USA
Arantxa Villanueva
Public University of Navarra, Spain
Rafael Cabeza
Public University of Navarra, Spain
Accurate detection of iris center and eye corners appears to be a promising
pproach for low cost gaze estimation.
In this paper we propose novel eye
inner corner detection methods. Appearance and feature based segmentation
pproaches are suggested. All these methods are exhaustively tested on a realistic
dataset containing images of subjects gazing at different points on a screen.
We have demonstrated that a method based on a neural network presents the
est performance even in light changing scenarios."
d03265ea9200a993af857b473c6bf12a095ca178,Multiple deep convolutional neural networks averaging for face alignment,"Multiple deep convolutional neural
networks averaging for face
lignment
Shaohua Zhang
Hua Yang
Zhouping Yin
Downloaded From: http://electronicimaging.spiedigitallibrary.org/ on 05/28/2015 Terms of Use: http://spiedl.org/terms"
d00c335fbb542bc628642c1db36791eae24e02b7,Deep Learning-Based Gaze Detection System for Automobile Drivers Using a NIR Camera Sensor,"Article
Deep Learning-Based Gaze Detection System for
Automobile Drivers Using a NIR Camera Sensor
Rizwan Ali Naqvi, Muhammad Arsalan, Ganbayar Batchuluun, Hyo Sik Yoon and
Kang Ryoung Park *
Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro, 1-gil, Jung-gu,
Seoul 100-715, Korea; (R.A.N.); (M.A.);
(G.B.); (H.S.Y.)
* Correspondence: Tel.: +82-10-3111-7022; Fax: +82-2-2277-8735
Received: 5 January 2018; Accepted: 1 February 2018; Published: 3 February 2018"
d0144d76b8b926d22411d388e7a26506519372eb,Improving Regression Performance with Distributional Losses,"Improving Regression Performance with Distributional Losses
Ehsan Imani 1 Martha White 1"
d0a21f94de312a0ff31657fd103d6b29db823caa,Facial Expression Analysis,"Facial Expression Analysis
Fernando De la Torre and Jeffrey F. Cohn"
d03e4e938bcbc25aa0feb83d8a0830f9cd3eb3ea,Face Recognition with Patterns of Oriented Edge Magnitudes,"Face Recognition with Patterns of Oriented
Edge Magnitudes
Ngoc-Son Vu1,2 and Alice Caplier2
Vesalis Sarl, Clermont Ferrand, France
Gipsa-lab, Grenoble INP, France"
d02c54192dbd0798b43231efe1159d6b4375ad36,3 D Reconstruction and Face Recognition Using Kernel-Based ICA and Neural Networks,"D Reconstruction and Face Recognition Using Kernel-Based
ICA and Neural Networks
Cheng-Jian Lin                  Ya-Tzu Huang
Chi-Yung Lee
Dept. of Electrical                Dept. of CSIE                    Dept. of CSIE
Engineering              Chaoyang University              Nankai Institute of
National University            of Technology                        Technology
of Kaohsiung"
d00787e215bd74d32d80a6c115c4789214da5edb,Faster and Lighter Online Sparse Dictionary Learning,"Faster and Lighter Online
Sparse Dictionary Learning
Project report
By: Shay Ben-Assayag, Omer Dahary
Supervisor: Jeremias Sulam"
be8c517406528edc47c4ec0222e2a603950c2762,Measuring Facial Action,"Harrigan / The new handbook of methods in nonverbal behaviour research 02-harrigan-chap02 Page Proof page 7
7.6.2005
5:45pm
B A S I C R E S E A RC H
M E T H O D S A N D
P RO C E D U R E S"
beb3fd2da7f8f3b0c3ebceaa2150a0e65736d1a2,Adaptive Histogram Equalization and Logarithm Transform with Rescaled Low Frequency DCT Coefficients for Illumination Normalization,"RESEARCH PAPER
International Journal of Recent Trends in Engineering Vol 1, No. 1, May 2009,
Adaptive Histogram Equalization and Logarithm
Transform with Rescaled Low Frequency DCT
Coefficients for Illumination Normalization
Virendra P. Vishwakarma, Sujata Pandey and M. N. Gupta
Department of Computer Science and Engineering
Amity School of Engineering Technology, 580, Bijwasan, New Delhi-110061, India
(Affiliated to Guru Gobind Singh Indraprastha University, Delhi, India)
Email:
illumination  normalization.  The
lighting  conditions.  Most  of  the"
be48b5dcd10ab834cd68d5b2a24187180e2b408f,Constrained Low-Rank Learning Using Least Squares-Based Regularization,"FOR PERSONAL USE ONLY
Constrained Low-rank Learning Using Least
Squares Based Regularization
Ping Li, Member, IEEE, Jun Yu, Member, IEEE, Meng Wang, Member, IEEE,
Luming Zhang, Member, IEEE, Deng Cai, Member, IEEE, and Xuelong Li, Fellow, IEEE,"
be437b53a376085b01ebd0f4c7c6c9e40a4b1a75,Face Recognition and Retrieval Using Cross Age Reference Coding,"ISSN (Online) 2321 – 2004
ISSN (Print) 2321 – 5526
INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH IN ELECTRICAL, ELECTRONICS, INSTRUMENTATION AND CONTROL ENGINEERING
Vol. 4, Issue 5, May 2016
IJIREEICE
Face Recognition and Retrieval Using Cross
Age Reference Coding
Sricharan H S1, Srinidhi K S1, Rajath D N1, Tejas J N1, Chandrakala B M2
BE, DSCE, Bangalore1
Assistant Professor, DSCE, Bangalore2"
be07f2950771d318a78d2b64de340394f7d6b717,3D HMM-based Facial Expression Recognition using Histogram of Oriented Optical Flow,"See	discussions,	stats,	and	author	profiles	for	this	publication	at:	https://www.researchgate.net/publication/290192867
D	HMM-based	Facial	Expression	Recognition
using	Histogram	of	Oriented	Optical	Flow
ARTICLE		in		SYNTHESIS	LECTURES	ON	ARTIFICIAL	INTELLIGENCE	AND	MACHINE	LEARNING	·	DECEMBER	2015
DOI:	10.14738/tmlai.36.1661
READS
AUTHORS,	INCLUDING:
Sheng	Kung
Oakland	University
Djamel	Bouchaffra
Institute	of	Electrical	and	Electronics	Engineers
PUBLICATION			0	CITATIONS
57	PUBLICATIONS			402	CITATIONS
SEE	PROFILE
SEE	PROFILE
All	in-text	references	underlined	in	blue	are	linked	to	publications	on	ResearchGate,
letting	you	access	and	read	them	immediately.
Available	from:	Djamel	Bouchaffra
Retrieved	on:	11	February	2016"
beb4546ae95f79235c5f3c0e9cc301b5d6fc9374,A Modular Approach to Facial Expression Recognition,"A Modular Approach to Facial Expression Recognition
Michal Sindlar
Cognitive Artificial Intelligence, Utrecht University, Heidelberglaan 6, 3584 CD, Utrecht
Marco Wiering
Intelligent Systems Group, Utrecht University, Padualaan 14, 3508 TB, Utrecht"
bebea83479a8e1988a7da32584e37bfc463d32d4,Discovery of Latent 3D Keypoints via End-to-end Geometric Reasoning,"Discovery of Latent 3D Keypoints via
End-to-end Geometric Reasoning
Supasorn Suwajanakorn∗ Noah Snavely
Jonathan Tompson Mohammad Norouzi
{supasorn, snavely, tompson,
Google AI"
bed06e7ff0b510b4a1762283640b4233de4c18e0,Face Interpretation Problems on Low Quality Images,"Bachelor Project
Czech
Technical
University
in Prague
Faculty of Electrical Engineering
Department of Cybernetics
Face Interpretation Problems on Low
Quality Images
Adéla Šubrtová
Supervisor: Ing. Jan Čech, Ph.D
May 2018"
beab10d1bdb0c95b2f880a81a747f6dd17caa9c2,DeepDeblur: Fast one-step blurry face images restoration,"DeepDeblur: Fast one-step blurry face images restoration
Lingxiao Wang, Yali Li, Shengjin Wang
Tsinghua Unversity"
b331ca23aed90394c05f06701f90afd550131fe3,Double regularized matrix factorization for image classification and clustering,"Zhou et al. EURASIP Journal on Image and Video Processing  (2018) 2018:49
https://doi.org/10.1186/s13640-018-0287-5
EURASIP Journal on Image
nd Video Processing
R ES EAR CH
Double regularized matrix factorization for
image classification and clustering
Wei Zhou1*
, Chengdong Wu2, Jianzhong Wang3,4, Xiaosheng Yu2 and Yugen Yi5
Open Access"
b37f57edab685dba5c23de00e4fa032a3a6e8841,Towards social interaction detection in egocentric photo-streams,"Towards Social Interaction Detection in Egocentric Photo-streams
Maedeh Aghaei, Mariella Dimiccoli, Petia Radeva
University of Barcelona and Computer Vision Centre, Barcelona, Spain
Recent advances in wearable camera technology have
led to novel applications in the field of Preventive Medicine.
For some of them, such as cognitive training of elderly peo-
ple by digital memories and detection of unhealthy social
trends associated to neuropsychological disorders, social in-
teraction are of special interest. Our purpose is to address
this problem in the domain of egocentric photo-streams cap-
tured by a low temporal resolution wearable camera (2fpm).
These cameras are suited for collecting visual information
for long period of time, as required by the aforementioned
pplications. The major difficulties to be handled in this
ontext are the sparsity of observations as well as the unpre-
dictability of camera motion and attention orientation due
to the fact that the camera is worn as part of clothing (see
Fig. 1). Inspired by the theory of F-formation which is a
pattern that people tend to follow when interacting [5], our
proposed approach consists of three steps: multi-faces as-"
b3cb91a08be4117d6efe57251061b62417867de9,Label propagation approach for predicting missing biographic labels in face-based biometric records,"T. Swearingen and A. Ross. ""A label propagation approach for predicting missing biographic labels in
A Label Propagation Approach for
Predicting Missing Biographic Labels
in Face-Based Biometric Records
Thomas Swearingen and Arun Ross"
b340f275518aa5dd2c3663eed951045a5b8b0ab1,Visual inference of human emotion and behaviour,"Visual Inference of Human Emotion and Behaviour
Shaogang Gong
Caifeng Shan
Tao Xiang
Dept of Computer Science
Queen Mary College, London
Dept of Computer Science
Queen Mary College, London
Dept of Computer Science
Queen Mary College, London
England, UK
England, UK
England, UK"
b375db63742f8a67c2a7d663f23774aedccc84e5,Brain-Inspired Classroom Occupancy Monitoring on a Low-Power Mobile Platform,"Brain-inspired Classroom Occupancy
Monitoring on a Low-Power Mobile Platform
Department of Electrical, Electronic and Information Engineering, University of Bologna, Italy
Francesco Conti∗, Antonio Pullini† and Luca Benini∗†
Integrated Systems Laboratory, ETH Zurich, Switzerland"
b3c60b642a1c64699ed069e3740a0edeabf1922c,Max-Margin Object Detection,"Max-Margin Object Detection
Davis E. King"
b3f7c772acc8bc42291e09f7a2b081024a172564,"A novel approach for performance parameter estimation of face recognition based on clustering , shape and corner detection","www.ijmer.com            Vol. 3, Issue. 5, Sep - Oct. 2013 pp-3225-3230                 ISSN: 2249-6645
International Journal of Modern Engineering Research (IJMER)
A novel approach for performance parameter estimation of face
recognition based on clustering, shape and corner detection
.Smt.Minj Salen Kujur , 2.Prof. Prashant Jain,
Department of Electronics & Communication Engineering college Jabalpur"
b3c398da38d529b907b0bac7ec586c81b851708f,Face recognition under varying lighting conditions using self quotient image,"Face Recognition under Varying Lighting Conditions Using Self Quotient
Haitao Wang, 2Stan Z Li, 1Yangsheng Wang
Image
Institute of Automation, Chinese Academy of
Sciences, Beijing, 100080, China,
Email:"
b32cf547a764a4efa475e9c99a72a5db36eeced6,Mimicry of ingroup and outgroup emotional expressions,"UvA-DARE (Digital Academic Repository)
Mimicry of ingroup and outgroup emotional expressions
Sachisthal, M.S.M.; Sauter, D.A.; Fischer, A.H.
Published in:
Comprehensive Results in Social Psychology
0.1080/23743603.2017.1298355
Link to publication
Citation for published version (APA):
Sachisthal, M. S. M., Sauter, D. A., & Fischer, A. H. (2016). Mimicry of ingroup and outgroup emotional
expressions. Comprehensive Results in Social Psychology, 1(1-3), 86-105. DOI:
0.1080/23743603.2017.1298355
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Download date: 08 Aug 2018"
b32631f456397462b3530757f3a73a2ccc362342,Discriminant Tensor Dictionary Learning with Neighbor Uncorrelation for Image Set Based Classification,Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17)
df90850f1c153bfab691b985bfe536a5544e438b,"Face Tracking Algorithm Robust to Pose , Illumination and Face Expression Changes : a 3 D Parametric Model Approach","FACE TRACKING ALGORITHM ROBUST TO POSE,
ILLUMINATION AND FACE EXPRESSION CHANGES: A 3D
PARAMETRIC MODEL APPROACH
Marco Anisetti, Valerio Bellandi
University of Milan - Department of Information Technology
via Bramante 65 - 26013, Crema (CR), Italy
Luigi Arnone, Fabrizio Beverina
STMicroelectronics - Advanced System Technology Group
via Olivetti 5 - 20041, Agrate Brianza, Italy
Keywords:
Face tracking, expression changes, FACS, illumination changes."
df8da144a695269e159fb0120bf5355a558f4b02,Face Recognition using PCA and Eigen Face Approach,"International Journal of Computer Applications (0975 – 8887)
International Conference on Recent Trends in engineering & Technology - 2013(ICRTET'2013)
Face Recognition using PCA and Eigen Face
Approach
Anagha A. Shinde
ME EXTC [VLSI & Embedded System]
Sinhgad Academy of Engineering
EXTC Department
Pune, India"
df577a89830be69c1bfb196e925df3055cafc0ed,"Shift: A Zero FLOP, Zero Parameter Alternative to Spatial Convolutions","Shift: A Zero FLOP, Zero Parameter Alternative to Spatial Convolutions
Bichen Wu, Alvin Wan∗, Xiangyu Yue∗, Peter Jin, Sicheng Zhao,
Noah Golmant, Amir Gholaminejad, Joseph Gonzalez, Kurt Keutzer
UC Berkeley"
df51dfe55912d30fc2f792561e9e0c2b43179089,Face Hallucination Using Linear Models of Coupled Sparse Support,"Face Hallucination using Linear Models of Coupled
Sparse Support
Reuben A. Farrugia, Member, IEEE, and Christine Guillemot, Fellow, IEEE
grid and fuse them to suppress the aliasing caused by under-
sampling [5], [6]. On the other hand, learning based meth-
ods use coupled dictionaries to learn the mapping relations
etween low- and high- resolution image pairs to synthesize
high-resolution images from low-resolution images [4], [7].
The research community has lately focused on the latter
ategory of super-resolution methods, since they can provide
higher quality images and larger magnification factors."
df054fa8ee6bb7d2a50909939d90ef417c73604c,Image Quality-aware Deep Networks Ensemble for Efficient Gender Recognition in the Wild,"Image Quality-Aware Deep Networks Ensemble for Efficient
Gender Recognition in the Wild
Mohamed Selim1, Suraj Sundararajan1, Alain Pagani2 and Didier Stricker1,2
Augmented Vision Lab, Technical University Kaiserslautern, Kaiserslautern, Germany
German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany
{mohamed.selim, alain.pagani, s
Keywords:
Gender, Face, Deep Neural Networks, Quality, In the Wild"
df80fed59ffdf751a20af317f265848fe6bfb9c9,Learning Deep Sharable and Structural Detectors for Face Alignment,"Learning Deep Sharable and Structural
Detectors for Face Alignment
Hao Liu, Jiwen Lu, Senior Member, IEEE, Jianjiang Feng, Member, IEEE, and Jie Zhou, Senior Member, IEEE"
dfa80e52b0489bc2585339ad3351626dee1a8395,Human Action Forecasting by Learning Task Grammars,"Human Action Forecasting by Learning Task Grammars
Tengda Han
Jue Wang
Anoop Cherian
Stephen Gould"
dfecaedeaf618041a5498cd3f0942c15302e75c3,A recursive framework for expression recognition: from web images to deep models to game dataset,"Noname manuscript No.
(will be inserted by the editor)
A Recursive Framework for Expression Recognition: From
Web Images to Deep Models to Game Dataset
Wei Li · Christina Tsangouri · Farnaz Abtahi · Zhigang Zhu
Received: date / Accepted: date"
df5fe0c195eea34ddc8d80efedb25f1b9034d07d,Robust modified Active Shape Model for automatic facial landmark annotation of frontal faces,"Robust Modified Active Shape Model for Automatic Facial Landmark
Annotation of Frontal Faces
Keshav Seshadri and Marios Savvides"
df2494da8efa44d70c27abf23f73387318cf1ca8,Supervised Filter Learning for Representation Based Face Recognition,"RESEARCH ARTICLE
Supervised Filter Learning for Representation
Based Face Recognition
Chao Bi1, Lei Zhang2, Miao Qi1, Caixia Zheng1, Yugen Yi3, Jianzhong Wang1*,
Baoxue Zhang4*
College of Computer Science and Information Technology, Northeast Normal University, Changchun,
China, 2 Changchun Institute of Optics, Fine Mechanics and Physics, CAS, Changchun, China, 3 School of
Software, Jiangxi Normal University, Nanchang, China, 4 School of Statistics, Capital University of
Economics and Business, Beijing, China
11111
* (JW); (BZ)"
df674dc0fc813c2a6d539e892bfc74f9a761fbc8,An Image Mining System for Gender Classification & Age Prediction Based on Facial Features,"IOSR Journal of Computer Engineering (IOSR-JCE)
e-ISSN: 2278-0661, p- ISSN: 2278-8727Volume 10, Issue 6 (May. - Jun. 2013), PP 21-29
www.iosrjournals.org
An Image Mining System for Gender Classification & Age
Prediction Based on Facial Features
1.Ms.Dhanashri Shirkey  , 2Prof.Dr.S.R.Gupta,
M.E(Scholar),Department Computer Science & Engineering, PRMIT & R, Badnera
Asstt.Prof. Department Computer Science & Engineering, PRMIT & R, Badnera"
dad7b8be074d7ea6c3f970bd18884d496cbb0f91,Super-Sparse Regression for Fast Age Estimation from Faces at Test Time,"Super-Sparse Regression for Fast Age
Estimation From Faces at Test Time
Ambra Demontis, Battista Biggio, Giorgio Fumera, and Fabio Roli
Dept. of Electrical and Electronic Engineering, University of Cagliari
Piazza d’Armi, 09123 Cagliari, Italy
WWW home page: http://prag.diee.unica.it"
da4170c862d8ae39861aa193667bfdbdf0ecb363,Multi-Task CNN Model for Attribute Prediction,"Multi-task CNN Model for Attribute Prediction
Abrar H. Abdulnabi, Student Member, IEEE, Gang Wang, Member, IEEE, , Jiwen Lu, Member, IEEE
nd Kui Jia, Member, IEEE"
dac2103843adc40191e48ee7f35b6d86a02ef019,Unsupervised Celebrity Face Naming in Web Videos,"Unsupervised Celebrity Face Naming in Web Videos
Lei Pang and Chong-Wah Ngo"
dae420b776957e6b8cf5fbbacd7bc0ec226b3e2e,Recognizing Emotions in Spontaneous Facial Expressions,"RECOGNIZING EMOTIONS IN SPONTANEOUS FACIAL EXPRESSIONS
Michael Grimm, Dhrubabrata Ghosh Dastidar, and Kristian Kroschel
Institut f¨ur Nachrichtentechnik
Universit¨at Karlsruhe (TH), Germany"
daa02cf195818cbf651ef81941a233727f71591f,Face recognition system on Raspberry Pi,"Face recognition system on Raspberry Pi
Olegs Nikisins, Rihards Fuksis, Arturs Kadikis, Modris Greitans
Institute of Electronics and Computer Science,
4 Dzerbenes Street, Riga, LV 1006, Latvia"
daefac0610fdeff415c2a3f49b47968d84692e87,Multimodal Frame Identification with Multilingual Evaluation,"New Orleans, Louisiana, June 1 - 6, 2018. c(cid:13)2018 Association for Computational Linguistics
Proceedings of NAACL-HLT 2018, pages 1481–1491"
b49affdff167f5d170da18de3efa6fd6a50262a2,Linking Names and Faces : Seeing the Problem in Different Ways,"Author manuscript, published in ""Workshop on Faces in 'Real-Life' Images: Detection, Alignment, and Recognition, Marseille : France
(2008)"""
b41374f4f31906cf1a73c7adda6c50a78b4eb498,Iterative Gaussianization: From ICA to Random Rotations,"This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination.
Iterative Gaussianization: From ICA to
Random Rotations
Valero Laparra, Gustavo Camps-Valls, Senior Member, IEEE, and Jesús Malo"
b4ee64022cc3ccd14c7f9d4935c59b16456067d3,Unsupervised Cross-Domain Image Generation,"Unsupervised Cross-Domain Image Generation
Xinru Hua, Davis Rempe, and Haotian Zhang"
b40290a694075868e0daef77303f2c4ca1c43269,Combining Local and Global Information for Hair Shape Modeling,"第 40 卷 第 4 期
014 年 4 月
自 动 化 学 报
ACTA AUTOMATICA SINICA
Vol. 40, No. 4
April, 2014
融合局部与全局信息的头发形状模型
王 楠 1 艾海舟 1
摘 要 头发在人体表观中具有重要作用, 然而, 因为缺少有效的形状模型, 头发分割仍然是一个非常具有挑战性的问题. 本
文提出了一种基于部件的模型, 它对头发形状以及环境变化更加鲁棒. 该模型将局部与全局信息相结合以描述头发的形状. 局
部模型通过一系列算法构建, 包括全局形状词表生成, 词表分类器学习以及参数优化; 而全局模型刻画不同的发型, 采用支持
向量机 (Support vector machine, SVM) 来学习, 它为所有潜在的发型配置部件并确定势函数. 在消费者图片上的实验证明
了本文算法在头发形状多变和复杂环境等条件下的准确性与有效性.
关键词 头发形状建模, 部件模型, 部件配置算法, 支持向量机
引用格式 王楠, 艾海舟. 融合局部与全局信息的头发形状模型. 自动化学报, 2014, 40(4): 615−623
DOI 10.3724/SP.J.1004.2014.00615
Combining Local and Global Information for Hair Shape Modeling
WANG Nan1
AI Hai-Zhou1"
b4b0bf0cbe1a2c114adde9fac64900b2f8f6fee4,Autonomous Learning Framework Based on Online Hybrid Classifier for Multi-view Object Detection in Video,"Autonomous Learning Framework Based on Online Hybrid
Classifier for Multi-view Object Detection in Video
Dapeng Luoa*Zhipeng Zenga Longsheng Weib Yongwen Liua Chen Luoc Jun Chenb Nong Sangd
School of Electronic Information and Mechanics, China University of Geosciences, Wuhan, Hubei 430074, China
School of Automation, China University of Geosciences, Wuhan, Hubei 430074, China
Huizhou School Affiliated to Beijing Normal University, Huizhou 516002, China
dNational Key Laboratory of Science and Technology on Multispectral Information Processing, School of Automation, Huazhong
University of Science and Technology, Wuhan, 430074, China"
a285b6edd47f9b8966935878ad4539d270b406d1,Facial Expression Recognition Based on Local Binary Patterns and Kernel Discriminant Isomap,"Sensors 2011, 11, 9573-9588; doi:10.3390/s111009573
OPEN ACCESS
sensors
ISSN 1424-8220
www.mdpi.com/journal/sensors
Article
Facial Expression Recognition Based on Local Binary Patterns
nd Kernel Discriminant Isomap
Xiaoming Zhao 1,* and Shiqing Zhang 2
Department of Computer Science, Taizhou University, Taizhou 317000, China
School of Physics and Electronic Engineering, Taizhou University, Taizhou 318000, China;
E-Mail:
*  Author to whom correspondence should be addressed; E-Mail:
Tel.: +86-576-8513-7178; Fax: ++86-576-8513-7178.
Received: 31 August 2011; in revised form: 27 September 2011 / Accepted: 9 October 2011 /
Published: 11 October 2011"
a2359c0f81a7eb032cff1fe45e3b80007facaa2a,Towards Structured Analysis of Broadcast Badminton Videos,"Towards Structured Analysis of Broadcast Badminton Videos
Anurag Ghosh
Suriya Singh
C.V.Jawahar
{anurag.ghosh,
CVIT, KCIS, IIIT Hyderabad"
a27735e4cbb108db4a52ef9033e3a19f4dc0e5fa,Intention from Motion,"Intention from Motion
Andrea Zunino, Jacopo Cavazza, Atesh Koul, Andrea Cavallo, Cristina Becchio and Vittorio Murino"
a2fbaa0b849ecc74f34ebb36d1442d63212b29d2,An Efficient Approach to Face Recognition of Surgically Altered Images,"Volume 5, Issue 6, June 2015                                           ISSN: 2277 128X
International Journal of Advanced Research in
Computer Science and Software Engineering
Research Paper
Available online at: www.ijarcsse.com
An Efficient Approach to Face Recognition of Surgically
Altered Images
Er. Supriya, Er. Sukhpreet Kaur
Department of computer science and engineering
SUS college of Engineering and Technology,
Tangori, District, Mohali, Punjab, India"
a50b4d404576695be7cd4194a064f0602806f3c4,Efficiently Estimating Facial Expression and Illumination in Appearance-based Tracking,"In Proceedings of BMVC, Edimburgh, UK, September 2006
Efficiently estimating facial expression and
illumination in appearance-based tracking
Jos´e M. Buenaposada†, Enrique Mu˜noz‡, Luis Baumela‡
ESCET, U. Rey Juan Carlos
C/ Tulip´an, s/n
8933 M´ostoles, Spain
Facultad Inform´atica, UPM
Campus de Montegancedo s/n
8660 Boadilla del Monte, Spain
http://www.dia.fi.upm.es/~pcr"
a5e5094a1e052fa44f539b0d62b54ef03c78bf6a,Detection without Recognition for Redaction,"Detection without Recognition for Redaction
Shagan Sah1, Ram Longman1, Ameya Shringi1, Robert Loce2, Majid Rabbani1, and Raymond Ptucha1
Rochester Institute of Technology - 83 Lomb Memorial Drive, Rochester, NY USA, 14623
Conduent, Conduent Labs - US, 800 Phillips Rd, MS128, Webster, NY USA, 14580
Email:"
a56c1331750bf3ac33ee07004e083310a1e63ddc,Efficient Point-to-Subspace Query in ℓ1 with Application to Robust Object Instance Recognition,"Vol. xx, pp. x
(cid:13) xxxx Society for Industrial and Applied Mathematics
Ef‌f‌icient Point-to-Subspace Query in (cid:96)1 with Application to Robust Object
Instance Recognition
Ju Sun∗, Yuqian Zhang†, and John Wright‡"
a54e0f2983e0b5af6eaafd4d3467b655a3de52f4,Face Recognition Using Convolution Filters and Neural Networks,"Face Recognition Using Convolution Filters and
Neural Networks
V. Rihani
Head, Dept. of E&E,PEC
Sec-12, Chandigarh – 160012
Amit Bhandari
Department of CSE & IT, PEC
Sec-12, Chandigarh – 160012
C.P. Singh
Physics Department, CFSL,
Sec-36, Chandigarh - 160036
to:  (a)
potential  method"
a5625cfe16d72bd00e987857d68eb4d8fc3ce4fb,VFSC: A Very Fast Sparse Clustering to Cluster Faces from Videos,"VFSC: A Very Fast Sparse Clustering to Cluster Faces
from Videos
Dinh-Luan Nguyen, Minh-Triet Tran
University of Science, VNU-HCMC, Ho Chi Minh city, Vietnam"
a546fd229f99d7fe3cf634234e04bae920a2ec33,Fast Fight Detection,"RESEARCH ARTICLE
Fast Fight Detection
Ismael Serrano Gracia1*, Oscar Deniz Suarez1*, Gloria Bueno Garcia1*, Tae-Kyun Kim2
Department of Systems Engineering and Automation, E.T.S.I. Industriales, Ciudad Real, Castilla-La
Mancha, Spain, 2 Department of Electrical and Electronic Engineering, Imperial College, London, UK
* (ISG); (ODS); (GBG)"
a5ae7fe2bb268adf0c1cd8e3377f478fca5e4529,Exemplar Hidden Markov Models for classification of facial expressions in videos,"Exemplar Hidden Markov Models for Classification of Facial Expressions in
Videos
Univ. of California San Diego
Univ. of Canberra, Australian
Univ. of California San Diego
Abhinav Dhall
Marian Bartlett
Karan Sikka
California, USA
National University
Australia
California, USA"
a55efc4a6f273c5895b5e4c5009eabf8e5ed0d6a,"Continuous Head Movement Estimator for Driver Assistance: Issues, Algorithms, and On-Road Evaluations","Continuous Head Movement Estimator for
Driver Assistance: Issues, Algorithms,
nd On-Road Evaluations
Ashish Tawari, Student Member, IEEE, Sujitha Martin, Student Member, IEEE, and
Mohan Manubhai Trivedi, Fellow, IEEE"
a51d5c2f8db48a42446cc4f1718c75ac9303cb7a,Cross-validating Image Description Datasets and Evaluation Metrics,"Cross-validating Image Description Datasets and Evaluation Metrics
Josiah Wang and Robert Gaizauskas
Department of Computer Science
University of Sheffield, UK
{j.k.wang,"
a52d9e9daf2cb26b31bf2902f78774bd31c0dd88,Understanding and Designing Convolutional Networks for Local Recognition Problems,"Understanding and Designing Convolutional Networks
for Local Recognition Problems
Jonathan Long
Electrical Engineering and Computer Sciences
University of California at Berkeley
Technical Report No. UCB/EECS-2016-97
http://www.eecs.berkeley.edu/Pubs/TechRpts/2016/EECS-2016-97.html
May 13, 2016"
a5a44a32a91474f00a3cda671a802e87c899fbb4,Moments in Time Dataset: one million videos for event understanding,"Moments in Time Dataset: one million
videos for event understanding
Mathew Monfort, Bolei Zhou, Sarah Adel Bargal,
Alex Andonian, Tom Yan, Kandan Ramakrishnan, Lisa Brown,
Quanfu Fan, Dan Gutfruend, Carl Vondrick, Aude Oliva"
bd0e100a91ff179ee5c1d3383c75c85eddc81723,Okutama-Action: An Aerial View Video Dataset for Concurrent Human Action Detection,"Okutama-Action: An Aerial View Video Dataset for Concurrent Human Action
Detection∗
Mohammadamin Barekatain1, Miquel Mart´ı2,3, Hsueh-Fu Shih4, Samuel Murray2, Kotaro Nakayama5,
Yutaka Matsuo5, Helmut Prendinger6
Technical University of Munich, Munich, 2KTH Royal Institute of Technology, Stockholm,
Polytechnic University of Catalonia, Barcelona, 4National Taiwan University, Taipei, 5University of
Tokyo, Tokyo, 6National Institute of Informatics, Tokyo"
bd07d1f68486052b7e4429dccecdb8deab1924db,Face representation under different illumination conditions,
bd13f50b8997d0733169ceba39b6eb1bda3eb1aa,Occlusion Coherence: Detecting and Localizing Occluded Faces,"Occlusion Coherence: Detecting and Localizing Occluded Faces
Golnaz Ghiasi, Charless C. Fowlkes
University of California at Irvine, Irvine, CA 92697"
bd78a853df61d03b7133aea58e45cd27d464c3cf,A Sparse Representation Approach to Facial Expression Recognition Based on LBP plus LFDA,"A Sparse Representation Approach to Facial
Expression Recognition Based on LBP plus LFDA
Ritesh Bora, V.A.Chakkarvar
Computer science and Engineering Department,
Government College of Engineering, Aurangabad [Autonomous]
Station Road, Aurangabad, Maharashtra, India."
bd2d7c7f0145028e85c102fe52655c2b6c26aeb5,Attribute-based People Search: Lessons Learnt from a Practical Surveillance System,"Attribute-based People Search: Lessons Learnt from a
Practical Surveillance System
Rogerio Feris
IBM Watson
http://rogerioferis.com
Russel Bobbitt
IBM Watson
Lisa Brown
IBM Watson
Sharath Pankanti
IBM Watson"
bdbba95e5abc543981fb557f21e3e6551a563b45,Speeding up the Hyperparameter Optimization of Deep Convolutional Neural Networks,"International Journal of Computational Intelligence and Applications
Vol. 17, No. 2 (2018) 1850008 (15 pages)
#.c The Author(s)
DOI: 10.1142/S1469026818500086
Speeding up the Hyperparameter Optimization of Deep
Convolutional Neural Networks
Tobias Hinz*, Nicolas Navarro-Guerrero†, Sven Magg‡
nd Stefan Wermter§
Knowledge Technology, Department of Informatics
Universit€at Hamburg
Vogt-K€olln-Str. 30, Hamburg 22527, Germany
Received 15 August 2017
Accepted 23 March 2018
Published 18 June 2018
Most learning algorithms require the practitioner to manually set the values of many hyper-
parameters before the learning process can begin. However, with modern algorithms, the
evaluation of a given hyperparameter setting can take a considerable amount of time and the
search space is often very high-dimensional. We suggest using a lower-dimensional represen-
tation of the original data to quickly identify promising areas in the hyperparameter space. This
information can then be used to initialize the optimization algorithm for the original, higher-"
d1dfdc107fa5f2c4820570e369cda10ab1661b87,Super SloMo: High Quality Estimation of Multiple Intermediate Frames for Video Interpolation,"Super SloMo: High Quality Estimation of Multiple Intermediate Frames
for Video Interpolation
Huaizu Jiang1
Deqing Sun2
Varun Jampani2
Ming-Hsuan Yang3,2
Erik Learned-Miller1
Jan Kautz2
UMass Amherst
NVIDIA 3UC Merced"
d1dae2993bdbb2667d1439ff538ac928c0a593dc,Gamma Correction Technique Based Feature Extraction for Face Recognition System,"International Journal of Computational Intelligence and Informatics, Vol. 3: No. 1, April - June 2013
Gamma Correction Technique Based Feature Extraction
for Face Recognition System
B Vinothkumar
P Kumar
Electronics and Communication Engineering
K S Rangasamy College of Technology
Electronics and Communication Engineering
K S Rangasamy College of Technology
Tamilnadu, India
Tamilnadu, India"
d1f58798db460996501f224fff6cceada08f59f9,Transferrable Representations for Visual Recognition,"Transferrable Representations for Visual Recognition
Jeffrey Donahue
Electrical Engineering and Computer Sciences
University of California at Berkeley
Technical Report No. UCB/EECS-2017-106
http://www2.eecs.berkeley.edu/Pubs/TechRpts/2017/EECS-2017-106.html
May 14, 2017"
d1a43737ca8be02d65684cf64ab2331f66947207,IJB–S: IARPA Janus Surveillance Video Benchmark,"IJB–S: IARPA Janus Surveillance Video Benchmark (cid:3)
Nathan D. Kalka y
Stephen Elliott z
Brianna Maze y
Kaleb Hebert y
James A. Duncan y
Julia Bryan z
Kevin O’Connor z
Anil K. Jain x"
d122d66c51606a8157a461b9d7eb8b6af3d819b0,Automated Recognition of Facial Expressions,"Vol-3 Issue-4 2017
IJARIIE-ISSN(O)-2395-4396
AUTOMATED RECOGNITION OF FACIAL
EXPRESSIONS
Pavan S. Ahire, PG Student, Dept. of Computer Engineering, METs Institute of Engineering,
Prof. R. P. Dahake, Dept. of Computer Engineering, METs Institute of  Engineering,
Adgoan,Nashik,Maharashtra.
Adgoan, Nashik, Maharashtra."
d142e74c6a7457e77237cf2a3ded4e20f8894e1a,Human Emotion Estimation from Eeg and Face Using Statistical Features and Svm,"HUMAN EMOTION ESTIMATION FROM
EEG AND FACE USING STATISTICAL
FEATURES AND SVM
Strahil Sokolov1, Yuliyan Velchev2, Svetla Radeva3 and Dimitar Radev4
,3Department of Information Technologies,
University of telecommunications and post, Sofia, Bulgaria
2,4Department of Telecommunications,
University of telecommunications and post, Sofia, Bulgaria"
d1082eff91e8009bf2ce933ac87649c686205195,Pruning of Error Correcting Output Codes by optimization of accuracy–diversity trade off,"(will be inserted by the editor)
Pruning of Error Correcting Output Codes by
Optimization of Accuracy-Diversity Trade off
S¨ureyya ¨Oz¨o˘g¨ur Aky¨uz · Terry
Windeatt · Raymond Smith
Received: date / Accepted: date"
d1d6f1d64a04af9c2e1bdd74e72bd3ffac329576,Neural Face Editing with Intrinsic Image Disentangling,"Neural Face Editing with Intrinsic Image Disentangling
Zhixin Shu1 Ersin Yumer2 Sunil Hadap2 Kalyan Sunkavalli2 Eli Shechtman 2 Dimitris Samaras1,3
Stony Brook University 2Adobe Research 3 CentraleSup´elec, Universit´e Paris-Saclay"
d69df51cff3d6b9b0625acdcbea27cd2bbf4b9c0,Robust Remote Heart Rate Determination for E-Rehabilitation - A Method that Overcomes Motion and Intensity Artefacts,
d61578468d267c2d50672077918c1cda9b91429b,Face Image Retrieval Using Pose Specific Set Sparse Feature Representation,"Abdul Afeef N et al, International Journal of Computer Science and Mobile Computing, Vol.3 Issue.9, September- 2014, pg. 314-323
Available Online at www.ijcsmc.com
International Journal of Computer Science and Mobile Computing
A Monthly Journal of Computer Science and Information Technology
ISSN 2320–088X
IJCSMC, Vol. 3, Issue. 9, September 2014, pg.314 – 323
RESEARCH ARTICLE
Face Image Retrieval Using Pose Specific
Set Sparse Feature Representation
Department of Computer Science, Viswajyothi College of Engineering and Technology Kerala, India
Assistant Professor of Computer Science, Viswajyothi College of Engineering and Technology Kerala, India
Abdul Afeef N1, Sebastian George2"
d687fa99586a9ad229284229f20a157ba2d41aea,Face Recognition Based on Wavelet Packet Coefficients and Radial Basis Function Neural Networks,"Journal of Intelligent Learning Systems and Applications, 2013, 5, 115-122
http://dx.doi.org/10.4236/jilsa.2013.52013 Published Online May 2013 (http://www.scirp.org/journal/jilsa)
Face Recognition Based on Wavelet Packet Coefficients
nd Radial Basis Function Neural Networks
Thangairulappan Kathirvalavakumar1*, Jeyasingh Jebakumari Beulah Vasanthi2
Department of Computer Science, Virudhunagar Hindu Nadars’ Senthikumara Nadar College, Virudhunagar, India; 2Department of
Computer Applications, Ayya Nadar Janaki Ammal College, Sivakasi, India.
Email:
Received December 12th, 2012; revised April 19th, 2013; accepted April 26th, 2013
Copyright © 2013 Thangairulappan Kathirvalavakumar, Jeyasingh Jebakumari Beulah Vasanthi. This is an open access article dis-
tributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any me-
dium, provided the original work is properly cited."
d6a9ea9b40a7377c91c705f4c7f206a669a9eea2,Visual Representations for Fine-grained Categorization,"Visual Representations for Fine-grained
Categorization
Ning Zhang
Electrical Engineering and Computer Sciences
University of California at Berkeley
Technical Report No. UCB/EECS-2015-244
http://www.eecs.berkeley.edu/Pubs/TechRpts/2015/EECS-2015-244.html
December 17, 2015"
d671a210990f67eba9b2d3dda8c2cb91575b4a7a,Social Environment Description from Data Collected with a Wearable Device,"Journal of Machine Learning Research ()
Submitted ; Published
Social Environment Description from Data Collected with a
Wearable Device
Pierluigi Casale
Computer Vision Center
Autonomous University of Barcelona
Barcelona, Spain
Editor: Radeva Petia, Pujol Oriol"
d65b82b862cf1dbba3dee6541358f69849004f30,2.5D Elastic graph matching,"Contents lists available at ScienceDirect
j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / c v i u
.5D Elastic graph matching
Stefanos Zafeiriou
, Maria Petrou
Imperial College, Department of Electrical and Electronic Engineering, London, UK
r t i c l e
i n f o
b s t r a c t
Article history:
Received 29 November 2009
Accepted 1 December 2010
Available online 17 March 2011
Keywords:
Elastic graph matching
D face recognition
Multiscale mathematical morphology
Geodesic distances
In this paper, we propose novel elastic graph matching (EGM) algorithms for face recognition assisted by
the availability of 3D facial geometry. More specifically, we conceptually extend the EGM algorithm in"
d6102a7ddb19a185019fd2112d2f29d9258f6dec,Fashion Style Generator,"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17)
GeneratorPatch……Global+…lstyle(2)lstyle(1)lcontent(1)lcontent(2)φθϕsϕcDiscriminatorDGXX(1)X(2)(a) Framework of the training stage(b) Examples of fashion style generationFigure1:Fashionstylegeneratorframeworkoverview.TheinputXconsistsofasetofclothingpatchesX(1)andfullclothingimagesX(2).Thesystemconsistsoftwocomponents:animagetransfor-mationnetworkGservedasfashionstylegenerator,andadiscrimi-natornetworkDcalculatesbothglobalandpatchbasedcontentandstylelosses.Gisaconvolutionalencoderdecodernetworkparam-eterizedbyweights(cid:18).Sixgeneratedshirtswithdifferentstylesbyourmethodareshownasexamples.(Wehighlyrecommendtozoominallthefigureswithcolorversionformoredetails.)recentneuralstyletransferworks[Gatysetal.,2015].Tak-ingVanGogh’s“StarryNight”astheexamplestyleimage,styleisbetweenthelow-levelcolor/texture(e.g.,blueandyellowcolor,roughorsmoothertexture)andthehigh-levelobjects(e.g.,houseandmountain).“Style”isarelativelyab-stractconcept.Fashionstylegenerationhasatleasttwoprac-ticalusages.Designerscouldquicklyseehowtheclothinglookslikeinagivenstyletofacilitatethedesignprocessing.Shopperscouldsynthesizetheclothingimagewiththeidealstyleandapplyclothingretrievaltools[Jiangetal.,2016b]tosearchthesimilaritems.Fashionstylegenerationisrelatedtoexistingneuralstyletransferworks[Gatysetal.,2015;LiandWand,2016a;EfrosandFreeman,2001],buthasitsownchallenges.Infashionstylegeneration,thesyntheticclothingimageshould"
d6bfa9026a563ca109d088bdb0252ccf33b76bc6,Unsupervised Temporal Segmentation of Facial Behaviour,"Unsupervised Temporal Segmentation of Facial Behaviour
Abhishek Kar
Advisors: Dr. Amitabha Mukerjee & Dr. Prithwijit Guha
Department of Computer Science and Engineering, IIT Kanpur"
d6c7092111a8619ed7a6b01b00c5f75949f137bf,A Novel Feature Extraction Technique for Facial Expression Recognition,"A Novel Feature Extraction Technique for Facial Expression
Recognition
*Mohammad Shahidul Islam1,  Surapong Auwatanamongkol2
1 Department of Computer Science, School of Applied Statistics,
National Institute of Development Administration,
Bangkok, 10240, Thailand
Department of Computer Science, School of Applied Statistics,
National Institute of Development Administration,
Bangkok, 10240, Thailand"
bcee40c25e8819955263b89a433c735f82755a03,Biologically Inspired Vision for Human-Robot Interaction,"Biologically inspired vision for human-robot
interaction
M. Saleiro, M. Farrajota, K. Terzi´c, S. Krishna, J.M.F. Rodrigues, and J.M.H.
du Buf
Vision Laboratory, LARSyS, University of the Algarve, 8005-139 Faro, Portugal,
{masaleiro, mafarrajota, kterzic, jrodrig,"
bc6de183cd8b2baeebafeefcf40be88468b04b74,Age Group Recognition using Human Facial Images,"Age Group Recognition using Human Facial Images
International Journal of Computer Applications (0975 – 8887)
Volume 126 – No.13, September 2015
Shailesh S. Kulkarni
Dept. of Electronics and Telecommunication
Government College of Engineering,
Aurangabad, Maharashtra, India"
bcf19b964e7d1134d00332cf1acf1ee6184aff00,Trajectory-Set Feature for Action Recognition,"IEICE TRANS. INF. & SYST., VOL.E100–D, NO.8 AUGUST 2017
LETTER
Trajectory-Set Feature for Action Recognition
Kenji MATSUI†, Nonmember, Toru TAMAKI†a), Member, Bisser RAYTCHEV†, Nonmember,
nd Kazufumi KANEDA†, Member
SUMMARY We propose a feature for action recognition called
Trajectory-Set (TS), on top of the improved Dense Trajectory (iDT).
The TS feature encodes only trajectories around densely sampled inter-
est points, without any appearance features. Experimental results on the
UCF50 action dataset demonstrates that TS is comparable to state-of-the-
rts, and outperforms iDT; the accuracy of 95.0%, compared to 91.7% by
key words: action recognition, trajectory, improved Dense Trajectory
the two-stream CNN [2] that uses a single frame and a opti-
al flow stack. In their paper stacking trajectories was also
reported but did not perform well, probably the sparseness
of trajectories does not fit to CNN architectures. In contrast,
we take a hand-crafted approach that can be fused later with
CNN outputs.
Introduction
Action recognition has been well studied in the computer"
bcc172a1051be261afacdd5313619881cbe0f676,A fast face clustering method for indexing applications on mobile phones,"978-1-5090-4117-6/17/$31.00 ©2017 IEEE
ICASSP 2017"
bc811a66855aae130ca78cd0016fd820db1603ec,Towards three-dimensional face recognition in the real Huibin,"Towards three-dimensional face recognition in the real
Huibin Li
To cite this version:
Huibin Li. Towards three-dimensional face recognition in the real. Other. Ecole Centrale de
Lyon, 2013. English. <NNT : 2013ECDL0037>. <tel-00998798>
HAL Id: tel-00998798
https://tel.archives-ouvertes.fr/tel-00998798
Submitted on 2 Jun 2014
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bc98027b331c090448492eb9e0b9721e812fac84,"Face Representation Using Combined Method of Gabor Filters, Wavelet Transformation and DCV and Recognition Using RBF","Journal of Intelligent Learning Systems and Applications, 2012, 4, 266-273
http://dx.doi.org/10.4236/jilsa.2012.44027 Published Online November 2012 (http://www.SciRP.org/journal/jilsa)
Face Representation Using Combined Method of Gabor
Filters, Wavelet Transformation and DCV and Recognition
Using RBF
Kathirvalavakumar Thangairulappan1*, Jebakumari Beulah Vasanthi Jeyasingh2
Department of Computer Science, VHNSN College, Virudhunagar, India; 2Department of Computer Applications, ANJA College,
Sivakasi, India.
Email:
Received April 27th, 2012; revised July 19th, 2012; accepted July 26th, 2012"
bc9af4c2c22a82d2c84ef7c7fcc69073c19b30ab,MoCoGAN: Decomposing Motion and Content for Video Generation,"MoCoGAN: Decomposing Motion and Content for Video Generation
Sergey Tulyakov,
Snap Research
Ming-Yu Liu, Xiaodong Yang,
NVIDIA
Jan Kautz"
bcac3a870501c5510df80c2a5631f371f2f6f74a,Structured Face Hallucination,"#1387
CVPR 2013 Submission #1387. CONFIDENTIAL REVIEW COPY. DO NOT DISTRIBUTE.
#1387
Structured Face Hallucination
Anonymous CVPR submission
Paper ID 1387"
ae8d5be3caea59a21221f02ef04d49a86cb80191,Skip RNN: Learning to Skip State Updates in Recurrent Neural Networks,"Published as a conference paper at ICLR 2018
SKIP RNN: LEARNING TO SKIP STATE UPDATES IN
RECURRENT NEURAL NETWORKS
V´ıctor Campos∗†, Brendan Jou‡, Xavier Gir´o-i-Nieto§, Jordi Torres†, Shih-Fu ChangΓ
Barcelona Supercomputing Center, ‡Google Inc,
§Universitat Polit`ecnica de Catalunya, ΓColumbia University
{victor.campos,"
ae2cf545565c157813798910401e1da5dc8a6199,Cascade of Boolean detector combinations,"Mahkonen et al. EURASIP Journal on Image and Video
Processing  (2018) 2018:61
https://doi.org/10.1186/s13640-018-0303-9
EURASIP Journal on Image
nd Video Processing
RESEARCH
Open Access
Cascade of Boolean detector
ombinations
Katariina Mahkonen*
, Tuomas Virtanen and Joni Kämäräinen"
aebb9649bc38e878baef082b518fa68f5cda23a5,A Multi - scale TVQI - based Illumination Normalization Model,
aeeea6eec2f063c006c13be865cec0c350244e5b,"Induced Disgust, Happiness and Surprise: an Addition to the MMI Facial Expression Database","Induced Disgust, Happiness and Surprise: an Addition to the MMI Facial
Expression Database
Michel F. Valstar, Maja Pantic
Imperial College London / Twente University
Department of Computing / EEMCS
80 Queen’s Gate / Drienerlolaan 5
London / Twente"
ae9257f3be9f815db8d72819332372ac59c1316b,Deciphering the enigmatic face: the importance of facial dynamics in interpreting subtle facial expressions.,"P SY CH O L O GIC AL SC I E NC E
Research Article
Deciphering the Enigmatic Face
The Importance of Facial Dynamics in Interpreting Subtle
Facial Expressions
Zara Ambadar,1 Jonathan W. Schooler,2 and Jeffrey F. Cohn1
University of Pittsburgh and 2University of British Columbia, Vancouver, British Columbia, Canada"
ae89b7748d25878c4dc17bdaa39dd63e9d442a0d,On evaluating face tracks in movies,"On evaluating face tracks in movies
Alexey Ozerov, Jean-Ronan Vigouroux, Louis Chevallier, Patrick Pérez
To cite this version:
Alexey Ozerov, Jean-Ronan Vigouroux, Louis Chevallier, Patrick Pérez. On evaluating face tracks
in movies. IEEE International Conference on Image Processing (ICIP 2013), Sep 2013, Melbourne,
Australia. 2013. <hal-00870059>
HAL Id: hal-00870059
https://hal.inria.fr/hal-00870059
Submitted on 4 Oct 2013
HAL is a multi-disciplinary open access
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destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d’enseignement et de
recherche français ou étrangers, des laboratoires"
aeff403079022683b233decda556a6aee3225065,DeepFace: Face Generation using Deep Learning,"DeepFace: Face Generation using Deep Learning
Hardie Cate
Fahim Dalvi
Zeshan Hussain"
ae753fd46a744725424690d22d0d00fb05e53350,Describing Clothing by Semantic Attributes,"Describing Clothing by Semantic Attributes
Anonymous ECCV submission
Paper ID 727"
ae85c822c6aec8b0f67762c625a73a5d08f5060d,Retrieving Similar Styles to Parse Clothing,"This is the author's version of an article that has been published in this journal. Changes were made to this version by the publisher prior to publication.
The final version of record is available at http://dx.doi.org/10.1109/TPAMI.2014.2353624
IEEE TRANSACTION ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. M, NO. N, MONTH YEAR
Retrieving Similar Styles to Parse Clothing
Kota Yamaguchi, Member, IEEE, M. Hadi Kiapour, Student Member, IEEE,
Luis E. Ortiz, and Tamara L. Berg, Member, IEEE"
d861c658db2fd03558f44c265c328b53e492383a,Automated face extraction and normalization of 3D Mesh Data,"Automated Face Extraction and Normalization of 3D Mesh Data
Jia Wu1, Raymond Tse2, Linda G. Shapiro1"
d8f0bda19a345fac81a1d560d7db73f2b4868836,Online Activity Understanding and Labeling in Natural Videos,"UNIVERSITY OF CALIFORNIA
RIVERSIDE
Online Activity Understanding and Labeling in Natural Videos
A Dissertation submitted in partial satisfaction
of the requirements for the degree of
Doctor of Philosophy
Computer Science
Md Mahmudul Hasan
August 2016
Dissertation Committee:
Dr. Amit K. Roy-Chowdhury, Chairperson
Dr. Eamonn Keogh
Dr. Evangelos Christidis
Dr. Christian Shelton"
d82b93f848d5442f82154a6011d26df8a9cd00e7,Neural Network Based Age Classification Using Linear Wavelet Transforms,"NEURAL NETWORK BASED AGE CLASSIFICATION USING
LINEAR WAVELET TRANSFORMS
NITHYASHRI JAYARAMAN1 & G.KULANTHAIVEL2
Department of Computer Science & Engineering,
Sathyabama University Old Mamallapuram Road, Chennai, India
Electronics Engineering, National Institute of  Technical Teachers
Training & Research, Taramani, Chennai, India
E-mail :"
d83d2fb5403c823287f5889b44c1971f049a1c93,Introducing the sick face,"Motiv Emot
DOI 10.1007/s11031-013-9353-6
O R I G I N A L P A P E R
Introducing the sick face
Sherri C. Widen • Joseph T. Pochedly •
Kerrie Pieloch • James A. Russell
Ó Springer Science+Business Media New York 2013"
d8b568392970b68794a55c090c4dd2d7f90909d2,PDA Face Recognition System Using Advanced Correlation Filters,"PDA Face  Recognition  System
Using  Advanced  Correlation
Filters
Chee  Kiat  Ng
Advisor:  Prof.  Khosla/Reviere"
d83ae5926b05894fcda0bc89bdc621e4f21272da,Frugal Forests: Learning a Dynamic and Cost Sensitive Feature Extraction Policy for Anytime Activity Classification,"The Thesis committee for Joshua Allen Kelle certifies that this is the approved
version of the following thesis:
Frugal Forests: Learning a Dynamic and Cost Sensitive
Feature Extraction Policy for Anytime Activity Classification
APPROVED BY
SUPERVISING COMMITTEE:
Kristen Grauman, Supervisor
Peter Stone"
d86fabd4498c8feaed80ec342d254fb877fb92f5,Region-Object Relevance-Guided Visual Relationship Detection,"Y. GOUTSU: REGION-OBJECT RELEVANCE-GUIDED VRD
Region-Object Relevance-Guided
Visual Relationship Detection
Yusuke Goutsu
National Institute of Informatics
Tokyo, Japan"
d850aff9d10a01ad5f1d8a1b489fbb3998d0d80e,Recognizing and Segmenting Objects in the Presence of Occlusion and Clutter,"UNIVERSITY OF CALIFORNIA,
IRVINE
Recognizing and Segmenting Objects in the Presence of Occlusion and Clutter
DISSERTATION
submitted in partial satisfaction of the requirements
for the degree of
DOCTOR OF PHILOSOPHY
in Computer Science
Golnaz Ghiasi
Dissertation Committee:
Professor Charless Fowlkes, Chair
Professor Deva Ramanan
Professor Alexander Ihler"
d89cfed36ce8ffdb2097c2ba2dac3e2b2501100d,Robust Face Recognition via Multimodal Deep Face Representation,"Robust Face Recognition via Multimodal Deep
Face Representation
Changxing Ding, Student Member, IEEE, Dacheng Tao, Fellow, IEEE"
ab8f9a6bd8f582501c6b41c0e7179546e21c5e91,Nonparametric Face Verification Using a Novel Face Representation,"Nonparametric Face Verification Using a Novel
Face Representation
Hae Jong Seo, Student Member, IEEE, Peyman Milanfar, Fellow, IEEE,"
ab58a7db32683aea9281c188c756ddf969b4cdbd,Efficient Solvers for Sparse Subspace Clustering,"Efficient Solvers for Sparse Subspace Clustering
Farhad Pourkamali-Anaraki and Stephen Becker"
aba770a7c45e82b2f9de6ea2a12738722566a149,Face Recognition in the Scrambled Domain via Salience-Aware Ensembles of Many Kernels,"Face Recognition in the Scrambled Domain via Salience-Aware
Ensembles of Many Kernels
Jiang, R., Al-Maadeed, S., Bouridane, A., Crookes, D., & Celebi, M. E. (2016). Face Recognition in the
Scrambled Domain via Salience-Aware Ensembles of Many Kernels. IEEE Transactions on Information
Forensics and Security, 11(8), 1807-1817. DOI: 10.1109/TIFS.2016.2555792
Published in:
Document Version:
Peer reviewed version
Queen's University Belfast - Research Portal:
Link to publication record in Queen's University Belfast Research Portal
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The Research Portal is Queen's institutional repository that provides access to Queen's research output. Every effort has been made to"
ab0f9bc35b777eaefff735cb0dd0663f0c34ad31,Semi-supervised Learning of Geospatial Objects through Multi-modal Data Integration,"Semi-Supervised Learning of Geospatial Objects
Through Multi-Modal Data Integration
Yi Yang and Shawn Newsam
Electrical Engineering and Computer Science
University of California, Merced, CA, 95343
Email:"
ab989225a55a2ddcd3b60a99672e78e4373c0df1,"Sample, computation vs storage tradeoffs for classification using tensor subspace models","Sample, Computation vs Storage Tradeoffs for
Classification Using Tensor Subspace Models
Mohammadhossein Chaghazardi and Shuchin Aeron, Senior Member, IEEE"
ab6776f500ed1ab23b7789599f3a6153cdac84f7,A Survey on Various Facial Expression Techniques,"International Journal of Scientific & Engineering Research, Volume 6, Issue 4, April-2015                                                                                                   1212
ISSN 2229-5518
A Survey on Various Facial Expression
Techniques
Md. Sarfaraz Jalil, Joy Bhattacharya"
ab1719f573a6c121d7d7da5053fe5f12de0182e7,Combining visual recognition and computational linguistics : linguistic knowledge for visual recognition and natural language descriptions of visual content,"Combining Visual Recognition
nd Computational Linguistics
Linguistic Knowledge for Visual Recognition
nd Natural Language Descriptions
of Visual Content
Thesis for obtaining the title of
Doctor of Engineering Science
(Dr.-Ing.)
of the Faculty of Natural Science and Technology I
of Saarland University
Marcus Rohrbach, M.Sc.
Saarbrücken
March 2014"
ab2b09b65fdc91a711e424524e666fc75aae7a51,Multi-modal Biomarkers to Discriminate Cognitive State *,"Multi-modal Biomarkers to Discriminate Cognitive State*
Thomas F. Quatieri 1, James R. Williamson1, Christopher J. Smalt1,
Joey Perricone, Tejash Patel, Laura Brattain, Brian S. Helfer, Daryush D. Mehta, Jeffrey Palmer
Kristin Heaton2, Marianna Eddy3, Joseph Moran3
MIT Lincoln Laboratory, Lexington, Massachusetts, USA
USARIEM, 3NSRDEC
. Introduction
Multimodal biomarkers based on behavorial, neurophysiolgical, and cognitive measurements have
recently obtained increasing popularity in the detection of cognitive stress- and neurological-based
disorders. Such conditions are significantly and adversely affecting human performance and quality
of life for a large fraction of the world’s population. Example modalities used in detection of these
onditions  include  voice,  facial  expression,  physiology,  eye  tracking,  gait,  and  EEG  analysis.
Toward  the  goal  of  finding  simple,  noninvasive  means  to  detect,  predict  and  monitor  cognitive
stress and neurological conditions, MIT Lincoln Laboratory is developing biomarkers that satisfy
three  criteria.  First,  we  seek  biomarkers  that  reflect  core  components  of  cognitive  status  such  as
working memory capacity, processing speed, attention, and arousal. Second, and as importantly, we
seek  biomarkers  that  reflect  timing  and  coordination  relations  both  within  components  of  each
modality and across different modalities. This is based on the hypothesis that neural coordination
cross different parts of the brain is essential in cognition (Figure 1). An example of timing and
oordination  within  a  modality  is  the  set  of  finely  timed  and  synchronized  physiological"
ab87dfccb1818bdf0b41d732da1f9335b43b74ae,Structured Dictionary Learning for Classification,"SUBMITTED TO IEEE TRANSACTIONS ON SIGNAL PROCESSING
Structured Dictionary Learning for Classification
Yuanming Suo, Student Member, IEEE, Minh Dao, Student Member, IEEE, Umamahesh Srinivas, Student
Member, IEEE, Vishal Monga, Senior Member, IEEE, and Trac D. Tran, Fellow, IEEE"
abc1ef570bb2d7ea92cbe69e101eefa9a53e1d72,Raisonnement abductif en logique de description exploitant les domaines concrets spatiaux pour l'interprétation d'images,"Raisonnement abductif en logique de
description exploitant les domaines concrets
spatiaux pour l’interprétation d’images
Yifan Yang 1, Jamal Atif 2, Isabelle Bloch 1
. LTCI, Télécom ParisTech, Université Paris-Saclay, Paris, France
. Université Paris-Dauphine, PSL Research University, CNRS, UMR 7243,
LAMSADE, 75016 Paris, France
RÉSUMÉ. L’interprétation d’images a pour objectif non seulement de détecter et reconnaître des
objets dans une scène mais aussi de fournir une description sémantique tenant compte des in-
formations contextuelles dans toute la scène. Le problème de l’interprétation d’images peut être
formalisé comme un problème de raisonnement abductif, c’est-à-dire comme la recherche de la
meilleure explication en utilisant une base de connaissances. Dans ce travail, nous présentons
une nouvelle approche utilisant une méthode par tableau pour la génération et la sélection
d’explications possibles d’une image donnée lorsque les connaissances, exprimées dans une
logique de description, comportent des concepts décrivant les objets mais aussi les relations
spatiales entre ces objets. La meilleure explication est sélectionnée en exploitant les domaines
oncrets pour évaluer le degré de satisfaction des relations spatiales entre les objets."
abeda55a7be0bbe25a25139fb9a3d823215d7536,Understanding Human-Centric Images: From Geometry to Fashion,"UNIVERSITATPOLITÈCNICADECATALUNYAProgramadeDoctorat:AUTOMÀTICA,ROBÒTICAIVISIÓTesiDoctoralUnderstandingHuman-CentricImages:FromGeometrytoFashionEdgarSimoSerraDirectors:FrancescMorenoNoguerCarmeTorrasMay2015"
ab8fb278db4405f7db08fa59404d9dd22d38bc83,Implicit and Automated Emotional Tagging of Videos,"UNIVERSITÉ DE GENÈVE
Département d'Informatique
FACULTÉ DES SCIENCES
Professeur Thierry Pun
Implicit and Automated Emotional
Tagging of Videos
THÈSE
présenté à la Faculté des sciences de l'Université de Genève
pour obtenir le grade de Docteur ès sciences, mention informatique
Mohammad SOLEYMANI
Téhéran (IRAN)
Thèse No 4368
GENÈVE
Repro-Mail - Université de Genève"
e5823a9d3e5e33e119576a34cb8aed497af20eea,DocFace+: ID Document to Selfie Matching,"DocFace+: ID Document to Selfie* Matching
Yichun Shi, Student Member, IEEE, and Anil K. Jain, Life Fellow, IEEE"
e510f2412999399149d8635a83eca89c338a99a1,Face Recognition using Block-Based DCT Feature Extraction,"Journal of Advanced Computer Science and Technology, 1 (4) (2012) 266-283
(cid:13)Science Publishing Corporation
www.sciencepubco.com/index.php/JACST
Face Recognition using Block-Based
DCT Feature Extraction
K Manikantan1, Vaishnavi Govindarajan1,
V V S Sasi Kiran1, S Ramachandran2
Department of Electronics and Communication Engineering,
M S Ramaiah Institute of Technology, Bangalore, Karnataka, India 560054
E-mail:
E-mail:
E-mail:
Department of Electronics and Communication Engineering,
S J B Institute of Technology, Bangalore, Karnataka, India 560060
E-mail:"
e56c4c41bfa5ec2d86c7c9dd631a9a69cdc05e69,Human Activity Recognition Based on Wearable Sensor Data: A Standardization of the State-of-the-Art,"Human Activity Recognition Based on Wearable
Sensor Data: A Standardization of the
State-of-the-Art
Artur Jord˜ao, Antonio C. Nazare Jr., Jessica Sena and William Robson Schwartz
Smart Surveillance Interest Group, Computer Science Department
Universidade Federal de Minas Gerais, Brazil
Email: {arturjordao, antonio.nazare, jessicasena,"
e5342233141a1d3858ed99ccd8ca0fead519f58b,Finger print and Palm print based Multibiometric Authentication System with GUI Interface,"ISSN: 2277 – 9043
International Journal of Advanced Research in Computer Science and Electronics Engineering (IJARCSEE)
Volume 2, Issue 2, February 2013
Finger print and Palm print based Multibiometric
Authentication System with GUI Interface
KALAIGNANASELVI.A#1, NARASIMMALOU.T*2
#PG Scholar, Dept. of CSE, Dr.Pauls Engineering College, Villupuram District, Tamilnadu, India.
*Assistant Professor, Dept. of CSE, Dr.Pauls Engineering College, Villupuram District, Tamilnadu, India."
e52be9a083e621d9ed29c8e9914451a6a327ff59,UvA - DARE ( Digital Academic Repository ) Communication and Automatic Interpretation of Affect from Facial Expressions,"UvA-DARE (Digital Academic Repository)
Communication and Automatic Interpretation of Affect from Facial Expressions
Salah, A.A.; Sebe, N.; Gevers, T.
Published in:
Affective computing and interaction: psychological, cognitive, and neuroscientific perspectives
Link to publication
Citation for published version (APA):
Salah, A. A., Sebe, N., & Gevers, T. (2010). Communication and Automatic Interpretation of Affect from Facial
Expressions. In D. Gökçay, & G. Yildirim (Eds.), Affective computing and interaction: psychological, cognitive,
nd neuroscientific perspectives (pp. 157-183). Hershey, PA: Information Science Reference.
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UvA-DARE is a service provided by the library of the University of Amsterdam (http://dare.uva.nl)"
e5799fd239531644ad9270f49a3961d7540ce358,Kinship classification by modeling facial feature heredity,"KINSHIP CLASSIFICATION BY MODELING FACIAL FEATURE HEREDITY
Ruogu Fang1, Andrew C. Gallagher1, Tsuhan Chen1, Alexander Loui2
Dept. of Elec. and Computer Eng., Cornell University 2Eastman Kodak Company"
e5eb7fa8c9a812d402facfe8e4672670541ed108,Performance of PCA Based Semi-supervised Learning in Face Recognition Using MPEG-7 Edge Histogram Descriptor,"Performance of PCA Based Semi-supervised
Learning in Face Recognition Using MPEG-7
Edge Histogram Descriptor
Shafin Rahman, Sheikh Motahar Naim, Abdullah Al Farooq and Md. Monirul Islam
Department of Computer Science and Engineering
Bangladesh University of Engineering and Technology(BUET)
Dhaka-1000, Bangladesh
Email: {shafin buet, naim sbh2007,"
e2d265f606cd25f1fd72e5ee8b8f4c5127b764df,Real-Time End-to-End Action Detection with Two-Stream Networks,"Real-Time End-to-End Action Detection
with Two-Stream Networks
Alaaeldin El-Nouby∗†, Graham W. Taylor∗†‡
School of Engineering, University of Guelph
Vector Institute for Artificial Intelligence
Canadian Institute for Advanced Research"
f437b3884a9e5fab66740ca2a6f1f3a5724385ea,Human identification technical challenges,"Human Identification Technical Challenges
P. Jonathon Phillips
DARPA
701 N. Fairfax Dr
Arlington, VA 22203"
f412d9d7bc7534e7daafa43f8f5eab811e7e4148,Running Head : Anxiety and Emotional Faces in WS 2,"Durham Research Online
Deposited in DRO:
6 December 2014
Version of attached le:
Accepted Version
Peer-review status of attached le:
Peer-reviewed
Citation for published item:
Kirk, H. E. and Hocking, D. R. and Riby, D. M. and Cornish, K. M. (2013) 'Linking social behaviour and
nxiety to attention to emotional faces in Williams syndrome.', Research in developmental disabilities., 34
(12). pp. 4608-4616.
Further information on publisher's website:
http://dx.doi.org/10.1016/j.ridd.2013.09.042
Publisher's copyright statement:
NOTICE: this is the author's version of a work that was accepted for publication in Research in Developmental
Disabilities. Changes resulting from the publishing process, such as peer review, editing, corrections, structural
formatting, and other quality control mechanisms may not be reected in this document. Changes may have been made
to this work since it was submitted for publication. A denitive version was subsequently published in Research in
Developmental Disabilities, 34, 12, December 2013, 10.1016/j.ridd.2013.09.042.
Additional information:"
f442a2f2749f921849e22f37e0480ac04a3c3fec,Critical Features for Face Recognition in Humans and Machines,"Critical Features for Face Recognition in Humans and Machines  Naphtali Abudarham1, Lior Shkiller1, Galit Yovel1,2 1School of Psychological Sciences, 2Sagol School of Neuroscience Tel Aviv University, Tel Aviv, Israel   Correspondence regarding this manuscript should be addressed to: Galit Yovel  School of Psychological Sciences & Sagol School of Neuroscience Tel Aviv University Tel Aviv, 69978, Israel Email:"
f4f6fc473effb063b7a29aa221c65f64a791d7f4,Facial expression recognition in the wild based on multimodal texture features,"Downloaded From: https://www.spiedigitallibrary.org/journals/Journal-of-Electronic-Imaging on 4/20/2018 Terms of Use: https://www.spiedigitallibrary.org/terms-of-use
FacialexpressionrecognitioninthewildbasedonmultimodaltexturefeaturesBoSunLiandongLiGuoyanZhouJunHeBoSun,LiandongLi,GuoyanZhou,JunHe,“Facialexpressionrecognitioninthewildbasedonmultimodaltexturefeatures,”J.Electron.Imaging25(6),061407(2016),doi:10.1117/1.JEI.25.6.061407."
f4373f5631329f77d85182ec2df6730cbd4686a9,Recognizing Gender from Human Facial Regions using Genetic Algorithm,"Soft Computing manuscript No.
(will be inserted by the editor)
Recognizing Gender from Human Facial Regions using
Genetic Algorithm
Avirup Bhattacharyya · Rajkumar Saini ·
Partha Pratim Roy · Debi Prosad Dogra ·
Samarjit Kar
Received: date / Accepted: date"
f47404424270f6a20ba1ba8c2211adfba032f405,Identification of Face Age range Group using Neural Network,"International Journal of Emerging Technology and Advanced Engineering
Website: www.ijetae.com (ISSN 2250-2459, Volume 2, Issue 5, May 2012)
Identification of Face Age range Group using Neural
Network
Sneha Thakur1, Ligendra Verma2
1M.Tech scholar, CSE, RITEE Raipur
2 Reader, MCA dept, RITEE Raipur"
f4ebbeb77249d1136c355f5bae30f02961b9a359,Human Computation for Attribute and Attribute Value Acquisition,"Human Computation for Attribute and Attribute Value Acquisition
Edith Law, Burr Settles, Aaron Snook, Harshit Surana, Luis von Ahn, Tom Mitchell
School of Computer Science
Carnegie Melon University"
f42dca4a4426e5873a981712102aa961be34539a,Next-Flow: Hybrid Multi-Tasking with Next-Frame Prediction to Boost Optical-Flow Estimation in the Wild,"Next-Flow: Hybrid Multi-Tasking with Next-Frame Prediction to Boost
Optical-Flow Estimation in the Wild
Nima Sedaghat
University of Freiburg
Germany"
f3d9e347eadcf0d21cb0e92710bc906b22f2b3e7,"NosePose: a competitive, landmark-free methodology for head pose estimation in the wild","NosePose: a competitive, landmark-free
methodology for head pose estimation in the wild
Fl´avio H. B. Zavan, Antonio C. P. Nascimento, Olga R. P. Bellon and Luciano Silva
IMAGO Research Group - Universidade Federal do Paran´a"
f3ea181507db292b762aa798da30bc307be95344,Covariance Pooling For Facial Expression Recognition,"Covariance Pooling for Facial Expression Recognition
Computer Vision Lab, ETH Zurich, Switzerland
VISICS, KU Leuven, Belgium
Dinesh Acharya†, Zhiwu Huang†, Danda Pani Paudel†, Luc Van Gool†‡
{acharyad, zhiwu.huang, paudel,"
f3cf10c84c4665a0b28734f5233d423a65ef1f23,Title Temporal Exemplar-based Bayesian Networks for facialexpression recognition,"Title
Temporal Exemplar-based Bayesian Networks for facial
expression recognition
Author(s)
Shang, L; Chan, KP
Citation
Proceedings - 7Th International Conference On Machine
Learning And Applications, Icmla 2008, 2008, p. 16-22
Issued Date
http://hdl.handle.net/10722/61208
Rights
This work is licensed under a Creative Commons Attribution-
NonCommercial-NoDerivatives 4.0 International License.;
International Conference on Machine Learning and Applications
Proceedings. Copyright © IEEE.; ©2008 IEEE. Personal use of
this material is permitted. However, permission to
reprint/republish this material for advertising or promotional
purposes or for creating new collective works for resale or
redistribution to servers or lists, or to reuse any copyrighted
omponent of this work in other works must be obtained from"
f3b7938de5f178e25a3cf477107c76286c0ad691,Object Detection with Deep Learning: A Review,"JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, MARCH 2017
Object Detection with Deep Learning: A Review
Zhong-Qiu Zhao, Member, IEEE, Peng Zheng,
Shou-tao Xu, and Xindong Wu, Fellow, IEEE"
eb526174fa071345ff7b1fad1fad240cd943a6d7,Deeply vulnerable: a study of the robustness of face recognition to presentation attacks,"Deeply Vulnerable – A Study of the Robustness of Face Recognition to
Presentation Attacks
Amir Mohammadi, Sushil Bhattacharjee, and S´ebastien Marcel ∗†"
eb566490cd1aa9338831de8161c6659984e923fd,From Lifestyle Vlogs to Everyday Interactions,"From Lifestyle Vlogs to Everyday Interactions
David F. Fouhey, Wei-cheng Kuo, Alexei A. Efros, Jitendra Malik
EECS Department, UC Berkeley"
eb9312458f84a366e98bd0a2265747aaed40b1a6,Facial Expression Sequence Synthesis Based on Shape and Texture Fusion Model,"-4244-1437-7/07/$20.00 ©2007 IEEE
IV - 473
ICIP 2007"
eb716dd3dbd0f04e6d89f1703b9975cad62ffb09, Visual Object Category Discovery in Images and Videos,"Copyright
Yong Jae Lee"
eb4d2ec77fae67141f6cf74b3ed773997c2c0cf6,A new soft biometric approach for keystroke dynamics based on gender recognition,"Int. J. Information Technology and Management, Vol. 11, Nos. 1/2, 2012
A new soft biometric approach for keystroke
dynamics based on gender recognition
Romain Giot* and Christophe Rosenberger
GREYC Research Lab,
ENSICAEN – Université de Caen Basse Normandie – CNRS,
4000 Caen, France
Fax: +33-231538110
E-mail:
E-mail:
*Corresponding author"
ebb7cc67df6d90f1c88817b20e7a3baad5dc29b9,Fast algorithms for Higher-order Singular Value Decomposition from incomplete data,"Journal of Computational Mathematics
Vol.xx, No.x, 200x, 1–25.
http://www.global-sci.org/jcm
doi:??
Fast algorithms for Higher-order Singular Value Decomposition
from incomplete data*
Department of Mathematics, University of Alabama, Tuscaloosa, AL
Yangyang Xu
Email:"
ebabd1f7bc0274fec88a3dabaf115d3e226f198f,Driver Drowsiness Detection System Based on Feature Representation Learning Using Various Deep Networks,"Driver drowsiness detection system based on feature
representation learning using various deep networks
Sanghyuk Park, Fei Pan, Sunghun Kang and Chang D. Yoo
School of Electrical Engineering, KAIST,
Guseong-dong, Yuseong-gu, Dajeon, Rep. of Korea
{shine0624, feipan, sunghun.kang, cd"
eb48a58b873295d719827e746d51b110f5716d6c,Face Alignment Using K-Cluster Regression Forests With Weighted Splitting,"Face Alignment Using K-cluster Regression Forests
With Weighted Splitting
Marek Kowalski and Jacek Naruniec"
ebd5df2b4105ba04cef4ca334fcb9bfd6ea0430c,Fast Localization of Facial Landmark Points,"Fast Localization of Facial Landmark Points
Nenad Markuˇs*, Miroslav Frljak*, Igor S. Pandˇzi´c*, J¨orgen Ahlberg†, and Robert Forchheimer†
* University of Zagreb, Faculty of Electrical Engineering and Computing, Unska 3, 10000 Zagreb, Croatia
Link¨oping University, Department of Electrical Engineering, SE-581 83 Link¨oping, Sweden
March 28, 2014"
ebf204e0a3e137b6c24e271b0d55fa49a6c52b41,Visual Tracking Using Deep Motion Features,"Master of Science Thesis in Electrical Engineering
Department of Electrical Engineering, Linköping University, 2016
Visual Tracking Using
Deep Motion Features
Susanna Gladh"
c7e4c7be0d37013de07b6d829a3bf73e1b95ad4e,Dynemo: a Video Database of Natural Facial Expressions of Emotions,"The International Journal of Multimedia & Its Applications (IJMA) Vol.5, No.5, October 2013
DYNEMO: A VIDEO DATABASE OF NATURAL FACIAL
EXPRESSIONS OF EMOTIONS
Anna Tcherkassof1, Damien Dupré1, Brigitte Meillon2, Nadine Mandran2,
Michel Dubois1 and Jean-Michel Adam2
LIP, Univ. Grenoble Alpes, BP 47 - 38040 Grenoble Cedex 9, France
LIG, Univ. Grenoble Alpes, BP 53 - 38041 Grenoble Cedex 9, France"
c7de0c85432ad17a284b5b97c4f36c23f506d9d1,RANSAC-Based Training Data Selection for Speaker State Recognition,"INTERSPEECH 2011
RANSAC-based Training Data Selection for Speaker State Recognition
Elif Bozkurt1, Engin Erzin1, C¸ i˘gdem Ero˘glu Erdem2, A.Tanju Erdem3
Multimedia, Vision and Graphics Laboratory, Koc¸ University, Istanbul, Turkey
Department of Electrical and Electronics Engineering, Bahc¸es¸ehir University, Istanbul, Turkey
Department of Electrical and Computer Engineering, ¨Ozye˘gin University, Istanbul, Turkey
ebozkurt,"
c7f752eea91bf5495a4f6e6a67f14800ec246d08,Exploring the Transfer Learning Aspect of Deep Neural Networks in Facial Information Processing,"EXPLORING THE TRANSFER
LEARNING ASPECT OF DEEP
NEURAL NETWORKS IN FACIAL
INFORMATION PROCESSING
A DISSERTATION SUBMITTED TO THE UNIVERSITY OF MANCHESTER
FOR THE DEGREE OF MASTER OF SCIENCE
IN THE FACULTY OF ENGINEERING AND PHYSICAL SCIENCES
Crefeda Faviola Rodrigues
School of Computer Science"
c758b9c82b603904ba8806e6193c5fefa57e9613,Heterogeneous Face Recognition with CNNs,"Heterogeneous Face Recognition with CNNs
Shreyas Saxena
Jakob Verbeek
INRIA Grenoble, Laboratoire Jean Kuntzmann"
c7c03324833ba262eeaada0349afa1b5990c1ea7,A Wearable Face Recognition System on Google Glass for Assisting Social Interactions,"A Wearable Face Recognition System on Google
Glass for Assisting Social Interactions
Bappaditya Mandal∗, Chia Shue Ching, Liyuan Li, Vijay Ramaseshan
Chandrasekhar, Cheston Tan Yin Chet and Lim Joo Hwee
Visual Computing Department, Institute for Infocomm Research, Singapore
Email address: (∗Contact author: Bappaditya Mandal);
{scchia, lyli, vijay, cheston-tan,"
c7c8d150ece08b12e3abdb6224000c07a6ce7d47,DeMeshNet: Blind Face Inpainting for Deep MeshFace Verification,"DeMeshNet: Blind Face Inpainting for Deep MeshFace Verification
National Laboratory of Pattern Recognition, CASIA
Center for Research on Intelligent Perception and Computing, CASIA
Shu Zhang Ran He Tieniu Tan"
c78fdd080df01fff400a32fb4cc932621926021f,Robust Automatic Facial Expression Detection Method,"Robust Automatic Facial Expression Detection
Method
Institute for Pattern Recognition and Artificial Intelligence/ Huazhong University of Science and Technology, Wuhan,
Institute for Pattern Recognition and Artificial Intelligence/ Huazhong University of Science and Technology, Wuhan,
Yan Ouyang
China
Nong Sang
China
Email:"
c03f48e211ac81c3867c0e787bea3192fcfe323e,Mahalanobis Metric Scoring Learned from Weighted Pairwise Constraints in I-Vector Speaker Recognition System,"INTERSPEECH 2016
September 8–12, 2016, San Francisco, USA
Mahalanobis Metric Scoring Learned from Weighted Pairwise Constraints in
I-vector Speaker Recognition System
Zhenchun Lei1, Yanhong Wan1, Jian Luo1, Yingen Yang1
School of Computer Information Engineering, Jiangxi Normal University, Nanchang, China"
c038beaa228aeec174e5bd52460f0de75e9cccbe,Temporal Segment Networks for Action Recognition in Videos,"Temporal Segment Networks for Action
Recognition in Videos
Limin Wang, Yuanjun Xiong, Zhe Wang, Yu Qiao, Dahua Lin, Xiaoou Tang, and Luc Van Gool"
c043f8924717a3023a869777d4c9bee33e607fb5,Emotion Separation Is Completed Early and It Depends on Visual Field Presentation,"Emotion Separation Is Completed Early and It Depends
on Visual Field Presentation
Lichan Liu1,2*, Andreas A. Ioannides1,2
Lab for Human Brain Dynamics, RIKEN Brain Science Institute, Wakoshi, Saitama, Japan, 2 Lab for Human Brain Dynamics, AAI Scientific Cultural Services Ltd., Nicosia,
Cyprus"
c05a7c72e679745deab9c9d7d481f7b5b9b36bdd,"Naval Postgraduate School Monterey, California Approved for Public Release; Distribution Is Unlimited Biometric Challenges for Future Deployments: a Study of the Impact of Geography, Climate, Culture, and Social Conditions on the Effective Collection of Biometrics","NPS-CS-11-005
NAVAL
POSTGRADUATE
SCHOOL
MONTEREY, CALIFORNIA
BIOMETRIC CHALLENGES FOR FUTURE DEPLOYMENTS:
A STUDY OF THE IMPACT OF GEOGRAPHY, CLIMATE, CULTURE,
AND SOCIAL CONDITIONS ON THE EFFECTIVE
COLLECTION OF BIOMETRICS
Paul C. Clark, Heather S. Gregg, with preface by Cynthia E. Irvine
April 2011
Approved for public release; distribution is unlimited"
c0ff7dc0d575658bf402719c12b676a34271dfcd,A New Incremental Optimal Feature Extraction Method for On-Line Applications,"A New Incremental Optimal Feature Extraction
Method for On-line Applications
Youness Aliyari Ghassabeh, Hamid Abrishami Moghaddam
Electrical Engineering Department, K. N. Toosi University of
Technology, Tehran, Iran"
c02847a04a99a5a6e784ab580907278ee3c12653,Fine Grained Video Classification for Endangered Bird Species Protection,"Fine Grained Video Classification for
Endangered Bird Species Protection
Non-Thesis MS Final Report
Chenyu Wang
.  Introduction
.1 Background
This project is about detecting eagles in videos. Eagles are endangered species at the brim of
extinction since 1980s. With the bans of harmful pesticides, the number of eagles keep increasing.
However, recent studies on golden eagles’ activities in the vicinity of wind turbines have shown
significant number of turbine blade collisions with eagles as the major cause of eagles’ mortality. [1]
This project is a part of a larger research project to build an eagle detection and deterrent system
on wind turbine toward reducing eagles’ mortality. [2] The critical component of this study is a
omputer vision system for eagle detection in videos. The key requirement are that the system should
work in real time and detect eagles at a far distance from the camera (i.e. in low resolution).
There are three different bird species in my dataset - falcon, eagle and seagull. The reason for
involving only these three species is based on the real world situation. Wind turbines are always
installed near coast and mountain hill where falcons and seagulls will be the majority. So my model
will classify the minority eagles out of other bird species during the immigration season and protecting
them by using the deterrent system.
.2 Brief Approach"
c0c8d720658374cc1ffd6116554a615e846c74b5,Modeling Multimodal Clues in a Hybrid Deep Learning Framework for Video Classification,"JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015
Modeling Multimodal Clues in a Hybrid Deep
Learning Framework for Video Classification
Yu-Gang Jiang, Zuxuan Wu, Jinhui Tang, Zechao Li, Xiangyang Xue, Shih-Fu Chang"
eee8a37a12506ff5df72c402ccc3d59216321346,Volume C,"Uredniki:
dr. Tomaž Erjavec
Odsek za tehnologije znanja
Institut »Jožef Stefan«, Ljubljana
dr. Jerneja Žganec Gros
Alpineon d.o.o, Ljubljana
Založnik: Institut »Jožef Stefan«, Ljubljana
Tisk: Birografika BORI d.o.o.
Priprava zbornika: Mitja Lasič
Oblikovanje naslovnice: dr. Damjan Demšar
Tiskano iz predloga avtorjev
Naklada:  50
Ljubljana, oktober 2008
Konferenco IS 2008 sofinancirata
Ministrstvo za visoko šolstvo, znanost in tehnologijo
Institut »Jožef Stefan«
Informacijska družba
ISSN 1581-9973
CIP - Kataložni zapis o publikaciji
Narodna in univerzitetna knjižnica, Ljubljana"
ee18e29a2b998eddb7f6663bb07891bfc7262248,Local Linear Discriminant Analysis Framework Using Sample Neighbors,"Local Linear Discriminant Analysis Framework
Using Sample Neighbors
Zizhu Fan, Yong Xu, Member, IEEE, and David Zhang, Fellow, IEEE"
eefb8768f60c17d76fe156b55b8a00555eb40f4d,Subspace Scores for Feature Selection in Computer Vision,"Subspace Scores for Feature Selection in Computer Vision
Cameron Musco
Christopher Musco"
ee463f1f72a7e007bae274d2d42cd2e5d817e751,Automatically Extracting Qualia Relations for the Rich Event Ontology,"Automatically Extracting Qualia Relations for the Rich Event Ontology
Ghazaleh Kazeminejad1, Claire Bonial2, Susan Windisch Brown1 and Martha Palmer1
{ghazaleh.kazeminejad, susan.brown,
University of Colorado Boulder, 2U.S. Army Research Lab"
eed1dd2a5959647896e73d129272cb7c3a2e145c,The Elements of Fashion Style,"INPUTSTYLE DOCUMENTTOP ITEMS“             ”I need an outfit for a beach wedding that I'm going to early this summer. I'm so excited -- it's going to be warm and exotic and tropical... I want my outfit to look effortless, breezy, flowy, like I’m floating over the sand! Oh, and obviously no white! For a tropical spot, I think my outfit should be bright and"
ee92d36d72075048a7c8b2af5cc1720c7bace6dd,Face recognition using mixtures of principal components,"FACE RECOGNITION USING MIXTURES OF PRINCIPAL COMPONENTS
Deepak S. Turaga and Tsuhan Chen
Video and Display Processing
Philips Research USA
Briarcliff Manor, NY 10510"
eedfb384a5e42511013b33104f4cd3149432bd9e,Multimodal probabilistic person tracking and identification in smart spaces,"Multimodal Probabilistic Person
Tracking and Identification
in Smart Spaces
zur Erlangung des akademischen Grades eines
Doktors der Ingenieurwissenschaften
der Fakultät für Informatik
der Universität Fridericiana zu Karlsruhe (TH)
genehmigte
Dissertation
Keni Bernardin
us Karlsruhe
Tag der mündlichen Prüfung: 20.11.2009
Erster Gutachter:
Zweiter Gutachter:
Prof. Dr. A. Waibel
Prof. Dr. R. Stiefelhagen"
c91103e6612fa7e664ccbc3ed1b0b5deac865b02,Automatic Facial Expression Recognition Using Statistical-Like Moments,"Automatic facial expression recognition using
statistical-like moments
Roberto D’Ambrosio, Giulio Iannello, and Paolo Soda
{r.dambrosio, g.iannello,
Integrated Research Center, Universit`a Campus Bio-Medico di Roma,
Via Alvaro del Portillo, 00128 Roma, Italy"
fc1e37fb16006b62848def92a51434fc74a2431a,A Comprehensive Analysis of Deep Regression,"DRAFT
A Comprehensive Analysis of Deep Regression
St´ephane Lathuili`ere, Pablo Mesejo, Xavier Alameda-Pineda, Member IEEE, and Radu Horaud"
fcd3d69b418d56ae6800a421c8b89ef363418665,Effects of Aging over Facial Feature Analysis and Face Recognition,"Effects of Aging over Facial Feature Analysis and Face
Recognition
Bilgin Esme & Bulent Sankur
Bogaziçi Un. Electronics Eng. Dept. March 2010"
fcd77f3ca6b40aad6edbd1dab9681d201f85f365,Machine Learning Based Attacks and Defenses in Computer Security: Towards Privacy and Utility Balance in Sensor Environments,"(cid:13)Copyright 2014
Miro Enev"
fcf8bb1bf2b7e3f71fb337ca3fcf3d9cf18daa46,Feature Selection via Sparse Approximation for Face Recognition,"MANUSCRIPT SUBMITTED TO IEEE TRANS. PATTERN ANAL. MACH. INTELL., JULY 2010
Feature Selection via Sparse Approximation for
Face Recognition
Yixiong Liang, Lei Wang, Yao Xiang, and Beiji Zou"
fcbf808bdf140442cddf0710defb2766c2d25c30,Unsupervised Semantic Action Discovery from Video Collections,"IJCV manuscript No.
(will be inserted by the editor)
Unsupervised Semantic Action Discovery from Video
Collections
Ozan Sener · Amir Roshan Zamir · Chenxia Wu · Silvio Savarese ·
Ashutosh Saxena
Received: date / Accepted: date"
fd4ac1da699885f71970588f84316589b7d8317b,Supervised Descent Method for Solving Nonlinear Least Squares Problems in Computer Vision,"JOURNAL OF LATEX CLASS FILES, VOL. 6, NO. 1, JANUARY 2007
Supervised Descent Method
for Solving Nonlinear Least Squares
Problems in Computer Vision
Xuehan Xiong, and Fernando De la Torre"
fdf533eeb1306ba418b09210387833bdf27bb756,Exploiting Unrelated Tasks in Multi-Task Learning,
fdda5852f2cffc871fd40b0cb1aa14cea54cd7e3,Im2Flow: Motion Hallucination from Static Images for Action Recognition,"Im2Flow: Motion Hallucination from Static Images for Action Recognition
Ruohan Gao
UT Austin
Bo Xiong
UT Austin
Kristen Grauman
UT Austin"
fdfaf46910012c7cdf72bba12e802a318b5bef5a,Computerized Face Recognition in Renaissance Portrait Art,"Computerized Face Recognition in Renaissance
Portrait Art
Ramya Srinivasan, Conrad Rudolph and Amit Roy-Chowdhury"
fdca08416bdadda91ae977db7d503e8610dd744f,ICT - 2009 . 7 . 1 KSERA Project 2010 - 248085,"ICT-2009.7.1
KSERA Project
010-248085
Deliverable D3.1
Deliverable D3.1
Human Robot Interaction
Human Robot Interaction
8 October 2010
Public Document
The KSERA project (http://www.ksera
KSERA project (http://www.ksera-project.eu) has received funding from the European Commission
project.eu) has received funding from the European Commission
under the 7th Framework Programme (FP7) for Research and Technological Development under grant
under the 7th Framework Programme (FP7) for Research and Technological Development under grant
under the 7th Framework Programme (FP7) for Research and Technological Development under grant
greement n°2010-248085."
fd96432675911a702b8a4ce857b7c8619498bf9f,Improved Face Detection and Alignment using Cascade Deep Convolutional Network,"Improved Face Detection and Alignment using Cascade
Deep Convolutional Network
Weilin Cong†, Sanyuan Zhao†, Hui Tian‡, and Jianbing Shen†
Beijing Key Laboratory of Intelligent Information Technology, School of
Computer Science,Beijing Institute of Technology, Beijing 100081, P.R.China
China Mobile Research Institute, Xuanwu Men West Street, Beijing"
fdb33141005ca1b208a725796732ab10a9c37d75,A connectionist computational method for face recognition,"Int.J.Appl. Math. Comput.Sci.,2016,Vol. 26,No. 2,451–465
DOI: 10.1515/amcs-2016-0032
A CONNECTIONIST COMPUTATIONAL METHOD FOR FACE RECOGNITION
FRANCISCO A. PUJOL a, HIGINIO MORA a,∗
, JOS ´E A. GIRONA-SELVA a
Department of Computer Technology
University of Alicante, 03690, San Vicente del Raspeig, Alicante, Spain
e-mail:
In this work, a modified version of the elastic bunch graph matching (EBGM) algorithm for face recognition is introduced.
First, faces are detected by using a fuzzy skin detector based on the RGB color space. Then, the fiducial points for the facial
graph are extracted automatically by adjusting a grid of points to the result of an edge detector. After that, the position of
the nodes, their relation with their neighbors and their Gabor jets are calculated in order to obtain the feature vector defining
each face. A self-organizing map (SOM) framework is shown afterwards. Thus, the calculation of the winning neuron and
the recognition process are performed by using a similarity function that takes into account both the geometric and texture
information of the facial graph. The set of experiments carried out for our SOM-EBGM method shows the accuracy of our
proposal when compared with other state-of the-art methods.
Keywords: pattern recognition, face recognition, neural networks, self-organizing maps.
Introduction
libraries,
In recent years, there has been intensive research carried"
fde0180735699ea31f6c001c71eae507848b190f,Face Detection and Sex Identification from Color Images using AdaBoost with SVM based Component Classifier,"International Journal of Computer Applications (0975 – 8887)
Volume 76– No.3, August 2013
Face Detection and Sex Identification from Color Images
using AdaBoost with SVM based Component Classifier
Tonmoy Das
Lecturer, Department of EEE
University of Information
Technology and Sciences
(UITS)
Dhaka, Bangladesh
Manamatha Sarnaker
B.Sc. in EEE
International University of
Business Agriculture and
Technology (IUBAT)
Dhaka-1230, Bangladesh
Md. Hafizur Rahman
Lecturer, Department of EEE
International University of
Business Agriculture and"
fd615118fb290a8e3883e1f75390de8a6c68bfde,Joint Face Alignment with Non-parametric Shape Models,"Joint Face Alignment with Non-Parametric
Shape Models
Brandon M. Smith and Li Zhang
University of Wisconsin – Madison
http://www.cs.wisc.edu/~lizhang/projects/joint-align/"
fdaf65b314faee97220162980e76dbc8f32db9d6,Face recognition using both visible light image and near-infrared image and a deep network,"Accepted Manuscript
Face recognition using both visible light image and near-infrared image and a deep
network
Kai Guo, Shuai Wu, Yong Xu
Reference:
S2468-2322(17)30014-8
0.1016/j.trit.2017.03.001
TRIT 41
To appear in:
CAAI Transactions on Intelligence Technology
Received Date: 30 January 2017
Accepted Date: 28 March 2017
Please cite this article as: K. Guo, S. Wu, Y. Xu, Face recognition using both visible light image and
near-infrared image and a deep network, CAAI Transactions on Intelligence Technology (2017), doi:
0.1016/j.trit.2017.03.001.
This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to
our customers we are providing this early version of the manuscript. The manuscript will undergo
opyediting, typesetting, and review of the resulting proof before it is published in its final form. Please
note that during the production process errors may be discovered which could affect the content, and all
legal disclaimers that apply to the journal pertain."
f2e9494d0dca9fb6b274107032781d435a508de6,Title of Dissertation : UNCONSTRAINED FACE RECOGNITION,
f2a7f9bd040aa8ea87672d38606a84c31163e171,Human Action Recognition without Human,"Human Action Recognition without Human
Yun He, Soma Shirakabe, Yutaka Satoh, Hirokatsu Kataoka
National Institute of Advanced Industrial Science and Technology (AIST)
Tsukuba, Ibaraki, Japan
{yun.he, shirakabe-s, yu.satou,"
f20e0eefd007bc310d2a753ba526d33a8aba812c,Accurate and robust face recognition from RGB-D images with a deep learning approach,"Lee et al.:  RGB-D FACE RECOGNITION WITH A DEEP LEARNING APPROACH
Accurate and robust face recognition from
RGB-D images with a deep learning
pproach
Yuancheng Lee
http://cv.cs.nthu.edu.tw/php/people/profile.php?uid=150
Jiancong Chen
http://cv.cs.nthu.edu.tw/php/people/profile.php?uid=153
Ching-Wei Tseng
http://cv.cs.nthu.edu.tw/php/people/profile.php?uid=156
Computer Vision Lab,
Department of
Computer Science,
National Tsing Hua
University,
Hsinchu, Taiwan
Shang-Hong Lai
http://www.cs.nthu.edu.tw/~lai/"
f231046d5f5d87e2ca5fae88f41e8d74964e8f4f,Perceived Age Estimation from Face Images,"We are IntechOpen,
the first native scientific
publisher of Open Access books
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08,000
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f5770dd225501ff3764f9023f19a76fad28127d4,Real Time Online Facial Expression Transfer with Single Video Camera,"Real Time Online Facial Expression Transfer
with Single Video Camera"
f558af209dd4c48e4b2f551b01065a6435c3ef33,An Enhanced Attribute Reranking Design for Web Image Search,"International Journal of Emerging Technology in Computer Science & Electronics (IJETCSE)
ISSN: 0976-1353 Volume 23 Issue 1 –JUNE 2016.
AN ENHANCED ATTRIBUTE
RERANKING DESIGN FOR WEB IMAGE
SEARCH
Sai Tejaswi Dasari#1 and G K Kishore Babu*2
#Student,Cse, CIET, Lam,Guntur, India
* Assistant Professort,Cse, CIET, Lam,Guntur , India"
e378ce25579f3676ca50c8f6454e92a886b9e4d7,Robust Video Super-Resolution with Learned Temporal Dynamics,"Robust Video Super-Resolution with Learned Temporal Dynamics
Ding Liu1 Zhaowen Wang2 Yuchen Fan1 Xianming Liu3
Zhangyang Wang4 Shiyu Chang5 Thomas Huang1
University of Illinois at Urbana-Champaign 2Adobe Research
Facebook 4Texas A&M University 5IBM Research"
e393a038d520a073b9835df7a3ff104ad610c552,Automatic temporal segment detection via bilateral long short-term memory recurrent neural networks,"Automatic temporal segment
detection via bilateral long short-
term memory recurrent neural
networks
Bo Sun
Siming Cao
Jun He
Lejun Yu
Liandong Li
Bo Sun, Siming Cao, Jun He, Lejun Yu, Liandong Li, “Automatic temporal segment
detection via bilateral long short-term memory recurrent neural networks,” J.
Electron. Imaging 26(2), 020501 (2017), doi: 10.1117/1.JEI.26.2.020501.
Downloaded From: http://electronicimaging.spiedigitallibrary.org/ on 03/03/2017 Terms of Use: http://spiedigitallibrary.org/ss/termsofuse.aspx"
e315959d6e806c8fbfc91f072c322fb26ce0862b,An Efficient Face Recognition System Based on Sub-Window Extraction Algorithm,"An Efficient Face Recognition System Based on Sub-Window
International Journal of Soft Computing and Engineering (IJSCE)
ISSN: 2231-2307, Volume-1, Issue-6, January 2012
Extraction Algorithm
Manish Gupta, Govind sharma"
e39a0834122e08ba28e7b411db896d0fdbbad9ba,Maximum Likelihood Estimation of Depth Maps Using Photometric Stereo,"Maximum Likelihood Estimation of Depth Maps
Using Photometric Stereo
Adam P. Harrison, Student Member, IEEE, and Dileepan Joseph, Member, IEEE"
e30dc2abac4ecc48aa51863858f6f60c7afdf82a,Facial Signs and Psycho-physical Status Estimation for Well-being Assessment,"Facial Signs and Psycho-physical Status Estimation for Well-being
Assessment
F. Chiarugi, G. Iatraki, E. Christinaki, D. Manousos, G. Giannakakis, M. Pediaditis,
A. Pampouchidou, K. Marias and M. Tsiknakis
Computational Medicine Laboratory, Institute of Computer Science, Foundation for Research and Technology - Hellas,
{chiarugi, giatraki, echrist, mandim, ggian, mped, pampouch, kmarias,
70013 Vasilika Vouton, Heraklion, Crete, Greece
Keywords:
Facial Expression, Stress, Anxiety, Feature Selection, Well-being Evaluation, FACS, FAPS, Classification."
e3917d6935586b90baae18d938295e5b089b5c62,Face localization and authentication using color and depth images,"Face Localization and Authentication
Using Color and Depth Images
Filareti Tsalakanidou, Sotiris Malassiotis, and Michael G. Strintzis, Fellow, IEEE"
e3144f39f473e238374dd4005c8b83e19764ae9e,Next-Flow: Hybrid Multi-Tasking with Next-Frame Prediction to Boost Optical-Flow Estimation in the Wild,"Next-Flow: Hybrid Multi-Tasking with Next-Frame Prediction to Boost
Optical-Flow Estimation in the Wild
Nima Sedaghat
University of Freiburg
Germany"
cfffae38fe34e29d47e6deccfd259788176dc213,Training bookcowgrass flower ? ? water sky doggrass water boat water chair road ? cow grass chair grass dog building ?,"TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. X, NO. X, DECEMBER 2012
Matrix Completion for Weakly-supervised
Multi-label Image Classification
Ricardo Cabral, Fernando De la Torre, João P. Costeira, Alexandre Bernardino"
cfd4004054399f3a5f536df71f9b9987f060f434,Person Recognition in Social Media Photos,"IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. ??, NO. ??, ?? 20??
Person Recognition in Personal Photo Collections
Seong Joon Oh,Rodrigo Benenson, Mario Fritz, and Bernt Schiele, Fellow, IEEE"
cfd933f71f4a69625390819b7645598867900eab,Person Authentication Using Face And Palm Vein: A Survey Of Recognition And Fusion Techniques,"INTERNATIONAL JOURNAL OF TECHNOLOGY ENHANCEMENTS AND EMERGING ENGINEERING RESEARCH, VOL 3, ISSUE 03                       55
ISSN 2347-4289
Person Authentication Using Face And Palm Vein:
A Survey Of Recognition And Fusion Techniques
Preethi M, Dhanashree Vaidya, Dr. S. Kar, Dr. A. M. Sapkal, Dr. Madhuri A. Joshi
Dept. of Electronics and Telecommunication, College of Engineering, Pune, India,
Image Processing & Machine Vision Section, Electronics & Instrumentation Services Division, BARC
Email:"
cf875336d5a196ce0981e2e2ae9602580f3f6243,"7 What 1 S It Mean for a Computer to ""have"" Emotions?","7  What 1
Rosalind W. Picard
It Mean for a Computer to  ""Have""  Emotions?
There  is a  lot  of  talk  about  giving machines  emotions,  some  of
it fluff. Recently at a large technical meeting, a researcher stood up
nd talked of how a Bamey stuffed animal [the purple dinosaur for
kids) ""has  emotions.""  He did not define what he meant by this, but
fter  repeating  it several  times,  it became  apparent  that  children
ttributed  emotions  to  Barney,  and that  Barney  had  deliberately
expressive behaviors that would  encourage the  kids to think. Bar-
ney had emotions. But kids have  attributed  emotions to  dolls and
stuffed animals for as long a s  we  know; and most of  my technical
olleagues would agree that such toys have never had and still do
not have emotions. What is different now that prompts a researcher
to make such a claim? Is the computational plush an example of  a
omputer that really does have emotions?
If  not Barney, then what would  be  an example  of  a  computa-
tional system that has emotions? I am not a philosopher, and this
paper will not be  a  discussion  of  the meaning  of  this question in
ny philosophical sense. However, as an engineer I am interested"
cfd8c66e71e98410f564babeb1c5fd6f77182c55,Comparative Study of Coarse Head Pose Estimation,"Comparative Study of Coarse Head Pose Estimation
Lisa M. Brown and Ying-Li Tian
IBM T.J. Watson Research Center
Hawthorne, NY 10532"
cfbb2d32586b58f5681e459afd236380acd86e28,Improving alignment of faces for recognition,"Improving Alignment of Faces for Recognition
Md. Kamrul Hasan
Christopher J. Pal
D´epartement de g´enie informatique et g´enie logiciel
´Ecole Polytechnique de Montr´eal,
D´epartement de g´enie informatique et g´enie logiciel
´Ecole Polytechnique de Montr´eal,
Qu´ebec, Canada
Qu´ebec, Canada"
cfa92e17809e8d20ebc73b4e531a1b106d02b38c,Parametric classification with soft labels using the evidential EM algorithm: linear discriminant analysis versus logistic regression,"Advances in Data Analysis and Classification manuscript No.
(will be inserted by the editor)
Parametric Classification with Soft Labels using the
Evidential EM Algorithm
Linear Discriminant Analysis vs. Logistic Regression
Benjamin Quost · Thierry Denœux ·
Shoumei Li
Received: date / Accepted: date"
cf5a0115d3f4dcf95bea4d549ec2b6bdd7c69150,Detection of emotions from video in non-controlled environment. (Détection des émotions à partir de vidéos dans un environnement non contrôlé),"Detection of emotions from video in non-controlled
environment
Rizwan Ahmed Khan
To cite this version:
Rizwan Ahmed Khan. Detection of emotions from video in non-controlled environment. Image
Processing. Universit´e Claude Bernard - Lyon I, 2013. English. <NNT : 2013LYO10227>.
<tel-01166539v2>
HAL Id: tel-01166539
https://tel.archives-ouvertes.fr/tel-01166539v2
Submitted on 23 Jun 2015
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destin´ee au d´epˆot et `a la diffusion de documents
scientifiques de niveau recherche, publi´es ou non,
´emanant des ´etablissements d’enseignement et de"
cfdc632adcb799dba14af6a8339ca761725abf0a,Probabilistic Formulations of Regression with Mixed Guidance,"Probabilistic Formulations of Regression with Mixed
Guidance
Aubrey Gress, Ian Davidson University of California, Davis"
cfc30ce53bfc204b8764ebb764a029a8d0ad01f4,Regularizing Deep Neural Networks by Noise: Its Interpretation and Optimization,"Regularizing Deep Neural Networks by Noise:
Its Interpretation and Optimization
Hyeonwoo Noh
Tackgeun You
Dept. of Computer Science and Engineering, POSTECH, Korea
Jonghwan Mun
Bohyung Han"
cf805d478aeb53520c0ab4fcdc9307d093c21e52,Finding Tiny Faces in the Wild with Generative Adversarial Network,"Finding Tiny Faces in the Wild with Generative Adversarial Network
Yancheng Bai1
Yongqiang Zhang1
Mingli Ding2
Bernard Ghanem1
Visual Computing Center, King Abdullah University of Science and Technology (KAUST)
School of Electrical Engineering and Automation, Harbin Institute of Technology (HIT)
Institute of Software, Chinese Academy of Sciences (CAS)
{zhangyongqiang,
Figure1. The detection results of tiny faces in the wild. (a) is the original low-resolution blurry face, (b) is the result of
re-sizing directly by a bi-linear kernel, (c) is the generated image by the super-resolution method, and our result (d) is learned
y the super-resolution (×4 upscaling) and refinement network simultaneously. Best viewed in color and zoomed in."
cf86616b5a35d5ee777585196736dfafbb9853b5,Learning Multiscale Active Facial Patches for Expression Analysis,"This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination.
Learning Multiscale Active Facial Patches for
Expression Analysis
Lin Zhong, Qingshan Liu, Peng Yang, Junzhou Huang, and Dimitris N. Metaxas, Senior Member, IEEE"
cacd51221c592012bf2d9e4894178c1c1fa307ca,Face and Expression Recognition Techniques: A Review,"ISSN: 2277-3754
ISO 9001:2008 Certified
International Journal of Engineering and Innovative Technology (IJEIT)
Volume 4, Issue 11, May 2015
Face and Expression Recognition Techniques: A
Review
Advanced Communication & Signal Processing Laboratory, Department of Electronics & Communication
engineering, Government College of Engineering Kannur, Kerala, India.
Rishin C. K, Aswani Pookkudi, A. Ranjith Ram"
ca0363d29e790f80f924cedaf93cb42308365b3d,Facial Expression Recognition in Image Sequences Using Geometric Deformation Features and Support Vector Machines,"Facial Expression Recognition in Image Sequences
using Geometric Deformation Features and Support
Vector Machines
Irene Kotsiay and Ioannis Pitasy,Senior Member IEEE
yAristotle University of Thessaloniki
Department of Informatics
Box 451
54124 Thessaloniki, Greece
email:"
cad52d74c1a21043f851ae14c924ac689e197d1f,From Ego to Nos-Vision: Detecting Social Relationships in First-Person Views,"From Ego to Nos-vision:
Detecting Social Relationships in First-Person Views
Stefano Alletto, Giuseppe Serra, Simone Calderara, Francesco Solera and Rita Cucchiara
Universit`a degli Studi di Modena e Reggio Emilia
Via Vignolese 905, 41125 Modena - Italy"
cad24ba99c7b6834faf6f5be820dd65f1a755b29,"Understanding hand-object manipulation by modeling the contextual relationship between actions, grasp types and object attributes","Understanding hand-object
manipulation by modeling the
ontextual relationship between actions,
grasp types and object attributes
Minjie Cai1, Kris M. Kitani2 and Yoichi Sato1
Journal Title
XX(X):1–14
(cid:13)The Author(s) 2016
Reprints and permission:
sagepub.co.uk/journalsPermissions.nav
DOI: 10.1177/ToBeAssigned
www.sagepub.com/"
cadba72aa3e95d6dcf0acac828401ddda7ed8924,Algorithms and VLSI Architectures for Low-Power Mobile Face Verification,"THÈSE PRÉSENTÉE À LA FACULTÉ DES SCIENCES
POUR L’OBTENTION DU GRADE DE DOCTEUR ÈS SCIENCES
Algorithms and VLSI Architectures
for Low-Power Mobile Face Verification
Jean-Luc Nagel
Acceptée sur proposition du jury:
Prof. F. Pellandini, directeur de thèse
PD Dr. M. Ansorge, co-directeur de thèse
Prof. P.-A. Farine, rapporteur
Dr. C. Piguet, rapporteur
Soutenue le 2 juin 2005
INSTITUT DE MICROTECHNIQUE
UNIVERSITÉ DE NEUCHÂTEL"
ca37eda56b9ee53610c66951ee7ca66a35d0a846,Semantic Concept Discovery for Large-Scale Zero-Shot Event Detection,"Semantic Concept Discovery for Large-Scale Zero-Shot Event Detection
Xiaojun Chang1,2, Yi Yang1, Alexander G. Hauptmann2, Eric P. Xing3 and Yao-Liang Yu3∗
Centre for Quantum Computation and Intelligent Systems, University of Technology Sydney.
Language Technologies Institute, Carnegie Mellon University.
Machine Learning Department, Carnegie Mellon University.
{cxj273, {alex, epxing,"
ca606186715e84d270fc9052af8500fe23befbda,"Using subclass discriminant analysis, fuzzy integral and symlet decomposition for face recognition","Using Subclass Discriminant Analysis, Fuzzy Integral and Symlet Decomposition for
Face Recognition
Seyed Mohammad Seyedzade
Department of Electrical Engineering,
Iran Univ. of Science and Technology,
Narmak, Tehran, Iran
Email:
Sattar Mirzakuchaki
Amir Tahmasbi
Department of Electrical Engineering,
Iran Univ. of Science and Technology,
Department of Electrical Engineering,
Iran Univ. of Science and Technology,
Narmak, Tehran, Iran
Email:
Narmak, Tehran, Iran
Email:"
e4bf70e818e507b54f7d94856fecc42cc9e0f73d,Face Recognition under Varying Blur in an Unconstrained Environment,"IJRET: International Journal of Research in Engineering and Technology        eISSN: 2319-1163 | pISSN: 2321-7308
FACE RECOGNITION UNDER VARYING BLUR IN AN
UNCONSTRAINED ENVIRONMENT
Anubha Pearline.S1, Hemalatha.M2
M.Tech, Information Technology,Madras Institute of Technology, TamilNadu,India,
Assistant Professor, Information Technology,Madras Institute of Technology, TamilNadu,India, email:,"
e4a1b46b5c639d433d21b34b788df8d81b518729,Side Information for Face Completion: a Robust PCA Approach,"JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015
Side Information for Face Completion: a Robust
PCA Approach
Niannan Xue, Student Member, IEEE, Jiankang Deng, Student Member,IEEE,
Shiyang Cheng, Student Member,IEEE, Yannis Panagakis, Member,IEEE,
nd Stefanos Zafeiriou, Member, IEEE"
e4c81c56966a763e021938be392718686ba9135e,Bio-Inspired Architecture for Clustering into Natural and Non-Natural Facial Expressions,",100+OPEN ACCESS BOOKS103,000+INTERNATIONALAUTHORS AND EDITORS106+ MILLIONDOWNLOADSBOOKSDELIVERED TO151 COUNTRIESAUTHORS AMONGTOP 1%MOST CITED SCIENTIST12.2%AUTHORS AND EDITORSFROM TOP 500 UNIVERSITIESSelection of our books indexed in theBook Citation Index in Web of Science™Core Collection (BKCI)Chapter from the book Visual Cortex - Current Status and PerspectivesDownloaded from: http://www.intechopen.com/books/visual-cortex-current-status-and-perspectivesPUBLISHED BYWorld's largest Science,Technology & Medicine Open Access book publisherInterested in publishing with InTechOpen?Contact us at"
e4e95b8bca585a15f13ef1ab4f48a884cd6ecfcc,Face Recognition with Independent Component Based Super-resolution,"Face Recognition with Independent Component Based
Super-resolution
Osman Gokhan Sezer†,a, Yucel Altunbasakb, Aytul Ercila
Faculty of Engineering and Natural Sciences, Sabanci Univ., Istanbul, Turkiye, 34956
School of Elec. and Comp. Eng. , Georgia Inst. of Tech., Atlanta, GA, USA, 30332-0250"
e43ea078749d1f9b8254e0c3df4c51ba2f4eebd5,Facial Expression Recognition Based on Constrained Local Models and Support Vector Machines,"Facial Expression Recognition Based on Constrained
Local Models and Support Vector Machines
Nikolay Neshov1, Ivo Draganov2, Agata Manolova3"
e4c2f8e4aace8cb851cb74478a63d9111ca550ae,Distributed One-class Learning,"DISTRIBUTED ONE-CLASS LEARNING
Ali Shahin Shamsabadi(cid:63), Hamed Haddadi†, Andrea Cavallaro(cid:63)
(cid:63)Queen Mary University of London,†Imperial College London"
e475e857b2f5574eb626e7e01be47b416deff268,Facial Emotion Recognition Using Nonparametric Weighted Feature Extraction and Fuzzy Classifier,"Facial Emotion Recognition Using Nonparametric
Weighted Feature Extraction and Fuzzy Classifier
Maryam Imani and Gholam Ali Montazer"
e4391993f5270bdbc621b8d01702f626fba36fc2,Head Pose Estimation Using Multi-scale Gaussian Derivatives,"Author manuscript, published in ""18th Scandinavian Conference on Image Analysis (2013)""
DOI : 10.1007/978-3-642-38886-6_31"
e4d8ba577cabcb67b4e9e1260573aea708574886,Um Sistema De Recomendaç˜ao Inteligente Baseado Em V ´ Idio Aulas Para Educaç˜ao a Distˆancia an Intelligent Recommendation System Based on Video Lectures for Distance Education (revelation),"UM SISTEMA DE RECOMENDAC¸ ˜AO INTELIGENTE BASEADO EM V´IDIO
AULAS PARA EDUCAC¸ ˜AO A DIST ˆANCIA
Gaspare Giuliano Elias Bruno
Tese de Doutorado apresentada ao Programa
de P´os-gradua¸c˜ao em Engenharia de Sistemas e
Computa¸c˜ao, COPPE, da Universidade Federal
do Rio de Janeiro, como parte dos requisitos
necess´arios `a obten¸c˜ao do t´ıtulo de Doutor em
Engenharia de Sistemas e Computa¸c˜ao.
Orientadores: Edmundo Albuquerque de
Souza e Silva
Rosa Maria Meri Le˜ao
Rio de Janeiro
Janeiro de 2016"
e4abc40f79f86dbc06f5af1df314c67681dedc51,Head Detection with Depth Images in the Wild,"Head Detection with Depth Images in the Wild
Diego Ballotta, Guido Borghi, Roberto Vezzani and Rita Cucchiara
Department of Engineering ”Enzo Ferrari”
University of Modena and Reggio Emilia, Italy
Keywords:
Head Detection, Head Localization, Depth Maps, Convolutional Neural Network"
e4d0e87d0bd6ead4ccd39fc5b6c62287560bac5b,Implicit video multi-emotion tagging by exploiting multi-expression relations,"Implicit Video Multi-Emotion Tagging by Exploiting Multi-Expression
Relations
Zhilei Liu, Shangfei Wang*, Zhaoyu Wang and Qiang Ji"
e48e94959c4ce799fc61f3f4aa8a209c00be8d7f,Design of an Efficient Real-Time Algorithm Using Reduced Feature Dimension for Recognition of Speed Limit Signs,"Hindawi Publishing Corporation
The Scientific World Journal
Volume 2013, Article ID 135614, 6 pages
http://dx.doi.org/10.1155/2013/135614
Research Article
Design of an Efficient Real-Time Algorithm Using Reduced
Feature Dimension for Recognition of Speed Limit Signs
Hanmin Cho,1 Seungwha Han,2 and Sun-Young Hwang1
Department of Electronic Engineering, Sogang University, Seoul 121-742, Republic of Korea
Samsung Techwin R&D Center, Security Solution Division, 701 Sampyeong-dong, Bundang-gu, Seongnam-si,
Gyeonggi 463-400, Republic of Korea
Correspondence should be addressed to Sun-Young Hwang;
Received 28 August 2013; Accepted 1 October 2013
Academic Editors: P. Daponte, M. Nappi, and N. Nishchal
Copyright © 2013 Hanmin Cho et al. This is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
We propose a real-time algorithm for recognition of speed limit signs from a moving vehicle. Linear Discriminant Analysis (LDA)
required for classification is performed by using Discrete Cosine Transform (DCT) coefficients. To reduce feature dimension in
LDA, DCT coefficients are selected by a devised discriminant function derived from information obtained by training. Binarization
nd thinning are performed on a Region of Interest (ROI) obtained by preprocessing a detected ROI prior to DCT for further"
e496d6be415038de1636bbe8202cac9c1cea9dbe,Facial Expression Recognition in Older Adults using Deep Machine Learning,"Facial Expression Recognition in Older Adults using
Deep Machine Learning
Andrea Caroppo, Alessandro Leone and Pietro Siciliano
National Research Council of Italy, Institute for Microelectronics and Microsystems, Lecce,
Italy"
e43cc682453cf3874785584fca813665878adaa7,Face Recognition using Local Derivative Pattern Face Descriptor,"www.ijecs.in
International Journal Of Engineering And Computer Science ISSN:2319-7242
Volume 3 Issue 10 October, 2014 Page No.8830-8834
Face Recognition using Local Derivative Pattern Face
Descriptor
Pranita R. Chavan1,  Dr. Dnyandeo J. Pete2
Department of Electronics and Telecommunication
Datta Meghe College of Engineering
Airoli, Navi Mumbai, India 1,2
Mob: 99206746061
Mob: 99870353142"
fec6648b4154fc7e0892c74f98898f0b51036dfe,"A Generic Face Processing Framework: Technologies, Analyses and Applications","A Generic Face Processing
Framework: Technologies,
Analyses and Applications
JANG Kim-fung
A Thesis Submitted in Partial Ful(cid:12)lment
of the Requirements for the Degree of
Master of Philosophy
Computer Science and Engineering
Supervised by
Prof. Michael R. Lyu
(cid:13)The Chinese University of Hong Kong
July 2003
The Chinese University of Hong Kong holds the copyright of this thesis. Any
person(s) intending to use a part or whole of the materials in the thesis in
proposed publication must seek copyright release from the Dean of the
Graduate School."
fea0a5ed1bc83dd1b545a5d75db2e37a69489ac9,Enhancing Recommender Systems for TV by Face Recognition,"Enhancing Recommender Systems for TV by Face Recognition
Toon De Pessemier, Damien Verlee and Luc Martens
iMinds - Ghent University, Technologiepark 15, B-9052 Ghent, Belgium
{toon.depessemier,
Keywords:
Recommender System, Face Recognition, Face Detection, TV, Emotion Detection."
fe9c460d5ca625402aa4d6dd308d15a40e1010fa,Neural Architecture for Temporal Emotion Classification,"Neural Architecture for Temporal Emotion
Classification
Roland Schweiger, Pierre Bayerl, and Heiko Neumann
Universit¨at Ulm, Neuroinformatik, Germany"
fe7e3cc1f3412bbbf37d277eeb3b17b8b21d71d5,Performance Evaluation of Gabor Wavelet Features for Face Representation and Recognition,"IOSR Journal of VLSI and Signal Processing (IOSR-JVSP)
Volume 6, Issue 2, Ver. I (Mar. -Apr. 2016), PP 47-53
e-ISSN: 2319 – 4200, p-ISSN No. : 2319 – 4197
www.iosrjournals.org
Performance Evaluation of Gabor Wavelet Features for Face
Representation and Recognition
M. E. Ashalatha1, Mallikarjun S. Holi2
Dept. of Biomedical Engineering, Bapuji Institute of Engineering & Technology Davanagere, Karnataka,India
Dept. of Electronics and Instrumentation Engineering, University B.D.T.College of Engineering, Visvesvaraya
Technological University,  Davanagere, Karnataka, India"
fea83550a21f4b41057b031ac338170bacda8805,Learning a Metric Embedding for Face Recognition using the Multibatch Method,"Learning a Metric Embedding
for Face Recognition
using the Multibatch Method
Oren Tadmor
Yonatan Wexler
Tal Rosenwein
Shai Shalev-Shwartz
Amnon Shashua
Orcam Ltd., Jerusalem, Israel"
fe0c51fd41cb2d5afa1bc1900bbbadb38a0de139,Bayesian face recognition using 2D Gaussian-Hermite moments,"Rahman et al. EURASIP Journal on Image and Video Processing  (2015) 2015:35
DOI 10.1186/s13640-015-0090-5
RESEARCH
Open Access
Bayesian face recognition using 2D
Gaussian-Hermite moments
S. M. Mahbubur Rahman1*, Shahana Parvin Lata2 and Tamanna Howlader2"
c8db8764f9d8f5d44e739bbcb663fbfc0a40fb3d,Modeling for part-based visual object detection based on local features,"Modeling for part-based visual object
detection based on local features
Von der Fakult¨at f¨ur Elektrotechnik und Informationstechnik
der Rheinisch-Westf¨alischen Technischen Hochschule Aachen
zur Erlangung des akademischen Grades eines Doktors
der Ingenieurwissenschaften genehmigte Dissertation
vorgelegt von
Diplom-Ingenieur
Mark Asbach
us Neuss
Berichter:
Univ.-Prof. Dr.-Ing. Jens-Rainer Ohm
Univ.-Prof. Dr.-Ing. Til Aach
Tag der m¨undlichen Pr¨ufung: 28. September 2011
Diese Dissertation ist auf den Internetseiten der
Hochschulbibliothek online verf¨ugbar."
c86e6ed734d3aa967deae00df003557b6e937d3d,Generative Adversarial Networks with Decoder-Encoder Output Noise,"Generative Adversarial Networks with
Decoder-Encoder Output Noise
Guoqiang Zhong, Member, IEEE, Wei Gao, Yongbin Liu, Youzhao Yang
onditional distribution of their neighbors. In [32], Portilla and
Simoncelli proposed a parametric texture model based on joint
statistics, which uses a decomposition method that is called
steerable pyramid decomposition to decompose the texture
of images. An example-based super-resolution algorithm [11]
was proposed in 2002, which uses a Markov network to model
the spatial relationship between the pixels of an image. A
scene completion algorithm [16] was proposed in 2007, which
pplied a semantic scene match technique. These traditional
lgorithms can be applied to particular image generation tasks,
such as texture synthesis and super-resolution. Their common
haracteristic is that they predict the images pixel by pixel
rather than generate an image as a whole, and the basic idea
of them is to make an interpolation according to the existing
part of the images. Here, the problem is, given a set of images,
an we generate totally new images with the same distribution
of the given ones?"
c8a4b4fe5ff2ace9ab9171a9a24064b5a91207a3,Locating facial landmarks with binary map cross-correlations,"LOCATING FACIAL LANDMARKS WITH BINARY MAP CROSS-CORRELATIONS
J´er´emie Nicolle
K´evin Bailly
Vincent Rapp
Mohamed Chetouani
Univ. Pierre & Marie Curie, ISIR - CNRS UMR 7222, F-75005, Paris - France
{nicolle, bailly, rapp,"
c866a2afc871910e3282fd9498dce4ab20f6a332,Surveillance Face Recognition Challenge,"Noname manuscript No.
(will be inserted by the editor)
Surveillance Face Recognition Challenge
Zhiyi Cheng · Xiatian Zhu · Shaogang Gong
Received: date / Accepted: date"
c84233f854bbed17c22ba0df6048cbb1dd4d3248,Exploring Locally Rigid Discriminative Patches for Learning Relative Attributes,"Y. VERMA, C. V. JAWAHAR: EXPLORING PATCHES FOR RELATIVE ATTRIBUTES
Exploring Locally Rigid Discriminative
Patches for Learning Relative Attributes
Yashaswi Verma
http://researchweb.iiit.ac.in/~yashaswi.verma/
C. V. Jawahar
http://www.iiit.ac.in/~jawahar/
IIIT-Hyderabad, India
http://cvit.iiit.ac.in"
c81ee278d27423fd16c1a114dcae486687ee27ff,Search Based Face Annotation Using Weakly Labeled Facial Images,"Search Based Face Annotation Using Weakly
Labeled Facial Images
Shital Shinde1, Archana Chaugule2
Computer Department, Savitribai Phule Pune University
D.Y.Patil Institute of Engineering and Technology, Pimpri, Pune-18
Mahatma Phulenagar, 120/2 Mahaganpati soc, Chinchwad, Pune-19, MH, India
D.Y.Patil Institute of Engineering and Technology, Pimpri, Pune-18, Savitribai Phule Pune University
DYPIET, Pimpri, Pune-18, MH, India"
c83a05de1b4b20f7cd7cd872863ba2e66ada4d3f,A Deep Learning Perspective on the Origin of Facial Expressions,"BREUER, KIMMEL: A DEEP LEARNING PERSPECTIVE ON FACIAL EXPRESSIONS
A Deep Learning Perspective on the Origin
of Facial Expressions
Ran Breuer
Ron Kimmel
Department of Computer Science
Technion - Israel Institute of Technology
Technion City, Haifa, Israel
Figure 1: Demonstration of the filter visualization process."
c8adbe00b5661ab9b3726d01c6842c0d72c8d997,Deep Architectures for Face Attributes,"Deep Architectures for Face Attributes
Tobi Baumgartner, Jack Culpepper
Computer Vision and Machine Learning Group, Flickr, Yahoo,
{tobi,"
fb4545782d9df65d484009558e1824538030bbb1,"Learning Visual Patterns: Imposing Order on Objects, Trajectories and Networks",
fbf196d83a41d57dfe577b3a54b1b7fa06666e3b,Extreme Learning Machine for Large-Scale Action Recognition,"Extreme Learning Machine for Large-Scale
Action Recognition
G¨ul Varol and Albert Ali Salah
Department of Computer Engineering, Bo˘gazi¸ci University, Turkey"
fba464cb8e3eff455fe80e8fb6d3547768efba2f,Survey Paper on Emotion Recognition,"International Journal of Engineering and Applied Sciences (IJEAS)
ISSN: 2394-3661, Volume-3, Issue-2, February 2016
Survey Paper on Emotion Recognition
Prachi Shukla, Sandeep Patil"
fbb2f81fc00ee0f257d4aa79bbef8cad5000ac59,Reading Hidden Emotions: Spontaneous Micro-expression Spotting and Recognition,"Reading Hidden Emotions: Spontaneous
Micro-expression Spotting and Recognition
Xiaobai Li, Student Member, IEEE, Xiaopeng Hong, Member, IEEE, Antti Moilanen, Xiaohua Huang, Student
Member, IEEE, Tomas Pfister, Guoying Zhao, Senior Member, IEEE, and Matti Pietik¨ainen, Fellow, IEEE"
fb9ad920809669c1b1455cc26dbd900d8e719e61,3 D Gaze Estimation from Remote RGB-D Sensors THÈSE,"D Gaze Estimation from Remote RGB-D Sensors
THÈSE NO 6680 (2015)
PRÉSENTÉE LE 9 OCTOBRE 2015
À LA FACULTÉ DES SCIENCES ET TECHNIQUES DE L'INGÉNIEUR
LABORATOIRE DE L'IDIAP
PROGRAMME DOCTORAL EN GÉNIE ÉLECTRIQUE
ÉCOLE POLYTECHNIQUE FÉDÉRALE DE LAUSANNE
POUR L'OBTENTION DU GRADE DE DOCTEUR ÈS SCIENCES
Kenneth Alberto FUNES MORA
cceptée sur proposition du jury:
Prof. K. Aminian, président du jury
Dr J.-M. Odobez,   directeur de thèse
Prof. L.-Ph. Morency, rapporteur
Prof. D. Witzner Hansen, rapporteur
Dr R. Boulic, rapporteur
Suisse"
ed28e8367fcb7df7e51963add9e2d85b46e2d5d6,A Novel Approach of Face Recognition Using Convolutional Neural Networks with Auto Encoder,"International J. of Engg. Research & Indu. Appls. (IJERIA).
ISSN 0974-1518, Vol.9, No. III (December 2016), pp.23-42
A NOVEL APPROACH OF FACE RECOGNITION USING
CONVOLUTIONAL NEURAL  NETWORKS WITH AUTO
ENCODER
T. SYED AKHEEL1 AND DR. S. A. K JILANI2
Research Scholar, Dept. of Electronics & Communication Engineering,
Rayalaseema University Kurnool, Andhra Pradesh.
2 Research Supervisor, Professor, Dept. of Electronics & Communication Engineering,
Madanapalle Institute of Technology & Science, Madanapalle, Andhra Pradesh."
ed08ac6da6f8ead590b390b1d14e8a9b97370794,An Efficient Approach for 3D Face Recognition Using ANN Based Classifiers,"ISSN(Online): 2320-9801
ISSN (Print):  2320-9798
International Journal of Innovative Research in Computer
nd Communication Engineering
(An ISO 3297: 2007 Certified Organization)
Vol. 3, Issue 9, September 2015
An Efficient Approach for 3D Face
Recognition Using ANN Based Classifiers
Vaibhav M. Pathak1, Suhas S.Satonkar2, Dr.Prakash B.Khanale3
Assistant Professor, Dept. of C.S., Shri Shivaji College, Parbhani, M.S, India1
Assistant Professor, Dept. of C.S., Arts, Commerce and Science College, Gangakhed, M.S, India2
Associate Professor, Dept. of C.S., Dnyanopasak College Parbhani, M.S, India3"
edef98d2b021464576d8d28690d29f5431fd5828,Pixel-Level Alignment of Facial Images for High Accuracy Recognition Using Ensemble of Patches,"Pixel-Level Alignment of Facial Images
for High Accuracy Recognition
Using Ensemble of Patches
Hoda Mohammadzade, Amirhossein Sayyafan, Benyamin Ghojogh"
ed04e161c953d345bcf5b910991d7566f7c486f7,Mirror my emotions! Combining facial expression analysis and synthesis on a robot,"Combining facial expression analysis and synthesis on a
Mirror my emotions!
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Stefan Sosnowski1 and Christoph Mayer2 and Kolja K¨uhnlenz3 and Bernd Radig4"
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Convex optimization techniques for the ef‌f‌icient recovery of a sparsely
orrupted low-rank matrix
Silvia Gandy and Isao Yamada
Received on August 10, 2010 / Revised on August 31, 2010"
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Sung Ju Hwang · Kristen Grauman
Received: 16 December 2010 / Accepted: 23 August 2011 / Published online: 18 October 2011
© Springer Science+Business Media, LLC 2011"
c1dfabe36a4db26bf378417985a6aacb0f769735,Describing Visual Scene through EigenMaps,"Journal of Computer Vision and Image Processing, NWPJ-201109-50
Describing Visual Scene through EigenMaps
Shizhi Chen, Student Member, IEEE, and YingLi Tian, Senior Member, IEEE"
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CVPR 2010 Submission #1369. CONFIDENTIAL REVIEW COPY. DO NOT DISTRIBUTE.
#1369
Subspace Regression: Predicting a Subspace from one Sample
Anonymous CVPR submission
Paper ID 1369"
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Peipei Li†, Yibo Hu†, Ran He Member, IEEE and Zhenan Sun Member, IEEE"
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Published in:
Proceedings of the 2017 IEEE International Conference on Acoustics, Speech and Signal Processing
Document Version:
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Take down policy"
c68ec931585847b37cde9f910f40b2091a662e83,A Comparative Evaluation of Dotted Raster-Stereography and Feature-Based Techniques for Automated Face Recognition,"(IJACSA) International Journal of Advanced Computer Science and Applications,
Vol. 9, No. 6, 2018
A Comparative Evaluation of Dotted Raster-
Stereography and Feature-Based Techniques for
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Muhammad Wasim
S. Talha Ahsan
Department of Computer Science
Department of Electrical Engineering
Usman Institute of Technology
Usman Institute of Technology
Karachi, Pakistan
Karachi, Pakistan
Lubaid Ahmed, Syed Faisal Ali,
Fauzan Saeed
Department of Computer Science
Usman Institute of Technology
Karachi, Pakistan
feature-based
system.  The"
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Facial Expression Recognition with Pyramid Gabor
Features and Complete Kernel Fisher Linear
Duan-Duan Yang1, Lian-Wen Jin1, Jun-Xun Yin1, Li-Xin Zhen2, Jian-Cheng Huang2
School of Electronic and Information Engineering, South China
University of Technology, Guangzhou, 510640, P.R.China
{ddyang,
Motorola China Research Center, Shanghai, 210000, P.R.China
{Li-Xin.Zhen,"
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http://dx.doi.org/10.1590/1678-4324-2017160480
ISSN 1678-4324 Online Edition
Engineering,Technology and Techniques
BRAZILIAN ARCHIVES OF
BIOLOGY AND TECHNOLOGY
A N   I N T E R N A T I O N A L   J O U R N A L
Face  Image  Retrieval  of  Efficient  Sparse  Code  words  and
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Suchitra S1*.
Srm Easwari Engineering College, Ramapuram, Bharathi Salai, Chennai, Tamil Nadu, India."
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Real-Time Video Surveillance Applications
Le Thanh Nguyen-Meidine1, Eric Granger 1, Madhu Kiran1 and Louis-Antoine Blais-Morin2
´Ecole de technologie sup´erieure, Universit´e du Qu´ebec, Montreal, Canada
Genetec Inc., Montreal, Canada"
c6ffa09c4a6cacbbd3c41c8ae7a728b0de6e10b6,Feature extraction using constrained maximum variance mapping,"This article appeared in a journal published by Elsevier. The attached
opy is furnished to the author for internal non-commercial research
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c62c07de196e95eaaf614fb150a4fa4ce49588b4,SSR-Net: A Compact Soft Stagewise Regression Network for Age Estimation,Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI-18)
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ec1e03ec72186224b93b2611ff873656ed4d2f74,D Reconstruction of “ Inthe-Wild ” Faces in Images and Videos,"JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015
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ec12f805a48004a90e0057c7b844d8119cb21b4a,Distance-Based Descriptors and Their Application in the Task of Object Detection,"Distance-Based Descriptors and Their
Application in the Task of Object Detection
Radovan Fusek(B) and Eduard Sojka
Department of Computer Science, Technical University of Ostrava, FEECS,
7. Listopadu 15, 708 33 Ostrava-Poruba, Czech Republic"
ec54000c6c0e660dd99051bdbd7aed2988e27ab8,Two in One: Joint Pose Estimation and Face Recognition with Pca,"TWO IN ONE: JOINT POSE ESTIMATION AND FACE RECOGNITION WITH P2CA1
Francesc Tarres*, Antonio Rama*
{tarres,
Davide Onofrio+, Stefano Tubaro+
{d.onofrio,
*Dept. Teoria del Senyal i Comunicacions - Universitat Politècnica de Catalunya, Barcelona, Spain
+Dipartimento di Elettronica e Informazione - Politecnico di Milano, Meiland, Italy"
ec0104286c96707f57df26b4f0a4f49b774c486b,An Ensemble CNN2ELM for Age Estimation,"An Ensemble CNN2ELM for Age Estimation
Mingxing Duan , Kenli Li, Senior Member, IEEE, and Keqin Li, Fellow, IEEE"
4e32fbb58154e878dd2fd4b06398f85636fd0cf4,A Hierarchical Matcher using Local Classifier Chains,"A Hierarchical Matcher using Local Classifier Chains
L. Zhang and I.A. Kakadiaris
Computational Biomedicine Lab, 4849 Calhoun Rd, Rm 373, Houston, TX 77204"
4ea53e76246afae94758c1528002808374b75cfa,A Review of Scholastic Examination and Models for Face Recognition and Retrieval in Video,"Lasbela, U. J.Sci. Techl., vol.IV , pp. 57-70, 2015
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ISSN 2306-8256
nd Retrieval in Video
Varsha Sachdeva1, Junaid Baber2, Maheen Bakhtyar2, Muzamil Bokhari3, Imran Ali4
Department of Computer Science, SBK Women’s University, Quetta, Balochistan
Department of CS and IT, University of Balochistan, Quetta
Department of Physics, University of Balochistan, Quetta
Institute of Biochemistry, University of Balochistan, Quetta"
4e97b53926d997f451139f74ec1601bbef125599,Discriminative Regularization for Generative Models,"Discriminative Regularization for Generative Models
Alex Lamb, Vincent Dumoulin and Aaron Courville
Montreal Institute for Learning Algorithms, Universit´e de Montr´eal"
4e27fec1703408d524d6b7ed805cdb6cba6ca132,SSD-Sface: Single shot multibox detector for small faces,"SSD-Sface: Single shot multibox detector for small faces
C. Thuis"
4e6c9be0b646d60390fe3f72ce5aeb0136222a10,Long-Term Temporal Convolutions for Action Recognition,"Long-term Temporal Convolutions
for Action Recognition
G¨ul Varol, Ivan Laptev, and Cordelia Schmid, Fellow, IEEE"
4ef0a6817a7736c5641dc52cbc62737e2e063420,Study of Face Recognition Techniques,"International Journal of Advanced Computer Research (ISSN (Print): 2249-7277   ISSN (Online): 2277-7970)
Volume-4 Number-4 Issue-17 December-2014
Study of Face Recognition Techniques
Sangeeta Kaushik1*, R. B. Dubey2 and Abhimanyu Madan3
Received: 10-November-2014; Revised: 18-December-2014; Accepted: 23-December-2014
©2014 ACCENTS"
4e0e49c280acbff8ae394b2443fcff1afb9bdce6,Automatic Learning of Gait Signatures for People Identification,"Automatic learning of gait signatures for people identification
F.M. Castro
Univ. of Malaga
fcastro<at>uma.es
M.J. Mar´ın-Jim´enez
Univ. of Cordoba
mjmarin<at>uco.es
N. Guil
Univ. of Malaga
nguil<at>uma.es
N. P´erez de la Blanca
Univ. of Granada
nicolas<at>ugr.es"
20a432a065a06f088d96965f43d0055675f0a6c1,The Effects of Regularization on Learning Facial Expressions with Convolutional Neural Networks,"In: Proc. of the 25th Int. Conference on Artificial Neural Networks (ICANN)
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The final publication is available at Springer via
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The Effects of Regularization on Learning Facial
Expressions with Convolutional Neural Networks
Tobias Hinz, Pablo Barros, and Stefan Wermter
University of Hamburg Department of Computer Science,
Vogt-Koelln-Strasse 30, 22527 Hamburg, Germany
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20a3ce81e7ddc1a121f4b13e439c4cbfb01adfba,Sparse-MVRVMs Tree for Fast and Accurate Head Pose Estimation in the Wild,"Sparse-MVRVMs Tree for Fast and Accurate
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Mohamed Selim, Alain Pagani, and Didier Stricker
Augmented Vision Research Group,
German Research Center for Artificial Intelligence (DFKI),
Tripstaddterstr. 122, 67663 Kaiserslautern, Germany
Technical University of Kaiserslautern
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2004afb2276a169cdb1f33b2610c5218a1e47332,Deep Convolutional Neural Network Used in Single Sample per Person Face Recognition,"Hindawi
Computational Intelligence and Neuroscience
Volume 2018, Article ID 3803627, 11 pages
https://doi.org/10.1155/2018/3803627
Research Article
Deep Convolutional Neural Network Used in Single Sample per
Person Face Recognition
Junying Zeng , Xiaoxiao Zhao , Junying Gan , Chaoyun Mai
nd Fan Wang
, Yikui Zhai,
School of Information Engineering, Wuyi University, Jiangmen 529020, China
Correspondence should be addressed to Xiaoxiao Zhao;
Received 27 November 2017; Revised 23 May 2018; Accepted 26 July 2018; Published 23 August 2018
Academic Editor: Jos´e Alfredo Hern´andez-P´erez
Copyright © 2018 Junying Zeng et al. 0is is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Face recognition (FR) with single sample per person (SSPP) is a challenge in computer vision. Since there is only one sample to be
trained, it makes facial variation such as pose, illumination, and disguise dif‌f‌icult to be predicted. To overcome this problem, this paper
proposes a scheme combined traditional and deep learning (TDL) method to process the task. First, it proposes an expanding sample
method based on traditional approach. Compared with other expanding sample methods, the method can be used easily and"
20e504782951e0c2979d9aec88c76334f7505393,Robust LSTM-Autoencoders for Face De-Occlusion in the Wild,"Robust LSTM-Autoencoders for Face De-Occlusion
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Fang Zhao, Jiashi Feng, Jian Zhao, Wenhan Yang, Shuicheng Yan"
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VISUAL GENOME
A THESIS
SUBMITTED TO THE DEPARTMENT OF COMPUTER SCIENCE
AND THE COMMITTEE ON GRADUATE STUDIES
OF STANFORD UNIVERSITY
IN PARTIAL FULFILLMENT OF THE REQUIREMENTS
FOR THE DEGREE OF
MASTERS OF SCIENCE
Ranjay Krishna
March 2016"
20ade100a320cc761c23971d2734388bfe79f7c5,Subspace Clustering via Good Neighbors,"IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
Subspace Clustering via Good Neighbors
Jufeng Yang, Jie Liang, Kai Wang, Ming-Hsuan Yang"
202d8d93b7b747cdbd6e24e5a919640f8d16298a,Face Classification via Sparse Approximation,"Face Classification via Sparse Approximation
Elena Battini S˝onmez1, Bulent Sankur2 and Songul Albayrak3
Computer Science Department, Bilgi University, Dolapdere, Istanbul, TR
Electric and Electronic Engineering Department, Bo¯gazici University, Istanbul, TR
Computer Engineering Department, Yıldız Teknik University, Istanbul, TR"
205b34b6035aa7b23d89f1aed2850b1d3780de35,Log-domain polynomial filters for illumination-robust face recognition,"014 IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP)
978-1-4799-2893-4/14/$31.00 ©2014 IEEE
Shenzhen Key Lab. of Information Sci&Tech,
♯Nagaoka University of Technology, Japan
RECOGNITION
. INTRODUCTION"
2059d2fecfa61ddc648be61c0cbc9bc1ad8a9f5b,Co-Localization of Audio Sources in Images Using Binaural Features and Locally-Linear Regression,"TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, VOL. 23, NO. 4, APRIL 2015
Co-Localization of Audio Sources in Images Using
Binaural Features and Locally-Linear Regression
Antoine Deleforge∗ Radu Horaud∗ Yoav Y. Schechner‡ Laurent Girin∗†
INRIA Grenoble Rhˆone-Alpes, Montbonnot Saint-Martin, France
Univ. Grenoble Alpes, GIPSA-Lab, France
Dept. Electrical Eng., Technion-Israel Inst. of Technology, Haifa, Israel"
20111924fbf616a13d37823cd8712a9c6b458cd6,Linear Regression Line based Partial Face Recognition,"International Journal of Computer Applications (0975 – 8887)
Volume 130 – No.11, November2015
Linear Regression Line based Partial Face Recognition
Naveena M.
Department of Studies in
Computer Science,
Manasagagothri,
Mysore.
G. Hemantha Kumar
Department of Studies in
Computer Science,
Manasagagothri,
Mysore.
P. Nagabhushan
Department of Studies in
Computer Science,
Manasagagothri,
Mysore.
images.  In"
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George Trigeorgis, Konstantinos Bousmalis, Student Member, IEEE, Stefanos Zafeiriou, Member, IEEE
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SPECIAL ISSUE
Facial expression recognition and tracking for intelligent human-robot
interaction
Y. Yang · S. S. Ge · T. H. Lee · C. Wang
Received: 27 June 2007 / Accepted: 6 December 2007 / Published online: 23 January 2008
© Springer-Verlag 2008"
20a0b23741824a17c577376fdd0cf40101af5880,Learning to Track for Spatio-Temporal Action Localization,"Learning to track for spatio-temporal action localization
Philippe Weinzaepfela
Zaid Harchaouia,b
NYU
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Cordelia Schmida"
18c72175ddbb7d5956d180b65a96005c100f6014,From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose,"IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 23, NO. 6,
JUNE 2001
From Few to Many: Illumination Cone
Models for Face Recognition under
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Athinodoros S. Georghiades, Student Member, IEEE, Peter N. Belhumeur, Member, IEEE, and
David J. Kriegman, Senior Member, IEEE"
18636347b8741d321980e8f91a44ee054b051574,Facial marks: Soft biometric for face recognition,"978-1-4244-5654-3/09/$26.00 ©2009 IEEE
ICIP 2009"
181045164df86c72923906aed93d7f2f987bce6c,Rheinisch-westfälische Technische Hochschule Aachen,"RHEINISCH-WESTFÄLISCHE TECHNISCHE
HOCHSCHULE AACHEN
KNOWLEDGE-BASED SYSTEMS GROUP
PROF. GERHARD LAKEMEYER, PH. D.
Detection and Recognition of Human
Faces using Random Forests for a
Mobile Robot
MASTER OF SCIENCE THESIS
VAISHAK BELLE
MATRICULATION NUMBER: 26 86 51
SUPERVISOR:
SECOND SUPERVISOR:
PROF. GERHARD LAKEMEYER, PH. D.
PROF. ENRICO BLANZIERI, PH. D.
ADVISERS:
STEFAN SCHIFFER, THOMAS DESELAERS"
18d5b0d421332c9321920b07e0e8ac4a240e5f1f,Collaborative Representation Classification Ensemble for Face Recognition,"Collaborative Representation Classification
Ensemble for Face Recognition
Xiao Chao Qu, Suah Kim, Run Cui and Hyoung Joong Kim"
18d51a366ce2b2068e061721f43cb798177b4bb7,Looking into your eyes: observed pupil size influences approach-avoidance responses.,"Cognition and Emotion
ISSN: 0269-9931 (Print) 1464-0600 (Online) Journal homepage: http://www.tandfonline.com/loi/pcem20
Looking into your eyes: observed pupil size
influences approach-avoidance responses
Marco Brambilla, Marco Biella & Mariska E. Kret
To cite this article: Marco Brambilla, Marco Biella & Mariska E. Kret (2018): Looking into your
eyes: observed pupil size influences approach-avoidance responses, Cognition and Emotion, DOI:
0.1080/02699931.2018.1472554
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1885acea0d24e7b953485f78ec57b2f04e946eaf,Combining Local and Global Features for 3D Face Tracking,"Combining Local and Global Features for 3D Face Tracking
Pengfei Xiong, Guoqing Li, Yuhang Sun
Megvii (face++) Research
{xiongpengfei, liguoqing,"
18a849b1f336e3c3b7c0ee311c9ccde582d7214f,"Efficiently Scaling up Crowdsourced Video Annotation A Set of Best Practices for High Quality, Economical Video Labeling","Int J Comput Vis
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Efficiently Scaling up Crowdsourced Video Annotation
A Set of Best Practices for High Quality, Economical Video Labeling
Carl Vondrick · Donald Patterson · Deva Ramanan
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© Springer Science+Business Media, LLC 2012"
18cd79f3c93b74d856bff6da92bfc87be1109f80,A N a Pplication to H Uman F Ace P Hoto - S Ketch S Ynthesis and R Ecognition,"International Journal of Advances in Engineering & Technology, May 2012.
©IJAET                                                                                                          ISSN: 2231-1963
AN APPLICATION TO HUMAN FACE PHOTO-SKETCH
SYNTHESIS AND RECOGNITION
Amit R. Sharma and 2Prakash. R. Devale
Student and 2Professor & Head,
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1886b6d9c303135c5fbdc33e5f401e7fc4da6da4,Knowledge Guided Disambiguation for Large-Scale Scene Classification With Multi-Resolution CNNs,"Knowledge Guided Disambiguation for Large-Scale
Scene Classification with Multi-Resolution CNNs
Limin Wang, Sheng Guo, Weilin Huang, Member, IEEE, Yuanjun Xiong, and Yu Qiao, Senior Member, IEEE"
1888bf50fd140767352158c0ad5748b501563833,A Guided Tour of Face Processing,"PA R T 1
THE BASICS"
185360fe1d024a3313042805ee201a75eac50131,Person De-Identification in Videos,"Person De-Identification in Videos
Prachi Agrawal and P. J. Narayanan"
1824b1ccace464ba275ccc86619feaa89018c0ad,One millisecond face alignment with an ensemble of regression trees,"One Millisecond Face Alignment with an Ensemble of Regression Trees
Vahid Kazemi and Josephine Sullivan
KTH, Royal Institute of Technology
Computer Vision and Active Perception Lab
Teknikringen 14, Stockholm, Sweden"
27a0a7837f9114143717fc63294a6500565294c2,Face Recognition in Unconstrained Environments: A Comparative Study,"Face Recognition in Unconstrained Environments: A
Comparative Study
Rodrigo Verschae, Javier Ruiz-Del-Solar, Mauricio Correa
To cite this version:
Rodrigo Verschae, Javier Ruiz-Del-Solar, Mauricio Correa. Face Recognition in Unconstrained
Environments: A Comparative Study: . Workshop on Faces in ’Real-Life’ Images: Detection,
Alignment, and Recognition, Oct 2008, Marseille, France. 2008. <inria-00326730>
HAL Id: inria-00326730
https://hal.inria.fr/inria-00326730
Submitted on 5 Oct 2008
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
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destin´ee au d´epˆot et `a la diffusion de documents
scientifiques de niveau recherche, publi´es ou non,
´emanant des ´etablissements d’enseignement et de"
276dbb667a66c23545534caa80be483222db7769,An Introduction to Image-based 3D Surface Reconstruction and a Survey of Photometric Stereo Methods,"D Res. 2, 03(2011)4
0.1007/3DRes.03(2011)4
DR REVIEW                                                            w
An  Introduction  to  Image-based  3D  Surface  Reconstruction  and  a
Survey of Photometric Stereo Methods
Steffen Herbort • Christian Wöhler
introduction
image-based  3D
techniques.  Then  we  describe
Received: 21Feburary 2011 / Revised: 20 March 2011 / Accepted: 11 May 2011
© 3D Research Center, Kwangwoon University and Springer 2011"
270733d986a1eb72efda847b4b55bc6ba9686df4,Recognizing Facial Expressions Using Model-Based Image Interpretation,"We are IntechOpen,
the first native scientific
publisher of Open Access books
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27169761aeab311a428a9dd964c7e34950a62a6b,Face Recognition Using 3D Head Scan Data Based on Procrustes Distance,"International Journal of the Physical Sciences Vol. 5(13), pp. 2020 -2029, 18 October, 2010
Available online at http://www.academicjournals.org/IJPS
ISSN 1992 - 1950 ©2010 Academic Journals
Full Length Research Paper
Face recognition using 3D head scan data based on
Ahmed Mostayed1, Sikyung Kim1, Mohammad Mynuddin Gani Mazumder1* and Se Jin Park2
Procrustes distance
Department of Electrical Engineering, Kongju National University, South Korea.
Korean Research Institute of Standards and Science (KRISS), Korea.
Accepted 6 July, 2010
Recently,  face  recognition  has  attracted  significant  attention  from  the  researchers  and  scientists  in
various  fields  of  research,  such  as  biomedical  informatics,  pattern  recognition,  vision,  etc  due  its
pplications in commercially available systems, defense and security purpose. In this paper a practical
method  for  face  reorganization  utilizing  head  cross  section  data  based  on  Procrustes  analysis  is
proposed. This proposed method relies on shape signatures of the contours extracted from face data.
The shape signatures are created by calculating the centroid distance of the boundary points, which is
translation  and  rotation  invariant  signature.  The  shape  signatures  for  a  selected  region  of  interest
(ROI)  are  used  as  feature  vectors  and  authentication  is  done  using  them.  After  extracting  feature
vectors  a  comparison  analysis  is  performed  utilizing  Procrustes  distance  to  differentiate  their  face
pattern from each other. The proposed scheme attains an equal error rate (EER) of 4.563% for the 400"
27173d0b9bb5ce3a75d05e4dbd8f063375f24bb5,Effect of Different Occlusion on Facial Expressions Recognition,"Ankita Vyas  Int. Journal of Engineering Research and Applications                               www.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 10( Part - 3), October 2014, pp.40-44
RESEARCH ARTICLE
OPEN ACCESS
Effect of Different Occlusion on Facial Expressions Recognition
Ankita Vyas*, Ramchand Hablani**
*(Department of Computer Science, RGPV University, Indore)
** (Department of Computer Science, RGPV University, Indore)"
2770b095613d4395045942dc60e6c560e882f887,GridFace: Face Rectification via Learning Local Homography Transformations,"GridFace: Face Rectification via Learning Local
Homography Transformations
Erjin Zhou, Zhimin Cao, and Jian Sun
Face++, Megvii Inc."
27cccf992f54966feb2ab4831fab628334c742d8,"Facial Expression Recognition by Statistical, Spatial Features and using Decision Tree","International Journal of Computer Applications (0975 – 8887)
Volume 64– No.18, February 2013
Facial Expression Recognition by Statistical, Spatial
Features and using Decision Tree
Nazil Perveen
Assistant Professor
CSIT Department
GGV BIlaspur, Chhattisgarh
India
Darshan Kumar
Assistant Professor
Electronics (ECE) Department
JECRC Jaipur, Rajasthan India
IshanBhardwaj
Student of Ph.D.
Electrical Department
NIT Raipur, Chhattisgarh India"
27f8b01e628f20ebfcb58d14ea40573d351bbaad,Events based Multimedia Indexing and Retrieval,"DEPARTMENT OF INFORMATION ENGINEERING AND COMPUTER SCIENCE
ICT International Doctoral School
Events based Multimedia Indexing
nd Retrieval
Kashif Ahmad
SUBMITTED TO THE DEPARTMENT OF
INFORMATION ENGINEERING AND COMPUTER SCIENCE (DISI)
IN THE PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE
DOCTOR OF PHILOSOPHY
Advisor:
Examiners: Prof. Marco Carli, Universit`a degli Studi di Roma Tre, Italy
Prof. Nicola Conci, Universit`a degli Studi di Trento, Italy
Prof. Pietro Zanuttigh, Universit`a degli Studi di Padova, Italy
Prof. Giulia Boato, Universit`a degli Studi di Trento, Italy
December 2017"
27b1670e1b91ab983b7b1ecfe9eb5e6ba951e0ba,Comparison between k-nn and svm method for speech emotion recognition,"Comparison between k-nn and svm method
for speech emotion recognition
Muzaffar Khan, Tirupati Goskula, Mohmmed Nasiruddin ,Ruhina Quazi
Anjuman College of Engineering & Technology ,Sadar, Nagpur, India"
27ee8482c376ef282d5eb2e673ab042f5ded99d7,Scale Normalization for the Distance Maps AAM,"Scale Normalization for the Distance Maps AAM.
Denis GIRI, Maxime ROSENWALD, Benjamin VILLENEUVE, Sylvain LE GALLOU and Renaud S ´EGUIER
Email: {denis.giri, maxime.rosenwald, benjamin.villeneuve, sylvain.legallou,
Avenue de la boulaie, BP 81127,
5 511 Cesson-S´evign´e, France
Sup´elec, IETR-SCEE Team"
4b4106614c1d553365bad75d7866bff0de6056ed,Unconstrained Facial Images: Database for Face Recognition Under Real-World Conditions,"Unconstrained Facial Images: Database for Face
Recognition under Real-world Conditions⋆
Ladislav Lenc1,2 and Pavel Kr´al1,2
Dept. of Computer Science & Engineering
University of West Bohemia
Plzeˇn, Czech Republic
NTIS - New Technologies for the Information Society
University of West Bohemia
Plzeˇn, Czech Republic"
4b89cf7197922ee9418ae93896586c990e0d2867,Unsupervised Discovery of Action Classes,"LATEX Author Guidelines for CVPR Proceedings
First Author
Institution1
Institution1 address"
4b04247c7f22410681b6aab053d9655cf7f3f888,Robust Face Recognition by Constrained Part-based Alignment,"Robust Face Recognition by Constrained Part-based
Alignment
Yuting Zhang, Kui Jia, Yueming Wang, Gang Pan, Tsung-Han Chan, Yi Ma"
4b60e45b6803e2e155f25a2270a28be9f8bec130,Attribute based object identification,"Attribute Based Object Identification
Yuyin Sun, Liefeng Bo and Dieter Fox"
4b48e912a17c79ac95d6a60afed8238c9ab9e553,Minimum Margin Loss for Deep Face Recognition,"JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015
Minimum Margin Loss for Deep Face Recognition
Xin Wei, Student Member, IEEE, Hui Wang, Member, IEEE, Bryan Scotney, and Huan Wan"
4b5eeea5dd8bd69331bd4bd4c66098b125888dea,Human Activity Recognition Using Conditional Random Fields and Privileged Information,"Human Activity Recognition Using Conditional
Random Fields and Privileged Information
DOCTORAL THESIS
submitted to
the designated by the General Assembly Composition of the
Department of Computer Science & Engineering Inquiry
Committee
Michalis Vrigkas
in partial fulfillment of the Requirements for the Degree of
DOCTOR OF PHILOSOPHY
February 2016"
4be03fd3a76b07125cd39777a6875ee59d9889bd,Content-based analysis for accessing audiovisual archives: Alternatives for concept-based indexing and search,"CONTENT-BASED ANALYSIS FOR ACCESSING AUDIOVISUAL ARCHIVES:
ALTERNATIVES FOR CONCEPT-BASED INDEXING AND SEARCH
Tinne Tuytelaars
ESAT/PSI - IBBT
KU Leuven, Belgium"
11f7f939b6fcce51bdd8f3e5ecbcf5b59a0108f5,Rolling Riemannian Manifolds to Solve the Multi-class Classification Problem,"Rolling Riemannian Manifolds to Solve the Multi-class Classification Problem
Rui Caseiro1, Pedro Martins1, João F. Henriques1, Fátima Silva Leite1,2, and Jorge Batista1
Institute of Systems and Robotics - University of Coimbra, Portugal
Department of Mathematics - University of Coimbra, Portugal ,
{ruicaseiro, pedromartins, henriques,"
11691f1e7c9dbcbd6dfd256ba7ac710581552baa,SoccerNet: A Scalable Dataset for Action Spotting in Soccer Videos,"SoccerNet: A Scalable Dataset for Action Spotting in Soccer Videos
Silvio Giancola, Mohieddine Amine, Tarek Dghaily, Bernard Ghanem
King Abdullah University of Science and Technology (KAUST), Saudi Arabia"
1149c6ac37ae2310fe6be1feb6e7e18336552d95,"Classification of Face Images for Gender, Age, Facial Expression, and Identity","Proc. Int. Conf. on Artificial Neural Networks (ICANN’05), Warsaw, LNCS 3696, vol. I, pp. 569-574, Springer Verlag 2005
Classification of Face Images for Gender, Age,
Facial Expression, and Identity1
Torsten Wilhelm, Hans-Joachim B¨ohme, and Horst-Michael Gross
Department of Neuroinformatics and Cognitive Robotics
Ilmenau Technical University, P.O.Box 100565, 98684 Ilmenau, Germany"
11f17191bf74c80ad0b16b9f404df6d03f7c8814,Recognition of Visually Perceived Compositional Human Actions by Multiple Spatio-Temporal Scales Recurrent Neural Networks,"Recognition of Visually Perceived Compositional
Human Actions by Multiple Spatio-Temporal Scales
Recurrent Neural Networks
Haanvid Lee, Minju Jung, and Jun Tani"
1198572784788a6d2c44c149886d4e42858d49e4,Learning Discriminative Features using Encoder-Decoder type Deep Neural Nets,"Learning Discriminative Features using Encoder/Decoder type Deep
Neural Nets
Vishwajeet Singh1, Killamsetti Ravi Kumar2, K Eswaran3
ALPES, Bolarum, Hyderabad 500010,
ALPES, Bolarum, Hyderabad 500010,
SNIST, Ghatkesar, Hyderabad 501301,"
11fe6d45aa2b33c2ec10d9786a71c15ec4d3dca8,Tied Factor Analysis for Face Recognition across Large Pose Differences,"JUNE 2008
Tied Factor Analysis for Face Recognition
cross Large Pose Differences
Simon J.D. Prince, Member, IEEE, James H. Elder, Member, IEEE,
Jonathan Warrell, Member, IEEE, and Fatima M. Felisberti"
1134a6be0f469ff2c8caab266bbdacf482f32179,Facial Expression Identification Using Four-bit Co- Occurrence Matrixfeatures and K-nn Classifier,"IJRET: International Journal of Research in Engineering and Technology        eISSN: 2319-1163 | pISSN: 2321-7308
FACIAL EXPRESSION IDENTIFICATION USING FOUR-BIT CO-
OCCURRENCE MATRIXFEATURES AND K-NN CLASSIFIER
Bonagiri  C S K Sunil Kumar1, V Bala Shankar2, Pullela S V V S R Kumar3
,2,3 Department of Computer Science & Engineering, Aditya College of Engineering, Surampalem, East Godavari
District, Andhra Pradesh, India"
111a9645ad0108ad472b2f3b243ed3d942e7ff16,Facial Expression Classification Using Combined Neural Networks,"Facial Expression Classification Using
Combined Neural Networks
Rafael V. Santos, Marley M.B.R. Vellasco, Raul Q. Feitosa, Ricardo Tanscheit
DEE/PUC-Rio, Marquês de São Vicente 225, Rio de Janeiro – RJ - Brazil"
111d0b588f3abbbea85d50a28c0506f74161e091,Facial Expression Recognition from Visual Information using Curvelet Transform,"International Journal of Computer Applications (0975 – 8887)
Volume 134 – No.10, January 2016
Facial Expression Recognition from Visual Information
using Curvelet Transform
Pratiksha Singh
Surabhi Group of Institution Bhopal
systems.  Further  applications"
7d98dcd15e28bcc57c9c59b7401fa4a5fdaa632b,Face Appearance Factorization for Expression Analysis and Synthesis,"FACE APPEARANCE FACTORIZATION FOR EXPRESSION ANALYSIS AND SYNTHESIS
Bouchra Abboud, Franck Davoine
Heudiasyc Laboratory, CNRS, University of Technology of Compi`egne.
BP 20529, 60205 COMPIEGNE Cedex, FRANCE.
E-mail:"
7de6e81d775e9cd7becbfd1bd685f4e2a5eebb22,Labeled Faces in the Wild: A Survey,"Labeled Faces in the Wild: A Survey
Erik Learned-Miller, Gary Huang, Aruni RoyChowdhury, Haoxiang Li, Gang Hua"
7d73adcee255469aadc5e926066f71c93f51a1a5,Face alignment by deep convolutional network with adaptive learning rate,"978-1-4799-9988-0/16/$31.00 ©2016 IEEE
ICASSP 2016"
7d9fe410f24142d2057695ee1d6015fb1d347d4a,Facial Expression Feature Extraction Based on FastLBP,"Facial Expression Feature Extraction Based on
FastLBP
Computer and Information Engineering Department of Beijing Technology and Business University, Beijing, China
Ya Zheng
Email:
Computer and Information Engineering Department of Beijing Technology and Business University, Beijing, China
Email:
Xiuxin Chen, Chongchong Yu and Cheng Gao
facial  expression"
7dffe7498c67e9451db2d04bb8408f376ae86992,LEAR-INRIA submission for the THUMOS workshop,"LEAR-INRIA submission for the THUMOS workshop
Heng Wang and Cordelia Schmid
LEAR, INRIA, France"
7d3f6dd220bec883a44596ddec9b1f0ed4f6aca2,Linear Regression for Face Recognition,"Linear Regression for Face Recognition
Imran Naseem,
Roberto Togneri, Senior Member, IEEE, and
Mohammed Bennamoun"
29ce6b54a87432dc8371f3761a9568eb3c5593b0,Age Sensitivity of Face Recognition Algorithms,"Kent Academic Repository
Full text document (pdf)
Citation for published version
Yassin, DK H. PHM and Hoque, Sanaul and Deravi, Farzin  (2013) Age Sensitivity of Face Recognition
pp. 12-15.
https://doi.org/10.1109/EST.2013.8
Link to record in KAR
http://kar.kent.ac.uk/43222/
Document Version
Author's Accepted Manuscript
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ontent is protected by copyright and in the absence of an open licence (eg Creative Commons), permissions
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For any further enquiries regarding the licence status of this document, please contact:"
292eba47ef77495d2613373642b8372d03f7062b,Deep Secure Encoding: An Application to Face Recognition,"Deep Secure Encoding: An Application to Face Recognition
Rohit Pandey
Yingbo Zhou
Venu Govindaraju"
29e96ec163cb12cd5bd33bdf3d32181c136abaf9,Regularized Locality Preserving Projections with Two-Dimensional Discretized Laplacian Smoothing,"Report No. UIUCDCS-R-2006-2748
UILU-ENG-2006-1788
Regularized Locality Preserving Projections with Two-Dimensional
Discretized Laplacian Smoothing
Deng Cai, Xiaofei He, and Jiawei Han
July 2006"
29e793271370c1f9f5ac03d7b1e70d1efa10577c,Face Recognition Based on Multi-classifierWeighted Optimization and Sparse Representation,"International Journal of Signal Processing, Image Processing and Pattern Recognition
Vol.6, No.5 (2013), pp.423-436
http://dx.doi.org/10.14257/ijsip.2013.6.5.37
Face Recognition Based on Multi-classifierWeighted Optimization
nd Sparse Representation
Deng Nan1, Zhengguang Xu2 and ShengQin Bian3
,2,3Institute of control science and engineering,
University of Science and Technology Beijing
,2,330 Xueyuan Road, Haidian District, Beijing 100083 P. R.China"
29c7dfbbba7a74e9aafb6a6919629b0a7f576530,Automatic Facial Expression Analysis and Emotional Classification,"Automatic Facial Expression Analysis and Emotional
Classification
Robert Fischer
Submitted to the Department of Math and Natural Sciences
in partial fulfillment of the requirements for the degree of a
Diplomingenieur der Optotechnik und Bildverarbeitung (FH)
(Diplom Engineer of Photonics and Image Processing)
t the
UNIVERSITY OF APPLIED SCIENCE DARMSTADT (FHD)
Accomplished and written at the
MASSACHUSETTS INSTITUTE OF TECHNOLOGY (MIT)
October 2004
Author . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Department of Math and Natural Sciences
October 30, 2004
Certified by . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Dr. Harald Scharfenberg
Professor at FHD
Thesis Supervisor
Accepted by . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ."
292c6b743ff50757b8230395c4a001f210283a34,Fast violence detection in video,"Fast Violence Detection in Video
O. Deniz1, I. Serrano1, G. Bueno1 and T-K. Kim2
VISILAB group, University of Castilla-La Mancha, E.T.S.I.Industriales, Avda. Camilo Jose Cela s.n, 13071 Spain
Department of Electrical and Electronic Engineering, Imperial College, South Kensington Campus, London SW7 2AZ, UK.
{oscar.deniz, ismael.serrano,
Keywords:
ction recognition, violence detection, fight detection"
294d1fa4e1315e1cf7cc50be2370d24cc6363a41,A modular non-negative matrix factorization for parts-based object recognition using subspace representation,"008 SPIE Digital Library -- Subscriber Archive Copy
Processing: Machine Vision Applications, edited by Kurt S. Niel, David Fofi, Proc. of SPIE-IS&T Electronic Imaging, SPIE Vol. 6813, 68130C, © 2008 SPIE-IS&T · 0277-786X/08/$18SPIE-IS&T/ Vol. 6813  68130C-1"
29d414bfde0dfb1478b2bdf67617597dd2d57fc6,Perfect histogram matching PCA for face recognition,"Multidim Syst Sign Process (2010) 21:213–229
DOI 10.1007/s11045-009-0099-y
Perfect histogram matching PCA for face recognition
Ana-Maria Sevcenco · Wu-Sheng Lu
Received: 10 August 2009 / Revised: 21 November 2009 / Accepted: 29 December 2009 /
Published online: 14 January 2010
© Springer Science+Business Media, LLC 2010"
290136947fd44879d914085ee51d8a4f433765fa,On a taxonomy of facial features,"On a Taxonomy of Facial Features
Brendan Klare and Anil K. Jain"
2957715e96a18dbb5ed5c36b92050ec375214aa6,InclusiveFaceNet: Improving Face Attribute Detection with Race and Gender Diversity,"Improving Face Attribute Detection with Race and Gender Diversity
InclusiveFaceNet:
Hee Jung Ryu 1 Hartwig Adam * 1 Margaret Mitchell * 1"
2965d092ed72822432c547830fa557794ae7e27b,Improving Representation and Classification of Image and Video Data for Surveillance Applications,"Improving Representation and Classification of Image and
Video Data for Surveillance Applications
Andres Sanin
BSc(Biol), MSc(Biol), MSc(CompSc)
A thesis submitted for the degree of Doctor of Philosophy at
The University of Queensland in 2012
School of Information Technology and Electrical Engineering"
2921719b57544cfe5d0a1614d5ae81710ba804fa,Face Recognition Enhancement Based on Image File Formats and Wavelet De - noising,"Face Recognition Enhancement Based on Image
File Formats and Wavelet De-noising
Isra’a Abdul-Ameer Abdul-Jabbar, Jieqing Tan, and Zhengfeng Hou"
29a013b2faace976f2c532533bd6ab4178ccd348,Hierarchical Manifold Learning With Applications to Supervised Classification for High-Resolution Remotely Sensed Images,"This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination.
Hierarchical Manifold Learning With Applications
to Supervised Classification for High-Resolution
Remotely Sensed Images
Hong-Bing Huang, Hong Huo, and Tao Fang"
29756b6b16d7b06ea211f21cdaeacad94533e8b4,Thresholding Approach based on GPU for Facial Expression Recognition,"Thresholding Approach based on GPU for Facial
Expression Recognition
Jesús García-Ramírez1, J. Arturo Olvera-López1, Ivan Olmos-Pineda1, Georgina
Flores-Becerra2, Adolfo Aguilar-Rico2
Benemérita Universidad Autónoma de Puebla, Faculty of Computer Science, Puebla, México
Instituto Tecnológico de Puebla, Puebla, México"
293193d24d5c4d2975e836034bbb2329b71c4fe7,Building a Corpus of Facial Expressions for Learning-Centered Emotions,"Building a Corpus of Facial Expressions
for Learning-Centered Emotions
María Lucía Barrón-Estrada, Ramón Zatarain-Cabada,
Bianca Giovanna Aispuro-Medina, Elvia Minerva Valencia-Rodríguez,
Ana Cecilia Lara-Barrera
Instituto Tecnológico de Culiacán, Culiacán, Sinaloa,
Mexico
{lbarron, rzatarain, m06170904, m95170906, m15171452}"
294bd7eb5dc24052237669cdd7b4675144e22306,Automatic Face Annotation,"International Journal of Science and Research (IJSR)
ISSN (Online): 2319-7064
Index Copernicus Value (2013): 6.14 | Impact Factor (2013): 4.438
Automatic Face Annotation
Ashna Shajahan
M.Tech Student, Dept. of Computer Science & Engineering, Mount Zion College of Engineering, Pathanamthitta, Kerala, India"
2988f24908e912259d7a34c84b0edaf7ea50e2b3,A Model of Brightness Variations Due to Illumination Changes and Non-rigid Motion Using Spherical Harmonics,"A Model of Brightness Variations Due to
Illumination Changes and Non-rigid Motion
Using Spherical Harmonics
Jos´e M. Buenaposada
Alessio Del Bue
Dep. Ciencias de la Computaci´on,
U. Rey Juan Carlos, Spain
http://www.dia.fi.upm.es/~pcr
Inst. for Systems and Robotics
Inst. Superior T´ecnico, Portugal
http://www.isr.ist.utl.pt/~adb
Enrique Mu˜noz
Facultad de Inform´atica,
U. Complutense de Madrid, Spain
Luis Baumela
Dep. de Inteligencia Artificial,
U. Polit´ecnica de Madrid, Spain
http://www.dia.fi.upm.es/~pcr
http://www.dia.fi.upm.es/~pcr"
7cee802e083c5e1731ee50e731f23c9b12da7d36,2^B3^C: 2 Box 3 Crop of Facial Image for Gender Classification with Convolutional Networks,"B3C: 2 Box 3 Crop of Facial Image for Gender Classification with Convolutional
Networks
Vandit Gajjar
Department of Electronics and Communication Engineering and
Computer Vision Group, L. D. College of Engineering, Ahmedabad, India"
7c47da191f935811f269f9ba3c59556c48282e80,Robust eye centers localization with zero-crossing encoded image projections,"Robust Eye Centers Localization
with Zero–Crossing Encoded Image Projections
Laura Florea
Image Processing and Analysis Laboratory
University ”Politehnica” of Bucharest, Romania, Address Splaiul Independent¸ei 313
Corneliu Florea
Image Processing and Analysis Laboratory
University ”Politehnica” of Bucharest, Romania, Address Splaiul Independent¸ei 313
Constantin Vertan
Image Processing and Analysis Laboratory
University ”Politehnica” of Bucharest, Romania, Address Splaiul Independent¸ei 313"
7c45b5824645ba6d96beec17ca8ecfb22dfcdd7f,News Image Annotation on a Large Parallel Text-image Corpus,"News image annotation on a large parallel text-image corpus
Pierre Tirilly, Vincent Claveau, Patrick Gros
Universit´e de Rennes 1/IRISA, CNRS/IRISA, INRIA Rennes-Bretagne Atlantique
Campus de Beaulieu
5042 Rennes Cedex, France"
7c0a6824b556696ad7bdc6623d742687655852db,MPCA+MDA: A novel approach for face recognition based on tensor objects,"8th Telecommunications forum TELFOR 2010
Serbia, Belgrade, November 23-25, 2010.
MPCA+DATER: A Novel Approach for Face
Recognition Based on Tensor Objects
Ali. A. Shams Baboli, Member, IEEE, G. Rezai-rad, Member, IEEE, Aref. Shams Baboli"
7c95449a5712aac7e8c9a66d131f83a038bb7caa,This is an author produced version of Facial first impressions from another angle: How social judgements are influenced by changeable and invariant facial properties. White Rose Research Online URL for this paper: http://eprints.whiterose.ac.uk/102935/,"This is an author produced version of Facial first impressions from another angle: How
social judgements are influenced by changeable and invariant facial properties.
White Rose Research Online URL for this paper:
http://eprints.whiterose.ac.uk/102935/
Article:
Sutherland, Clare, Young, Andrew William orcid.org/0000-0002-1202-6297 and Gillian,
Rhodes (2017) Facial first impressions from another angle: How social judgements are
influenced by changeable and invariant facial properties. British journal of psychology. pp.
97-415. ISSN 0007-1269
https://doi.org/10.1111/bjop.12206
promoting access to
White Rose research papers
http://eprints.whiterose.ac.uk/"
7c3e09e0bd992d3f4670ffacb4ec3a911141c51f,Transferring Object-Scene Convolutional Neural Networks for Event Recognition in Still Images,"Noname manuscript No.
(will be inserted by the editor)
Transferring Object-Scene Convolutional Neural Networks for
Event Recognition in Still Images
Limin Wang · Zhe Wang · Yu Qiao · Luc Van Gool
Received: date / Accepted: date"
7c7b0550ec41e97fcfc635feffe2e53624471c59,"Head, Eye, and Hand Patterns for Driver Activity Recognition","051-4651/14 $31.00 © 2014 IEEE
DOI 10.1109/ICPR.2014.124"
7c119e6bdada2882baca232da76c35ae9b5277f8,Facial expression recognition using embedded Hidden Markov Model,"Facial Expression Recognition Using Embedded
Hidden Markov Model
Languang He, Xuan Wang, Member, IEEE, Chenglong Yu, Member, IEEE, Kun Wu
Intelligence Computing Research Center
HIT Shenzhen Graduate School
Shenzhen, China
{telent, wangxuan, ycl, wukun}"
7c9a65f18f7feb473e993077d087d4806578214e,SpringerLink - Zeitschriftenbeitrag,"SpringerLink - Zeitschriftenbeitrag
http://www.springerlink.com/content/93hr862660nl1164/?p=abe5352...
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7c1e1c767f7911a390d49bed4f73952df8445936,Non-Rigid Object Detection with LocalInterleaved Sequential Alignment (LISA),"NON-RIGID OBJECT DETECTION WITH LOCAL INTERLEAVED SEQUENTIAL ALIGNMENT (LISA)
Non-Rigid Object Detection with Local
Interleaved Sequential Alignment (LISA)
Karel Zimmermann, Member, IEEE,, David Hurych, Member, IEEE,
nd Tom´aˇs Svoboda, Member, IEEE"
7cf579088e0456d04b531da385002825ca6314e2,Emotion Detection on TV Show Transcripts with Sequence-based Convolutional Neural Networks,"Emotion Detection on TV Show Transcripts with
Sequence-based Convolutional Neural Networks
Sayyed M. Zahiri
Jinho D. Choi
Mathematics and Computer Science
Mathematics and Computer Science
Emory University
Atlanta, GA 30322, USA
Emory University
Atlanta, GA 30322, USA"
7c349932a3d083466da58ab1674129600b12b81c,Leveraging Multiple Features for Image Retrieval and Matching,
162403e189d1b8463952fa4f18a291241275c354,Action Recognition with Spatio-Temporal Visual Attention on Skeleton Image Sequences,"Action Recognition with Spatio-Temporal
Visual Attention on Skeleton Image Sequences
Zhengyuan Yang, Student Member, IEEE, Yuncheng Li, Jianchao Yang, Member, IEEE,
nd Jiebo Luo, Fellow, IEEE
With a strong ability of modeling sequential data, Recur-
rent Neural Networks (RNN) with Long Short-Term Memory
(LSTM) neurons outperform the previous hand-crafted feature
ased methods [9], [10]. Each skeleton frame is converted into
feature vector and the whole sequence is fed into the RNN.
Despite the strong ability in modeling temporal sequences,
RNN structures lack the ability to efficiently learn the spatial
relations between the joints. To better use spatial information,
hierarchical structure is proposed in [11], [12] that feeds
the joints into the network as several pre-defined body part
groups. However,
limit
the effectiveness of representing spatial relations. A spatio-
temporal 2D LSTM (ST-LSTM) network [13] is proposed
to learn the spatial and temporal relations simultaneously.
Furthermore, a two-stream RNN structure [14] is proposed to"
160259f98a6ec4ec3e3557de5e6ac5fa7f2e7f2b,Discriminant multi-label manifold embedding for facial Action Unit detection,"Discriminant Multi-Label Manifold Embedding for Facial Action Unit
Detection
Signal Procesing Laboratory (LTS5), ´Ecole Polytechnique F´ed´erale de Lausanne, Switzerland
Anıl Y¨uce, Hua Gao and Jean-Philippe Thiran"
16671b2dc89367ce4ed2a9c241246a0cec9ec10e,Detecting the Number of Clusters in n-Way Probabilistic Clustering,"Detecting the Number of Clusters
in n-Way Probabilistic Clustering
Zhaoshui He, Andrzej Cichocki, Senior Member, IEEE,
Shengli Xie, Senior Member, IEEE, and Kyuwan Choi"
16395b40e19cbc6d5b82543039ffff2a06363845,Action Recognition in Video Using Sparse Coding and Relative Features,"Action Recognition in Video Using Sparse Coding and Relative Features
Anal´ı Alfaro
Domingo Mery
Alvaro Soto
P. Universidad Catolica de Chile
P. Universidad Catolica de Chile
P. Universidad Catolica de Chile
Santiago, Chile
Santiago, Chile
Santiago, Chile"
16c884be18016cc07aec0ef7e914622a1a9fb59d,Exploiting Multimodal Data for Image Understanding,"UNIVERSITÉ DE GRENOBLE
No attribué par la bibliothèque
THÈSE
pour obtenir le grade de
DOCTEUR DE L’UNIVERSITÉ DE GRENOBLE
Spécialité : Mathématiques et Informatique
préparée au Laboratoire Jean Kuntzmann
dans le cadre de l’École Doctorale Mathématiques,
Sciences et Technologies de l’Information, Informatique
présentée et soutenue publiquement
Matthieu Guillaumin
le 27 septembre 2010
Exploiting Multimodal Data for Image Understanding
Données multimodales pour l’analyse d’image
Directeurs de thèse : Cordelia Schmid et Jakob Verbeek
M. Éric Gaussier
M. Antonio Torralba
Mme Tinne Tuytelaars Katholieke Universiteit Leuven
M. Mark Everingham University of Leeds
Mme Cordelia Schmid"
1630e839bc23811e340bdadad3c55b6723db361d,Exploiting relationship between attributes for improved face verification,"SONG, TAN, CHEN: EXPLOITING RELATIONSHIP BETWEEN ATTRIBUTES
Exploiting Relationship between Attributes for
Improved Face Verification
Fengyi Song
Xiaoyang Tan
Songcan Chen
Department of Computer Science and
Technology, Nanjing University of Aero-
nautics and Astronautics, Nanjing 210016,
P.R. China"
16286fb0f14f6a7a1acc10fcd28b3ac43f12f3eb,"All Smiles are Not Created Equal: Morphology and Timing of Smiles Perceived as Amused, Polite, and Embarrassed/Nervous.","J Nonverbal Behav
DOI 10.1007/s10919-008-0059-5
O R I G I N A L P A P E R
All Smiles are Not Created Equal: Morphology
nd Timing of Smiles Perceived as Amused, Polite,
nd Embarrassed/Nervous
Zara Ambadar Æ Jeffrey F. Cohn Æ Lawrence Ian Reed
Ó Springer Science+Business Media, LLC 2008"
166186e551b75c9b5adcc9218f0727b73f5de899,Automatic Age and Gender Recognition in Human Face Image Dataset using Convolutional Neural Network System,"Volume 4, Issue 2, February 2016
International Journal of Advance Research in
Computer Science and Management Studies
Research Article / Survey Paper / Case Study
Available online at: www.ijarcsms.com
ISSN: 2321-7782 (Online)
Automatic Age and Gender Recognition in Human Face Image
Dataset using Convolutional Neural Network System
Subhani Shaik1
Assoc. Prof & Head of the Department
Department of CSE,
Anto A. Micheal2
Associate Professor
Department of CSE,
St.Mary’s Group of Institutions Guntur
St.Mary’s Group of Institutions Guntur
Chebrolu(V&M),Guntur(Dt),
Andhra Pradesh - India
Chebrolu(V&M),Guntur(Dt),
Andhra Pradesh - India"
16d9b983796ffcd151bdb8e75fc7eb2e31230809,GazeDirector: Fully Articulated Eye Gaze Redirection in Video,"EUROGRAPHICS 2018 / D. Gutierrez and A. Sheffer
(Guest Editors)
Volume 37 (2018), Number 2
GazeDirector: Fully Articulated Eye Gaze Redirection in Video
ID: paper1004"
162c33a2ec8ece0dc96e42d5a86dc3fedcf8cd5e,Large-Scale Classification by an Approximate Least Squares One-Class Support Vector Machine Ensemble,"Mygdalis, V., Iosifidis, A., Tefas, A., & Pitas, I. (2016). Large-Scale
Classification by an Approximate Least Squares One-Class Support Vector
of a meeting held 20-22 August 2015, Helsinki, Finland (Vol. 2, pp. 6-10).
Institute of Electrical and Electronics Engineers (IEEE). DOI:
0.1109/Trustcom.2015.555
Peer reviewed version
Link to published version (if available):
0.1109/Trustcom.2015.555
Link to publication record in Explore Bristol Research
PDF-document
University of Bristol - Explore Bristol Research
General rights
This document is made available in accordance with publisher policies. Please cite only the published
version using the reference above. Full terms of use are available:
http://www.bristol.ac.uk/pure/about/ebr-terms"
161eb88031f382e6a1d630cd9a1b9c4bc6b47652,Automatic facial expression recognition using features of salient facial patches,"Automatic Facial Expression Recognition
Using Features of Salient Facial Patches
S L Happy and Aurobinda Routray"
4209783b0cab1f22341f0600eed4512155b1dee6,Accurate and Efficient Similarity Search for Large Scale Face Recognition,"Accurate and Efficient Similarity Search for Large Scale Face Recognition
Ce Qi
Zhizhong Liu
Fei Su"
42e3dac0df30d754c7c7dab9e1bb94990034a90d,PANDA: Pose Aligned Networks for Deep Attribute Modeling,"PANDA: Pose Aligned Networks for Deep Attribute Modeling
Ning Zhang1,2, Manohar Paluri1, Marc’Aurelio Ranzato1, Trevor Darrell2, Lubomir Bourdev1
EECS, UC Berkeley
{mano, ranzato,
Facebook AI Research
{nzhang,"
42cc9ea3da1277b1f19dff3d8007c6cbc0bb9830,Coordinated Local Metric Learning,"Coordinated Local Metric Learning
Shreyas Saxena
Jakob Verbeek
Inria∗"
42350e28d11e33641775bef4c7b41a2c3437e4fd,Multilinear Discriminant Analysis for Face Recognition,"Multilinear Discriminant Analysis
for Face Recognition
Shuicheng Yan, Member, IEEE, Dong Xu, Qiang Yang, Senior Member, IEEE, Lei Zhang, Member, IEEE,
Xiaoou Tang, Senior Member, IEEE, and Hong-Jiang Zhang, Fellow, IEEE"
42e155ea109eae773dadf74d713485be83fca105,Sparse reconstruction of facial expressions with localized gabor moments,
4270460b8bc5299bd6eaf821d5685c6442ea179a,"Partial Similarity of Objects, or How to Compare a Centaur to a Horse","Int J Comput Vis (2009) 84: 163–183
DOI 10.1007/s11263-008-0147-3
Partial Similarity of Objects, or How to Compare a Centaur
to a Horse
Alexander M. Bronstein · Michael M. Bronstein · Alfred
M. Bruckstein · Ron Kimmel
Received: 30 September 2007 / Accepted: 3 June 2008 / Published online: 26 July 2008
© Springer Science+Business Media, LLC 2008"
429d4848d03d2243cc6a1b03695406a6de1a7abd,"Face Recognition based on Logarithmic Fusion of SVD and KT Ramachandra A C , Raja K B , Venugopal K R , L M Patnaik","Face Recognition based on Logarithmic Fusion
International Journal of Soft Computing and Engineering (IJSCE)
ISSN: 2231-2307, Volume-2, Issue-3, July 2012
of SVD and KT
Ramachandra A C, Raja K B, Venugopal K R, L M Patnaik"
42dc36550912bc40f7faa195c60ff6ffc04e7cd6,Visible and Infrared Face Identification via Sparse Representation,"Hindawi Publishing Corporation
ISRN Machine Vision
Volume 2013, Article ID 579126, 10 pages
http://dx.doi.org/10.1155/2013/579126
Research Article
Visible and Infrared Face Identification via
Sparse Representation
Pierre Buyssens1 and Marinette Revenu2
LITIS EA 4108-QuantIF Team, University of Rouen, 22 Boulevard Gambetta, 76183 Rouen Cedex, France
GREYC UMR CNRS 6072 ENSICAEN-Image Team, University of Caen Basse-Normandie, 6 Boulevard Mar´echal Juin,
4050 Caen, France
Correspondence should be addressed to Pierre Buyssens;
Received 4 April 2013; Accepted 27 April 2013
Academic Editors: O. Ghita, D. Hernandez, Z. Hou, M. La Cascia, and J. M. Tavares
Copyright © 2013 P. Buyssens and M. Revenu. This is an open access article distributed under the Creative Commons Attribution
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly
ited.
We present a facial recognition technique based on facial sparse representation. A dictionary is learned from data, and patches
extracted from a face are decomposed in a sparse manner onto this dictionary. We particularly focus on the design of dictionaries
that play a crucial role in the final identification rates. Applied to various databases and modalities, we show that this approach"
42ecfc3221c2e1377e6ff849afb705ecd056b6ff,Pose Invariant Face Recognition Under Arbitrary Unknown Lighting Using Spherical Harmonics,"Pose Invariant Face Recognition under Arbitrary
Unknown Lighting using Spherical Harmonics
Lei Zhang and Dimitris Samaras
Department of Computer Science,
SUNY at Stony Brook, NY, 11790
{lzhang,"
421955c6d2f7a5ffafaf154a329a525e21bbd6d3,Evolutionary Pursuit and Its Application to Face Recognition,"IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 22, NO. 6,
JUNE 2000
Evolutionary Pursuit and Its
Application to Face Recognition
Chengjun Liu, Member, IEEE, and Harry Wechsler, Fellow, IEEE"
42df75080e14d32332b39ee5d91e83da8a914e34,Illumination Compensation Using Oriented Local Histogram Equalization and its Application to Face Recognition,"Illumination Compensation Using Oriented
Local Histogram Equalization and
Its Application to Face Recognition
Ping-Han Lee, Szu-Wei Wu, and Yi-Ping Hung"
89945b7cd614310ebae05b8deed0533a9998d212,Divide-and-Conquer Method for L1 Norm Matrix Factorization in the Presence of Outliers and Missing Data,"Divide-and-Conquer Method for L1 Norm Matrix
Factorization in the Presence of Outliers and
Missing Data
Deyu Meng and Zongben Xu"
89c84628b6f63554eec13830851a5d03d740261a,Image Enhancement and Automated Target Recognition Techniques for Underwater Electro-Optic Imagery,"Image Enhancement and Automated Target Recognition
Techniques for Underwater Electro-Optic Imagery
Thomas Giddings (PI), Cetin Savkli and Joseph Shirron
Metron, Inc.
1911 Freedom Dr., Suite 800
Reston, VA 20190
phone: (703) 437-2428   fax: (703) 787-3518    email:
Contract Number N00014-07-C-0351
http:www.metsci.com
LONG TERM GOALS
The long-term goal of this project is to provide a flexible, accurate and extensible automated target
recognition (ATR) system for use with a variety of imaging and non-imaging sensors.  Such an ATR
system, once it achieves a high level of performance, can relieve human operators from the tedious
usiness of pouring over vast quantities of mostly mundane data, calling the operator in only when the
omputer assessment involves an unacceptable level of  ambiguity. The ATR system will provide most
leading edge algorithms for detection, segmentation, and classification while incorporating many novel
lgorithms that we are developing at Metron.  To address one of the most critical challenges in ATR
technology, the system will also provide powerful feature extraction routines designed for specific
pplications of current interest.
OBJECTIVES"
89c51f73ec5ebd1c2a9000123deaf628acf3cdd8,Face Recognition Based on Nonlinear Feature Approach Eimad,"American Journal of Applied Sciences 5 (5): 574-580, 2008
ISSN 1546-9239
© 2008 Science Publications
Face Recognition Based on Nonlinear Feature Approach
Eimad E.A. Abusham, 1Andrew T.B. Jin, 1Wong E. Kiong and 2G. Debashis
Faculty of Information Science and Technology,
Faculty of Engineering and Technology, Multimedia University (Melaka Campus),
Jalan Ayer Keroh Lama, 75450 Bukit Beruang, Melaka, Malaysia"
89c73b1e7c9b5e126a26ed5b7caccd7cd30ab199,Application of an Improved Mean Shift Algorithm in Real-time Facial Expression Recognition,"Application of an Improved Mean Shift Algorithm
in Real-time Facial Expression Recognition
School of Computer and Communication, Hunan University of Technology, Hunan, Zhuzhou, 412008，china
School of Electrical and Information Engineering, Hunan University of Technology, Hunan, Zhuzhou, 412008，china
School of Computer and Communication, Hunan University of Technology, Hunan, Zhuzhou, 412008，china
Zhao-yi PENG
Yu ZHOU
Yan-hui  ZHU
Email:
Zhi-qiang WEN
Email:
School of Computer and Communication, Hunan University of Technology, Hunan, Zhuzhou, 412008，china
facial
real-time
expression"
893239f17dc2d17183410d8a98b0440d98fa2679,UvA-DARE ( Digital Academic Repository ) Expression-Invariant Age Estimation,"UvA-DARE (Digital Academic Repository)
Expression-Invariant Age Estimation
Alnajar, F.; Lou, Z.; Alvarez Lopez, J.M.; Gevers, T.
Published in:
Proceedings of the British Machine Vision Conference 2014
0.5244/C.28.14
Link to publication
Citation for published version (APA):
Alnajar, F., Lou, Z., Alvarez, J., & Gevers, T. (2014). Expression-Invariant Age Estimation. In M. Valstar, A.
French, & T. Pridmore (Eds.), Proceedings of the British Machine Vision Conference 2014 (pp. 14.1-14.11).
BMVA Press. DOI: 10.5244/C.28.14
General rights
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The Netherlands. You will be contacted as soon as possible.
Download date: 04 Aug 2017"
8913a5b7ed91c5f6dec95349fbc6919deee4fc75,BigBIRD: A large-scale 3D database of object instances,"BigBIRD: A Large-Scale 3D Database of Object Instances
Arjun Singh, James Sha, Karthik S. Narayan, Tudor Achim, Pieter Abbeel"
89d3a57f663976a9ac5e9cdad01267c1fc1a7e06,Neural Class-Specific Regression for face verification,"Neural Class-Specific Regression for face
verification
Guanqun Cao, Alexandros Iosifidis, Moncef Gabbouj"
89bc311df99ad0127383a9149d1684dfd8a5aa34,Towards ontology driven learning of visual concept detectors,"Towards ontology driven learning of
visual concept detectors
Sanchit ARORA, Chuck CHO, Paul FITZPATRICK, Franc¸ois SCHARFFE 1
Dextro Robotics, Inc. 101 Avenue of the Americas, New York, USA"
898a66979c7e8b53a10fd58ac51fbfdb6e6e6e7c,Dynamic vs. Static Recognition of Facial Expressions,"Dynamic vs. Static Recognition of Facial
Expressions
No Author Given
No Institute Given"
89d7cc9bbcd2fdc4f4434d153ecb83764242227b,Face-Name Graph Matching For The Personalities In Movie Screen,"Einstein.J, DivyaBaskaran / International Journal of Engineering Research and Applications
(IJERA)             ISSN: 2248-9622           www.ijera.com
Vol. 3, Issue 2, March -April 2013, pp.351-355
Face-Name Graph Matching For The Personalities In Movie
Screen
*(Asst. Professor, Dept. of IT, VelTech HighTech Dr. Rangarajan Dr.Sakunthala Engineering College,
Einstein.J*, DivyaBaskaran**
** (Final Year Student, M.Tech IT, Vel Tech Dr. RR &Dr. SR Technical University, Chennai.)
Chennai.)"
891b10c4b3b92ca30c9b93170ec9abd71f6099c4,2 New Statement for Structured Output Regression Problems,"Facial landmark detection using structured output deep
neural networks
Soufiane Belharbi ∗1, Cl´ement Chatelain∗1, Romain H´erault∗1, and S´ebastien
Adam∗2
LITIS EA 4108, INSA de Rouen, Saint ´Etienne du Rouvray 76800, France
LITIS EA 4108, UFR des Sciences, Universit´e de Rouen, France.
September 24, 2015"
45c340c8e79077a5340387cfff8ed7615efa20fd,Assessment of the Emotional States of Students during e-Learning,
45e7ddd5248977ba8ec61be111db912a4387d62f,Adversarial Learning of Structure-Aware Fully Convolutional Networks for Landmark Localization,"CHEN ET AL.: ADVERSARIAL POSENET
Adversarial Learning of Structure-Aware Fully
Convolutional Networks for Landmark
Localization
Yu Chen1, Chunhua Shen2, Hao Chen2, Xiu-Shen Wei3, Lingqiao Liu2 and Jian Yang1"
4526992d4de4da2c5fae7a5ceaad6b65441adf9d,System for Medical Mask Detection in the Operating Room Through Facial Attributes,"System for Medical Mask Detection
in the Operating Room Through
Facial Attributes
A. Nieto-Rodr´ıguez, M. Mucientes(B), and V.M. Brea
Center for Research in Information Technologies (CiTIUS),
University of Santiago de Compostela, Santiago de Compostela, Spain"
45efd6c2dd4ca19eed38ceeb7c2c5568231451e1,Comparative Analysis of Statistical Approach for Face Recognition,"Comparative Analysis of Statistical Approach
for Face Recognition
S.Pradnya1, M.Riyajoddin2, M.Janga Reddy3
CMR Institute of Technology, Hyderabad, (India)"
4560491820e0ee49736aea9b81d57c3939a69e12,Investigating the Impact of Data Volume and Domain Similarity on Transfer Learning Applications,"Investigating the Impact of Data Volume and
Domain Similarity on Transfer Learning
Applications
Michael Bernico, Yuntao Li, and Dingchao Zhang
State Farm Insurance, Bloomington IL 61710, USA,"
4571626d4d71c0d11928eb99a3c8b10955a74afe,Geometry Guided Adversarial Facial Expression Synthesis,"Geometry Guided Adversarial Facial Expression Synthesis
Lingxiao Song1,2
Zhihe Lu1,3 Ran He1,2,3
Zhenan Sun1,2
Tieniu Tan1,2,3
National Laboratory of Pattern Recognition, CASIA
Center for Research on Intelligent Perception and Computing, CASIA
Center for Excellence in Brain Science and Intelligence Technology, CAS"
4534d78f8beb8aad409f7bfcd857ec7f19247715,Transformation-Based Models of Video Sequences,"Under review as a conference paper at ICLR 2017
TRANSFORMATION-BASED MODELS OF VIDEO
SEQUENCES
Joost van Amersfoort ∗, Anitha Kannan, Marc’Aurelio Ranzato,
Arthur Szlam, Du Tran & Soumith Chintala
Facebook AI Research
{akannan, ranzato, aszlam, trandu,"
459e840ec58ef5ffcee60f49a94424eb503e8982,One-shot Face Recognition by Promoting Underrepresented Classes,"One-shot Face Recognition by Promoting Underrepresented Classes
Yandong Guo, Lei Zhang
Microsoft
One Microsoft Way, Redmond, Washington, United States
{yandong.guo,"
451c42da244edcb1088e3c09d0f14c064ed9077e,Using subclasses in discriminant non-negative subspace learning for facial expression recognition,"© EURASIP, 2011  -  ISSN 2076-1465
9th European Signal Processing Conference (EUSIPCO 2011)
INTRODUCTION"
4568063b7efb66801e67856b3f572069e774ad33,Correspondence driven adaptation for human profile recognition,"Correspondence Driven Adaptation for Human Profile Recognition
Ming Yang1, Shenghuo Zhu1, Fengjun Lv2, Kai Yu1
NEC Laboratories America, Inc.
Huawei Technologies (USA)
Cupertino, CA 95014
Santa Clara, CA 95050"
45e459462a80af03e1bb51a178648c10c4250925,LCrowdV: Generating Labeled Videos for Simulation-based Crowd Behavior Learning,"LCrowdV: Generating Labeled Videos for
Simulation-based Crowd Behavior Learning
Ernest Cheung1, Tsan Kwong Wong1, Aniket Bera1, Xiaogang Wang2, and
Dinesh Manocha1
The University of North Carolina at Chapel Hill"
458677de7910a5455283a2be99f776a834449f61,Face Image Retrieval Using Facial Attributes By K-Means,"Face Image Retrieval Using Facial Attributes By
K-Means
[1]I.Sudha,  [2]V.Saradha, [3]M.Tamilselvi, [4]D.Vennila
[1]AP, Department of CSE ,[2][3][4] B.Tech(CSE)
Achariya college of Engineering Technology-
Puducherry"
45a6333fc701d14aab19f9e2efd59fe7b0e89fec,Dataset Creation for Gesture Recognition,"HAND POSTURE DATASET CREATION FOR GESTURE
RECOGNITION
Luis Anton-Canalis
Instituto de Sistemas Inteligentes y Aplicaciones Numericas en Ingenieria
Campus Universitario de Tafira, 35017 Gran Canaria, Spain
Elena Sanchez-Nielsen
Departamento de E.I.O. y Computacion
8271 Universidad de La Laguna, Spain
Keywords:
Image understanding, Gesture recognition, Hand dataset."
1ffe20eb32dbc4fa85ac7844178937bba97f4bf0,Face Clustering: Representation and Pairwise Constraints,"Face Clustering: Representation and Pairwise
Constraints
Yichun Shi, Student Member, IEEE, Charles Otto, Member, IEEE, and Anil K. Jain, Fellow, IEEE"
1f8304f4b51033d2671147b33bb4e51b9a1e16fe,Beyond Trees: MAP Inference in MRFs via Outer-Planar Decomposition,"Noname manuscript No.
(will be inserted by the editor)
Beyond Trees:
MAP Inference in MRFs via Outer-Planar Decomposition
Dhruv Batra · Andrew C. Gallagher · Devi Parikh · Tsuhan Chen
Received: date / Accepted: date"
1f9ae272bb4151817866511bd970bffb22981a49,An Iterative Regression Approach for Face Pose Estimation from RGB Images,"An Iterative Regression Approach for Face Pose Estima-
tion from RGB Images
Wenye He
This paper presents a iterative optimization method, explicit shape regression, for face pose
detection and localization. The regression function is learnt to find out the entire facial shape
nd minimize the alignment errors. A cascaded learning framework is employed to enhance
shape constraint during detection. A combination of a two-level boosted regression, shape
performance. In this paper, we have explain the advantage of ESR for deformable object like
face pose estimation and reveal its generic applications of the method. In the experiment,
we compare the results with different work and demonstrate the accuracy and robustness in
different scenarios.
Introduction
Pose estimation is an important problem in computer vision, and has enabled many practical ap-
plication from face expression 1 to activity tracking 2. Researchers design a new algorithm called
explicit shape regression (ESR) to find out face alignment from a picture 3. Figure 1 shows how
the system uses ESR to learn a shape of a human face image. A simple way to identify a face is to
find out facial landmarks like eyes, nose, mouth and chin. The researchers define a face shape S
nd S is composed of Nf p facial landmarks. Therefore, they get S = [x1, y1, ..., xNf p, yNf p]T . The
objective of the researchers is to estimate a shape S of a face image. The way to know the accuracy"
1fc249ec69b3e23856b42a4e591c59ac60d77118,Evaluation of a 3D-aided pose invariant 2D face recognition system,"Evaluation of a 3D-aided Pose Invariant 2D Face Recognition System
Xiang Xu, Ha A. Le, Pengfei Dou, Yuhang Wu, Ioannis A. Kakadiaris
{xxu18, hale4, pdou, ywu35,
Computational Biomedicine Lab
800 Calhoun Rd. Houston, TX, USA"
1fbde67e87890e5d45864e66edb86136fbdbe20e,The Action Similarity Labeling Challenge,"The Action Similarity Labeling Challenge
Orit Kliper-Gross, Tal Hassner, and
Lior Wolf, Member, IEEE"
1f41a96589c5b5cee4a55fc7c2ce33e1854b09d6,Demographic Estimation from Face Images: Human vs. Machine Performance,"Demographic Estimation from Face Images:
Human vs. Machine Performance
Hu Han, Member, IEEE, Charles Otto, Student Member, IEEE, Xiaoming Liu, Member, IEEE
nd Anil K. Jain, Fellow, IEEE"
1f8e44593eb335c2253d0f22f7f9dc1025af8c0d,Fine-Tuning Regression Forests Votes for Object Alignment in the Wild,"Fine-tuning regression forests votes for object alignment in the wild.
Yang, H; Patras, I
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1f94734847c15fa1da68d4222973950d6b683c9e,Embedding Label Structures for Fine-Grained Feature Representation,"Embedding Label Structures for Fine-Grained Feature Representation
Xiaofan Zhang
UNC Charlotte
Charlotte, NC 28223
Feng Zhou
NEC Lab America
Cupertino, CA 95014
Yuanqing Lin
NEC Lab America
Cupertino, CA 95014
Shaoting Zhang
UNC Charlotte
Charlotte, NC 28223"
1fff309330f85146134e49e0022ac61ac60506a9,Data-Driven Sparse Sensor Placement for Reconstruction,"Data-Driven Sparse Sensor Placement for Reconstruction
Krithika Manohar∗, Bingni W. Brunton, J. Nathan Kutz, and Steven L. Brunton
Corresponding author:"
73f467b4358ac1cafb57f58e902c1cab5b15c590,Combination of Dimensionality Reduction Techniques for Face Image Retrieval: A Review,"ISSN 0976 3724                                                                                                                                  47
Combination of Dimensionality Reduction Techniques for Face
Image Retrieval: A Review
Fousiya K.K 1, Jahfar Ali P 2
M.Tech Scholar, MES College of Engineering, Kuttippuram,
Kerala
Asst. Professor, MES College of Engineering, Kuttippuram,
Kerala"
7323b594d3a8508f809e276aa2d224c4e7ec5a80,An Experimental Evaluation of Covariates Effects on Unconstrained Face Verification,"JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015
An Experimental Evaluation of Covariates
Effects on Unconstrained Face Verification
Boyu Lu, Student Member, IEEE, Jun-Cheng Chen, Member, IEEE, Carlos D Castillo, Member, IEEE
nd Rama Chellappa, Fellow, IEEE"
732e8d8f5717f8802426e1b9debc18a8361c1782,Unimodal Probability Distributions for Deep Ordinal Classification,"Unimodal Probability Distributions for Deep Ordinal Classification
Christopher Beckham 1 Christopher Pal 1"
73ed64803d6f2c49f01cffef8e6be8fc9b5273b8,Cooking in the kitchen: Recognizing and Segmenting Human Activities in Videos,"Noname manuscript No.
(will be inserted by the editor)
Cooking in the kitchen: Recognizing and Segmenting Human
Activities in Videos
Hilde Kuehne · Juergen Gall · Thomas Serre
Received: date / Accepted: date"
7306d42ca158d40436cc5167e651d7ebfa6b89c1,Transductive Zero-Shot Action Recognition by Word-Vector Embedding,"Noname manuscript No.
(will be inserted by the editor)
Transductive Zero-Shot Action Recognition by
Word-Vector Embedding
Xun Xu · Timothy Hospedales · Shaogang Gong
Received: date / Accepted: date"
734cdda4a4de2a635404e4c6b61f1b2edb3f501d,Automatic landmark point detection and tracking for human facial expressions,"Tie and Guan EURASIP Journal on Image and Video Processing 2013, 2013:8
http://jivp.eurasipjournals.com/content/2013/1/8
R ES EAR CH
Open Access
Automatic landmark point detection and tracking
for human facial expressions
Yun Tie* and Ling Guan"
7373c4a23684e2613f441f2236ed02e3f9942dd4,Feature extraction through Binary Pattern of Phase Congruency for facial expression recognition,"This document is downloaded from DR-NTU, Nanyang Technological
University Library, Singapore.
Title
Feature extraction through binary pattern of phase
ongruency for facial expression recognition
Author(s)
Shojaeilangari, Seyedehsamaneh; Yau, Wei-Yun; Li, Jun;
Teoh, Eam Khwang
Citation
Shojaeilangari, S., Yau, W. Y., Li, J., & Teoh, E. K.
(2012). Feature extraction through binary pattern of
phase congruency for facial expression recognition. 12th
International Conference on Control Automation Robotics
& Vision (ICARCV), 166-170.
http://hdl.handle.net/10220/18012
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© 2012 IEEE. Personal use of this material is permitted.
Permission from IEEE must be obtained for all other
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reprinting/republishing this material for advertising or"
732686d799d760ccca8ad47b49a8308b1ab381fb,Teachers’ differing classroom behaviors: The role of emotional sensitivity and cultural tolerance,"Running head: TEACHERS’ DIFFERING BEHAVIORS
Graduate School of Psychology
RESEARCH MASTER’S PSYCHOLOGY THESıS REPORT
Teachers’ differing classroom behaviors:
The role of emotional sensitivity and cultural tolerance
Ceren Su Abacıoğlu
Supervisor: prof. dr. Agneta Fischer
Second supervisor: dr. Disa Sauter
External Supervisor: prof. dr. Monique Volman
Research Master’s, Social Psychology
Ethics Committee Reference Code: 2016-SP-7084"
73fbdd57270b9f91f2e24989178e264f2d2eb7ae,Kernel linear regression for low resolution face recognition under variable illumination,"978-1-4673-0046-9/12/$26.00 ©2012 IEEE
ICASSP 2012"
73c9cbbf3f9cea1bc7dce98fce429bf0616a1a8c,Unsupervised Learning of Object Landmarks by Factorized Spatial Embeddings,"imagesViewpoint factorizationLearned landmarksFigure1.Wepresentanovelmethodthatcanlearnviewpointin-variantlandmarkswithoutanysupervision.Themethodusesaprocessofviewpointfactorizationwhichlearnsadeeplandmarkdetectorcompatiblewithimagedeformations.Itcanbeappliedtorigidanddeformableobjectsandobjectcategories.terns.Achievingadeeperunderstandingofobjectsrequiresmodelingtheirintrinsicviewpoint-independentstructure.Oftenthisstructureisdefinedmanuallybyspecifyingen-titiessuchaslandmarks,parts,andskeletons.Givensuffi-cientmanualannotations,itispossibletoteachdeepneuralnetworksandothermodelstorecognizesuchstructuresinimages.However,theproblemoflearningsuchstructureswithoutmanualsupervisionremainslargelyopen.Inthispaper,wecontributeanewapproachtolearnviewpoint-independentrepresentationsofobjectsfromim-ageswithoutmanualsupervision(fig.1).Weformulatethistaskasafactorizationproblem,wheretheeffectsofimagedeformations,forexamplearisingfromaviewpointchange,areexplainedbythemotionofareferenceframeattachedtotheobjectandindependentoftheviewpoint.Afterdescribingthegeneralprinciple(sec.3.1),wein-1"
8796f2d54afb0e5c924101f54d469a1d54d5775d,Illumination Invariant Face Recognition Using Fuzzy LDA and FFNN,"Journal of Signal and Information Processing, 2012, 3, 45-50
http://dx.doi.org/10.4236/jsip.2012.31007 Published Online February 2012 (http://www.SciRP.org/journal/jsip)
Illumination Invariant Face Recognition Using Fuzzy LDA
nd FFNN
Behzad Bozorgtabar, Hamed Azami, Farzad Noorian
School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran.
Email:
Received October 20th, 2011; revised November 24th, 2011; accepted December 10th, 2011"
87f285782d755eb85d8922840e67ed9602cfd6b9,Incorporating Boltzmann Machine Priors for Semantic Labeling in Images and Videos,"INCORPORATING BOLTZMANN MACHINE PRIORS
FOR SEMANTIC LABELING IN IMAGES AND VIDEOS
A Dissertation Presented
ANDREW KAE
Submitted to the Graduate School of the
University of Massachusetts Amherst in partial fulfillment
of the requirements for the degree of
DOCTOR OF PHILOSOPHY
May 2014
Computer Science"
871f5f1114949e3ddb1bca0982086cc806ce84a8,Discriminative learning of apparel features,"Discriminative Learning of Apparel Features
Rasmus Rothe1, Marko Ristin1, Matthias Dantone1, and Luc Van Gool1,2
Computer Vision Laboratory, D-ITET, ETH Z¨urich, Switzerland
ESAT - PSI / IBBT, K.U. Leuven, Belgium"
87bee0e68dfc86b714f0107860d600fffdaf7996,Automated 3D Face Reconstruction from Multiple Images Using Quality Measures,"Automated 3D Face Reconstruction from Multiple Images
using Quality Measures
Marcel Piotraschke and Volker Blanz
Institute for Vision and Graphics, University of Siegen, Germany"
878169be6e2c87df2d8a1266e9e37de63b524ae7,Image interpretation above and below the object level.,"CBMM Memo No. 089
May 10, 2018
Image interpretation above and below the object level
Guy Ben-Yosef, Shimon Ullman"
878301453e3d5cb1a1f7828002ea00f59cbeab06,Faceness-Net: Face Detection through Deep Facial Part Responses,"Faceness-Net: Face Detection through
Deep Facial Part Responses
Shuo Yang, Ping Luo, Chen Change Loy, Senior Member, IEEE and Xiaoou Tang, Fellow, IEEE"
87e592ee1a7e2d34e6b115da08700a1ae02e9355,Deep Pictorial Gaze Estimation,"Deep Pictorial Gaze Estimation
Seonwook Park, Adrian Spurr, and Otmar Hilliges
AIT Lab, Department of Computer Science, ETH Zurich"
87dd3fd36bccbe1d5f1484ac05f1848b51c6eab5,Spatio-temporal Maximum Average Correlation Height Templates in Action Recognition and Video Summarization,"SPATIO-TEMPORAL MAXIMUM AVERAGE CORRELATION
HEIGHT TEMPLATES IN ACTION RECOGNITION AND VIDEO
SUMMARIZATION
MIKEL RODRIGUEZ
B.A. Earlham College, Richmond Indiana
M.S. University of Central Florida
A dissertation submitted in partial fulfillment of the requirements
for the degree of Doctor of Philosophy
in the School of Electrical Engineering and Computer Science
in the College of Engineering and Computer Science
t the University of Central Florida
Orlando, Florida
Summer Term
Major Professor: Mubarak Shah"
87bb183d8be0c2b4cfceb9ee158fee4bbf3e19fd,Craniofacial Image Analysis,"Craniofacial Image Analysis
Ezgi Mercan, Indriyati Atmosukarto, Jia Wu, Shu Liang and Linda G. Shapiro"
8006219efb6ab76754616b0e8b7778dcfb46603d,Contributions to large-scale learning for image classification. (Contributions à l'apprentissage grande échelle pour la classification d'images),"CONTRIBUTIONSTOLARGE-SCALELEARNINGFORIMAGECLASSIFICATIONZeynepAkataPhDThesisl’´EcoleDoctoraleMath´ematiques,SciencesetTechnologiesdel’Information,InformatiquedeGrenoble"
804b4c1b553d9d7bae70d55bf8767c603c1a09e3,Subspace clustering with a learned dimensionality reduction projection,"978-1-4799-9988-0/16/$31.00 ©2016 IEEE
ICASSP 2016"
800cbbe16be0f7cb921842d54967c9a94eaa2a65,Multimodal Recognition of Emotions Multimodal Recognition of Emotions,"MULTIMODAL RECOGNITION OF
EMOTIONS"
80135ed7e34ac1dcc7f858f880edc699a920bf53,Efficient Action and Event Recognition in Videos Using Extreme Learning Machines,"EFFICIENT ACTION AND EVENT RECOGNITION IN VIDEOS USING
EXTREME LEARNING MACHINES
G¨ul Varol
B.S., Computer Engineering, Bo˘gazi¸ci University, 2013
Submitted to the Institute for Graduate Studies in
Science and Engineering in partial fulfillment of
the requirements for the degree of
Master of Science
Graduate Program in Computer Engineering
Bo˘gazi¸ci University"
803c92a3f0815dbf97e30c4ee9450fd005586e1a,Max-Mahalanobis Linear Discriminant Analysis Networks,"Max-Mahalanobis Linear Discriminant Analysis Networks
Tianyu Pang 1 Chao Du 1 Jun Zhu 1"
80c8d143e7f61761f39baec5b6dfb8faeb814be9,Local Directional Pattern based Fuzzy Co- occurrence Matrix Features for Face recognition,"Local Directional Pattern based Fuzzy Co-
occurrence Matrix Features for Face recognition
Dr. P Chandra Sekhar Reddy
Professor, CSE Dept.
Gokaraju Rangaraju Institute of Engineering and Technology, Hyd."
80345fbb6bb6bcc5ab1a7adcc7979a0262b8a923,Soft Biometrics for a Socially Assistive Robotic Platform,"Research Article
Pierluigi Carcagnì*, Dario Cazzato, Marco Del Coco, Pier Luigi Mazzeo, Marco Leo, and
Cosimo Distante
Soft Biometrics for a Socially Assistive Robotic
Platform
Open Access"
80a6bb337b8fdc17bffb8038f3b1467d01204375,Subspace LDA Methods for Solving the Small Sample Size Problem in Face Recognition,"Proceedings of the International Conference on Computer and Information Science and Technology
Ottawa, Ontario, Canada, May 11 – 12, 2015
Paper No. 126
Subspace LDA Methods for Solving the Small Sample Size
Problem in Face Recognition
Ching-Ting Huang, Chaur-Chin Chen
Department of Computer Science/National Tsing Hua University
01 KwanFu Rd., Sec. 2, Hsinchu, Taiwan"
80097a879fceff2a9a955bf7613b0d3bfa68dc23,Active Self-Paced Learning for Cost-Effective and Progressive Face Identification,"Active Self-Paced Learning for Cost-Effective and
Progressive Face Identification
Liang Lin, Keze Wang, Deyu Meng, Wangmeng Zuo, and Lei Zhang"
74408cfd748ad5553cba8ab64e5f83da14875ae8,Facial Expressions Tracking and Recognition: Database Protocols for Systems Validation and Evaluation,"Facial Expressions Tracking and Recognition: Database Protocols for Systems Validation
nd Evaluation"
74dbe6e0486e417a108923295c80551b6d759dbe,An HMM based Model for Prediction of Emotional Composition of a Facial Expression using both Significant and Insignificant Action Units and Associated Gender Differences,"International Journal of Computer Applications (0975 – 8887)
Volume 45– No.11, May 2012
An HMM based Model for Prediction of Emotional
Composition of a Facial Expression using both
Significant and Insignificant Action Units and
Associated Gender Differences
Suvashis Das
Koichi Yamada
Department of Management and Information
Department of Management and Information
Systems Science
603-1 Kamitomioka, Nagaoka
Niigata, Japan
Systems Science
603-1 Kamitomioka, Nagaoka
Niigata, Japan"
747c25bff37b96def96dc039cc13f8a7f42dbbc7,EmoNets: Multimodal deep learning approaches for emotion recognition in video,"EmoNets: Multimodal deep learning approaches for emotion
recognition in video
Samira Ebrahimi Kahou · Xavier Bouthillier · Pascal Lamblin · Caglar Gulcehre ·
Vincent Michalski · Kishore Konda · S´ebastien Jean · Pierre Froumenty · Yann
Dauphin · Nicolas Boulanger-Lewandowski · Raul Chandias Ferrari · Mehdi Mirza ·
David Warde-Farley · Aaron Courville · Pascal Vincent · Roland Memisevic ·
Christopher Pal · Yoshua Bengio"
744fa8062d0ae1a11b79592f0cd3fef133807a03,Deep Pain: Exploiting Long Short-Term Memory Networks for Facial Expression Classification.,"Aalborg Universitet
Deep Pain
Rodriguez, Pau; Cucurull, Guillem; Gonzàlez, Jordi; M. Gonfaus, Josep ; Nasrollahi, Kamal;
Moeslund, Thomas B.; Xavier Roca, F.
Published in:
I E E E Transactions on Cybernetics
DOI (link to publication from Publisher):
0.1109/TCYB.2017.2662199
Publication date:
Document Version
Accepted author manuscript, peer reviewed version
Link to publication from Aalborg University
Citation for published version (APA):
Rodriguez, P., Cucurull, G., Gonzàlez, J., M. Gonfaus, J., Nasrollahi, K., Moeslund, T. B., & Xavier Roca, F.
(2017). Deep Pain: Exploiting Long Short-Term Memory Networks for Facial Expression Classification. I E E E
Transactions on Cybernetics, 1-11. DOI: 10.1109/TCYB.2017.2662199
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743e582c3e70c6ec07094887ce8dae7248b970ad,Face Recognition based on Deep Neural Network,"International Journal of Signal Processing, Image Processing and Pattern Recognition
Vol.8, No.10 (2015), pp.29-38
http://dx.doi.org/10.14257/ijsip.2015.8.10.04
Face Recognition based on Deep Neural Network
Li Xinhua,Yu Qian
Shandong Women’s University"
74b0095944c6e29837c208307a67116ebe1231c8,Manifold learning using Euclidean k-nearest neighbor graphs [image processing examples],"be
independentandidenticallydis-tributed(i.i.d.)randomvectorswithvaluesinacompactsubsetof.The(-)nearestneighborofinisgivenby!""$%&(*,.%13575where575istheusualEuclidean(<=)distanceinbe-tweenvectorand.Forgeneralinteger?,the-nearestneighborofapointisdefinedinasimilarway.The-NNgraphputsanedgebetweeneachpointinandits-nearestneighbors.LetBCDBCDFHbethesetof-nearestneighborsofin.Thetotaledgelengthofthe-NNgraphisdefinedas:<JDCFHMN
M%&QRS1575J(1)whereVWXisapowerweightingconstant.2.1.ConvergencetoExtrinsicZ-EntropyThe-NNedgelengthliesinthelargeclassoffunctionalscalledcontinuousquasi-additiveEuclideanfunctionals[7].Othergraphsinthisclassincludetheminimalspanningtree,theminimalmatch-inggraphorthetravelingsalesmantouramongothers.Thesefunc-tionalshaveremarkableasymptoticbehavioras
increases:Theorem1([7,Theorem8.3])Let
bei.i.d.randomvectorswithvaluesinacompactsubsetofandLebesgueden-sity\.Let]?_,aVb]anddefineZF]7VHf].Then,withprobability(w.p.)gh""jk<JDCFH
moDJDCp\mFrHtr(2)whereoDJDCisaconstantindependentof\.Furthermore,themeanlengthuv<JDCFHwf
mconvergestothesamelimit.Thequantitythatdeterminesthelimit(2)inTheorem1istheex-trinsicR´enyiZ-entropyofthemultivariateLebesguedensity\:yz{mF\H7Zg!pz{\mFrHtr(3)III - 9880-7803-8484-9/04/$20.00 ©2004 IEEEICASSP 2004(cid:224)"
74156a11c2997517061df5629be78428e1f09cbd,"Preparatory coordination of head, eyes and hands: Experimental study at intersections","Cancún Center, Cancún, México, December 4-8, 2016
978-1-5090-4846-5/16/$31.00 ©2016 IEEE"
749d605dd12a4af58de1fae6f5ef5e65eb06540e,Multi-Task Video Captioning with Video and Entailment Generation,"Multi-Task Video Captioning with Video and Entailment Generation
Ramakanth Pasunuru and Mohit Bansal
UNC Chapel Hill
{ram,"
749382d19bfe9fb8d0c5e94d0c9b0a63ab531cb7,A Modular Framework to Detect and Analyze Faces for Audience Measurement Systems,"A Modular Framework to Detect and Analyze Faces for
Audience Measurement Systems
Andreas Ernst, Tobias Ruf, Christian Kueblbeck
Fraunhofer Institute for Integrated Circuits IIS
Department Electronic Imaging
Am Wolfsmantel 33, 91058 Erlangen, Germany
{andreas.ernst, tobias.ruf,"
74c19438c78a136677a7cb9004c53684a4ae56ff,RESOUND: Towards Action Recognition without Representation Bias,"RESOUND: Towards Action Recognition
without Representation Bias
Yingwei Li, Yi Li, and Nuno Vasconcelos
UC San Diego"
74618fb4ce8ce0209db85cc6069fe64b1f268ff4,Rendering and animating expressive caricatures,"Rendering and Animating Expressive
Caricatures
Mohammad Obaid* t, Ramakrishnan
Mukundan
*HITLab New Zealand,
University
of Canterbury,
t, and Mark Billinghurst*
Christchurch,
New Zealand
tComputer
Science
nd Software Engineering
Email: {mohammad.obaid,
Dept., University
of Canterbury,
New Zealand
stylized
nd control
on the generated caricature."
74eae724ef197f2822fb7f3029c63014625ce1ca,Feature Extraction based on Local Directional Pattern with SVM Decision-level Fusion for Facial Expression Recognition,"International Journal of Bio-Science and Bio-Technology
Vol. 5, No. 2, April, 2013
Feature Extraction based on Local Directional Pattern with SVM
Decision-level Fusion for Facial Expression Recognition
Juxiang Zhou1, Tianwei Xu1,2 and Jianhou Gan1
Key Laboratory of Education Informalization for Nationalities, Ministry of
Education, Yunnan Normal University, Kunming, China
College of Information, Yunnan Normal University, Kunming, China"
7480d8739eb7ab97c12c14e75658e5444b852e9f,MLBoost Revisited: A Faster Metric Learning Algorithm for Identity-Based Face Retrieval,"NEGREL ET AL.: REVISITED MLBOOST FOR FACE RETRIEVAL
MLBoost Revisited: A Faster Metric
Learning Algorithm for Identity-Based Face
Retrieval
Romain Negrel
Alexis Lechervy
Frederic Jurie
Normandie Univ, UNICAEN,
ENSICAEN, CNRS
France"
74ba4ab407b90592ffdf884a20e10006d2223015,Partial Face Detection in the Mobile Domain,"Partial Face Detection in the Mobile Domain
Upal Mahbub, Student Member, IEEE, Sayantan Sarkar, Student Member, IEEE,
nd Rama Chellappa, Fellow, IEEE"
7405ed035d1a4b9787b78e5566340a98fe4b63a0,Self-Expressive Decompositions for Matrix Approximation and Clustering,"Self-Expressive Decompositions for
Matrix Approximation and Clustering
Eva L. Dyer, Member, IEEE, Tom A. Goldstein, Member, IEEE, Raajen Patel, Student Member, IEEE,
Konrad P. K¨ording, and Richard G. Baraniuk, Fellow, IEEE"
744db9bd550bf5e109d44c2edabffec28c867b91,FX e-Makeup for Muscle Based Interaction,"FX e-Makeup for Muscle Based Interaction
Katia Canepa Vega1, Abel Arrieta2, Felipe Esteves3, and Hugo Fuks1
Department of Informatics, PUC-Rio, Rio de Janeiro, Brazil
Department of Mechanical Engineering, PUC-Rio, Rio de Janeiro, Brazil
Department of Administration, PUC-Rio, Rio de Janeiro, Brazil"
74325f3d9aea3a810fe4eab8863d1a48c099de11,Regression-Based Image Alignment for General Object Categories,"Regression-Based Image Alignment
for General Object Categories
Hilton Bristow1 and Simon Lucey2
Queensland University of Technology (QUT)
Brisbane QLD 4000, Australia
Carnegie Mellon University (CMU)
Pittsburgh PA 15289, USA"
744d23991a2c48d146781405e299e9b3cc14b731,Aging Face Recognition: A Hierarchical Learning Model Based on Local Patterns Selection,"This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TIP.2016.2535284, IEEE
Transactions on Image Processing
Aging Face Recognition: A Hierarchical Learning
Model Based on Local Patterns Selection
Zhifeng Li, Senior Member, IEEE, Dihong Gong, Xuelong Li, Fellow, IEEE, and Dacheng Tao, Fellow, IEEE"
1a45ddaf43bcd49d261abb4a27977a952b5fff12,LDOP: Local Directional Order Pattern for Robust Face Retrieval,"LDOP: Local Directional Order Pattern for Robust
Face Retrieval
Shiv Ram Dubey, Member, IEEE, and Snehasis Mukherjee, Member, IEEE"
1a849b694f2d68c3536ed849ed78c82e979d64d5,This is a repository copy of Symmetric Shape Morphing for 3 D Face and Head Modelling,"This is a repository copy of Symmetric Shape Morphing for 3D Face and Head Modelling.
White Rose Research Online URL for this paper:
http://eprints.whiterose.ac.uk/131760/
Version: Accepted Version
Proceedings Paper:
Dai, Hang, Pears, Nicholas Edwin orcid.org/0000-0001-9513-5634, Smith, William Alfred
Peter orcid.org/0000-0002-6047-0413 et al. (1 more author) (2018) Symmetric Shape
Morphing for 3D Face and Head Modelling. In: The 13th IEEE Conference on Automatic
Face and Gesture Recognition. IEEE .
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1a878e4667fe55170252e3f41d38ddf85c87fcaf,Discriminative Machine Learning with Structure,"Discriminative Machine Learning with Structure
Simon Lacoste-Julien
Electrical Engineering and Computer Sciences
University of California at Berkeley
Technical Report No. UCB/EECS-2010-4
http://www.eecs.berkeley.edu/Pubs/TechRpts/2010/EECS-2010-4.html
January 12, 2010"
1a41831a3d7b0e0df688fb6d4f861176cef97136,A Biological Model of Object Recognition with Feature Learning,"massachusetts institute of technology — artificial intelligence laboratory
A Biological Model of Object
Recognition with Feature Learning
Jennifer Louie
AI Technical Report 2003-009
CBCL Memo 227
June 2003
© 2 0 0 3   m a s s a c h u s e t t s   i n s t i t u t e   o f
t e c h n o l o g y, c a m b r i d g e , m a   0 2 1 3 9   u s a   —   w w w. a i . m i t . e d u"
1a6c3c37c2e62b21ebc0f3533686dde4d0103b3f,Implementation of Partial Face Recognition using Directional Binary Code,"International Journal of Linguistics and Computational Applications (IJLCA)                          ISSN 2394-6385 (Print)
Volume 4, Issue 1, January – March 2017                                                                                   ISSN 2394-6393 (Online)
Implementation of Partial Face Recognition
using Directional Binary Code
N.Pavithra #1, A.Sivapriya*2, K.Hemalatha*3 , D.Lakshmi*4
,2,3Final Year, Department of Computer Science and Engineering, PanimalarInstitute of Technology,
Assistant Professor, Department of Computer Science and Engineering, PanimalarInstitute of Technology, Tamilnadu, India,
faith
is  proposed.  It
face  alignment  and"
1a3eee980a2252bb092666cf15dd1301fa84860e,PCA Gaussianization for image processing,"PCA GAUSSIANIZATION FOR IMAGE PROCESSING
Valero Laparra, Gustavo Camps-Valls and Jes´us Malo
Image Processing Laboratory (IPL), Universitat de Val`encia
Catedr´atico A. Escardino - 46980 Paterna, Val`encia, Spain"
1a031378cf1d2b9088a200d9715d87db8a1bf041,D Eep D Ictionary L Earning : S Ynergizing R E - Construction and C Lassification,"Workshop track - ICLR 2018
DEEP DICTIONARY LEARNING: SYNERGIZING RE-
CONSTRUCTION AND CLASSIFICATION
Shahin Mahdizadehaghdam, Ashkan Panahi, Hamid Krim & Liyi Dai"
1afd481036d57320bf52d784a22dcb07b1ca95e2,Automated Content Metadata Extraction Services Based on MPEG Standards,"The Computer Journal Advance Access published December 6, 2012
© The Author 2012. Published by Oxford University Press on behalf of The British Computer Society. All rights reserved.
For Permissions, please email:
doi:10.1093/comjnl/bxs146
Automated Content Metadata Extraction
Services Based on MPEG Standards
D.C. Gibbon∗, Z. Liu, A. Basso and B. Shahraray
AT&T Labs Research, Middletown, NJ, USA
Corresponding author:
This paper is concerned with the generation, acquisition, standardized representation and transport
of video metadata. The use of MPEG standards in the design and development of interoperable
media architectures and web services is discussed. A high-level discussion of several algorithms
for metadata extraction is presented. Some architectural and algorithmic issues encountered when
designing services for real-time processing of video streams, as opposed to traditional offline media
processing, are addressed. A prototype real-time video analysis system for generating MPEG-7
Audiovisual Description Profile from MPEG-2 transport stream encapsulated video is presented.
Such a capability can enable a range of new services such as content-based personalization of live
roadcasts given that the MPEG-7 based data models fit in well with specifications for advanced
television services such as TV-Anytime andAlliance for Telecommunications Industry Solutions IPTV
Interoperability Forum."
1a9a192b700c080c7887e5862c1ec578012f9ed1,Discriminant Subspace Analysis for Face Recognition with Small Number of Training Samples,"IEEE TRANSACTIONS ON SYSTEM, MAN AND CYBERNETICS, PART B
Discriminant Subspace Analysis for Face
Recognition with Small Number of Training
Samples
Hui Kong, Xuchun Li, Matthew Turk, and Chandra Kambhamettu"
1a8ccc23ed73db64748e31c61c69fe23c48a2bb1,Extensive Facial Landmark Localization with Coarse-to-Fine Convolutional Network Cascade,"Extensive Facial Landmark Localization
with Coarse-to-fine Convolutional Network Cascade
Erjin Zhou Haoqiang Fan Zhimin Cao Yuning Jiang Qi Yin
Megvii Inc."
1ad97cce5fa8e9c2e001f53f6f3202bddcefba22,Grassmann Averages for Scalable Robust PCA,"Grassmann Averages for Scalable Robust PCA
Aasa Feragen
DIKU and MPIs T¨ubingen∗
Denmark and Germany
Søren Hauberg
DTU Compute∗
Lyngby, Denmark"
1a7a2221fed183b6431e29a014539e45d95f0804,Person Identification Using Text and Image Data,"Person Identification Using Text and Image Data
David S. Bolme, J. Ross Beveridge and Adele E. Howe
Computer Science Department
Colorado State Univeristy
Fort Collins, Colorado 80523"
1a5b39a4b29afc5d2a3cd49087ae23c6838eca2b,Competitive Game Designs for Improving the Cost Effectiveness of Crowdsourcing,"Competitive Game Designs for Improving the Cost
Effectiveness of Crowdsourcing
Markus Rokicki, Sergiu Chelaru, Sergej Zerr, Stefan Siersdorfer
L3S Research Center, Hannover, Germany"
287795991fad3c61d6058352879c7d7ae1fdd2b6,Biometrics Security: Facial Marks Detection from the Low Quality Images,"International Journal of Computer Applications (0975 – 8887)
Volume 66– No.8, March 2013
Biometrics Security: Facial Marks Detection from the
Low Quality Images
nd  facial  marks  are  detected  using  LoG  with  morphological
operator.  This  method  though  was  not  enough  to  detect  the
facial  marks  from  the  low  quality  images  [7].  But,  facial
marks  have  been  used  to  speed  up  the  retrieval  process  in
order to differentiate the human faces [15].
Ziaul Haque Choudhury                                                      K.M.Mehata
B.S.Abdur Rahman University                                                     B.S.Abdur Rahman University
Dept. Of Information Technology                                        Dept. Of Computer Science & Engineering
Chennai, India                                                                               Chennai, India"
28d7029cfb73bcb4ad1997f3779c183972a406b4,Discriminative Nonlinear Analysis Operator Learning: When Cosparse Model Meets Image Classification,"Discriminative Nonlinear Analysis Operator
Learning: When Cosparse Model Meets Image
Classification
Zaidao Wen, Biao Hou, Member, IEEE, and Licheng Jiao, Senior Member, IEEE"
280d59fa99ead5929ebcde85407bba34b1fcfb59,Online Nonnegative Matrix Factorization With Outliers,"978-1-4799-9988-0/16/$31.00 ©2016 IEEE
ICASSP 2016"
28cd46a078e8fad370b1aba34762a874374513a5,"cvpaper.challenge in 2016: Futuristic Computer Vision through 1, 600 Papers Survey","CVPAPER.CHALLENGE IN 2016, JULY 2017
vpaper.challenge in 2016: Futuristic Computer
Vision through 1,600 Papers Survey
Hirokatsu Kataoka, Soma Shirak-
be, Yun He, Shunya Ueta, Teppei Suzuki, Kaori Abe, Asako Kanezaki, Shin’ichiro
Morita, Toshiyuki Yabe, Yoshihiro Kanehara, Hiroya Yatsuyanagi, Shinya Maruyama, Ryosuke Taka-
sawa, Masataka Fuchida, Yudai Miyashita, Kazushige Okayasu, Yuta Matsuzaki"
28b5b5f20ad584e560cd9fb4d81b0a22279b2e7b,A New Fuzzy Stacked Generalization Technique and Analysis of its Performance,"A New Fuzzy Stacked Generalization Technique
nd Analysis of its Performance
Mete Ozay, Student Member, IEEE, Fatos T. Yarman Vural, Member, IEEE"
28bc378a6b76142df8762cd3f80f737ca2b79208,Understanding Objects in Detail with Fine-Grained Attributes,"Understanding Objects in Detail with Fine-grained Attributes
Andrea Vedaldi1
Siddharth Mahendran2
Stavros Tsogkas3
Subhransu Maji4
Ross Girshick5
Juho Kannala6
Esa Rahtu6
Matthew B. Blaschko3
David Weiss7
Ben Taskar8
Naomi Saphra2
Sammy Mohamed9
Iasonas Kokkinos3
Karen Simonyan1"
28bcf31f794dc27f73eb248e5a1b2c3294b3ec9d,Improved Combination of LBP plus LFDA for Facial Expression Recognition using SRC,"International Journal of Computer Applications (0975 – 8887)
Volume 96– No.13, June 2014
Improved Combination of LBP plus LFDA for Facial
Expression Recognition using SRC
Ritesh Bora
Research Scholar, CSE Department,
Government College of Engineering, Aurangabad
human
facial
expression
recognition"
28fe6e785b32afdcd2c366c9240a661091b850cf,Facial Expression Recognition using Patch based Gabor Features,"International Journal of Applied Information Systems (IJAIS) – ISSN : 2249-0868
Foundation of Computer Science FCS, New York, USA
Volume 10 – No.7, March 2016 – www.ijais.org
Facial Expression Recognition using Patch based Gabor
Features
Electronics & Telecommunication Engg
Electronics & Telecommunication Engg
St. Francis Institute of Technology
St. Francis Institute of Technology
Vaqar Ansari
Department
Mumbai, India
Anju Chandran
Department
Mumbai, India"
28c9198d30447ffe9c96176805c1cd81615d98c8,No evidence that a range of artificial monitoring cues influence online donations to charity in an MTurk sample,"rsos.royalsocietypublishing.org
Research
Cite this article: Saunders TJ, Taylor AH,
Atkinson QD. 2016 No evidence that a range of
rtificial monitoring cues influence online
donations to charity in an MTurk sample.
R. Soc. open sci. 3: 150710.
http://dx.doi.org/10.1098/rsos.150710
Received: 22 December 2015
Accepted: 13 September 2016
Subject Category:
Psychology and cognitive neuroscience
Subject Areas:
ehaviour/psychology/evolution
Keywords:
prosociality, eye images, charity donation,
reputation, online behaviour
Author for correspondence:
Quentin D. Atkinson
e-mail:"
2866cbeb25551257683cf28f33d829932be651fe,A Two-Step Learning Method For Detecting Landmarks on Faces From Different Domains,"In Proceedings of the 2018 IEEE International Conference on Image Processing (ICIP)
The final publication is available at: http://dx.doi.org/10.1109/ICIP.2018.8451026
A TWO-STEP LEARNING METHOD FOR DETECTING LANDMARKS
ON FACES FROM DIFFERENT DOMAINS
Bruna Vieira Frade
Erickson R. Nascimento
Universidade Federal de Minas Gerais (UFMG), Brazil
{brunafrade,"
28aa89b2c827e5dd65969a5930a0520fdd4a3dc7,Characterization and Classification of Faces across Age Progression,
28b061b5c7f88f48ca5839bc8f1c1bdb1e6adc68,Predicting User Annoyance Using Visual Attributes,"Predicting User Annoyance Using Visual Attributes
Gordon Christie
Virginia Tech
Amar Parkash
Goibibo
Ujwal Krothapalli
Virginia Tech
Devi Parikh
Virginia Tech"
17a85799c59c13f07d4b4d7cf9d7c7986475d01c,Extending Procrustes Analysis: Building Multi-view 2-D Models from 3-D Human Shape Samples,"ADVERTIMENT.  La  consulta  d’aquesta  tesi  queda  condicionada  a  l’acceptació  de  les  següents
ondicions  d'ús:  La  difusió  d’aquesta  tesi  per  mitjà  del  servei  TDX  (www.tesisenxarxa.net)  ha
estat  autoritzada  pels  titulars  dels  drets  de  propietat  intel·lectual  únicament  per  a  usos  privats
emmarcats en activitats d’investigació i docència. No s’autoritza la seva reproducció amb finalitats
de lucre ni la seva difusió i posada a disposició des d’un lloc aliè al servei TDX. No s’autoritza la
presentació  del  seu  contingut  en  una  finestra  o  marc  aliè  a  TDX  (framing).  Aquesta  reserva  de
drets afecta tant al resum de presentació de la tesi com als seus continguts. En la utilització o cita
de parts de la tesi és obligat indicar el nom de la persona autora.
ADVERTENCIA. La consulta de esta tesis queda condicionada a la aceptación de las siguientes
ondiciones de uso: La difusión de esta tesis por medio del servicio TDR (www.tesisenred.net) ha
sido autorizada por los titulares de los derechos de propiedad intelectual únicamente para usos
privados enmarcados en actividades de investigación y docencia. No se autoriza su reproducción
on finalidades de lucro ni su difusión y puesta a disposición desde un sitio ajeno al servicio TDR.
No se autoriza la presentación de su contenido en una ventana o marco ajeno a TDR (framing).
Esta  reserva  de  derechos  afecta  tanto  al  resumen  de  presentación  de  la  tesis  como  a  sus
ontenidos.  En  la  utilización  o  cita  de  partes  de  la  tesis  es  obligado  indicar  el  nombre  de  la
persona autora.
WARNING.  On  having  consulted  this  thesis  you’re  accepting  the  following  use  conditions:
Spreading  this  thesis  by  the  TDX  (www.tesisenxarxa.net)  service  has  been  authorized  by  the
titular of the intellectual property rights only for private uses placed in investigation and teaching"
17cf838720f7892dbe567129dcf3f7a982e0b56e,Global-Local Face Upsampling Network,"Global-Local Face Upsampling Network
Oncel Tuzel
Yuichi Taguchi
John R. Hershey
Mitsubishi Electric Research Labs (MERL), Cambridge, MA, USA"
178a82e3a0541fa75c6a11350be5bded133a59fd,BioHDD: a dataset for studying biometric identification on heavily degraded data,"Techset Composition Ltd, Salisbury
{IEE}BMT/Articles/Pagination/BMT20140045.3d
www.ietdl.org
Received on 15th July 2014
Revised on 17th September 2014
Accepted on 23rd September 2014
doi: 10.1049/iet-bmt.2014.0045
ISSN 2047-4938
BioHDD: a dataset for studying biometric
identification on heavily degraded data
Gil Santos1, Paulo T. Fiadeiro2, Hugo Proença1
Department of Computer Science, IT – Instituto de Telecomunicações, University of Beira Interior, Covilhã, Portugal
Department of Physics, Remote Sensing Unit – Optics, Optometry and Vision Sciences Group, University of Beira Interior,
Covilhã, Portugal
E-mail:"
17a995680482183f3463d2e01dd4c113ebb31608,Structured Label Inference for Visual Understanding,"IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. X, NO. Y, MONTH Z
Structured Label Inference for
Visual Understanding
Nelson Nauata, Hexiang Hu, Guang-Tong Zhou, Zhiwei Deng,
Zicheng Liao and Greg Mori"
1742ffea0e1051b37f22773613f10f69d2e4ed2c,Interactive Mirror for Smart Home,
174930cac7174257515a189cd3ecfdd80ee7dd54,Multi-view Face Detection Using Deep Convolutional Neural Networks,"Multi-view Face Detection Using Deep Convolutional
Neural Networks
Sachin Sudhakar Farfade
Yahoo
Mohammad Saberian
inc.com
Yahoo
Li-Jia Li
Yahoo"
1750db78b7394b8fb6f6f949d68f7c24d28d934f,Detecting Facial Retouching Using Supervised Deep Learning,"Detecting Facial Retouching Using Supervised
Deep Learning
Aparna Bharati, Richa Singh, Senior Member, IEEE, Mayank Vatsa, Senior Member, IEEE, Kevin W.
Bowyer, Fellow, IEEE"
173657da03e3249f4e47457d360ab83b3cefbe63,HKU-Face : A Large Scale Dataset for Deep Face Recognition Final Report,"HKU-Face: A Large Scale Dataset for
Deep Face Recognition
Final Report
Haicheng Wang
035140108
COMP4801 Final Year Project
Project Code: 17007"
7bbaa09c9e318da4370a83b126bcdb214e7f8428,"FaaSter, Better, Cheaper: The Prospect of Serverless Scientific Computing and HPC","FaaSter, Better, Cheaper: The Prospect of
Serverless Scientific Computing and HPC
Josef Spillner1, Cristian Mateos2, and David A. Monge3
Zurich University of Applied Sciences, School of Engineering
Service Prototyping Lab (blog.zhaw.ch/icclab/), 8401 Winterthur, Switzerland
ISISTAN Research Institute - CONICET - UNICEN
Campus Universitario, Paraje Arroyo Seco, Tandil (7000), Buenos Aires, Argentina
ITIC Research Institute, National University of Cuyo
Padre Jorge Contreras 1300, M5502JMA Mendoza, Argentina"
7b9961094d3e664fc76b12211f06e12c47a7e77d,Bridging biometrics and forensics,"Bridging Biometrics and Forensics
Yanjun Yan and Lisa Ann Osadciw
EECS, Syracuse University, Syracuse, NY, USA
{yayan,"
7b9b3794f79f87ca8a048d86954e0a72a5f97758,Passing an Enhanced Turing Test - Interacting with Lifelike Computer Representations of Specific Individuals,"DOI 10.1515/jisys-2013-0016      Journal of Intelligent Systems 2013; 22(4): 365–415
Avelino J. Gonzalez*, Jason Leigh, Ronald F. DeMara, Andrew
Johnson, Steven Jones, Sangyoon Lee, Victor Hung, Luc
Renambot, Carlos Leon-Barth, Maxine Brown, Miguel Elvir,
James Hollister and Steven Kobosko
Passing an Enhanced Turing Test –
Interacting with Lifelike Computer
Representations of Specific Individuals"
7bce4f4e85a3bfcd6bfb3b173b2769b064fce0ed,A Psychologically-Inspired Match-Score Fusion Model for Video-Based Facial Expression Recognition,"A Psychologically-Inspired Match-Score Fusion Model
for Video-Based Facial Expression Recognition
Albert Cruz, Bir Bhanu, Songfan Yang,
VISLab, EBUII-216, University of California Riverside,
Riverside, California, USA, 92521-0425
{acruz, bhanu,"
7b0f1fc93fb24630eb598330e13f7b839fb46cce,Learning to Find Eye Region Landmarks for Remote Gaze Estimation in Unconstrained Settings,"Learning to Find Eye Region Landmarks for Remote Gaze
Estimation in Unconstrained Settings
Seonwook Park
ETH Zurich
Xucong Zhang
MPI for Informatics
Andreas Bulling
MPI for Informatics
Otmar Hilliges
ETH Zurich"
7bdcd85efd1e3ce14b7934ff642b76f017419751,Learning Discriminant Face Descriptor,"Learning Discriminant Face Descriptor
Zhen Lei, Member, IEEE, Matti Pietika¨ inen, Fellow, IEEE, and Stan Z. Li, Fellow, IEEE"
7b3b7769c3ccbdf7c7e2c73db13a4d32bf93d21f,"On the design and evaluation of robust head pose for visual user interfaces: algorithms, databases, and comparisons","On the Design and Evaluation of Robust Head Pose for
Visual User Interfaces: Algorithms, Databases, and
Comparisons
Sujitha Martin
Laboratory of Intelligent and
Safe Automobiles
UCSD - La Jolla, CA, USA
Ashish Tawari
Laboratory of Intelligent and
Safe Automobiles
UCSD - La Jolla, CA, USA
Erik Murphy-Chutorian
Laboratory of Intelligent and
Safe Automobiles
UCSD - La Jolla, CA, USA
Shinko Y. Cheng
Laboratory of Intelligent and
Safe Automobiles
UCSD - La Jolla, CA, USA
Mohan Trivedi"
8f6d05b8f9860c33c7b1a5d704694ed628db66c7,Non-linear dimensionality reduction and sparse representation models for facial analysis. (Réduction de la dimension non-linéaire et modèles de la représentations parcimonieuse pour l'analyse du visage),"Non-linear dimensionality reduction and sparse
representation models for facial analysis
Yuyao Zhang
To cite this version:
Yuyao Zhang. Non-linear dimensionality reduction and sparse representation models for facial analysis.
Medical Imaging. INSA de Lyon, 2014. English. <NNT : 2014ISAL0019>. <tel-01127217>
HAL Id: tel-01127217
https://tel.archives-ouvertes.fr/tel-01127217
Submitted on 7 Mar 2015
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d’enseignement et de
recherche français ou étrangers, des laboratoires"
8f772d9ce324b2ef5857d6e0b2a420bc93961196,Facial Landmark Point Localization using Coarse-to-Fine Deep Recurrent Neural Network,"MAHPOD et al.: CFDRNN
Facial Landmark Point Localization using
Coarse-to-Fine Deep Recurrent Neural Network
Shahar Mahpod, Rig Das, Emanuele Maiorana, Yosi Keller, and Patrizio Campisi,"
8fda2f6b85c7e34d3e23927e501a4b4f7fc15b2a,Feature Selection with Annealing for Big Data Learning,"Feature Selection with Annealing for Big Data
Learning
Adrian Barbu, Yiyuan She, Liangjing Ding, Gary Gramajo"
8fa3478aaf8e1f94e849d7ffbd12146946badaba,Attributes for Classifier Feedback,"Attributes for Classifier Feedback
Amar Parkash1 and Devi Parikh2
Indraprastha Institute of Information Technology (Delhi, India)
Toyota Technological Institute (Chicago, US)"
8f9c37f351a91ed416baa8b6cdb4022b231b9085,Generative Adversarial Style Transfer Networks for Face Aging,"Generative Adversarial Style Transfer Networks for Face Aging
Sveinn Palsson
D-ITET, ETH Zurich
Eirikur Agustsson
D-ITET, ETH Zurich"
8f8c0243816f16a21dea1c20b5c81bc223088594,Local Directional Number Based Classification and Recognition of Expressions Using Subspace Methods,
8f3e3f0f97844d3bfd9e9ec566ac7a54f6931b09,"A Survey on Human Emotion Recognition Approaches, Databases and Applications","Electronic Letters on Computer Vision and Image Analysis 14(2):24-44; 2015
A Survey on Human Emotion Recognition Approaches,
Databases and Applications
C.Vinola*, K.Vimaladevi†
* Department of Computer Science and Engineering, Francis Xavier Engineering College, Tirunelveli,Tamilnadu,India
Department of Computer Science and Engineering, P.S.R Engineering College, Sivakasi, Tamilnadu,India
Received 7th Aug 2015; accepted 30th Nov 2015"
8f89aed13cb3555b56fccd715753f9ea72f27f05,Attended End-to-end Architecture for Age Estimation from Facial Expression Videos,"Attended End-to-end Architecture for Age
Estimation from Facial Expression Videos
Wenjie Pei, Hamdi Dibeklio˘glu, Member, IEEE, Tadas Baltruˇsaitis and David M.J. Tax"
8fd9c22b00bd8c0bcdbd182e17694046f245335f,Recognizing Facial Expressions in Videos,"Recognizing Facial Expressions in Videos
Lin Su, Matthew Balazsi"
8acdc4be8274e5d189fb67b841c25debf5223840,Improving clustering performance using independent component analysis and unsupervised feature learning,"Gultepe and Makrehchi
Hum. Cent. Comput. Inf. Sci.  (2018) 8:25
https://doi.org/10.1186/s13673-018-0148-3
RESEARCH
Improving clustering performance
using independent component analysis
nd unsupervised feature learning
Open Access
Eren Gultepe* and Masoud Makrehchi
*Correspondence:
Department of Electrical
nd Computer Engineering,
University of Ontario Institute
of Technology, 2000 Simcoe
St N, Oshawa, ON L1H 7K4,
Canada"
8a54f8fcaeeede72641d4b3701bab1fe3c2f730a,What do you think of my picture? Investigating factors of influence in profile images context perception,"What do you think of my picture? Investigating factors
of influence in profile images context perception
Filippo Mazza, Matthieu Perreira da Silva, Patrick Le Callet, Ingrid
Heynderickx
To cite this version:
Filippo Mazza, Matthieu Perreira da Silva, Patrick Le Callet, Ingrid Heynderickx. What do you
think of my picture? Investigating factors of influence in profile images context perception. Human
Vision and Electronic Imaging XX, Mar 2015, San Francisco, United States. Proc. SPIE 9394, Hu-
man Vision and Electronic Imaging XX, 9394, <http://spie.org/EI/conferencedetails/human-vision-
electronic-imaging>. <10.1117/12.2082817>. <hal-01149535>
HAL Id: hal-01149535
https://hal.archives-ouvertes.fr/hal-01149535
Submitted on 7 May 2015
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est"
8aae23847e1beb4a6d51881750ce36822ca7ed0b,Comparison Between Geometry-Based and Gabor-Wavelets-Based Facial Expression Recognition Using Multi-Layer Perceptron,"Comparison Between Geometry-Based and Gabor-Wavelets-Based
Facial Expression Recognition Using Multi-Layer Perceptron
Zhengyou Zhang

Shigeru Akamatsu
 Michael Lyons Michael Schuster
 ATR Human Information Processing Research Laboratories
 ATR Interpreting Telecommunications Research Laboratories
-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-02, Japan
INRIA, 2004 route des Lucioles, BP 93, F-06902 Sophia-Antipolis Cedex, France
e-mail:"
8a866bc0d925dfd8bb10769b8b87d7d0ff01774d,WikiArt Emotions: An Annotated Dataset of Emotions Evoked by Art,"WikiArt Emotions: An Annotated Dataset of Emotions Evoked by Art
Saif M. Mohammad and Svetlana Kiritchenko
National Research Council Canada"
8adb2fcab20dab5232099becbd640e9c4b6a905a,Beyond Euclidean Eigenspaces: Bayesian Matching for Visual Recognition,"Beyond Euclidean Eigenspaces:
Bayesian Matching for Visual Recognition
Baback Moghaddam
Alex Pentland
Mitsubishi Electric Research Laboratory
MIT Media Laboratory

 Broadway
 Ames St.
Cambridge, MA 
	, USA
Cambridge, MA 
	, USA"
8a91ad8c46ca8f4310a442d99b98c80fb8f7625f,2D Segmentation Using a Robust Active Shape Model With the EM Algorithm,"D Segmentation Using a Robust Active
Shape Model With the EM Algorithm
Carlos Santiago, Jacinto C. Nascimento, Member, IEEE, and Jorge S. Marques"
8aed6ec62cfccb4dba0c19ee000e6334ec585d70,Localizing and Visualizing Relative Attributes,"Localizing and Visualizing Relative Attributes
Fanyi Xiao and Yong Jae Lee"
8a336e9a4c42384d4c505c53fb8628a040f2468e,Detecting Visually Observable Disease Symptoms from Faces,"Wang and Luo EURASIP Journal on Bioinformatics
nd Systems Biology  (2016) 2016:13
DOI 10.1186/s13637-016-0048-7
R ES EAR CH
Detecting Visually Observable Disease
Symptoms from Faces
Kuan Wang* and Jiebo Luo
Open Access"
7e3367b9b97f291835cfd0385f45c75ff84f4dc5,Improved local binary pattern based action unit detection using morphological and bilateral filters,"Improved Local Binary Pattern Based Action Unit Detection Using
Morphological and Bilateral Filters
Anıl Y¨uce1, Matteo Sorci2 and Jean-Philippe Thiran1
Signal Processing Laboratory (LTS5)
´Ecole Polytechnique F´ed´erale de Lausanne,
Switzerland
nViso SA
Lausanne, Switzerland"
7ef0cc4f3f7566f96f168123bac1e07053a939b2,Triangular Similarity Metric Learning: a Siamese Architecture Approach. ( L'apprentissage de similarité triangulaire en utilisant des réseaux siamois),"Triangular Similarity Metric Learning: a Siamese
Architecture Approach
Lilei Zheng
To cite this version:
Lilei Zheng. Triangular Similarity Metric Learning: a Siamese Architecture Approach. Com-
puter Science [cs]. UNIVERSITE DE LYON, 2016. English. <NNT : 2016LYSEI045>. <tel-
01314392>
HAL Id: tel-01314392
https://hal.archives-ouvertes.fr/tel-01314392
Submitted on 11 May 2016
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destin´ee au d´epˆot et `a la diffusion de documents
scientifiques de niveau recherche, publi´es ou non,
´emanant des ´etablissements d’enseignement et de"
7ee53d931668fbed1021839db4210a06e4f33190,What If We Do Not have Multiple Videos of the Same Action? &#x2014; Video Action Localization Using Web Images,"What if we do not have multiple videos of the same action? —
Video Action Localization Using Web Images
Center for Research in Computer Vision (CRCV), University of Central Florida (UCF)
Waqas Sultani, Mubarak Shah"
7e9df45ece7843fe050033c81014cc30b3a8903a,Audio-visual intent-to-speak detection for human-computer interaction,"AUDIO-VISUAL INTENT-TO-SPEAK DETECTION FOR HUMAN-COMPUTER
INTERACTION
Philippe de Cuetos
Institut Eurecom
	, route des Cr^etes, BP 
	
	 Sophia-Antipolis Cedex, FRANCE
Chalapathy Neti, Andrew W. Senior
IBM T.J. Watson Research Center
Yorktown Heights, NY 
	, USA
cneti,aws"
7ebd323ddfe3b6de8368c4682db6d0db7b70df62,Location-based Face Recognition Using Smart Mobile Device Sensors,"Proceedings of the International Conference on Computer and Information Science and Technology
Ottawa, Ontario, Canada, May 11 – 12, 2015
Paper No. 111
Location-based Face Recognition Using Smart Mobile Device
Sensors
Nina Taherimakhsousi, Hausi A. Müller
Department of Computer Science
University of Victoria, Victoria, Canada"
7ed6ff077422f156932fde320e6b3bd66f8ffbcb,State of 3D Face Biometrics for Homeland Security Applications,"State of 3D Face Biometrics for Homeland Security Applications
Anshuman Razdan1, Gerald Farin2, Myung Soo-Bae3 and Mahesh
Chaudhari4"
7e507370124a2ac66fb7a228d75be032ddd083cc,Dynamic Pose-Robust Facial Expression Recognition by Multi-View Pairwise Conditional Random Forests,"This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TAFFC.2017.2708106, IEEE
Transactions on Affective Computing
Dynamic Pose-Robust Facial Expression
Recognition by Multi-View Pairwise Conditional
Random Forests
Arnaud Dapogny1 and Kevin Bailly1 and S´everine Dubuisson1
Sorbonne Universit´es, UPMC Univ Paris 06
CNRS, UMR 7222, F-75005, Paris, France"
10e7dd3bbbfbc25661213155e0de1a9f043461a2,Cross Euclidean-to-Riemannian Metric Learning with Application to Face Recognition from Video,"Cross Euclidean-to-Riemannian Metric Learning
with Application to Face Recognition from Video
Zhiwu Huang, Member, IEEE, Ruiping Wang, Member, IEEE, Shiguang Shan, Senior Member, IEEE,
Luc Van Gool, Member, IEEE and Xilin Chen, Fellow, IEEE"
10ce3a4724557d47df8f768670bfdd5cd5738f95,Fisher Light-Fields for Face Recognition across Pose and Illumination,"Fihe igh	Fied f Face Recgii
Ac e ad 
	
iai
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ai ahew ad Si
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The Rbic 
i	e Caegie e Uiveiy
5000 Fbe Ave	e ib	gh A 15213
Abac. 
 
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age 
ay be avaiabe each ca	ed f

di(cid:11)ee e ad 	de a di(cid:11)ee i	
iai. We e a face
ecgii agih
 which ca 	e ay 	
be f gaey i
age e
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iai
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iai. The agih
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age. achig bewee he be ad gaey i he ef
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he Fihe igh	(cid:12)ed.

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age ae
di(cid:11)ee. The gaey cai he i
age 	ed d	ig aiig f he agih
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 ae eed wih he i
age i he be e. F exa
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102e374347698fe5404e1d83f441630b1abf62d9,Facial Image Analysis for Fully Automatic Prediction of Difficult Endotracheal Intubation,"Facial Image Analysis for Fully-Automatic
Prediction of Difficult Endotracheal Intubation
Gabriel L. Cuendet, Student Member, IEEE, Patrick Schoettker, Anıl Y¨uce Student Member, IEEE, Matteo Sorci,
Hua Gao, Christophe Perruchoud, Jean-Philippe Thiran, Senior Member, IEEE"
100641ed8a5472536dde53c1f50fa2dd2d4e9be9,Visual attributes for enhanced human-machine communication,"Visual Attributes for Enhanced Human-Machine Communication*
Devi Parikh1"
101569eeef2cecc576578bd6500f1c2dcc0274e2,Multiaccuracy: Black-Box Post-Processing for Fairness in Classification,"Multiaccuracy: Black-Box Post-Processing for Fairness in
Michael P. Kim∗†
Classification
Amirata Ghorbani∗
James Zou"
106732a010b1baf13c61d0994552aee8336f8c85,Expanded Parts Model for Semantic Description of Humans in Still Images,"Expanded Parts Model for Semantic Description
of Humans in Still Images
Gaurav Sharma, Member, IEEE, Fr´ed´eric Jurie, and Cordelia Schmid, Fellow, IEEE"
102b27922e9bd56667303f986404f0e1243b68ab,Multiscale recurrent regression networks for face alignment,"Wang et al. Appl Inform  (2017) 4:13
DOI 10.1186/s40535-017-0042-5
RESEARCH
Multiscale recurrent regression networks
for face alignment
Open Access
Caixun Wang1,2,3, Haomiao Sun1,2,3, Jiwen Lu1,2,3*, Jianjiang Feng1,2,3 and Jie Zhou1,2,3
*Correspondence:
State Key Lab of Intelligent
Technologies and Systems,
Beijing 100084, People’s
Republic of China
Full list of author information
is available at the end of the
rticle"
10fcbf30723033a5046db791fec2d3d286e34daa,On-Line Cursive Handwriting Recognition: A Survey of Methods and Performances,"On-Line Cursive Handwriting Recognition: A Survey of Methods
nd Performances
Dzulkifli Mohamad* ,  2Muhammad Faisal Zafar*,    and  3Razib M. Othman*
*Faculty of Computer Science & Information Systems, Universiti Teknologi Malaysia (UTM) , 81310
Skudai, Johor, Malaysia."
108b2581e07c6b7ca235717c749d45a1fa15bb24,Using Stereo Matching with General Epipolar Geometry for 2D Face Recognition across Pose,"Using Stereo Matching with General Epipolar
Geometry for 2D Face Recognition
cross Pose
Carlos D. Castillo, Student Member, IEEE, and
David W. Jacobs, Member, IEEE"
10d334a98c1e2a9e96c6c3713aadd42a557abb8b,Scene Text Recognition Using Part-Based Tree-Structured Character Detection,"Scene Text Recognition using Part-based Tree-structured Character Detection
Cunzhao Shi, Chunheng Wang, Baihua Xiao, Yang Zhang, Song Gao and Zhong Zhang
State Key Laboratory of Management and Control for Complex Systems, CASIA, Beijing, China"
1048c753e9488daa2441c50577fe5fdba5aa5d7c,Recognising faces in unseen modes: A tensor based approach,"Recognising faces in unseen modes: a tensor based approach
Santu Rana, Wanquan Liu, Mihai Lazarescu and Svetha Venkatesh
{santu.rana, wanquan, m.lazarescu,
Dept. of Computing, Curtin University of Technology
GPO Box U1987, Perth, WA 6845, Australia."
19841b721bfe31899e238982a22257287b9be66a,Recurrent Neural Networks,"Published as a conference paper at ICLR 2018
SKIP RNN: LEARNING TO SKIP STATE UPDATES IN
RECURRENT NEURAL NETWORKS
V´ıctor Campos∗†, Brendan Jou‡, Xavier Gir´o-i-Nieto§, Jordi Torres†, Shih-Fu ChangΓ
Barcelona Supercomputing Center, ‡Google Inc,
§Universitat Polit`ecnica de Catalunya, ΓColumbia University
{victor.campos,"
192723085945c1d44bdd47e516c716169c06b7c0,Vision and Attention Theory Based Sampling for Continuous Facial Emotion Recognition,"This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation
Vision and Attention Theory Based Sampling
for Continuous Facial Emotion Recognition
Albert C. Cruz, Student Member, IEEE, Bir Bhanu, Fellow, IEEE, and
Ninad S. Thakoor, Member, IEEE"
19fb5e5207b4a964e5ab50d421e2549ce472baa8,Online emotional facial expression dictionary,"International Conference on Computer Systems and Technologies - CompSysTech’14
Online Emotional Facial Expression Dictionary
Léon Rothkrantz"
1962e4c9f60864b96c49d85eb897141486e9f6d1,Locality preserving embedding for face and handwriting digital recognition,"Neural Comput & Applic (2011) 20:565–573
DOI 10.1007/s00521-011-0577-7
O R I G I N A L A R T I C L E
Locality preserving embedding for face and handwriting digital
recognition
Zhihui Lai • MingHua Wan • Zhong Jin
Received: 3 December 2008 / Accepted: 11 March 2011 / Published online: 1 April 2011
Ó Springer-Verlag London Limited 2011
supervised manifold
the local sub-manifolds."
191674c64f89c1b5cba19732869aa48c38698c84,Face Image Retrieval Using Attribute - Enhanced Sparse Codewords,"International Journal of Advanced Technology in Engineering and Science                 www.ijates.com
Volume No.03, Issue No. 03, March 2015                                                   ISSN (online): 2348 – 7550
FACE IMAGE RETRIEVAL USING ATTRIBUTE -
ENHANCED SPARSE CODEWORDS
E.Sakthivel1 , M.Ashok kumar2
PG scholar, Communication Systems, Adhiyamaan College of Engineeing,Hosur,(India)
Asst. Prof., Electronics And Communication Engg., Adhiyamaan College of Engg.,Hosur,(India)"
190d8bd39c50b37b27b17ac1213e6dde105b21b8,Mining Weakly Labeled Web Facial Images for Search-Based Face Annotation,"This document is downloaded from DR-NTU, Nanyang Technological
University Library, Singapore.
Title
Mining weakly labeled web facial images for search-
ased face annotation
Author(s) Wang, Dayong; Hoi, Steven C. H.; He, Ying; Zhu, Jianke
Citation
Wang, D., Hoi, S. C. H., He, Y., & Zhu, J. (2014). Mining
weakly labeled web facial images for search-based face
nnotation. IEEE Transactions on Knowledge and Data
Engineering, 26(1), 166-179.
http://hdl.handle.net/10220/18955
Rights
© 2014 IEEE. Personal use of this material is permitted.
Permission from IEEE must be obtained for all other
uses, in any current or future media, including
reprinting/republishing this material for advertising or
promotional purposes, creating new collective works, for
resale or redistribution to servers or lists, or reuse of any
opyrighted component of this work in other works."
19af008599fb17bbd9b12288c44f310881df951c,Discriminative Local Sparse Representations for Robust Face Recognition,"Discriminative Local Sparse Representations for
Robust Face Recognition
Yi Chen, Umamahesh Srinivas, Thong T. Do, Vishal Monga, and Trac D. Tran"
19296e129c70b332a8c0a67af8990f2f4d4f44d1,Is that you? Metric learning approaches for face identification,"Metric Learning Approaches for Face Identification
Is that you?
M. Guillaumin, J. Verbeek and C. Schmid
LEAR team, INRIA Rhˆone-Alpes, France
Supplementary Material"
19666b9eefcbf764df7c1f5b6938031bcf777191,Group Component Analysis for Multiblock Data: Common and Individual Feature Extraction,"Group Component Analysis for Multi-block Data:
Common and Individual Feature Extraction
Guoxu Zhou, Andrzej Cichocki Fellow, IEEE, Yu Zhang, and Danilo Mandic Fellow, IEEE"
198b6beb53e0e61357825d57938719f614685f75,Vaulted Verification: A Scheme for Revocable Face Recognition,"Vaulted Verification: A Scheme for Revocable Face
Recognition
Michael Wilber
University of Colorado, Colorado Springs"
195d331c958f2da3431f37a344559f9bce09c0f7,Parsing occluded people by flexible compositions,"Parsing Occluded People by Flexible Compositions
Xianjie Chen, Alan Yuille
University of California, Los Angeles.
Figure 1: An illustration of the flexible compositions. Each connected sub-
tree of the full graph (include the full graph itself) is a flexible composition.
The flexible compositions that do not have certain parts are suitable for the
people with those parts occluded.
Figure 2: The absence of body parts evidence can help to predict occlusion.
However, absence of evidence is not evidence of absence.
It can fail in
some challenging scenes. The local image measurements near the occlusion
oundary (i.e., around the right elbow and left shoulder) can reliably provide
evidence of occlusion.
This paper presents an approach to parsing humans when there is signifi-
ant occlusion. We model humans using a graphical model which has a tree
structure building on recent work [1, 6] and exploit the connectivity prior
that, even in presence of occlusion, the visible nodes form a connected sub-
tree of the graphical model. We call each connected subtree a flexible com-
position of object parts. This involves a novel method for learning occlusion
ues. During inference we need to search over a mixture of different flexible"
19c0c7835dba1a319b59359adaa738f0410263e8,Natural Image Statistics and Low-Complexity Feature Selection,"Natural Image Statistics and
Low-Complexity Feature Selection
Manuela Vasconcelos and Nuno Vasconcelos, Senior Member, IEEE"
19d583bf8c5533d1261ccdc068fdc3ef53b9ffb9,FaceNet: A unified embedding for face recognition and clustering,"FaceNet: A Unified Embedding for Face Recognition and Clustering
Florian Schroff
Dmitry Kalenichenko
James Philbin
Google Inc.
Google Inc.
Google Inc."
1910f5f7ac81d4fcc30284e88dee3537887acdf3,Semantic Based Hypergraph Reranking Model for Web Image Search,"Volume 6, Issue 5, May 2016                                   ISSN: 2277 128X
International Journal of Advanced Research in
Computer Science and Software Engineering
Research Paper
Available online at: www.ijarcsse.com
Semantic Based Hypergraph Reranking Model for Web
Image Search
Amol Darkunde, 2Manoj Jalan, 3Yelmar Mahesh, 4Shivadatta Shinde, 5Dnyanda Patil
, 2, 3, 4 B. E.  Dept of CSE, 5 Asst. Prof. Dept of CSE
, 2, 3, 4, 5 Dr.D.Y.Patil College of Engineering, Pune, Maharashtra, India"
197c64c36e8a9d624a05ee98b740d87f94b4040c,Regularized Greedy Column Subset Selection,"Regularized Greedy Column Subset Selection
Bruno Ordozgoiti*a, Alberto Mozoa, Jes´us Garc´ıa L´opez de Lacalleb
Department of Computer Systems, Universidad Polit´ecnica de Madrid
Department of Applied Mathematics, Universidad Polit´ecnica de Madrid"
19d4855f064f0d53cb851e9342025bd8503922e2,Learning SURF Cascade for Fast and Accurate Object Detection,"Learning SURF Cascade for Fast and Accurate Object Detection
Jianguo Li, Yimin Zhang
Intel Labs China"
193ec7bb21321fcf43bbe42233aed06dbdecbc5c,Automatic 3D Facial Expression Analysis in Videos,"UC Santa Barbara
UC Santa Barbara Previously Published Works
Title
Automatic 3D facial expression analysis in videos
Permalink
https://escholarship.org/uc/item/3g44f7k8
Authors
Chang, Y
Vieira, M
Turk, M
et al.
Publication Date
005-01-01
Peer reviewed
eScholarship.org
Powered by the California Digital Library
University of California"
4c6e1840451e1f86af3ef1cb551259cb259493ba,Hand Posture Dataset Creation for Gesture Recognition,"HAND POSTURE DATASET CREATION FOR GESTURE
RECOGNITION
Instituto de Sistemas Inteligentes y Aplicaciones Numericas en Ingenieria
Luis Anton-Canalis
Campus Universitario de Tafira, 35017 Gran Canaria, Spain
Elena Sanchez-Nielsen
Departamento de E.I.O. y Computacion
8271 Universidad de La Laguna, Spain
Keywords:
Image understanding, Gesture recognition, Hand dataset."
4c815f367213cc0fb8c61773cd04a5ca8be2c959,Facial expression recognition using curvelet based local binary patterns,"978-1-4244-4296-6/10/$25.00 ©2010 IEEE
ICASSP 2010"
4c4e49033737467e28aa2bb32f6c21000deda2ef,Improving Landmark Localization with Semi-Supervised Learning,"Improving Landmark Localization with Semi-Supervised Learning
Sina Honari1∗, Pavlo Molchanov2, Stephen Tyree2, Pascal Vincent1,4,5, Christopher Pal1,3, Jan Kautz2
MILA-University of Montreal, 2NVIDIA, 3Ecole Polytechnique of Montreal, 4CIFAR, 5Facebook AI Research.
{honaris,
{pmolchanov, styree,"
4c81c76f799c48c33bb63b9369d013f51eaf5ada,Multi-modal Score Fusion and Decision Trees for Explainable Automatic Job Candidate Screening from Video CVs,"Multi-modal Score Fusion and Decision Trees for Explainable Automatic Job
Candidate Screening from Video CVs
Heysem Kaya1, Furkan G¨urpınar2, and Albert Ali Salah2
Department of Computer Engineering, Namık Kemal University, Tekirda˘g, Turkey
Department of Computer Engineering, Bo˘gazic¸i University, Istanbul, Turkey"
4c1ce6bced30f5114f135cacf1a37b69bb709ea1,Gaze direction estimation by component separation for recognition of Eye Accessing Cues,"Gaze Direction Estimation by Component Separation for
Recognition of Eye Accessing Cues
Ruxandra Vrˆanceanu
Image Processing and Analysis Laboratory
University ”Politehnica” of Bucharest, Romania, Address Splaiul Independent¸ei 313
Corneliu Florea
Image Processing and Analysis Laboratory
University ”Politehnica” of Bucharest, Romania, Address Splaiul Independent¸ei 313
Laura Florea
Image Processing and Analysis Laboratory
University ”Politehnica” of Bucharest, Romania, Address Splaiul Independent¸ei 313
Constantin Vertan
Image Processing and Analysis Laboratory
University ”Politehnica” of Bucharest, Romania, Address Splaiul Independent¸ei 313"
2661f38aaa0ceb424c70a6258f7695c28b97238a,Multilayer Architectures for Facial Action Unit Recognition,"IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART B: CYBERNETICS, VOL. 42, NO. 4, AUGUST 2012
Multilayer Architectures for Facial
Action Unit Recognition
Tingfan Wu, Nicholas J. Butko, Paul Ruvolo, Jacob Whitehill, Marian S. Bartlett, and Javier R. Movellan"
264a84f4d27cd4bca94270620907cffcb889075c,Deep motion features for visual tracking,"Deep Motion Features for Visual Tracking
Susanna Gladh, Martin Danelljan, Fahad Shahbaz Khan, Michael Felsberg
Computer Vision Laboratory, Department of Electrical Engineering, Link¨oping University, Sweden"
26a72e9dd444d2861298d9df9df9f7d147186bcd,Collecting and annotating the large continuous action dataset,"DOI 10.1007/s00138-016-0768-4
ORIGINAL PAPER
Collecting and annotating the large continuous action dataset
Daniel Paul Barrett1 · Ran Xu2 · Haonan Yu1 · Jeffrey Mark Siskind1
Received: 18 June 2015 / Revised: 18 April 2016 / Accepted: 22 April 2016 / Published online: 21 May 2016
© The Author(s) 2016. This article is published with open access at Springerlink.com"
266766818dbc5a4ca1161ae2bc14c9e269ddc490,Boosting a Low-Cost Smart Home Environment with Usage and Access Control Rules,"Article
Boosting a Low-Cost Smart Home Environment with
Usage and Access Control Rules
Paolo Barsocchi * ID , Antonello Calabrò, Erina Ferro, Claudio Gennaro ID and Eda Marchetti and
Claudio Vairo
Institute of Information Science and Technologies of CNR (CNR-ISTI)-Italy, 56124 Pisa, Italy;
(A.C.); (E.F.); (C.G.);
(E.M.); (C.V.)
* Correspondence: Tel.: +39-050-315-2965
Received: 27 April 2018; Accepted: 31 May 2018; Published: 8 June 2018"
265af79627a3d7ccf64e9fe51c10e5268fee2aae,A Mixture of Transformed Hidden Markov Models for Elastic Motion Estimation,"A Mixture of Transformed Hidden Markov
Models for Elastic Motion Estimation
Huijun Di, Linmi Tao, and Guangyou Xu, Senior Member, IEEE"
26af867977f90342c9648ccf7e30f94470d40a73,Joint Gender and Face Recognition System for RGB-D Images with Texture and DCT Features,"IJIRST –International Journal for Innovative Research in Science & Technology| Volume 3 | Issue 04 | September 2016
ISSN (online): 2349-6010
Joint Gender and Face Recognition System for
RGB-D Images with Texture and DCT Features
Jesny Antony
PG Student
Department of Computer Science & Information Systems
Federal Institute of Science and Technology,  Mookkannoor
PO, Angamaly, Ernakulam, Kerala 683577, India
Prasad J. C.
Associate Professor
Department of Computer Science & Engineering
Federal Institute of Science and Technology,  Mookkannoor
PO, Angamaly, Ernakulam, Kerala 683577, India"
26c884829897b3035702800937d4d15fef7010e4,Facial Expression Recognition by Supervised Independent Component Analysis Using MAP Estimation,"IEICE TRANS. INF. & SYST., VOL.Exx–??, NO.xx XXXX 200x
PAPER
Facial Expression Recognition by Supervised Independent
Component Analysis using MAP Estimation
Fan CHEN
, Nonmember and Kazunori KOTANI
, Member
SUMMARY Permutation ambiguity of the classical Inde-
pendent Component Analysis (ICA) may cause problems in fea-
ture extraction for pattern classification. Especially when only a
small subset of components is derived from data, these compo-
nents may not be most distinctive for classification, because ICA
is an unsupervised method. We include a selective prior for de-
mixing coef‌f‌icients into the classical ICA to alleviate the problem.
Since the prior is constructed upon the classification information
from the training data, we refer to the proposed ICA model with
selective prior as a supervised ICA (sICA). We formulated the
learning rule for sICA by taking a Maximum a Posteriori (MAP)
scheme and further derived a fixed point algorithm for learning
the de-mixing matrix. We investigate the performance of sICA"
26ad6ceb07a1dc265d405e47a36570cb69b2ace6,Neural Correlates of Cross-Cultural Adaptation,"RESEARCH AND EXPLOR ATORY
DEVELOPMENT DEPARTMENT
REDD-2015-384
Neural Correlates of Cross-Cultural
How to Improve the Training and Selection for
Military Personnel Involved in Cross-Cultural
Operating Under Grant #N00014-12-1-0629/113056
Adaptation
September, 2015
Interactions
Jonathon Kopecky
Jason Spitaletta
Mike Wolmetz
Alice Jackson
Prepared for:
Office of Naval Research"
26437fb289cd7caeb3834361f0cc933a02267766,Innovative Assessment Technologies: Comparing ‘Face-to-Face’ and Game-Based Development of Thinking Skills in Classroom Settings,"012 International Conference on Management and Education Innovation
IPEDR vol.37 (2012) © (2012) IACSIT Press, Singapore
Innovative Assessment Technologies: Comparing ‘Face-to-Face’ and
Game-Based Development of Thinking Skills in Classroom Settings
Gyöngyvér Molnár 1 + and András Lőrincz 2
University of Szeged, 2 Eötvös Loránd University"
26e570049aaedcfa420fc8c7b761bc70a195657c,Hybrid Facial Regions Extraction for Micro-expression Recognition System,"J Sign Process Syst
DOI 10.1007/s11265-017-1276-0
Hybrid Facial Regions Extraction for Micro-expression
Recognition System
Sze-Teng Liong1,2,3 · John See4 · Raphael C.-W. Phan2 · KokSheik Wong5 ·
Su-Wei Tan2
Received: 2 February 2016 / Revised: 20 October 2016 / Accepted: 10 August 2017
© Springer Science+Business Media, LLC 2017"
21ef129c063bad970b309a24a6a18cbcdfb3aff5,Individual and Inter-related Action Unit Detection in Videos for Affect Recognition,"POUR L'OBTENTION DU GRADE DE DOCTEUR ÈS SCIENCESacceptée sur proposition du jury:Dr J.-M. Vesin, président du juryProf. J.-Ph. Thiran, Prof. D. Sander, directeurs de thèseProf. M. F. Valstar, rapporteurProf. H. K. Ekenel, rapporteurDr S. Marcel, rapporteurIndividual and Inter-related Action Unit Detection in Videos for Affect RecognitionTHÈSE NO 6837 (2016)ÉCOLE POLYTECHNIQUE FÉDÉRALE DE LAUSANNEPRÉSENTÉE LE 19 FÉVRIER 2016À LA FACULTÉ DES SCIENCES ET TECHNIQUES DE L'INGÉNIEURLABORATOIRE DE TRAITEMENT DES SIGNAUX 5PROGRAMME DOCTORAL EN GÉNIE ÉLECTRIQUE Suisse2016PARAnıl YÜCE"
218b2c5c9d011eb4432be4728b54e39f366354c1,Enhancing Training Collections for Image Annotation: An Instance-Weighted Mixture Modeling Approach,"Enhancing Training Collections for Image
Annotation: An Instance-Weighted Mixture
Modeling Approach
Neela Sawant, Student Member, IEEE, James Z. Wang, Senior Member, IEEE, Jia Li, Senior Member, IEEE."
2162654cb02bcd10794ae7e7d610c011ce0fb51b,Joint gaze-correction and beautification of DIBR-synthesized human face via dual sparse coding,"978-1-4799-5751-4/14/$31.00 ©2014 IEEE
http://www.skype.com/
http://www.google.com/hangouts/
tification, sparse coding"
21f3c5b173503185c1e02a3eb4e76e13d7e9c5bc,Rotation Invariant Real-time Face Detection and Recognition System,"m a s s a c h u s e t t s   i n s t i t u t e   o f
t e c h n o l o g y   — a r t i f i c i a l   i n t e l l i g e n c e   l a b o r a t o r y
Rotation Invariant Real-time
Face Detection and
Recognition System
Purdy Ho
AI Memo 2001-010
CBCL Memo 197
May 31, 2001
© 2 0 0 1   m a s s a c h u s e t t s   i n s t i t u t e   o f
t e c h n o l o g y, c a m b r i d g e , m a   0 2 1 3 9   u s a   —   w w w. a i . m i t . e d u"
214db8a5872f7be48cdb8876e0233efecdcb6061,Semantic-Aware Co-Indexing for Image Retrieval,"Semantic-aware Co-indexing for Image Retrieval
Shiliang Zhang2, Ming Yang1, Xiaoyu Wang1, Yuanqing Lin1, Qi Tian2
NEC Laboratories America, Inc.
Dept. of CS, Univ. of Texas at San Antonio
Cupertino, CA 95014
San Antonio, TX 78249"
214ac8196d8061981bef271b37a279526aab5024,Face Recognition Using Smoothed High-Dimensional Representation,"Face Recognition Using Smoothed High-Dimensional
Representation
Juha Ylioinas, Juho Kannala, Abdenour Hadid, and Matti Pietik¨ainen
Center for Machine Vision Research, PO Box 4500,
FI-90014 University of Oulu, Finland"
213a579af9e4f57f071b884aa872651372b661fd,Automatic and Efficient Human Pose Estimation for Sign Language Videos,"Int J Comput Vis
DOI 10.1007/s11263-013-0672-6
Automatic and Efficient Human Pose Estimation for Sign
Language Videos
James Charles · Tomas Pfister · Mark Everingham ·
Andrew Zisserman
Received: 4 February 2013 / Accepted: 29 October 2013
© Springer Science+Business Media New York 2013"
21626caa46cbf2ae9e43dbc0c8e789b3dbb420f1,Transductive VIS-NIR face matching,"978-1-4673-2533-2/12/$26.00 ©2012 IEEE
ICIP 2012"
21b16df93f0fab4864816f35ccb3207778a51952,Recognition of Static Gestures Applied to Brazilian Sign Language (Libras),"Recognition of Static Gestures applied to Brazilian Sign Language (Libras)
Igor L. O. Bastos
Math Institute
Michele F. Angelo, Angelo C. Loula
Department of Technology, Department of Exact Sciences
Federal University of Bahia (UFBA),
State University of Feira de Santana (UEFS)
Salvador, Brazil
Feira de Santana, Brazil"
4d49c6cff198cccb21f4fa35fd75cbe99cfcbf27,Topological principal component analysis for face encoding and recognition,"Topological Principal Component Analysis for
face encoding and recognition
Albert Pujol 
, Jordi Vitri(cid:18)a, Felipe Lumbreras,
Juan J. Villanueva
Computer Vision Center and Departament d’Inform(cid:18)atica, Edi(cid:12)ci O, Universitat
Aut(cid:18)onoma de Barcelona 
	, Cerdanyola, Spain"
4da735d2ed0deeb0cae4a9d4394449275e316df2,"The rhythms of head, eyes and hands at intersections","Gothenburg, Sweden, June 19-22, 2016
978-1-5090-1820-8/16/$31.00 ©2016 IEEE"
4d530a4629671939d9ded1f294b0183b56a513ef,Facial Expression Classification Method Based on Pseudo Zernike Moment and Radial Basis Function Network,"International Journal of Machine Learning and Computing, Vol. 2, No. 4, August 2012
Facial Expression Classification Method Based on Pseudo
Zernike Moment and Radial Basis Function Network
Tran Binh Long, Le Hoang Thai, and Tran Hanh"
4d2975445007405f8cdcd74b7fd1dd547066f9b8,Image and Video Processing for Affective Applications,"Image and Video Processing
for Affective Applications
Maja Pantic and George Caridakis"
4db9e5f19366fe5d6a98ca43c1d113dac823a14d,"Are 1, 000 Features Worth A Picture? Combining Crowdsourcing and Face Recognition to Identify Civil War Soldiers","Combining Crowdsourcing and Face Recognition to Identify Civil War Soldiers
Are 1,000 Features Worth A Picture?
Vikram Mohanty, David Thames, Kurt Luther
Department of Computer Science and Center for Human-Computer Interaction
Virginia Tech, Arlington, VA, USA"
4de757faa69c1632066391158648f8611889d862,Review of Face Recognition Technology Using Feature Fusion Vector,"International Journal of Advanced Engineering Research and Science (IJAERS)                             Vol-3, Issue-3 , March- 2016]
ISSN: 2349-6495
Review of Face Recognition Technology Using
Feature Fusion Vector
Shrutika Shukla, Prof. Anuj Bhargav, Prof. Prashant Badal
Department of Electronics and Communication, S.R.C.E.M, Banmore, RGPV, University, Bhopal, Madhya Pradesh, India"
4d7e1eb5d1afecb4e238ba05d4f7f487dff96c11,Largest center-specific margin for dimension reduction,"978-1-5090-4117-6/17/$31.00 ©2017 IEEE
ICASSP 2017"
4d6ad0c7b3cf74adb0507dc886993e603c863e8c,Human Activity Recognition Based on Wearable Sensor Data : A Standardization of the State-ofthe-Art,"Human Activity Recognition Based on Wearable
Sensor Data: A Standardization of the
State-of-the-Art
Artur Jord˜ao, Antonio C. Nazare Jr., Jessica Sena and William Robson Schwartz
Smart Surveillance Interest Group, Computer Science Department
Universidade Federal de Minas Gerais, Brazil
Email: {arturjordao, antonio.nazare, jessicasena,"
4dca3d6341e1d991c902492952e726dc2a443d1c,Learning towards Minimum Hyperspherical Energy,"Learning towards Minimum Hyperspherical Energy
Weiyang Liu1,*, Rongmei Lin2,*, Zhen Liu1,*, Lixin Liu3,*, Zhiding Yu4, Bo Dai1,5, Le Song1,6
Georgia Institute of Technology 2Emory University
South China University of Technology 4NVIDIA 5Google Brain 6Ant Financial"
4d0ef449de476631a8d107c8ec225628a67c87f9,Face system evaluation toolkit: Recognition is harder than it seems,"© 2010 IEEE. Personal use of this material is permitted. Permission from IEEE
must  be  obtained  for  all  other  uses,  in  any  current  or  future  media,  including
reprinting/republishing  this  material  for  advertising  or  promotional  purposes,
reating  new  collective  works,  for  resale  or  redistribution  to  servers  or  lists,  or
reuse of any copyrighted component of this work in other works.
Pre-print of article that appeared at BTAS 2010.
The published article can be accessed from:
http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=5634517"
4d47261b2f52c361c09f7ab96fcb3f5c22cafb9f,Deep multi-frame face super-resolution,"Deep multi-frame face super-resolution
Evgeniya Ustinova, Victor Lempitsky
October 17, 2017"
4df3143922bcdf7db78eb91e6b5359d6ada004d2,The Chicago face database: A free stimulus set of faces and norming data.,"Behav Res (2015) 47:1122–1135
DOI 10.3758/s13428-014-0532-5
The Chicago face database: A free stimulus set of faces
nd norming data
Debbie S. Ma & Joshua Correll & Bernd Wittenbrink
Published online: 13 January 2015
# Psychonomic Society, Inc. 2015"
75879ab7a77318bbe506cb9df309d99205862f6c,Analysis of emotion recognition from facial expressions using spatial and transform domain methods,"Analysis Of Emotion Recognition From Facial
Expressions Using Spatial And Transform Domain
Methods
Ms. P. Suja* and Dr. Shikha Tripathi"
75503aff70a61ff4810e85838a214be484a674ba,Improved facial expression recognition via uni-hyperplane classification,"Improved Facial Expression Recognition via Uni-Hyperplane Classification
S.W. Chew∗, S. Lucey†, P. Lucey‡, S. Sridharan∗, and J.F. Cohn‡"
75308067ddd3c53721430d7984295838c81d4106,Rapid Facial Reactions in Response to Facial Expressions of Emotion Displayed by Real Versus Virtual Faces,"Article
Rapid Facial Reactions
in Response to Facial
Expressions of Emotion
Displayed by Real Versus
Virtual Faces
i-Perception
018 Vol. 9(4), 1–18
! The Author(s) 2018
DOI: 10.1177/2041669518786527
journals.sagepub.com/home/ipe
Leonor Philip, Jean-Claude Martin and Ce´ line Clavel
LIMSI, CNRS, University of Paris-Sud, Orsay, France"
759a3b3821d9f0e08e0b0a62c8b693230afc3f8d,Attribute and simile classifiers for face verification,"Attribute and Simile Classifiers for Face Verification
Neeraj Kumar
Alexander C. Berg
Peter N. Belhumeur
Columbia University∗
Shree K. Nayar"
75859ac30f5444f0d9acfeff618444ae280d661d,Multibiometric Cryptosystems Based on Feature-Level Fusion,"Multibiometric Cryptosystems based on Feature
Level Fusion
Abhishek Nagar, Student Member, IEEE, Karthik Nandakumar, Member, IEEE, and Anil K. Jain, Fellow, IEEE"
758d7e1be64cc668c59ef33ba8882c8597406e53,"AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild","IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
AffectNet: A Database for Facial Expression,
Valence, and Arousal Computing in the Wild
Ali Mollahosseini, Student Member, IEEE, Behzad Hasani, Student Member, IEEE,
nd Mohammad H. Mahoor, Senior Member, IEEE"
7553fba5c7f73098524fbb58ca534a65f08e91e7,A Practical Approach for Determination of Human Gender & Age,"Harpreet Kaur Bhatia et al, International Journal of Computer Science and Mobile Computing, Vol.3 Issue.6, June- 2014, pg. 816-824
Available Online at www.ijcsmc.com
International Journal of Computer Science and Mobile Computing
A Monthly Journal of Computer Science and Information Technology
ISSN 2320–088X
IJCSMC, Vol. 3, Issue. 6, June 2014, pg.816 – 824
RESEARCH ARTICLE
A Practical Approach for Determination
of Human Gender & Age
Harpreet Kaur Bhatia1, Ahsan Hussain2
CSE Dept. & CSVTU University, India
CSE Dept. & CSVTU University, India"
75249ebb85b74e8932496272f38af274fbcfd696,Face Identification in Large Galleries,"Face Identification in Large Galleries
Rafael H. Vareto, Filipe Costa, William Robson Schwartz
Smart Surveillance Interest Group, Department of Computer Science
Universidade Federal de Minas Gerais, Belo Horizonte, Brazil"
81a142c751bf0b23315fb6717bc467aa4fdfbc92,Pairwise Trajectory Representation for Action Recognition,"978-1-5090-4117-6/17/$31.00 ©2017 IEEE
ICASSP 2017"
81bfe562e42f2eab3ae117c46c2e07b3d142dade,A Hajj And Umrah Location Classification System For Video Crowded Scenes,"A Hajj And Umrah Location Classification System For Video
Crowded Scenes
Hossam M. Zawbaa†
Salah A. Aly†‡
Adnan A. Gutub†
Center of Research Excellence in Hajj and Umrah, Umm Al-Qura University, Makkah, KSA
College of Computers and Information Systems, Umm Al-Qura University, Makkah, KSA"
81695fbbbea2972d7ab1bfb1f3a6a0dbd3475c0f,Comparison of Face Recognition Neural Networks,"UNIVERSITY OF TARTU
FACULTY OF SCIENCE AND TECHNOLOGY
Institute of Computer Science
Computer Science
Zepp Uibo
Comparison of Face Recognition
Neural Networks
Bachelor's thesis (6 ECST)
Supervisor: Tambet Matiisen
Tartu 2016"
8147ee02ec5ff3a585dddcd000974896cb2edc53,Angular Embedding: A Robust Quadratic Criterion,"Angular Embedding:
A Robust Quadratic Criterion
Stella X. Yu, Member,"
8199803f476c12c7f6c0124d55d156b5d91314b6,The iNaturalist Species Classification and Detection Dataset,"The iNaturalist Species Classification and Detection Dataset
Grant Van Horn1 Oisin Mac Aodha1 Yang Song2 Yin Cui3 Chen Sun2
Alex Shepard4 Hartwig Adam2
Pietro Perona1
Serge Belongie3
Caltech
Google
Cornell Tech
iNaturalist"
81706277ed180a92d2eeb94ac0560f7dc591ee13,Emotion based Contextual Semantic Relevance Feedback in Multimedia Information Retrieval,"International Journal of Computer Applications (0975 – 8887)
Volume 55– No.15, October 2012
Emotion based Contextual Semantic Relevance
Feedback in Multimedia Information Retrieval
Karm Veer Singh
Department of Computer Engineering, Indian
Institute of Technology, Banaras Hindu
University,Varanasi, 221005, India
Anil K. Tripathi
Department of Computer Engineering, Indian
Institute of Technology, Banaras Hindu
University,Varanasi, 221005, India
find  some
issued  by  a  user"
81b2a541d6c42679e946a5281b4b9dc603bc171c,Semi-supervised learning with committees: exploiting unlabeled data using ensemble learning algorithms,"Universit¨at Ulm | 89069 Ulm | Deutschland
Fakult¨at f¨ur Ingenieurwissenschaften und Informatik
Institut f¨ur Neuroinformatik
Direktor: Prof. Dr. G¨unther Palm
Semi-Supervised Learning with Committees:
Exploiting Unlabeled Data Using Ensemble
Learning Algorithms
Dissertation zur Erlangung des Doktorgrades
Doktor der Naturwissenschaften (Dr. rer. nat.)
der Fakult¨at f¨ur Ingenieurwissenschaften und Informatik
der Universit¨at Ulm
vorgelegt von
Mohamed Farouk Abdel Hady
us Kairo, ¨Agypten
Ulm, Deutschland"
8160b3b5f07deaa104769a2abb7017e9c031f1c1,Exploiting discriminant information in nonnegative matrix factorization with application to frontal face verification,"Exploiting Discriminant Information in Nonnegative
Matrix Factorization With Application
to Frontal Face Verification
Stefanos Zafeiriou, Anastasios Tefas, Member, IEEE, Ioan Buciu, and Ioannis Pitas, Senior Member, IEEE"
814d091c973ff6033a83d4e44ab3b6a88cc1cb66,The EU-Emotion Stimulus Set: A validation study.,"Behav Res (2016) 48:567–576
DOI 10.3758/s13428-015-0601-4
The EU-Emotion Stimulus Set: A validation study
Helen O’Reilly 1,2 & Delia Pigat 1 & Shimrit Fridenson 5 & Steve Berggren 3,4 & Shahar Tal 5 &
Ofer Golan 5 & Sven Bölte 3,4 & Simon Baron-Cohen 1,6 & Daniel Lundqvist 3
Published online: 30 September 2015
# Psychonomic Society, Inc. 2015"
816eff5e92a6326a8ab50c4c50450a6d02047b5e,fLRR: Fast Low-Rank Representation Using Frobenius Norm,"fLRR: Fast Low-Rank Representation Using
Frobenius Norm
Haixian Zhang, Zhang Yi, and Xi Peng
Low Rank Representation (LRR) intends to find the representation
with lowest-rank of a given data set, which can be formulated as a
rank minimization problem. Since the rank operator is non-convex and
discontinuous, most of the recent works use the nuclear norm as a convex
relaxation. This letter theoretically shows that under some conditions,
Frobenius-norm-based optimization problem has an unique solution that
is also a solution of the original LRR optimization problem. In other
words, it is feasible to apply Frobenius-norm as a surrogate of the
nonconvex matrix rank function. This replacement will largely reduce the
time-costs for obtaining the lowest-rank solution. Experimental results
show that our method (i.e., fast Low Rank Representation, fLRR),
performs well in terms of accuracy and computation speed in image
lustering and motion segmentation compared with nuclear-norm-based
LRR algorithm.
Introduction: Given a data set X ∈ Rm×n(m < n) composed of column
vectors, let A be a data set composed of vectors with the same dimension
s those in X. Both X and A can be considered as matrices. A linear"
8149c30a86e1a7db4b11965fe209fe0b75446a8c,Semi-supervised multiple instance learning based domain adaptation for object detection,"Semi-Supervised Multiple Instance Learning based
Domain Adaptation for Object Detection
Siemens Corporate Research
Siemens Corporate Research
Siemens Corporate Research
Amit Kale
Bangalore
Chhaya Methani
Bangalore
{chhaya.methani,
Rahul Thota
Bangalore
rahul.thota,"
86614c2d2f6ebcb9c600d4aef85fd6bf6eab6663,Benchmarks for Cloud Robotics,"Benchmarks for Cloud Robotics
Arjun Singh
Electrical Engineering and Computer Sciences
University of California at Berkeley
Technical Report No. UCB/EECS-2016-142
http://www.eecs.berkeley.edu/Pubs/TechRpts/2016/EECS-2016-142.html
August 12, 2016"
86b69b3718b9350c9d2008880ce88cd035828432,Improving Face Image Extraction by Using Deep Learning Technique,"Improving Face Image Extraction by Using Deep Learning Technique
Zhiyun Xue, Sameer Antani, L. Rodney Long, Dina Demner-Fushman, George R. Thoma
National Library of Medicine, NIH, Bethesda, MD"
86904aee566716d9bef508aa9f0255dc18be3960,Learning Anonymized Representations with Adversarial Neural Networks,"Learning Anonymized Representations with
Adversarial Neural Networks
Cl´ement Feutry, Pablo Piantanida, Yoshua Bengio, and Pierre Duhamel"
867e709a298024a3c9777145e037e239385c0129,Analytical Representation of Undersampled Face Recognition Approach Based on Dictionary Learning and Sparse Representation,"INTERNATIONAL JOURNAL
OF PROFESSIONAL ENGINEERING STUDIES                                                                                                            Volume VIII /Issue 2 / FEB 2017
ANALYTICAL REPRESENTATION OF UNDERSAMPLED FACE
RECOGNITION APPROACH BASED ON DICTIONARY LEARNING
AND  SPARSE REPRESENTATION
Murala Sandeep1 A.Mallikarjuna Reddy2 P.Rajashaker Reddy3 Dr. G. Vishnu murthy4
(M.Tech)1, Assistant Professor2, Assistant Professor3, HOD of CSE Department4
Anurag group of institutions Ghatkesar, Ranga Reddy, Hyderabad, India"
869a2fbe42d3fdf40ed8b768edbf54137be7ac71,Relative Attributes for Enhanced Human-Machine Communication,"Relative Attributes for Enhanced Human-Machine Communication
Devi Parikh1, Adriana Kovashka3, Amar Parkash2, and Kristen Grauman3
Toyota Technological Institute, Chicago
Indraprastha Institute of Information Technology, Delhi
University of Texas, Austin"
86c053c162c08bc3fe093cc10398b9e64367a100,Cascade of forests for face alignment,"Cascade of Forests for Face Alignment
Heng Yang, Changqing Zou, Ioannis Patras"
861802ac19653a7831b314cd751fd8e89494ab12,"Time-of-Flight and Depth Imaging. Sensors, Algorithms, and Applications","Marcin Grzegorzek, Christian Theobalt, Reinhard Koch,
Andreas Kolb
Time-of-Flight and Depth Imaging. Sensors, Algorithms
nd Applications: Dagstuhl Seminar 2012 and GCPR
Workshop on Imaging New Modalities (Lecture ... Vision,
Pattern Recognition, and Graphics)
Publisher: Springer; 2013 edition
(November 8, 2013)
Language: English
Pages: 320
ISBN: 978-3642449635
Size: 20.46 MB
Format: PDF / ePub / Kindle
Cameras for 3D depth imaging, using
either time-of-flight (ToF) or
structured light sensors, have received
lot of attention recently and have
een improved considerably over the
last few years. The present
techniques..."
861b12f405c464b3ffa2af7408bff0698c6c9bf0,An Effective Technique for Removal of Facial Dupilcation by SBFA,"International Journal on Recent and Innovation Trends in Computing and Communication                                                     ISSN: 2321-8169
Volume: 3 Issue: 5
3337 - 3342
_______________________________________________________________________________________________
An Effective Technique for Removal of Facial Dupilcation by SBFA
Miss. Deepika B. Patil
Computer Department,
GHRCEM,
Pune, India
Dr. Ayesha Butalia
Computer Department,
GHRCEM,
Pune, India"
86e1bdbfd13b9ed137e4c4b8b459a3980eb257f6,The Kinetics Human Action Video Dataset,"The Kinetics Human Action Video Dataset
Will Kay
Jo˜ao Carreira
Karen Simonyan
Brian Zhang
Chloe Hillier
Sudheendra Vijayanarasimhan
Fabio Viola
Tim Green
Trevor Back
Paul Natsev
Mustafa Suleyman
Andrew Zisserman"
86b6de59f17187f6c238853810e01596d37f63cd,Competitive Representation Based Classification Using Facial Noise Detection,"(IJACSA) International Journal of Advanced Computer Science and Applications,
Vol. 7, No. 3, 2016
Competitive Representation Based Classification
Using Facial Noise Detection
Tao Liu
Ying Liu
Chongqing Key Laboratory of Computational Intelligence
College of Computer Science and Technology, Chongqing
Chongqing Key Laboratory of Computational Intelligence
College of Computer Science and Technology, Chongqing
University of Posts and Telecommunications
University of Posts and Telecommunications
Chongqing, China
Chongqing, China
Cong Li
Chao Li
Chongqing Key Laboratory of Computational Intelligence
College of Computer Science and Technology, Chongqing
Chongqing Key Laboratory of Computational Intelligence
College of Computer Science and Technology, Chongqing"
86b105c3619a433b6f9632adcf9b253ff98aee87,A Mutual Information based Face Clustering Algorithm for Movies,"­4244­0367­7/06/$20.00 ©2006 IEEE
ICME 2006"
72a87f509817b3369f2accd7024b2e4b30a1f588,Fault diagnosis of a railway device using semi-supervised independent factor analysis with mixing constraints,"Fault diagnosis of a railway device using semi-supervised
independent factor analysis with mixing constraints
Etienne Côme, Latifa Oukhellou, Thierry Denoeux, Patrice Aknin
To cite this version:
Etienne Côme, Latifa Oukhellou, Thierry Denoeux, Patrice Aknin. Fault diagnosis of a railway device
using semi-supervised independent factor analysis with mixing constraints. Pattern Analysis and
Applications, Springer Verlag, 2012, 15 (3), pp.313-326. <hal-00750589>
HAL Id: hal-00750589
https://hal.archives-ouvertes.fr/hal-00750589
Submitted on 11 Nov 2012
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d’enseignement et de"
72a00953f3f60a792de019a948174bf680cd6c9f,Understanding the role of facial asymmetry in human face identification,"Stat Comput (2007) 17:57–70
DOI 10.1007/s11222-006-9004-9
Understanding the role of facial asymmetry in human face
identification
Sinjini Mitra · Nicole A. Lazar · Yanxi Liu
Received: May 2005 / Accepted: September 2006 / Published online: 30 January 2007
C(cid:1) Springer Science + Business Media, LLC 2007"
727ecf8c839c9b5f7b6c7afffe219e8b270e7e15,Leveraging Geo-referenced Digital Photographs a Dissertation Submitted to the Department of Computer Science and the Committee on Graduate Studies of Stanford University in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy,"LEVERAGING GEO-REFERENCED DIGITAL PHOTOGRAPHS
A DISSERTATION
SUBMITTED TO THE DEPARTMENT OF COMPUTER SCIENCE
AND THE COMMITTEE ON GRADUATE STUDIES
OF STANFORD UNIVERSITY
IN PARTIAL FULFILLMENT OF THE REQUIREMENTS
FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY
Mor Naaman
July 2005"
72ecaff8b57023f9fbf8b5b2588f3c7019010ca7,Facial Keypoints Detection,"Facial Keypoints Detection
Shenghao Shi"
72591a75469321074b072daff80477d8911c3af3,Group Component Analysis for Multiblock Data: Common and Individual Feature Extraction,"Group Component Analysis for Multi-block Data:
Common and Individual Feature Extraction
Guoxu Zhou, Andrzej Cichocki Fellow, IEEE, Yu Zhang, and Danilo Mandic Fellow, IEEE"
729a9d35bc291cc7117b924219bef89a864ce62c,Recognizing Material Properties from Images,"Recognizing Material Properties from Images
Gabriel Schwartz and Ko Nishino, Senior Member, IEEE"
721d9c387ed382988fce6fa864446fed5fb23173,Assessing Facial Expressions in Virtual Reality Environments,
72c0c8deb9ea6f59fde4f5043bff67366b86bd66,Age progression in Human Faces : A Survey,"Age progression in Human Faces : A Survey
Narayanan Ramanathan, Rama Chellappa and Soma Biswas"
72f4aaf7e2e3f215cd8762ce283988220f182a5b,Active illumination and appearance model for face alignment,"Turk J Elec Eng & Comp Sci, Vol.18, No.4, 2010, c(cid:2) T ¨UB˙ITAK
doi:10.3906/elk-0906-48
Active illumination and appearance model for face
lignment
Fatih KAHRAMAN1, Muhittin G ¨OKMEN 2, Sune DARKNER3, Rasmus LARSEN3
Institute of Informatics, ˙Istanbul Technical University, ˙Istanbul, 34469, TURKEY
Department of Computer Engineering, ˙Istanbul Technical University, ˙Istanbul, 34469, TURKEY
DTU Informatics, Technical University of Denmark, DK-2800 Kgs. Lyngby, DENMARK
e-mail:
e-mail:
e-mail: {sda,"
72a55554b816b66a865a1ec1b4a5b17b5d3ba784,Real-Time Face Identification via CNN and Boosted Hashing Forest,"Real-Time Face Identification
via CNN
nd Boosted Hashing Forest
Yury Vizilter, Vladimir Gorbatsevich, Andrey Vorotnikov and Nikita Kostromov
State Research Institute of Aviation Systems (GosNIIAS), Moscow, Russia
IEEE Computer Society Workshop on Biometrics
In conjunction with CVPR 2016, June 26, 2016"
72bf9c5787d7ff56a1697a3389f11d14654b4fcf,Robust Face Recognition Using Symmetric Shape-from-Shading,"RobustFaceRecognitionUsing
SymmetricShape-from-Shading
W.Zhao
RamaChellappa
CenterforAutomationResearchand
ElectricalandComputerEngineeringDepartment
UniversityofMaryland
CollegePark,MD-
ThesupportoftheO(cid:14)ceofNavalResearchunderGrantN
-	-
-
isgratefullyacknowledged.DRAFT"
4414a328466db1e8ab9651bf4e0f9f1fe1a163e4,Weighted voting of sparse representation classifiers for facial expression recognition,"© EURASIP, 2010   ISSN 2076-1465
8th European Signal Processing Conference (EUSIPCO-2010)
INTRODUCTION"
4439746eeb7c7328beba3f3ef47dc67fbb52bcb3,YASAMAN HEYDARZADEH at al: AN EFFICIENT FACE DETECTION METHOD USING ADABOOST,"YASAMAN HEYDARZADEH at al: AN EFFICIENT FACE DETECTION METHOD USING ADABOOST . . .
An Efficient Face Detection Method Using Adaboost and Facial Parts
Yasaman Heydarzadeh, Abolfazl Toroghi Haghighat
Computer, IT and Electronic department
Azad University of Qazvin
Tehran, Iran
qiau.ac.ir ,"
446a99fdedd5bb32d4970842b3ce0fc4f5e5fa03,A Pose-Adaptive Constrained Local Model for Accurate Head Pose Tracking,"A Pose-Adaptive Constrained Local Model For
Accurate Head Pose Tracking
Lucas Zamuner
Eikeo
1 rue Leon Jouhaux,
F-75010, Paris, France
Kevin Bailly
Sorbonne Universit´es
UPMC Univ Paris 06
CNRS UMR 7222, ISIR
F-75005, Paris, France
Erwan Bigorgne
Eikeo
1 rue Leon Jouhaux,
F-75010, Paris, France"
44b1399e8569a29eed0d22d88767b1891dbcf987,Learning Multi-modal Latent Attributes,"This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
Learning Multi-modal Latent Attributes
Yanwei Fu, Timothy M. Hospedales, Tao Xiang and Shaogang Gong"
446dc1413e1cfaee0030dc74a3cee49a47386355,Recent Advances in Zero-shot Recognition,"Recent Advances in Zero-shot Recognition
Yanwei Fu, Tao Xiang, Yu-Gang Jiang, Xiangyang Xue, Leonid Sigal, and Shaogang Gong"
44a3ec27f92c344a15deb8e5dc3a5b3797505c06,A Taxonomy of Part and Attribute Discovery Techniques,"A Taxonomy of Part and Attribute Discovery
Techniques
Subhransu Maji"
44dd150b9020b2253107b4a4af3644f0a51718a3,An Analysis of the Sensitivity of Active Shape Models to Initialization When Applied to Automatic Facial Landmarking,"An Analysis of the Sensitivity of Active Shape
Models to Initialization when Applied to Automatic
Facial Landmarking
Keshav Seshadri, Student Member, IEEE and Marios Savvides, Member, IEEE"
447d8893a4bdc29fa1214e53499ffe67b28a6db5,Electronic Transport in Quantum Confined Systems,"THÈSEPour obtenir le titre deDOCTEUR DE L’UNIVERSITÉSpécialitéSCIENCES DES MATÉRIAUXParMaxime BERTHEElectronic transport in quantum confined systemsSoutenue le 11 décembre 2007 devant la commission d’examen composée de:B. DJAFARI-ROUHANIS. ROUSSETD. RODITCHEVF. CHARRAD. STIÉVENARDH. SHIGEKAWAB. GRANDIDIERPrésidentRapporteurRapporteurExaminateurDirecteur de thèseCo-directeur de thèseCo-directeur de thèsel’Université des Sciences et Technologies de LilleEcole Doctorale Sciences de la Matière, du Rayonnement et de l’EnvironnementPrésentée à"
44f65e3304bdde4be04823fd7ca770c1c05c2cef,On the use of phase of the Fourier transform for face recognition under variations in illumination,"SIViP
DOI 10.1007/s11760-009-0125-4
ORIGINAL PAPER
On the use of phase of the Fourier transform for face recognition
under variations in illumination
Anil Kumar Sao · B. Yegnanarayana
Received: 17 November 2008 / Revised: 20 February 2009 / Accepted: 7 July 2009
© Springer-Verlag London Limited 2009"
447a5e1caf847952d2bb526ab2fb75898466d1bc,Learning Non-linear Transform with Discrim- Inative and Minimum Information Loss Priors,"Under review as a conference paper at ICLR 2018
LEARNING NON-LINEAR TRANSFORM WITH DISCRIM-
INATIVE AND MINIMUM INFORMATION LOSS PRIORS
Anonymous authors
Paper under double-blind review"
2a7bca56e2539c8cf1ae4e9da521879b7951872d,Exploiting Unrelated Tasks in Multi-Task Learning,"Exploiting Unrelated Tasks in Multi-Task Learning
Anonymous Author 1
Unknown Institution 1
Anonymous Author 2
Unknown Institution 2
Anonymous Author 3
Unknown Institution 3"
2a0efb1c17fbe78470acf01e4601a75735a805cc,Illumination-Insensitive Face Recognition Using Symmetric Shape-from-Shading,"Illumination-InsensitiveFaceRecognitionUsing
SymmetricShape-from-Shading
WenYiZhao
RamaChellappa
CenterforAutomationResearch
UniversityofMaryland,CollegePark,MD-"
2aec012bb6dcaacd9d7a1e45bc5204fac7b63b3c,Robust Registration and Geometry Estimation from Unstructured Facial Scans,"Robust Registration and Geometry Estimation from Unstructured
Facial Scans
Maxim Bazik1 and Daniel Crispell2"
2ae139b247057c02cda352f6661f46f7feb38e45,Combining modality specific deep neural networks for emotion recognition in video,"Combining Modality Specific Deep Neural Networks for
Emotion Recognition in Video
Samira Ebrahimi Kahou1, Christopher Pal1, Xavier Bouthillier2, Pierre Froumenty1,
Ça˘glar Gülçehre2,∗ , Roland Memisevic2, Pascal Vincent2, Aaron Courville2, & Yoshua Bengio2
École Polytechique de Montréal, Université de Montréal, Montréal, Canada
Laboratoire d’Informatique des Systèmes Adaptatifs, Université de Montréal, Montréal, Canada
{samira.ebrahimi-kahou, christopher.pal,
{bouthilx, gulcehrc, memisevr, vincentp, courvila,"
2ad0ee93d029e790ebb50574f403a09854b65b7e,Acquiring linear subspaces for face recognition under variable lighting,"Acquiring Linear Subspaces for Face
Recognition under Variable Lighting
Kuang-Chih Lee, Student Member, IEEE, Jeffrey Ho, Member, IEEE, and
David Kriegman, Senior Member, IEEE"
2ff9618ea521df3c916abc88e7c85220d9f0ff06,Facial Tic Detection Using Computer Vision,"Facial Tic Detection Using Computer Vision
Christopher D. Leveille
Advisor: Prof. Aaron Cass
March 20, 2014"
2fda461869f84a9298a0e93ef280f79b9fb76f94,OpenFace: An open source facial behavior analysis toolkit,"OpenFace: an open source facial behavior analysis toolkit
Tadas Baltruˇsaitis
Peter Robinson
Louis-Philippe Morency"
2ffcd35d9b8867a42be23978079f5f24be8d3e35,Satellite based Image Processing using Data mining,"ISSN XXXX XXXX © 2018 IJESC
Research Article                                                                                                                            Volume 8 Issue No.6
Satellite based Image Processing using Data mining
E.Malleshwari1, S.Nirmal Kumar2, J.Dhinesh3
Professor1, Assistant Professor2, PG Scholar3
Department of Information Technology1, 2, Master of Computer Applications3
Vel Tech High Tech Dr Rangarajan Dr Sakunthala Engineering College, Avadi, Chennai, India"
2f7e9b45255c9029d2ae97bbb004d6072e70fa79,cvpaper.challenge in 2015 - A review of CVPR2015 and DeepSurvey,"Noname manuscript No.
(will be inserted by the editor)
vpaper.challenge in 2015
A review of CVPR2015 and DeepSurvey
Hirokatsu Kataoka · Yudai Miyashita · Tomoaki Yamabe · Soma
Shirakabe · Shin’ichi Sato · Hironori Hoshino · Ryo Kato · Kaori Abe ·
Takaaki Imanari · Naomichi Kobayashi · Shinichiro Morita · Akio
Nakamura
Received: date / Accepted: date"
2f489bd9bfb61a7d7165a2f05c03377a00072477,Structured Semi-supervised Forest for Facial Landmarks Localization with Face Mask Reasoning,"JIA, YANG: STRUCTURED SEMI-SUPERVISED FOREST
Structured Semi-supervised Forest for
Facial Landmarks Localization with Face
Mask Reasoning
Department of Computer Science
The Univ. of Hong Kong, HK
School of EECS
Queen Mary Univ. of London, UK
Xuhui Jia1
Heng Yang2
Angran Lin1
Kwok-Ping Chan1
Ioannis Patras2"
2f59f28a1ca3130d413e8e8b59fb30d50ac020e2,Children Gender Recognition Under Unconstrained Conditions Based on Contextual Information,"Children Gender Recognition Under Unconstrained
Conditions Based on Contextual Information
Riccardo Satta, Javier Galbally and Laurent Beslay
Joint Research Centre, European Commission, Ispra, Italy
Email:"
2f78e471d2ec66057b7b718fab8bfd8e5183d8f4,An Investigation of a New Social Networks Contact Suggestion Based on Face Recognition Algorithm,"SOFTWARE ENGINEERING
VOLUME: 14 | NUMBER: 5 | 2016 | DECEMBER
An Investigation of a New Social Networks
Contact Suggestion Based on Face Recognition
Algorithm
Ivan ZELINKA1,2, Petr SALOUN 2, Jakub STONAWSKI 2, Adam ONDREJKA2
Modeling Evolutionary Algorithms Simulation and Artificial Intelligence, Faculty of Electrical & Electronics
Engineering, Ton Duc Thang University, 19 Nguyen Huu Tho Street, Ho Chi Minh City, Vietman
Department of Computer Science, Faculty of Electrical Engineering and Computer Science,
VSB–Technical University of Ostrava, 17. listopadu 15, 708 33 Ostrava, Czech Republic
DOI: 10.15598/aeee.v14i5.1116"
2f88d3189723669f957d83ad542ac5c2341c37a5,Attribute-correlated local regions for deep relative attributes learning,"Downloaded From: https://www.spiedigitallibrary.org/journals/Journal-of-Electronic-Imaging on 9/13/2018
Terms of Use: https://www.spiedigitallibrary.org/terms-of-use
Attribute-correlatedlocalregionsfordeeprelativeattributeslearningFenZhangXiangweiKongZeJiaFenZhang,XiangweiKong,ZeJia,“Attribute-correlatedlocalregionsfordeeprelativeattributeslearning,”J.Electron.Imaging27(4),043021(2018),doi:10.1117/1.JEI.27.4.043021."
2fda164863a06a92d3a910b96eef927269aeb730,Names and faces in the news,"Names and Faces in the News
Tamara L. Berg, Alexander C. Berg, Jaety Edwards, Michael Maire,
Ryan White, Yee-Whye Teh, Erik Learned-Miller and D.A. Forsyth
Computer Science Division
U.C. Berkeley
Berkeley, CA 94720"
2f8ef26bfecaaa102a55b752860dbb92f1a11dc6,A Graph Based Approach to Speaker Retrieval in Talk Show Videos with Transcript-Based Supervision,"A Graph Based Approach to Speaker Retrieval in Talk
Show Videos with Transcript-Based Supervision
Yina Han  1, Guizhong Liu, Hichem Sahbi, Gérard Chollet"
2f17f6c460e02bd105dcbf14c9b73f34c5fb59bd,Robust Face Recognition Using the Deep C2D-CNN Model Based on Decision-Level Fusion,"Article
Robust Face Recognition Using the Deep C2D-CNN
Model Based on Decision-Level Fusion
Jing Li 1,2,†, Tao Qiu 3,†, Chang Wen 3,*, Kai Xie 1,2 and Fang-Qing Wen 1,2
School of Electronic and Information, Yangtze University, Jingzhou 434023, China;
(J.L.); (K.X.); (F-Q.W.)
National Demonstration Center for Experimental Electrical and Electronic Education, Yangtze University,
Jingzhou 434023, China
School of Computer Science, Yangtze University, Jingzhou 434023, China;
* Correspondence: Tel.: +86-136-9731-5482
These authors contributed equally to this work.
Received: 20 May 2018; Accepted: 25 June 2018; Published: 28 June 2018"
2f184c6e2c31d23ef083c881de36b9b9b6997ce9,Polichotomies on Imbalanced Domains by One-per-Class Compensated Reconstruction Rule,"Polichotomies on Imbalanced Domains
y One-per-Class Compensated Reconstruction Rule
Roberto D’Ambrosio and Paolo Soda
Integrated Research Centre, Universit´a Campus Bio-Medico of Rome, Rome, Italy"
2f13dd8c82f8efb25057de1517746373e05b04c4,Evaluation of state-of-the-art algorithms for remote face recognition,"EVALUATION OF STATE-OF-THE-ART ALGORITHMS FOR REMOTE FACE
RECOGNITION
Jie Ni and Rama Chellappa
Department of Electrical and Computer Engineering and Center for Automation Research, University
of Maryland, College Park, MD 20742, USA"
2fa1fc116731b2b5bb97f06d2ac494cb2b2fe475,A novel approach to personal photo album representation and management,"A novel approach to personal photo album representation
nd management
Edoardo Ardizzone, Marco La Cascia, and Filippo Vella
Universit`a di Palermo - Dipartimento di Ingegneria Informatica
Viale delle Scienze, 90128, Palermo, Italy"
2f882ceaaf110046e63123b495212d7d4e99f33d,High Frequency Component Compensation based Super-Resolution Algorithm for Face Video Enhancement,"High Frequency Component Compensation based Super-resolution
Algorithm for Face Video Enhancement
Junwen Wu, Mohan Trivedi, Bhaskar Rao
CVRR Lab, UC San Diego, La Jolla, CA 92093, USA"
2f95340b01cfa48b867f336185e89acfedfa4d92,Face expression recognition with a 2-channel Convolutional Neural Network,"Face Expression Recognition with a 2-Channel
Convolutional Neural Network
Dennis Hamester, Pablo Barros, Stefan Wermter
University of Hamburg — Department of Informatics
Vogt-K¨olln-Straße 30, 22527 Hamburg, Germany
http://www.informatik.uni-hamburg.de/WTM/"
2faa09413162b0a7629db93fbb27eda5aeac54ca,Quantifying how lighting and focus affect face recognition performance,"NISTIR 7674
Quantifying How Lighting and Focus
Affect Face Recognition Performance
Phillips, P. J.
Beveridge, J. R.
Draper, B.
Bolme, D.
Givens, G. H.
Lui, Y. M."
433bb1eaa3751519c2e5f17f47f8532322abbe6d,Face Recognition,
43bb20ccfda7b111850743a80a5929792cb031f0,Discrimination of Computer Generated versus Natural Human Faces,"PhD Dissertation
International Doctorate School in Information and
Communication Technologies
DISI - University of Trento
Discrimination of Computer Generated
versus Natural Human Faces
Duc-Tien Dang-Nguyen
Advisor:
Prof. Giulia Boato
Universit`a degli Studi di Trento
Co-Advisor:
Prof. Francesco G. B. De Natale
Universit`a degli Studi di Trento
February 2014"
439ac8edfa1e7cbc65474cab544a5b8c4c65d5db,Face authentication with undercontrolled pose and illumination,"SIViP (2011) 5:401–413
DOI 10.1007/s11760-011-0244-6
ORIGINAL PAPER
Face authentication with undercontrolled pose and illumination
Maria De Marsico · Michele Nappi · Daniel Riccio
Received: 15 September 2010 / Revised: 14 December 2010 / Accepted: 17 February 2011 / Published online: 7 August 2011
© Springer-Verlag London Limited 2011"
43f6953804964037ff91a4f45d5b5d2f8edfe4d5,Multi-feature fusion in advanced robotics applications,"Multi-Feature Fusion in Advanced Robotics Applications
Zahid Riaz, Christoph Mayer, Michael Beetz,
Bernd Radig
Institut für Informatik
Technische Universität München
D-85748 Garching, Germany"
439ec47725ae4a3660e509d32828599a495559bf,Facial Expressions Tracking and Recognition: Database Protocols for Systems Validation and Evaluation,"Facial Expressions Tracking and Recognition: Database Protocols for Systems Validation
nd Evaluation"
434bf475addfb580707208618f99c8be0c55cf95,DeXpression: Deep Convolutional Neural Network for Expression Recognition,"UNDER CONSIDERATION FOR PUBLICATION IN PATTERN RECOGNITION LETTERS
DeXpression: Deep Convolutional Neural
Network for Expression Recognition
Peter Burkert∗‡, Felix Trier∗‡, Muhammad Zeshan Afzal†‡,
Andreas Dengel†‡ and Marcus Liwicki‡
German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany
University of Kaiserslautern, Gottlieb-Daimler-Str., Kaiserslautern 67663, Germany"
43836d69f00275ba2f3d135f0ca9cf88d1209a87,Effective hyperparameter optimization using Nelder-Mead method in deep learning,"Ozaki et al. IPSJ Transactions on Computer Vision and
Applications  (2017) 9:20
DOI 10.1186/s41074-017-0030-7
IPSJ Transactions on Computer
Vision and Applications
RESEARCH PAPER
Open Access
Effective hyperparameter optimization
using Nelder-Mead method in deep learning
Yoshihiko Ozaki1,2, Masaki Yano1,2 and Masaki Onishi1,2*"
4362368dae29cc66a47114d5ffeaf0534bf0159c,"Performance Analysis of FDA Based Face Recognition Using Correlation, ANN and SVM","UACEE International Journal of Artificial Intelligence and Neural Networks ISSN:- 2250-3749 (online)
Performance Analysis of FDA Based Face
Recognition Using Correlation, ANN and SVM
Mahesh Goyani
Akash Dhorajiya
Ronak Paun
Department of Computer Engineering
Department of Computer Engineering
Department of Computer Engineering
GCET, Sardar Patel University
GCET, Sardar Patel University
GCET, Sardar Patel University
Anand, INDIA
Anand, INDIA
Anand, INDIA
e- mail :
e- mail :
e- mail :"
4350bb360797a4ade4faf616ed2ac8e27315968e,Edge Suppression by Gradient Field Transformation Using Cross-Projection Tensors,"MITSUBISHI ELECTRIC RESEARCH LABORATORIES
http://www.merl.com
Edge Suppression by Gradient Field
Transformation using Cross-Projection
Tensors
Amit Agrawal, Ramesh Raskar, Rama Chellappa
TR2006-058
June 2006"
43476cbf2a109f8381b398e7a1ddd794b29a9a16,A Practical Transfer Learning Algorithm for Face Verification,"A Practical Transfer Learning Algorithm for Face Verification
Xudong Cao
David Wipf
Fang Wen
Genquan Duan
Jian Sun"
4353d0dcaf450743e9eddd2aeedee4d01a1be78b,Learning Discriminative LBP-Histogram Bins for Facial Expression Recognition,"Learning Discriminative LBP-Histogram Bins
for Facial Expression Recognition
Caifeng Shan and Tommaso Gritti
Philips Research, High Tech Campus 36, Eindhoven 5656 AE, The Netherlands
{caifeng.shan,"
43b8b5eeb4869372ef896ca2d1e6010552cdc4d4,Large-scale Supervised Hierarchical Feature Learning for Face Recognition,"Large-scale Supervised Hierarchical Feature Learning for Face Recognition
Jianguo Li, Yurong Chen
Intel Labs China"
43ae4867d058453e9abce760ff0f9427789bab3a,Graph Embedded Nonparametric Mutual Information for Supervised Dimensionality Reduction,"Graph Embedded Nonparametric Mutual
Information For Supervised
Dimensionality Reduction
Dimitrios Bouzas, Nikolaos Arvanitopoulos, Student Member, IEEE, and Anastasios Tefas, Member, IEEE"
438b88fe40a6f9b5dcf08e64e27b2719940995e0,Building a classification cascade for visual identification from one example,"Building a Classi(cid:2)cation Cascade for Visual Identi(cid:2)cation from One Example
Andras Ferencz
Erik G. Learned-Miller
Computer Science, U.C. Berkeley
Computer Science, UMass Amherst
Jitendra Malik
Computer Science, U.C. Berkeley"
43fb9efa79178cb6f481387b7c6e9b0ca3761da8,Mixture of parts revisited: Expressive part interactions for Pose Estimation,"Mixture of Parts Revisited: Expressive Part Interactions for Pose Estimation
Anoop R Katti
IIT Madras
Chennai, India
Anurag Mittal
IIT Madras
Chennai, India"
43ed518e466ff13118385f4e5d039ae4d1c000fb,Classification of Occluded Objects Using Fast Recurrent Processing,"Classification of Occluded Objects using Fast Recurrent
Processing
Ozgur Yilmaza,∗
Turgut Ozal University, Department of Computer Engineering, Ankara Turkey"
43d7d0d0d0e2d6cf5355e60c4fe5b715f0a1101a,Playlist Generation using Facial Expression Analysis and Task Extraction,"Pobrane z czasopisma Annales AI- Informatica http://ai.annales.umcs.pl
Data: 04/05/2018 16:53:32
U M CS"
88c6d4b73bd36e7b5a72f3c61536c8c93f8d2320,Image patch modeling in a light field,"Image patch modeling in a light field
Zeyu Li
Electrical Engineering and Computer Sciences
University of California at Berkeley
Technical Report No. UCB/EECS-2014-81
http://www.eecs.berkeley.edu/Pubs/TechRpts/2014/EECS-2014-81.html
May 15, 2014"
889bc64c7da8e2a85ae6af320ae10e05c4cd6ce7,Using Support Vector Machines to Enhance the Performance of Bayesian Face Recognition,"Using Support Vector Machines to Enhance the
Performance of Bayesian Face Recognition
Zhifeng Li, Member, IEEE, and Xiaoou Tang, Senior Member, IEEE"
88a898592b4c1dfd707f04f09ca58ec769a257de,MobileFace: 3D Face Reconstruction with Efficient CNN Regression,"MobileFace: 3D Face Reconstruction
with Ef‌f‌icient CNN Regression
Nikolai Chinaev1, Alexander Chigorin1, and Ivan Laptev1,2
VisionLabs, Amsterdam, The Netherlands
{n.chinaev,
Inria, WILLOW, Departement d’Informatique de l’Ecole Normale Superieure, PSL
Research University, ENS/INRIA/CNRS UMR 8548, Paris, France"
881066ec43bcf7476479a4146568414e419da804,From Traditional to Modern: Domain Adaptation for Action Classification in Short Social Video Clips,"From Traditional to Modern : Domain Adaptation for
Action Classification in Short Social Video Clips
Aditya Singh, Saurabh Saini, Rajvi Shah, and P J Narayanan
Center for Visual Information Technology, IIIT Hyderabad, India"
8813368c6c14552539137aba2b6f8c55f561b75f,Trunk-Branch Ensemble Convolutional Neural Networks for Video-Based Face Recognition,"Trunk-Branch Ensemble Convolutional Neural
Networks for Video-based Face Recognition
Changxing Ding, Student Member, IEEE, Dacheng Tao, Fellow, IEEE"
883006c0f76cf348a5f8339bfcb649a3e46e2690,Weakly supervised pain localization using multiple instance learning,"Weakly Supervised Pain Localization using Multiple Instance Learning
Karan Sikka, Abhinav Dhall and Marian Bartlett"
88850b73449973a34fefe491f8836293fc208580,XBeats-An Emotion Based Music Player,"www.ijaret.org                                                                                                                                                     Vol. 2, Issue I, Jan. 2014
ISSN 2320-6802
INTERNATIONAL JOURNAL FOR ADVANCE RESEARCH IN
ENGINEERING AND TECHNOLOGY
WINGS TO YOUR THOUGHTS…..
XBeats-An Emotion Based Music Player
Sayali Chavan1, Ekta Malkan2, Dipali Bhatt3, Prakash H. Paranjape4
U.G. Student, Dept. of Computer Engineering,
D.J. Sanghvi College of Engineering,
Vile Parle (W), Mumbai-400056.
U.G. Student, Dept. of Computer Engineering,
D.J. Sanghvi College of Engineering,
Vile Parle (W), Mumbai-400056.
U.G. Student, Dept. of Computer Engineering,
D.J. Sanghvi College of Engineering,
Vile Parle (W), Mumbai-400056.
Assistant Professor, Dept. of Computer Engineering,
D.J. Sanghvi College of Engineering,
Vile Parle (W), Mumbai-400056."
88f2952535df5859c8f60026f08b71976f8e19ec,A neural network framework for face recognition by elastic bunch graph matching,"A neural network framework for face
recognition by elastic bunch graph matching
Francisco A. Pujol López, Higinio Mora Mora*, José A. Girona Selva"
887b7676a4efde616d13f38fcbfe322a791d1413,Deep Temporal Appearance-Geometry Network for Facial Expression Recognition,"Deep Temporal Appearance-Geometry Network
for Facial Expression Recognition
Injae Lee‡ Chunghyun Ahn‡
Junmo Kim†
Heechul Jung† Sihaeng Lee† Sunjeong Park†
Korea Advanced Institute of Science and Technology†
Electronics and Telecommunications Research Institute‡
{heechul, haeng, sunny0414, {ninja,"
8878871ec2763f912102eeaff4b5a2febfc22fbe,Human Action Recognition in Unconstrained Videos by Explicit Motion Modeling,"Human Action Recognition in Unconstrained
Videos by Explicit Motion Modeling
Yu-Gang Jiang, Qi Dai, Wei Liu, Xiangyang Xue, and Chong-Wah Ngo"
8855d6161d7e5b35f6c59e15b94db9fa5bbf2912,COGNITION IN PREGNANCY AND THE POSTPARTUM PERIOD COGNITIVE REORGANIZATION AND PROTECTIVE MECHANISMS IN PREGNANCY AND THE POSTPARTUM PERIOD By,COGNITION IN PREGNANCY AND THE POSTPARTUM PERIOD
88bee9733e96958444dc9e6bef191baba4fa6efa,Extending Face Identification to Open-Set Face Recognition,"Extending Face Identification to
Open-Set Face Recognition
Cassio E. dos Santos Jr., William Robson Schwartz
Department of Computer Science
Universidade Federal de Minas Gerais
Belo Horizonte, Brazil"
88fd4d1d0f4014f2b2e343c83d8c7e46d198cc79,Joint action recognition and summarization by sub-modular inference,"978-1-4799-9988-0/16/$31.00 ©2016 IEEE
ICASSP 2016"
9fa1be81d31fba07a1bde0275b9d35c528f4d0b8,Identifying Persons by Pictorial and Contextual Cues,"Identifying Persons by Pictorial and
Contextual Cues
Nicholas Leonard Pi¨el
Thesis submitted for the degree of Master of Science
Supervisor:
Prof. dr. Theo Gevers
April 2009"
9f094341bea610a10346f072bf865cb550a1f1c1,Recognition and volume estimation of food intake using a mobile device,"Recognition and Volume Estimation of Food Intake using a Mobile Device
Manika Puri Zhiwei Zhu Qian Yu Ajay Divakaran Harpreet Sawhney
Sarnoff Corporation
01 Washington Rd,
Princeton, NJ, 08540
{mpuri, zzhu, qyu, adivakaran,"
6bcfcc4a0af2bf2729b5bc38f500cfaab2e653f0,Facial Expression Recognition in the Wild Using Improved Dense Trajectories and Fisher Vector Encoding,"Facial expression recognition in the wild using improved dense trajectories and
Fisher vector encoding
Sadaf Afshar1
Albert Ali Salah2
Computational Science and Engineering Program, Bo˘gazic¸i University, Istanbul, Turkey
Department of Computer Engineering, Bo˘gazic¸i University, Istanbul, Turkey
{sadaf.afshar,"
6b089627a4ea24bff193611e68390d1a4c3b3644,Cross-Pollination of Normalization Techniques From Speaker to Face Authentication Using Gaussian Mixture Models,"CROSS-POLLINATION OF NORMALISATION
TECHNIQUES FROM SPEAKER TO FACE
AUTHENTICATION USING GAUSSIAN
MIXTURE MODELS
Roy Wallace        Mitchell McLaren        Chris McCool
Sébastien Marcel
Idiap-RR-03-2012
JANUARY 2012
Centre du Parc, Rue Marconi 19, P.O. Box 592, CH - 1920 Martigny
T +41 27 721 77 11  F +41 27 721 77 12   www.idiap.ch"
6be0ab66c31023762e26d309a4a9d0096f72a7f0,Enhance Visual Recognition under Adverse Conditions via Deep Networks,"Enhance Visual Recognition under Adverse
Conditions via Deep Networks
Ding Liu, Student Member, IEEE, Bowen Cheng, Zhangyang Wang, Member, IEEE,
Haichao Zhang, Member, IEEE, and Thomas S. Huang, Life Fellow, IEEE"
6b18628cc8829c3bf851ea3ee3bcff8543391819,Face recognition based on subset selection via metric learning on manifold,"Hong Shao, Shuang Chen, Jie-yi Zhao, Wen-cheng Cui, Tian-shu Yu, 2015.
Face recognition based on subset selection via metric learning on manifold.
058.  [doi:10.1631/FITEE.1500085]
Face recognition based on subset
selection via metric learning on manifold
Key words: Face recognition, Sparse representation, Manifold structure,
Metric learning, Subset selection
Contact: Shuang Chen
E-mail:
ORCID: http://orcid.org/0000-0001-7441-4749
Front Inform Technol & Electron Eng"
6b6493551017819a3d1f12bbf922a8a8c8cc2a03,Pose Normalization for Local Appearance-Based Face Recognition,"Pose Normalization for Local Appearance-Based
Face Recognition
Hua Gao, Hazım Kemal Ekenel, and Rainer Stiefelhagen
Computer Science Department, Universit¨at Karlsruhe (TH)
Am Fasanengarten 5, Karlsruhe 76131, Germany
http://isl.ira.uka.de/cvhci"
6b6ff9d55e1df06f8b3e6f257e23557a73b2df96,Survey of Threats to the Biometric Authentication Systems and Solutions,"International Journal of Computer Applications (0975 – 8887)
Volume 61– No.17, January 2013
Survey of Threats to the Biometric Authentication
Systems and Solutions
Sarika Khandelwal
Research Scholor,Mewar
University,Chitorgarh. (INDIA)
P.C.Gupta
Kota University,Kota(INDIA)
Khushboo Mantri
M.tech.student, Arya College of
engineering ,Jaipur(INDIA)"
0728f788107122d76dfafa4fb0c45c20dcf523ca,The Best of BothWorlds: Combining Data-Independent and Data-Driven Approaches for Action Recognition,"The Best of Both Worlds: Combining Data-independent and Data-driven
Approaches for Action Recognition
Zhenzhong Lan, Dezhong Yao, Ming Lin, Shoou-I Yu, Alexander Hauptmann
{lanzhzh, minglin, iyu,"
07ea3dd22d1ecc013b6649c9846d67f2bf697008,Human-centric Video Understanding with Weak Supervision a Dissertation Submitted to the Department of Computer Science and the Committee on Graduate Studies of Stanford University in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy,"HUMAN-CENTRIC VIDEO UNDERSTANDING WITH WEAK
SUPERVISION
A DISSERTATION
SUBMITTED TO THE DEPARTMENT OF COMPUTER SCIENCE
AND THE COMMITTEE ON GRADUATE STUDIES
OF STANFORD UNIVERSITY
IN PARTIAL FULFILLMENT OF THE REQUIREMENTS
FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY
Vignesh Ramanathan
June 2016"
071099a4c3eed464388c8d1bff7b0538c7322422,Facial expression recognition in the wild using rich deep features,"FACIAL EXPRESSION RECOGNITION IN THE WILD USING RICH DEEP FEATURES
Abubakrelsedik Karali, Ahmad Bassiouny and Motaz El-Saban
Microsoft Advanced Technology labs, Microsoft Technology and Research, Cairo, Egypt"
070ab604c3ced2c23cce2259043446c5ee342fd6,An Active Illumination and Appearance (AIA) Model for Face Alignment,"AnActiveIlluminationandAppearance(AIA)ModelforFaceAlignment
FatihKahraman,MuhittinGokmen
IstanbulTechnicalUniversity,
ComputerScienceDept.,Turkey
{fkahraman,
InformaticsandMathematicalModelling,Denmark
SuneDarkner,RasmusLarsen
TechnicalUniversityofDenmark"
071135dfb342bff884ddb9a4d8af0e70055c22a1,Temporal 3D ConvNets: New Architecture and Transfer Learning for Video Classification,"New Architecture and Transfer Learning for Video Classification
Temporal 3D ConvNets:
Ali Diba1,4,(cid:63), Mohsen Fayyaz2,(cid:63), Vivek Sharma3, Amir Hossein Karami4, Mohammad Mahdi Arzani4,
Rahman Yousefzadeh4, Luc Van Gool1,4
ESAT-PSI, KU Leuven, 2University of Bonn, 3CV:HCI, KIT, Karlsruhe, 4Sensifai"
0754e769eb613fd3968b6e267a301728f52358be,Towards a Watson that sees: Language-guided action recognition for robots,"Towards a Watson That Sees: Language-Guided Action Recognition for
Robots
Ching L. Teo, Yezhou Yang, Hal Daum´e III, Cornelia Ferm¨uller and Yiannis Aloimonos"
07c83f544d0604e6bab5d741b0bf9a3621d133da,Learning Spatio-Temporal Features with 3D Residual Networks for Action Recognition,"Learning Spatio-Temporal Features with 3D Residual Networks
for Action Recognition
Kensho Hara, Hirokatsu Kataoka, Yutaka Satoh
National Institute of Advanced Industrial Science and Technology (AIST)
Tsukuba, Ibaraki, Japan
{kensho.hara, hirokatsu.kataoka,"
0717b47ab84b848de37dbefd81cf8bf512b544ac,Robust Face Recognition and Tagging in Visual Surveillance System,"International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622
International Conference on Humming Bird ( 01st March 2014)
RESEARCH ARTICLE
OPEN ACCESS
Robust Face Recognition and Tagging in Visual Surveillance
Kavitha MS 1, Siva Pradeepa S2
System
Kavitha MS Author is currently pursuing M.E(CSE)in VINS Christian college of Engineering,Nagercoil.
Siva pradeepa,Assistant Lecturer in VINS Christian college of Engineering"
0750a816858b601c0dbf4cfb68066ae7e788f05d,CosFace: Large Margin Cosine Loss for Deep Face Recognition,"CosFace: Large Margin Cosine Loss for Deep Face Recognition
Hao Wang, Yitong Wang, Zheng Zhou, Xing Ji, Dihong Gong, Jingchao Zhou,
Zhifeng Li∗, and Wei Liu∗
Tencent AI Lab"
078d507703fc0ac4bf8ca758be101e75ea286c80,Large - Scale Content Based Face Image Retrieval using Attribute Enhanced,"ISSN: 2321-8169
International Journal on Recent and Innovation Trends in Computing and Communication
Volume: 3 Issue: 8
5287 - 5296
________________________________________________________________________________________________________________________________
Large- Scale Content Based Face Image Retrieval using Attribute Enhanced
Sparse Codewords.
Chaitra R,
Mtech Digital Coomunication Engineering
Acharya Institute Of Technology
Bangalore"
0716e1ad868f5f446b1c367721418ffadfcf0519,Interactively Guiding Semi-Supervised Clustering via Attribute-Based Explanations,"Interactively Guiding Semi-Supervised
Clustering via Attribute-Based Explanations
Shrenik Lad and Devi Parikh
Virginia Tech, Blacksburg, VA, USA"
0726a45eb129eed88915aa5a86df2af16a09bcc1,Introspective perception: Learning to predict failures in vision systems,"Introspective Perception: Learning to Predict Failures in Vision Systems
Shreyansh Daftry, Sam Zeng, J. Andrew Bagnell and Martial Hebert"
0742d051caebf8a5d452c03c5d55dfb02f84baab,Real-time geometric motion blur for a deforming polygonal mesh,"Real-Time Geometric Motion Blur for a Deforming Polygonal Mesh
Nathan Jones
Formerly: Texas A&M University
Currently: The Software Group"
38d56ddcea01ce99902dd75ad162213cbe4eaab7,Sense Beauty by Label Distribution Learning,Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17)
389334e9a0d84bc54bcd5b94b4ce4c5d9d6a2f26,Facial parameter extraction system based on active contours,"FACIAL PARAMETER EXTRACTION SYSTEM BASED ON ACTIVE CONTOURS
Montse Pardàs, Marcos Losada
Universitat Politècnica de Catalunya, Barcelona, Spain"
38f7f3c72e582e116f6f079ec9ae738894785b96,A New Technique for Face Matching after Plastic Surgery in Forensics,"IJARCCE
ISSN (Online) 2278-1021
ISSN (Print) 2319 5940
International Journal of Advanced Research in Computer and Communication Engineering
Vol. 4, Issue 11, November 2015
A New Technique for Face Matching after
Plastic Surgery in Forensics
Anju Joseph1, Nilu Tressa Thomas2, Neethu C. Sekhar3
Student, Dept. of CSE, Amal Jyothi College of Engineering, Kanjirappally, India 1,2
Asst. Professor, Dept. of CSE, Amal Jyothi College of Engineering, Kanjirappally, India 3
I.  INTRODUCTION
Facial  recognition  is  one  of  the  most  important    task  that
forensic  examiners  execute
their
investigation. This work focuses on analysing the effect of
plastic  surgery  in  face  recognition  algorithms.  It  is
imperative for the subsequent facial recognition systems to
e  capable  of  addressing  this  significant  issue  and
ccordingly  there  is  a  need  for  more  research  in  this
important area."
38679355d4cfea3a791005f211aa16e76b2eaa8d,Evolutionary Cross-Domain Discriminative Hessian Eigenmaps,"Title
Evolutionary cross-domain discriminative Hessian Eigenmaps
Author(s)
Si, S; Tao, D; Chan, KP
Citation
Issued Date
http://hdl.handle.net/10722/127357
Rights
This work is licensed under a Creative Commons Attribution-
NonCommercial-NoDerivatives 4.0 International License.; ©2010
IEEE. Personal use of this material is permitted. However,
permission to reprint/republish this material for advertising or
promotional purposes or for creating new collective works for
resale or redistribution to servers or lists, or to reuse any
opyrighted component of this work in other works must be
obtained from the IEEE."
38682c7b19831e5d4f58e9bce9716f9c2c29c4e7,Movie Character Identification Using Graph Matching Algorithm,"International Journal of Computer Trends and Technology (IJCTT) – Volume 18 Number 5 – Dec 2014
Movie Character Identification Using Graph Matching
Algorithm
Shaik. Kartheek.*1, A.Srinivasa Reddy*2
M.Tech Scholar, Dept of CSE, QISCET, ONGOLE, Dist: Prakasam, AP, India.
Associate Professor, Department of CSE, QISCET, ONGOLE, Dist: Prakasam, AP, India"
3803b91e784922a2dacd6a18f61b3100629df932,Temporal Multimodal Fusion for Video Emotion Classification in the Wild,"Temporal Multimodal Fusion
for Video Emotion Classification in the Wild
Valentin Vielzeuf∗
Orange Labs
Cesson-Sévigné, France
Stéphane Pateux
Orange Labs
Cesson-Sévigné, France
Frédéric Jurie
Normandie Univ., UNICAEN,
ENSICAEN, CNRS
Caen, France"
38eea307445a39ee7902c1ecf8cea7e3dcb7c0e7,Multi-distance Support Matrix Machines,"Noname manuscript No.
(will be inserted by the editor)
Multi-distance Support Matrix Machine
Yunfei Ye1
· Dong Han1
Received: date / Accepted: date"
384f972c81c52fe36849600728865ea50a0c4670,"Multi-Fold Gabor, PCA and ICA Filter Convolution Descriptor for Face Recognition","Multi-Fold Gabor, PCA and ICA Filter
Convolution Descriptor for Face Recognition
Cheng Yaw Low, Andrew Beng Jin Teoh, Senior Member, IEEE, Cong Jie Ng"
38f1fac3ed0fd054e009515e7bbc72cdd4cf801a,Finding Person Relations in Image Data of the Internet Archive,"Finding Person Relations in Image Data of the
Internet Archive
Eric M¨uller-Budack1,2[0000−0002−6802−1241],
Kader Pustu-Iren1[0000−0003−2891−9783], Sebastian Diering1, and
Ralph Ewerth1,2[0000−0003−0918−6297]
Leibniz Information Centre for Science and Technology (TIB), Hannover, Germany
L3S Research Center, Leibniz Universit¨at Hannover, Germany"
380d5138cadccc9b5b91c707ba0a9220b0f39271,Deep Imbalanced Learning for Face Recognition and Attribute Prediction,"Deep Imbalanced Learning for Face Recognition
nd Attribute Prediction
Chen Huang, Yining Li, Chen Change Loy, Senior Member, IEEE and Xiaoou Tang, Fellow, IEEE"
38215c283ce4bf2c8edd597ab21410f99dc9b094,The SEMAINE Database: Annotated Multimodal Records of Emotionally Colored Conversations between a Person and a Limited Agent,"The SEMAINE Database: Annotated Multimodal Records of
Emotionally Colored Conversations between a Person and a Limited
Agent
McKeown, G., Valstar, M., Cowie, R., Pantic, M., & Schröder, M. (2012). The SEMAINE Database: Annotated
Multimodal Records of Emotionally Colored Conversations between a Person and a Limited Agent. IEEE
Transactions on Affective Computing, 3(1), 5-17. DOI: 10.1109/T-AFFC.2011.20
Published in:
Document Version:
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Download date:05. Nov. 2018"
38183fe28add21693729ddeaf3c8a90a2d5caea3,Scale-Aware Face Detection,"Scale-Aware Face Detection
Zekun Hao1, Yu Liu1, Hongwei Qin2, Junjie Yan2, Xiu Li2, Xiaolin Hu2
SenseTime, 2Tsinghua University
{haozekun,"
3802da31c6d33d71b839e260f4022ec4fbd88e2d,Deep Attributes for One-Shot Face Recognition,"Deep Attributes for One-Shot Face Recognition
Aishwarya Jadhav1,3, Vinay P. Namboodiri2, and K. S. Venkatesh 3
Xerox Research Center India, 2Department of Computer Science,
Department of Electrical Engineering, IIT Kanpur"
00fb2836068042c19b5197d0999e8e93b920eb9c,Genetic Algorithm for Weight Optimization in Descriptor based Face Recognition Methods,
0077cd8f97cafd2b389783858a6e4ab7887b0b6b,Face Image Reconstruction from Deep Templates,"MAI et al.: ON THE RECONSTRUCTION OF DEEP FACE TEMPLATES
On the Reconstruction of Deep Face Templates
Guangcan Mai, Kai Cao, Pong C. Yuen, Senior Member, IEEE, and Anil K. Jain, Life Fellow, IEEE"
00214fe1319113e6649435cae386019235474789,Face Recognition using Distortion Models,"Bachelorarbeit im Fach Informatik
Face Recognition using
Distortion Models
Mathematik, Informatik und Naturwissenschaften der
RHEINISCH-WESTFÄLISCHEN TECHNISCHEN HOCHSCHULE AACHEN
Der Fakultät für
Lehrstuhl für Informatik VI
Prof. Dr.-Ing. H. Ney
vorgelegt von:
Harald Hanselmann
Matrikelnummer 252400
Gutachter:
Prof. Dr.-Ing. H. Ney
Prof. Dr. B. Leibe
Betreuer:
Dipl.-Inform. Philippe Dreuw
September 2009"
004e3292885463f97a70e1f511dc476289451ed5,Quadruplet-Wise Image Similarity Learning,"Quadruplet-wise Image Similarity Learning
Marc T. Law
Nicolas Thome
Matthieu Cord
LIP6, UPMC - Sorbonne University, Paris, France
{Marc.Law, Nicolas.Thome,"
00f0ed04defec19b4843b5b16557d8d0ccc5bb42,Modeling Spatial and Temporal Cues for Multi-label Facial Action Unit Detection,
0037bff7be6d463785d4e5b2671da664cd7ef746,Multiple Instance Metric Learning from Automatically Labeled Bags of Faces,"Author manuscript, published in ""European Conference on Computer Vision (ECCV '10) 6311 (2010) 634--647""
DOI : 10.1007/978-3-642-15549-9_46"
00d9d88bb1bdca35663946a76d807fff3dc1c15f,Subjects and Their Objects: Localizing Interactees for a Person-Centric View of Importance,"Subjects and Their Objects: Localizing Interactees for a
Person-Centric View of Importance
Chao-Yeh Chen · Kristen Grauman"
00a3cfe3ce35a7ffb8214f6db15366f4e79761e3,Using Kinect for real-time emotion recognition via facial expressions,"Qi-rong Mao, Xin-yu Pan, Yong-zhao Zhan, Xiang-jun Shen, 2015. Using
Kinect for real-time emotion recognition via facial expressions. Frontiers of
Information Technology & Electronic Engineering, 16(4):272-282.
[doi:10.1631/FITEE.1400209]
Using Kinect for real-time emotion
recognition via facial expressions
Key words: Kinect, Emotion recognition, Facial expression, Real-time
lassification, Fusion algorithm, Support vector machine (SVM)
Contact: Qi-rong Mao
E-mail:
ORCID: http://orcid.org/0000-0002-5021-9057
Front Inform Technol & Electron Eng"
004a1bb1a2c93b4f379468cca6b6cfc6d8746cc4,Balanced k-Means and Min-Cut Clustering,"Balanced k-Means and Min-Cut Clustering
Xiaojun Chang, Feiping Nie, Zhigang Ma, and Yi Yang"
00d94b35ffd6cabfb70b9a1d220b6823ae9154ee,Discriminative Bayesian Dictionary Learning for Classification,"Discriminative Bayesian Dictionary Learning
for Classification
Naveed Akhtar, Faisal Shafait, and Ajmal Mian"
006f283a50d325840433f4cf6d15876d475bba77,Preserving Structure in Model-Free Tracking,"Preserving Structure in Model-Free Tracking
Lu Zhang and Laurens van der Maaten"
00d931eccab929be33caea207547989ae7c1ef39,The Natural Input Memory Model,"The Natural Input Memory Model
Joyca P.W. Lacroix
Department of Computer Science, IKAT, Universiteit Maastricht, St. Jacobsstraat 6, 6211 LB Maastricht, The Netherlands
Department of Psychology, Universiteit van Amsterdam, Roeterstraat 15, 1018 WB Amsterdam, The Netherlands
Jaap M.J. Murre
Department of Computer Science, IKAT, Universiteit Maastricht, St. Jacobsstraat 6, 6211 LB Maastricht, The Netherlands
Eric O. Postma
H. Jaap van den Herik"
0052de4885916cf6949a6904d02336e59d98544c,Generalized Low Rank Approximations of Matrices,"005 Springer Science + Business Media, Inc. Manufactured in The Netherlands.
DOI: 10.1007/s10994-005-3561-6
Generalized Low Rank Approximations of Matrices
JIEPING YE
Department of Computer Science & Engineering,University of Minnesota-Twin Cities, Minneapolis,
MN 55455, USA
Editor:
Peter Flach
Published online: 12 August 2005"
6e198f6cc4199e1c4173944e3df6f39a302cf787,MORPH-II: Inconsistencies and Cleaning Whitepaper,"MORPH-II: Inconsistencies and Cleaning Whitepaper
Participants: G. Bingham, B. Yip, M. Ferguson, and C. Nansalo
Mentors: C. Chen, Y. Wang, and T. Kling
NSF-REU Site at UNC Wilmington, Summer 2017"
6eba25166fe461dc388805cc2452d49f5d1cdadd,"ALBANIE, VEDALDI: LEARNING GRIMACES BY WATCHING TV 1 Learning Grimaces by Watching TV","Pages 122.1-122.12
DOI: https://dx.doi.org/10.5244/C.30.122"
6e8a81d452a91f5231443ac83e4c0a0db4579974,Illumination robust face representation based on intrinsic geometrical information,"Illumination robust face representation based on intrinsic geometrical
information
Soyel, H; Ozmen, B; McOwan, PW
This is a pre-copyedited, author-produced PDF of an article accepted for publication in IET
Conference on Image Processing (IPR 2012). The version of record is available
http://ieeexplore.ieee.org/document/6290632/?arnumber=6290632&tag=1
For additional information about this publication click this link.
http://qmro.qmul.ac.uk/xmlui/handle/123456789/16147
Information about this research object was correct at the time of download; we occasionally
make corrections to records, please therefore check the published record when citing. For
more information contact"
6ed738ff03fd9042965abdfaa3ed8322de15c116,K-MEAP: Generating Specified K Clusters with Multiple Exemplars by Efficient Affinity Propagation,"This document is downloaded from DR-NTU, Nanyang Technological
University Library, Singapore.
Title
K-MEAP: Generating Specified K Clusters with Multiple
Exemplars by Efficient Affinity Propagation
Author(s) Wang, Yangtao; Chen, Lihui
Citation
Wang, Y & Chen, L. (2014). K-MEAP: Generating
Specified K Clusters with Multiple Exemplars by Efficient
Affinity Propagation. 2014 IEEE International Conference
on Data Mining (ICDM), 1091-1096.
http://hdl.handle.net/10220/39690
Rights
© 2014 IEEE. Personal use of this material is permitted.
Permission from IEEE must be obtained for all other
uses, in any current or future media, including
reprinting/republishing this material for advertising or
promotional purposes, creating new collective works, for
resale or redistribution to servers or lists, or reuse of any
opyrighted component of this work in other works. The"
6ecd4025b7b5f4894c990614a9a65e3a1ac347b2,Automatic Naming of Character using Video Streaming for Face Recognition with Graph Matching,"International Journal on Recent and Innovation Trends in Computing and Communication
ISSN: 2321-8169
Volume: 2 Issue: 5
1275– 1281
_______________________________________________________________________________________________
Automatic Naming of Character using Video Streaming for Face
Recognition with Graph Matching
Nivedita.R.Pandey
Ranjan.P.Dahake
PG Student at MET’s IOE Bhujbal Knowledge City,
PG Student at MET’s IOE Bhujbal Knowledge City,
Nasik, Maharashtra, India,
Nasik, Maharashtra, India,"
6e9a8a34ab5b7cdc12ea52d94e3462225af2c32c,Fusing Aligned and Non-aligned Face Information for Automatic Affect Recognition in the Wild: A Deep Learning Approach,"Fusing Aligned and Non-Aligned Face Information
for Automatic Affect Recognition in the Wild: A Deep Learning Approach
Bo-Kyeong Kim, Suh-Yeon Dong, Jihyeon Roh, Geonmin Kim, Soo-Young Lee
Computational NeuroSystems Laboratory (CNSL)
Korea Advanced Institute of Science and Technology (KAIST)
{bokyeong1015, {rohleejh, gmkim90,"
6e3a181bf388dd503c83dc324561701b19d37df1,Finding a low-rank basis in a matrix subspace,"Finding a low-rank basis in a matrix subspace
Yuji Nakatsukasa · Tasuku Soma ·
Andr´e Uschmajew"
6ef1996563835b4dfb7fda1d14abe01c8bd24a05,Nonparametric Part Transfer for Fine-Grained Recognition,"Nonparametric Part Transfer for Fine-grained Recognition
Christoph G¨oring, Erik Rodner, Alexander Freytag, and Joachim Denzler∗
Computer Vision Group, Friedrich Schiller University Jena
www.inf-cv.uni-jena.de"
6e8c3b7d25e6530a631ea01fbbb93ac1e8b69d2f,"Deep Episodic Memory: Encoding, Recalling, and Predicting Episodic Experiences for Robot Action Execution","Deep Episodic Memory: Encoding, Recalling, and Predicting
Episodic Experiences for Robot Action Execution
Jonas Rothfuss∗†, Fabio Ferreira∗†, Eren Erdal Aksoy ‡, You Zhou† and Tamim Asfour†"
6e911227e893d0eecb363015754824bf4366bdb7,Wasserstein Divergence for GANs,"Wasserstein Divergence for GANs
Jiqing Wu1, Zhiwu Huang1, Janine Thoma1, Dinesh Acharya1, and
Luc Van Gool1,2
Computer Vision Lab, ETH Zurich, Switzerland
VISICS, KU Leuven, Belgium"
6ee8a94ccba10062172e5b31ee097c846821a822,How to solve classification and regression problems on high-dimensional data with a supervised extension of slow feature analysis,"Submitted 3/13; Revised 10/13; Published 12/13
How to Solve Classification and Regression Problems on
High-Dimensional Data with a Supervised
Extension of Slow Feature Analysis
Alberto N. Escalante-B.
Laurenz Wiskott
Institut f¨ur Neuroinformatik
Ruhr-Universit¨at Bochum
Bochum D-44801, Germany
Editor: David Dunson"
6ee64c19efa89f955011531cde03822c2d1787b8,Table S1: Review of Existing Facial Expression Databases That Are Often Used in Social Psycholgy,"Table S1: Review of existing facial expression databases that are often used in social
psycholgy.
Author
database
Expressions1
Format
Short summary
GEMEP Corpus
Mind Reading: the
interactive
guide
to emotions
udio
video
record-
Videos
nger,
muse-
dmiration,
ment,"
6e379f2d34e14efd85ae51875a4fa7d7ae63a662,A New Multi-modal Biometric System Based on Fingerprint and Finger Vein Recognition,"A NEW MULTI-MODAL BIOMETRIC SYSTEM
BASED ON FINGERPRINT AND FINGER
VEIN RECOGNITION
Naveed AHMED
Master's Thesis
Department of Software Engineering
Advisor: Prof. Dr. Asaf VAROL
JULY-2014"
6e0a05d87b3cc7e16b4b2870ca24cf5e806c0a94,Random Graphs for Structure Discovery in High-dimensional Data,"RANDOM GRAPHS FOR STRUCTURE
DISCOVERY IN HIGH-DIMENSIONAL DATA
Jos¶e Ant¶onio O. Costa
A dissertation submitted in partial fulflllment
of the requirements for the degree of
Doctor of Philosophy
(Electrical Engineering: Systems)
in The University of Michigan
Doctoral Committee:
Professor Alfred O. Hero III, Chair
Professor Jefirey A. Fessler
Professor Susan A. Murphy
Professor David L. Neuhofi"
6e1802874ead801a7e1072aa870681aa2f555f35,Exploring Feature Descritors for Face Recognition,"­4244­0728­1/07/$20.00 ©2007 IEEE
I ­ 629
ICASSP 2007
*22+),)164,7+616DAIK??AIIB=B=?AHA?CEJE=CHEJDCHA=JOHAEAI.EIDAHB=?A -*/ 4A?AJO?=*E=HO2=JJAH*22+),)"
6ed22b934e382c6f72402747d51aa50994cfd97b,Customized expression recognition for performance-driven cutout character animation,"Customized Expression Recognition for Performance-Driven
Cutout Character Animation
Xiang Yu†
NEC Laboratories America
Jianchao Yang‡ Wilmot Li§
Snapchat"
6e93fd7400585f5df57b5343699cb7cda20cfcc2,Comparing a novel model based on the transferable belief model with humans during the recognition of partially occluded facial expressions.,"http://journalofvision.org/9/2/22/
Comparing a novel model based on the transferable
elief model with humans during the recognition of
partially occluded facial expressions
Zakia Hammal
Martin Arguin
Frédéric Gosselin
Département de Psychologie, Université de Montréal,
Canada
Département de Psychologie, Université de Montréal,
Canada
Département de Psychologie, Université de Montréal,
Canada
Humans recognize basic facial expressions effortlessly. Yet, despite a considerable amount of research, this task remains
elusive for computer vision systems. Here, we compared the behavior of one of the best computer models of facial
expression recognition (Z. Hammal, L. Couvreur, A. Caplier, & M. Rombaut, 2007) with the behavior of human observers
during the M. Smith, G. Cottrell, F. Gosselin, and P. G. Schyns (2005) facial expression recognition task performed on
stimuli randomly sampled using Gaussian apertures. The modelVwhich we had to significantly modify in order to give the
bility to deal with partially occluded stimuliVclassifies the six basic facial expressions (Happiness, Fear, Sadness,
Surprise, Anger, and Disgust) plus Neutral from static images based on the permanent facial feature deformations and the"
9ab463d117219ed51f602ff0ddbd3414217e3166,Weighted Transmedia Relevance Feedback for Image Retrieval and Auto-annotation,"Weighted Transmedia
Relevance Feedback for
Image Retrieval and
Auto-annotation
Thomas Mensink, Jakob Verbeek, Gabriela Csurka
TECHNICAL
REPORT
N° 0415
December 2011
Project-Teams LEAR - INRIA
nd TVPA - XRCE"
9ac82909d76b4c902e5dde5838130de6ce838c16,Recognizing Facial Expressions Automatically from Video,"Recognizing Facial Expressions Automatically
from Video
Caifeng Shan and Ralph Braspenning
Introduction
Facial expressions, resulting from movements of the facial muscles, are the face
hanges in response to a person’s internal emotional states, intentions, or social
ommunications. There is a considerable history associated with the study on fa-
ial expressions. Darwin (1872) was the first to describe in details the specific fa-
ial expressions associated with emotions in animals and humans, who argued that
ll mammals show emotions reliably in their faces. Since that, facial expression
nalysis has been a area of great research interest for behavioral scientists (Ekman,
Friesen, and Hager, 2002). Psychological studies (Mehrabian, 1968; Ambady and
Rosenthal, 1992) suggest that facial expressions, as the main mode for non-verbal
ommunication, play a vital role in human face-to-face communication. For illus-
tration, we show some examples of facial expressions in Fig. 1.
Computer recognition of facial expressions has many important applications in
intelligent human-computer interaction, computer animation, surveillance and se-
urity, medical diagnosis, law enforcement, and awareness systems (Shan, 2007).
Therefore, it has been an active research topic in multiple disciplines such as psy-
hology, cognitive science, human-computer interaction, and pattern recognition."
9ac15845defcd0d6b611ecd609c740d41f0c341d,Robust Color-based Vision for Mobile Robots,"Copyright
Juhyun Lee"
9af1cf562377b307580ca214ecd2c556e20df000,International Journal of Advanced Studies in Computer Science and Engineering,"Feb. 28
International Journal of Advanced Studies in Computer Science and Engineering
IJASCSE, Volume 4, Issue 2, 2015
Video-Based Facial Expression Recognition
Using Local Directional Binary Pattern
Sahar Hooshmand, Ali Jamali Avilaq, Amir Hossein Rezaie
Electrical Engineering Dept., AmirKabir Univarsity of Technology
Tehran, Iran"
9a23a0402ae68cc6ea2fe0092b6ec2d40f667adb,High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs,"High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
Ting-Chun Wang1 Ming-Yu Liu1
Jun-Yan Zhu2 Andrew Tao1
Jan Kautz1 Bryan Catanzaro1
NVIDIA Corporation
UC Berkeley
Figure 1: We propose a generative adversarial framework for synthesizing 2048 × 1024 images from semantic label maps
(lower left corner in (a)). Compared to previous work [5], our results express more natural textures and details. (b) We can
hange labels in the original label map to create new scenes, like replacing trees with buildings. (c) Our framework also
llows a user to edit the appearance of individual objects in the scene, e.g. changing the color of a car or the texture of a road.
Please visit our website for more side-by-side comparisons as well as interactive editing demos."
9a7858eda9b40b16002c6003b6db19828f94a6c6,Mooney face classification and prediction by learning across tone,"MOONEY FACE CLASSIFICATION AND PREDICTION BY LEARNING ACROSS TONE
Tsung-Wei Ke(cid:63)†
Stella X. Yu(cid:63)†
David Whitney(cid:63)
(cid:63) UC Berkeley / †ICSI"
9a276c72acdb83660557489114a494b86a39f6ff,Emotion Classification through Lower Facial Expressions using Adaptive Support Vector Machines,"Emotion Classification through Lower Facial Expressions using Adaptive
Support Vector Machines
Porawat Visutsak
Department of Information Technology, Faculty of Industrial Technology and Management,
King Mongkut’s University of Technology North Bangkok,"
9a1a9dd3c471bba17e5ce80a53e52fcaaad4373e,Automatic Recognition of Spontaneous Facial Actions,"Automatic Recognition of Spontaneous Facial
Actions
Marian Stewart Bartlett1, Gwen C. Littlewort1, Mark G. Frank2, Claudia Lainscsek1,
Ian R. Fasel1, Javier R. Movellan1
Institute for Neural Computation, University of California, San Diego.
Department of Communication, University at Buffalo, State University of New York."
9a42c519f0aaa68debbe9df00b090ca446d25bc4,Face Recognition via Centralized Coordinate Learning,"Face Recognition via Centralized Coordinate
Learning
Xianbiao Qi, Lei Zhang"
9aad8e52aff12bd822f0011e6ef85dfc22fe8466,Temporal-Spatial Mapping for Action Recognition,"Temporal-Spatial Mapping for Action Recognition
Xiaolin Song, Cuiling Lan, Wenjun Zeng, Junliang Xing, Jingyu Yang, and Xiaoyan Sun"
363ca0a3f908859b1b55c2ff77cc900957653748,Local Binary Patterns and Linear Programming using Facial Expression,"International Journal of Computer Trends and Technology (IJCTT) – volume 1 Issue 3 Number 4 – Aug 2011
Local Binary Patterns and Linear Programming using
Facial Expression
Ms.P.Jennifer
#MCA Department, Bharath Institute of Science and Technology
+B.Tech (C.S.E), Bharath University , Chennai – 73.
Dr. A. Muthu kumaravel
#MCA Department, Bharath Institute of Science and Technology
+B.Tech (C.S.E), Bharath University , Chennai – 73."
36939e6a365e9db904d81325212177c9e9e76c54,"Assessing the Accuracy of Four Popular Face Recognition Tools for Inferring Gender, Age, and Race","Assessing the Accuracy of Four Popular Face Recognition Tools for
Inferring Gender, Age, and Race
Soon-Gyo Jung, Jisun An, Haewoon Kwak, Joni Salminen, Bernard J. Jansen
Qatar Computing Research Institute, HBKU
HBKU Research Complex, Doha, P.O. Box 34110, Qatar"
3646b42511a6a0df5470408bc9a7a69bb3c5d742,Detection of Facial Parts based on ABLATA,"International Journal of Computer Applications (0975 – 8887)
Applications of Computers and Electronics for the Welfare of Rural Masses (ACEWRM) 2015
Detection of Facial Parts based on ABLATA
Siddhartha Choubey
Shri Shankaracharya
Technical Campus, Bhilai
Vikas Singh
Shri Shankaracharya
Technical Campus, Bhilai
Abha Choubey
Shri Shankaracharya
Technical Campus, Bhilai"
36fe39ed69a5c7ff9650fd5f4fe950b5880760b0,Tracking von Gesichtsmimik mit Hilfe von Gitterstrukturen zur Klassifikation von schmerzrelevanten Action Units,"Tracking von Gesichtsmimik
mit Hilfe von Gitterstrukturen
zur Klassifikation von schmerzrelevanten Action
Units
Christine Barthold1, Anton Papst1, Thomas Wittenberg1
Christian K¨ublbeck1, Stefan Lautenbacher2, Ute Schmid2, Sven Friedl1,3
Fraunhofer-Institut f¨ur Integrierte Schaltungen IIS, Erlangen,
Otto-Friedrich-Universit¨at Bamberg, 3Universit¨atsklinkum Erlangen
Kurzfassung. In der Schmerzforschung werden schmerzrelevante Mi-
mikbewegungen von Probanden mittels des Facial Action Coding System
klassifiziert. Die manuelle Klassifikation hierbei ist aufw¨andig und eine
utomatische (Vor-)klassifikation k¨onnte den diagnostischen Wert dieser
Analysen erh¨ohen sowie den klinischen Workflow unterst¨utzen. Der hier
vorgestellte regelbasierte Ansatz erm¨oglicht eine automatische Klassifika-
tion ohne große Trainingsmengen vorklassifizierter Daten. Das Verfahren
erkennt und verfolgt Mimikbewegungen, unterst¨utzt durch ein Gitter,
und ordnet diese Bewegungen bestimmten Gesichtsarealen zu. Mit die-
sem Wissen kann aus den Bewegungen auf die zugeh¨origen Action Units
geschlossen werden.
Einleitung"
36fc4120fc0638b97c23f97b53e2184107c52233,Introducing Celebrities in an Images using HAAR Cascade algorithm,"National Conference on Innovative Paradigms in Engineering & Technology (NCIPET-2013)
Proceedings published by International Journal of Computer Applications® (IJCA)
Introducing Celebrities in an Images using HAAR
Cascade algorithm
Jaya M. Jadhav
Deipali V. Gore
Asst. Professor
Rashmi R. Tundalwar
PES Modern College of Engg.
PES Modern College of Engg.
PES Modern College of Engg.
Shivaji Nagar, Pune
Shivaji Nagar, Pune
Shivaji Nagar, Pune"
36ce0b68a01b4c96af6ad8c26e55e5a30446f360,Facial expression recognition based on a mlp neural network using constructive training algorithm,"Multimed Tools Appl
DOI 10.1007/s11042-014-2322-6
Facial expression recognition based on a mlp neural
network using constructive training algorithm
Hayet Boughrara · Mohamed Chtourou ·
Chokri Ben Amar · Liming Chen
Received: 5 February 2014 / Revised: 22 August 2014 / Accepted: 13 October 2014
© Springer Science+Business Media New York 2014"
3674f3597bbca3ce05e4423611d871d09882043b,Facial Expression Spacial Charts for Describing Dynamic Diversity of Facial Expressions,"ISSN 1796-2048
Volume 7, Number 4, August 2012
Contents
Special Issue: Multimedia Contents Security in Social Networks Applications
Guest Editors: Zhiyong Zhang and Muthucumaru Maheswaran
Guest Editorial
Zhiyong Zhang and Muthucumaru Maheswaran
SPECIAL ISSUE PAPERS
DRTEMBB: Dynamic Recommendation Trust Evaluation Model Based on Bidding
Gang Wang and Xiao-lin Gui
Block-Based Parallel Intra Prediction Scheme for HEVC
Jie Jiang, Baolong, Wei Mo, and Kefeng Fan
Optimized LSB Matching Steganography Based on Fisher Information
Yi-feng Sun, Dan-mei Niu, Guang-ming Tang, and Zhan-zhan Gao
A Novel Robust Zero-Watermarking Scheme Based on Discrete Wavelet Transform
Yu Yang, Min Lei, Huaqun Liu, Yajian Zhou, and Qun Luo
Stego Key Estimation in LSB Steganography
Jing Liu and Guangming Tang
REGULAR PAPERS
Facial Expression Spacial Charts for Describing Dynamic Diversity of Facial Expressions"
362bfeb28adac5f45b6ef46c07c59744b4ed6a52,Incorporating Scalability in Unsupervised Spatio- Temporal Feature Learning,"INCORPORATING SCALABILITY IN UNSUPERVISED SPATIO-TEMPORAL FEATURE
LEARNING
Sujoy Paul, Sourya Roy and Amit K. Roy-Chowdhury
Dept. of Electrical and Computer Engineering, University of California, Riverside, CA 92521"
365866dc937529c3079a962408bffaa9b87c1f06,Facial Feature Expression Based Approach for Human Face Recognition: A Review,"IJISET - International Journal of Innovative Science, Engineering & Technology, Vol. 1 Issue 3, May 2014.
www.ijiset.com
ISSN 2348 – 7968
Facial Feature Expression Based Approach for Human Face
Recognition: A Review
Jageshvar K. Keche1, Mahendra P. Dhore2
Department of Computer Science, SSESA, Science College, Congress Nagar, Nagpur, (MS)-India,
Department of Electronics & Computer Science, RTM Nagpur University, Campus Nagpur, (MS)-India.
required
extraction  of"
362a70b6e7d55a777feb7b9fc8bc4d40a57cde8c,A partial least squares based ranker for fast and accurate age estimation,"978-1-4799-9988-0/16/$31.00 ©2016 IEEE
ICASSP 2016"
3630324c2af04fd90f8668f9ee9709604fe980fd,Image Classification With Tailored Fine-Grained Dictionaries,"This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TCSVT.2016.2607345, IEEE
Transactions on Circuits and Systems for Video Technology
Image Classification with Tailored Fine-Grained
Dictionaries
Xiangbo Shu, Jinhui Tang, Guo-Jun Qi, Zechao Li, Yu-Gang Jiang and Shuicheng Yan"
36cf96fe11a2c1ea4d999a7f86ffef6eea7b5958,RGB-D Face Recognition With Texture and Attribute Features,"RGB-D Face Recognition with Texture and
Attribute Features
Gaurav Goswami, Student Member, IEEE, Mayank Vatsa, Senior Member, IEEE, and Richa Singh, Senior
Member, IEEE"
36018404263b9bb44d1fddaddd9ee9af9d46e560,Occluded Face Recognition by Using Gabor Features,"OCCLUDED FACE RECOGNITION BY USING GABOR
FEATURES
Burcu Kepenekci 1,2, F. Boray Tek 1,2, Gozde Bozdagi Akar 1
Department of Electrical And Electronics Engineering, METU, Ankara, Turkey
7h%ł7$.(cid:3)%ł/7(1(cid:15)(cid:3)$QNDUD(cid:15)(cid:3)7XUNH\"
366595171c9f4696ec5eef7c3686114fd3f116ad,Algorithms and Representations for Visual Recognition,"Algorithms and Representations for Visual
Recognition
Subhransu Maji
Electrical Engineering and Computer Sciences
University of California at Berkeley
Technical Report No. UCB/EECS-2012-53
http://www.eecs.berkeley.edu/Pubs/TechRpts/2012/EECS-2012-53.html
May 1, 2012"
5c6de2d9f93b90034f07860ae485a2accf529285,Compensating for pose and illumination in unconstrained periocular biometrics,"Int. J. Biometrics, Vol. X, No. Y, xxxx
Compensating for pose and illumination in
unconstrained periocular biometrics
Chandrashekhar N. Padole and
Hugo Proença*
Department of Computer Science,
IT – Instituto de Telecomunicações,
University of Beira Interior,
6200-Covilhã, Portugal
Fax: +351-275-319899
E-mail:
E-mail:
*Corresponding author"
5c2e264d6ac253693469bd190f323622c457ca05,Improving large-scale face image retrieval using multi-level features,"978-1-4799-2341-0/13/$31.00 ©2013 IEEE
ICIP 2013"
5c5e1f367e8768a9fb0f1b2f9dbfa060a22e75c0,Reference Face Graph for Face Recognition,"Reference Face Graph for Face Recognition
Mehran Kafai, Member, IEEE, Le An, Student Member, IEEE, and Bir Bhanu, Fellow, IEEE"
5c35ac04260e281141b3aaa7bbb147032c887f0c,Face Detection and Tracking Control with Omni Car,"Face Detection and Tracking Control with Omni Car
Jheng-Hao Chen, Tung-Yu Wu
CS 231A Final Report
June 31, 2016"
5c435c4bc9c9667f968f891e207d241c3e45757a,"""How old are you?"" : Age Estimation with Tensors of Binary Gaussian Receptive Maps","RUIZ-HERNANDEZ, CROWLEY, LUX: HOW OLD ARE YOU?
""How old are you?"" : Age Estimation with
Tensors of Binary Gaussian Receptive Maps
John A. Ruiz-Hernandez
James L. Crowley
Augustin Lux
INRIA Grenoble Rhones-Alpes
Research Center and Laboratoire
d’Informatique de Grenoble (LIG)
655 avenue de l’Europe
8 334 Saint Ismier Cedex, France"
5c02bd53c0a6eb361972e8a4df60cdb30c6e3930,Multimedia stimuli databases usage patterns: a survey report,"Multimedia stimuli databases usage patterns: a
survey report
M. Horvat1, S. Popović1 and K. Ćosić1
University of Zagreb, Faculty of Electrical Engineering and Computing
Department of Electric Machines, Drives and Automation
Zagreb, Croatia"
5c717afc5a9a8ccb1767d87b79851de8d3016294,A novel eye region based privacy protection scheme,"978-1-4673-0046-9/12/$26.00 ©2012 IEEE
ICASSP 2012"
096eb8b4b977aaf274c271058feff14c99d46af3,Multi-observation visual recognition via joint dynamic sparse representation,"REPORT DOCUMENTATION PAGE
Form Approved OMB NO. 0704-0188
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09137e3c267a3414314d1e7e4b0e3a4cae801f45,Two Birds with One Stone: Transforming and Generating Facial Images with Iterative GAN,"Noname manuscript No.
(will be inserted by the editor)
Two Birds with One Stone: Transforming and Generating
Facial Images with Iterative GAN
Dan Ma · Bin Liu · Zhao Kang · Jiayu Zhou · Jianke Zhu · Zenglin Xu
Received: date / Accepted: date"
09926ed62511c340f4540b5bc53cf2480e8063f8,Tubelet Detector for Spatio-Temporal Action Localization,"Action Tubelet Detector for Spatio-Temporal Action Localization
Vicky Kalogeiton1,2
Philippe Weinzaepfel3
Vittorio Ferrari2
Cordelia Schmid1"
09718bf335b926907ded5cb4c94784fd20e5ccd8,"Recognizing partially occluded, expression variant faces from single training image per person with SOM and soft k-NN ensemble","Recognizing Partially Occluded, Expression Variant
Faces From Single Training Image per Person
With SOM and Soft k-NN Ensemble
Xiaoyang Tan, Songcan Chen, Zhi-Hua Zhou, Member, IEEE, and Fuyan Zhang"
0903bb001c263e3c9a40f430116d1e629eaa616f,An Empirical Study of Context in Object Detection,"CVPR 2009 Submission #987. CONFIDENTIAL REVIEW COPY. DO NOT DISTRIBUTE.
An Empirical Study of Context in Object Detection
Anonymous CVPR submission
Paper ID 987"
09df62fd17d3d833ea6b5a52a232fc052d4da3f5,Mejora de Contraste y Compensación en Cambios de la Iluminación,"ISSN: 1405-5546
Instituto Politécnico Nacional
México
Rivas Araiza, Edgar A.; Mendiola Santibañez, Jorge D.; Herrera Ruiz, Gilberto; González Gutiérrez,
Carlos A.; Trejo Perea, Mario; Ríos Moreno, G. J.
Mejora de Contraste y Compensación en Cambios de la Iluminación
Instituto Politécnico Nacional
Distrito Federal, México
Disponible en: http://www.redalyc.org/articulo.oa?id=61509703
Cómo citar el artículo
Número completo
Más información del artículo
Página de la revista en redalyc.org
Sistema de Información Científica
Red de Revistas Científicas de América Latina, el Caribe, España y Portugal
Proyecto académico sin fines de lucro, desarrollado bajo la iniciativa de acceso abierto"
09f853ce12f7361c4b50c494df7ce3b9fad1d221,Random Forests for Real Time 3D Face Analysis,"myjournal manuscript No.
(will be inserted by the editor)
Random forests for real time 3D face analysis
Gabriele Fanelli · Matthias Dantone · Juergen Gall · Andrea Fossati ·
Luc Van Gool
Received: date / Accepted: date"
09750c9bbb074bbc4eb66586b20822d1812cdb20,Estimation of the neutral face shape using Gaussian Mixture Models,"978-1-4673-0046-9/12/$26.00 ©2012 IEEE
ICASSP 2012"
097f674aa9e91135151c480734dda54af5bc4240,Face Recognition Based on Multiple Region Features,"Proc. VIIth Digital Image Computing: Techniques and Applications, Sun C., Talbot H., Ourselin S. and Adriaansen T. (Eds.), 10-12 Dec. 2003, Sydney
Face Recognition Based on Multiple Region Features
Jiaming Li, Geoff  Poulton, Ying Guo,  Rong-Yu Qiao
CSIRO Telecommunications & Industrial Physics
Australia
Tel: 612 9372 4104, Fax: 612 9372 4411, Email:"
5da740682f080a70a30dc46b0fc66616884463ec,Real-Time Head Pose Estimation Using Multi-variate RVM on Faces in the Wild,"Real-Time Head Pose Estimation Using
Multi-Variate RVM on Faces in the Wild
Mohamed Selim, Alain Pagani, Didier Stricker
Augmented Vision Research Group,
German Research Center for Artificial Intelligence (DFKI),
Tripstaddterstr. 122, 67663 Kaiserslautern, Germany
Technical University of Kaiserslautern
http://www.av.dfki.de"
5da139fc43216c86d779938d1c219b950dd82a4c,A Generalized Multiple Instance Learning Algorithm for Iterative Distillation and Cross-Granular Propagation of Video Annotations,"-4244-1437-7/07/$20.00 ©2007 IEEE
II - 205
ICIP 2007"
5d185d82832acd430981ffed3de055db34e3c653,A Fuzzy Reasoning Model for Recognition of Facial Expressions,"A Fuzzy Reasoning Model for Recognition
of Facial Expressions
Oleg Starostenko1, Renan Contreras1, Vicente Alarcón Aquino1, Leticia Flores Pulido1,
Jorge Rodríguez Asomoza1, Oleg Sergiyenko2, and Vira Tyrsa3
Research Center CENTIA, Department of Computing, Electronics and Mechatronics,
Universidad de las Américas, 72820, Puebla, Mexico
{oleg.starostenko; renan.contrerasgz; vicente.alarcon; leticia.florespo;
Engineering Institute, Autonomous University of Baja California, Blvd. Benito Juárez,
Insurgentes Este, 21280, Mexicali, Baja California, Mexico
Universidad Politécnica de Baja California, Mexicali, Baja California, Mexico"
5d233e6f23b1c306cf62af49ce66faac2078f967,Optimal Geometrical Set for Automated Marker Placement to Virtualized Real-Time Facial Emotions,"RESEARCH ARTICLE
Optimal Geometrical Set for Automated
Marker Placement to Virtualized Real-Time
Facial Emotions
Vasanthan Maruthapillai, Murugappan Murugappan*
School of Mechatronic Engineering, Universiti Malaysia Perlis, 02600, Ulu Pauh, Arau, Perlis, West Malaysia"
5db075a308350c083c3fa6722af4c9765c4b8fef,The Novel Method of Moving Target Tracking Eyes Location based on SIFT Feature Matching and Gabor Wavelet Algorithm,"The Novel Method of Moving Target Tracking Eyes
Location based on SIFT Feature Matching and Gabor
Wavelet Algorithm
* Jing Zhang, Caixia Yang, Kecheng Liu
College of Computer and Information Engineering, Nanyang Institute of Technology,
Henan Nanyang, 473004, China
* Tel.: 0086+13838972861
* E-mail:
Sensors & Transducers, Vol. 154, Issue 7, July 2013, pp. 129-137
SSSeeennnsssooorrrsss   &&&   TTTrrraaannnsssddduuuccceeerrrsss
© 2013 by IFSA
http://www.sensorsportal.com
Received: 28 April 2013   /Accepted: 19 July 2013   /Published: 31 July 2013"
5d7f8eb73b6a84eb1d27d1138965eb7aef7ba5cf,Robust Registration of Dynamic Facial Sequences,"Robust Registration of Dynamic Facial Sequences
Evangelos Sariyanidi, Hatice Gunes, and Andrea Cavallaro"
5db4fe0ce9e9227042144758cf6c4c2de2042435,Recognition of Facial Expression Using Haar Wavelet Transform,"INTERNATIONAL JOURNAL OF ELECTRICAL AND ELECTRONIC SYSTEMS RESEARCH, VOL.3, JUNE 2010
Recognition of Facial Expression Using Haar
Wavelet Transform
M. Satiyan,  M.Hariharan,  R.Nagarajan
paper
features
investigates"
5d5cd6fa5c41eb9d3d2bab3359b3e5eb60ae194e,Face Recognition Algorithms,"Face Recognition Algorithms
Proyecto Fin de Carrera
June 16, 2010
Ion Marqu´es
Supervisor:
Manuel Gra˜na"
5d09d5257139b563bd3149cfd5e6f9eae3c34776,Pattern recognition with composite correlation filters designed with multi-objective combinatorial optimization,"Optics Communications 338 (2015) 77–89
Contents lists available at ScienceDirect
Optics Communications
journal homepage: www.elsevier.com/locate/optcom
Pattern recognition with composite correlation filters designed with
multi-objective combinatorial optimization
Victor H. Diaz-Ramirez a,n, Andres Cuevas a, Vitaly Kober b, Leonardo Trujillo c,
Abdul Awwal d
Instituto Politécnico Nacional – CITEDI, Ave. del Parque 1310, Mesade Otay, Tijuana B.C. 22510, México
Department of Computer Science, CICESE, Carretera Ensenada-Tijuana 3918, Ensenada B.C. 22860, México
Instituto Tecnológico de Tijuana, Blvd. Industrial y Ave. ITR TijuanaS/N, Mesa de Otay, Tijuana B.C. 22500, México
d National Ignition Facility, Lawrence Livermore National Laboratory, Livermore, CA 94551, USA
r t i c l e i n f o
b s t r a c t
Article history:
Received 12 July 2014
Accepted 16 November 2014
Available online 23 October 2014
Keywords:
Object recognition"
5d197c8cd34473eb6cde6b65ced1be82a3a1ed14,A Face Image Database for Evaluating Out-of-Focus Blur,"0AFaceImageDatabaseforEvaluatingOut-of-FocusBlurQiHan,QiongLiandXiamuNiuHarbinInstituteofTechnologyChina1.IntroductionFacerecognitionisoneofthemostpopularresearchfieldsofcomputervisionandmachinelearning(Tores(2004);Zhaoetal.(2003)).Alongwithinvestigationoffacerecognitionalgorithmsandsystems,manyfaceimagedatabaseshavebeencollected(Gross(2005)).Facedatabasesareimportantfortheadvancementoftheresearchfield.Becauseofthenonrigidityandcomplex3Dstructureofface,manyfactorsinfluencetheperformanceoffacedetectionandrecognitionalgorithmssuchaspose,expression,age,brightness,contrast,noise,blurandetc.Someearlyfacedatabasesgatheredunderstrictlycontrolledenvironment(Belhumeuretal.(1997);Samaria&Harter(1994);Turk&Pentland(1991))onlyallowslightexpressionvariation.Toinvestigatetherelationshipsbetweenalgorithms’performanceandtheabovefactors,morefacedatabaseswithlargerscaleandvariouscharacterswerebuiltinthepastyears(Bailly-Bailliereetal.(2003);Flynnetal.(2003);Gaoetal.(2008);Georghiadesetal.(2001);Hallinan(1995);Phillipsetal.(2000);Simetal.(2003)).Forinstance,The""CAS-PEAL"",""FERET"",""CMUPIE"",and""YaleB""databasesincludevariousposes(Gaoetal.(2008);Georghiadesetal.(2001);Phillipsetal.(2000);Simetal.(2003));The""HarvardRL"",""CMUPIE""and""YaleB""databasesinvolvemorethan40differentconditionsinillumination(Georghiadesetal.(2001);Hallinan(1995);Simetal.(2003));Andthe""BANCA"",and""NDHID""databasescontainover10timesgathering(Bailly-Bailliereetal.(2003);Flynnetal.(2003)).Thesedatabaseshelpresearcherstoevaluateandimprovetheiralgorithmsaboutfacedetection,recognition,andotherpurposes.Blurisnotthemostimportantbutstillanotablefactoraffectingtheperformanceofabiometricsystem(Fronthaleretal.(2006);Zamanietal.(2007)).Themainreasonsleadingblurconsistinout-of-focusofcameraandmotionofobject,andtheout-of-focusblurismoresignificantintheapplicationenvironmentoffacerecognition(Eskicioglu&Fisher(1995);Kimetal.(1998);Tanakaetal.(2007);Yitzhaky&Kopeika(1996)).Toinvestigatetheinfluenceofbluronafacerecognitionsystem,afaceimagedatabasewithdifferentconditionsofclarityandefficientblurevaluatingalgorithmsareneeded.Thischapterintroducesanewfacedatabasebuiltforthepurposeofblurevaluation.Theapplicationenvironmentsoffacerecognitionareanalyzedfirstly,thenaimagegatheringschemeisdesigned.Twotypicalgatheringfacilitiesareusedandthefocusstatusaredividedinto11steps.Further,theblurassessmentalgorithmsaresummarizedandthecomparisonbetweenthemisraisedonthevarious-claritydatabase.The7www.intechopen.com"
5da2ae30e5ee22d00f87ebba8cd44a6d55c6855e,"When facial expressions do and do not signal minds: The role of face inversion, expression dynamism, and emotion type.","This is an Open Access document downloaded from ORCA, Cardiff University's institutional
repository: http://orca.cf.ac.uk/111659/
This is the author’s version of a work that was submitted to / accepted for publication.
Citation for final published version:
Krumhuber, Eva G, Lai, Yukun, Rosin, Paul and Hugenberg, Kurt 2018. When facial expressions
Publishers page:
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Changes made as a result of publishing processes such as copy-editing, formatting and page
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made available in ORCA are retained by the copyright holders."
31625522950e82ad4dffef7ed0df00fdd2401436,Motion Representation with Acceleration Images,"Motion Representation with Acceleration Images
Hirokatsu Kataoka, Yun He, Soma Shirakabe, Yutaka Satoh
National Institute of Advanced Industrial Science and Technology (AIST)
Tsukuba, Ibaraki, Japan
{hirokatsu.kataoka, yun.he, shirakabe-s,"
318e7e6daa0a799c83a9fdf7dd6bc0b3e89ab24a,Sparsity in Dynamics of Spontaneous Subtle Emotions: Analysis and Application,"Sparsity in Dynamics of Spontaneous
Subtle Emotions: Analysis & Application
Anh Cat Le Ngo, Member, IEEE, John See, Member, IEEE, Raphael C.-W. Phan, Member, IEEE"
31c0968fb5f587918f1c49bf7fa51453b3e89cf7,Deep Transfer Learning for Person Re-identification,"Deep Transfer Learning for Person Re-identification
Mengyue Geng
Yaowei Wang
Tao Xiang
Yonghong Tian"
316e67550fbf0ba54f103b5924e6537712f06bee,Multimodal semi-supervised learning for image classification,"Multimodal semi-supervised learning
for image classification
Matthieu Guillaumin, Jakob Verbeek, Cordelia Schmid
LEAR team, INRIA Grenoble, France"
31ef5419e026ef57ff20de537d82fe3cfa9ee741,Facial Expression Analysis Based on High Dimensional Binary Features,"Facial Expression Analysis Based on
High Dimensional Binary Features
Samira Ebrahimi Kahou, Pierre Froumenty, and Christopher Pal
´Ecole Polytechique de Montr´eal, Universit´e de Montr´eal, Montr´eal, Canada
{samira.ebrahimi-kahou, pierre.froumenty,"
3176ee88d1bb137d0b561ee63edf10876f805cf0,Recombinator Networks: Learning Coarse-to-Fine Feature Aggregation,"Recombinator Networks: Learning Coarse-to-Fine Feature Aggregation
Sina Honari1, Jason Yosinski2, Pascal Vincent1,4, Christopher Pal3
University of Montreal, 2Cornell University, 3Ecole Polytechnique of Montreal, 4CIFAR
{honaris,"
31ace8c9d0e4550a233b904a0e2aabefcc90b0e3,Learning Deep Face Representation,"Learning Deep Face Representation
Haoqiang Fan
Megvii Inc.
Zhimin Cao
Megvii Inc.
Yuning Jiang
Megvii Inc.
Qi Yin
Megvii Inc.
Chinchilla Doudou
Megvii Inc."
31afdb6fa95ded37e5871587df38976fdb8c0d67,Quantized fuzzy LBP for face recognition,"QUANTIZED FUZZY LBP FOR FACE RECOGNITION
Jianfeng
Xudong Jiang,
Junsong
BeingThere
Centre
Institute
of Media Innovation
Nanyang
50 Nanyang
Technological
Singapore
Drive,
637553.
University
School of Electrical
& Electronics
Engineering
Nanyang
50 Nanyang"
91811203c2511e919b047ebc86edad87d985a4fa,Expression Subspace Projection for Face Recognition from Single Sample per Person,"Expression Subspace Projection for Face
Recognition from Single Sample per Person
Hoda Mohammadzade, Student Member, IEEE, and Dimitrios Hatzinakos, Senior Member, IEEE"
9117fd5695582961a456bd72b157d4386ca6a174,Recognition Using Dee Networks,"Facial Expression
n Recognition Using Dee
ep Neural
Networks
Junnan Li and Edmund Y. Lam
Departm
ment of Electrical and Electronic Engineering
he University of Hong Kong, Pokfulam,
Hong Kong"
91067f298e1ece33c47df65236853704f6700a0b,Local Binary Pattern and Local Linear Regression for Pose Invariant Face Recognition,"IJSTE - International Journal of Science Technology & Engineering | Volume 2 | Issue 11 | May 2016
ISSN (online): 2349-784X
Local Binary Pattern and Local Linear
Regression for Pose Invariant Face Recognition
Raju Dadasab Patil
M. Tech Student
Shreekumar T
Associate Professor
Department of Computer Science & Engineering
Department of Computer Science & Engineering
Mangalore Institute of Engineering & Technology, Badaga
Mangalore Institute of Engineering & Technology, Badaga
Mijar, Moodbidri, Mangalore
Mijar, Moodbidri, Mangalore
Karunakara K
Professor & Head of Dept.
Department of Information Science & Engineering
Sri SidarthaInstitute of Technology, Tumkur"
919d3067bce76009ce07b070a13728f549ebba49,Time Based Re-ranking for Web Image Search,"International Journal of Scientific and Research Publications, Volume 4, Issue 6, June 2014
ISSN 2250-3153
Time Based Re-ranking for Web Image Search
Ms. A.Udhayabharadhi *, Mr. R.Ramachandran **
* MCA Student, Sri Manakula Vinayagar Engineering College, Pondicherry-605106
** Assistant Professor dept of MCA, Sri Manakula Vinayagar Engineering College, Pondicherry-605106"
91e57667b6fad7a996b24367119f4b22b6892eca,Probabilistic Corner Detection for Facial Feature,"Probabilistic  Corner  Detection  for  Facial  Feature
Extraction
Article
Accepted version
E. Ardizzone, M. La Cascia, M. Morana
In Lecture Notes in Computer Science Volume 5716, 2009
It  is  advisable  to  refer  to  the  publisher's  version  if  you  intend  to  cite
from the work.
Publisher: Springer
http://link.springer.com/content/pdf/10.1007%2F978-3-
642-04146-4_50.pdf"
917bea27af1846b649e2bced624e8df1d9b79d6f,Ultra Power-Efficient CNN Domain Specific Accelerator with 9.3TOPS/Watt for Mobile and Embedded Applications,"Ultra Power-Efficient CNN Domain Specific Accelerator with 9.3TOPS/Watt for
Mobile and Embedded Applications
Baohua Sun,
Lin Yang,
Patrick Dong, Wenhan Zhang,
Gyrfalcon Technology Inc.
Jason Dong, Charles Young
900 McCarthy Blvd. Milpitas, CA 95035"
91b1a59b9e0e7f4db0828bf36654b84ba53b0557,Simultaneous Hallucination and Recognition of Low-Resolution Faces Based on Singular Value Decomposition,"This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI
> REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE-CLICK HERE TO EDIT) <
Simultaneous Hallucination and Recognition of
Low-Resolution Faces Based on Singular Value
Decomposition
Muwei Jian,  Kin-Man Lam*, Senior Member, IEEE
(SVD)
for  performing  both"
911bef7465665d8b194b6b0370b2b2389dfda1a1,Learning Human Optical Flow,"RANJAN, ROMERO, BLACK: LEARNING HUMAN OPTICAL FLOW
Learning Human Optical Flow
MPI for Intelligent Systems
Tübingen, Germany
Amazon Inc.
Anurag Ranjan1
Javier Romero∗,2
Michael J. Black1"
91ead35d1d2ff2ea7cf35d15b14996471404f68d,Combining and Steganography of 3D Face Textures,"Combining and Steganography of 3D Face Textures
Mohsen Moradi and Mohammad-Reza Rafsanjani-Sadeghi"
91d513af1f667f64c9afc55ea1f45b0be7ba08d4,Automatic Face Image Quality Prediction,"Automatic Face Image Quality Prediction
Lacey Best-Rowden, Student Member, IEEE, and Anil K. Jain, Life Fellow, IEEE"
91e58c39608c6eb97b314b0c581ddaf7daac075e,Pixel-wise Ear Detection with Convolutional Encoder-Decoder Networks,"Pixel-wise Ear Detection with Convolutional
Encoder-Decoder Networks
ˇZiga Emerˇsiˇc 1, Luka Lan Gabriel 2, Vitomir ˇStruc 3 and Peter Peer 1"
9131c990fad219726eb38384976868b968ee9d9c,Deep Facial Expression Recognition: A Survey,"Deep Facial Expression Recognition: A Survey
Shan Li and Weihong Deng∗, Member, IEEE"
911505a4242da555c6828509d1b47ba7854abb7a,Improved Active Shape Model for Facial Feature Localization,"IMPROVED ACTIVE SHAPE MODEL FOR FACIAL FEATURE LOCALIZATION
Hui-Yu Huang and Shih-Hang Hsu
National Formosa University, Taiwan
Email:"
915d4a0fb523249ecbc88eb62cb150a60cf60fa0,Comparison of Feature Extraction Techniques in Automatic Face Recognition Systems for Security Applications,"Comparison of Feature Extraction Techniques in Automatic
Face Recognition Systems for Security Applications
S .  Cruz-Llanas, J. Ortega-Garcia, E. Martinez-Torrico, J. Gonzalez-Rodriguez
Dpto. Ingenieria Audiovisual y Comunicaciones, EUIT Telecomunicacion, Univ. PolitCcnica de Madrid, Spain
{cruzll, jortega, etorrico,
http://www.atvs.diac.upm.es"
65126e0b1161fc8212643b8ff39c1d71d262fbc1,Occlusion Coherence: Localizing Occluded Faces with a Hierarchical Deformable Part Model,"Occlusion Coherence: Localizing Occluded Faces with a
Hierarchical Deformable Part Model
Golnaz Ghiasi Charless C. Fowlkes
Dept. of Computer Science, University of California, Irvine"
6582f4ec2815d2106957215ca2fa298396dde274,Discriminative Learning and Recognition of Image Set Classes Using Canonical Correlations,"JUNE 2007
Discriminative Learning and Recognition
of Image Set Classes Using
Canonical Correlations
Tae-Kyun Kim, Josef Kittler, Member, IEEE, and Roberto Cipolla, Member, IEEE"
65b1209d38c259fe9ca17b537f3fb4d1857580ae,Information Constraints on Auto-Encoding Variational Bayes,"Information Constraints on Auto-Encoding Variational Bayes
Romain Lopez1, Jeffrey Regier1, Michael I. Jordan1,2, and Nir Yosef1,3,4
{romain_lopez, regier,
Department of Electrical Engineering and Computer Sciences, University of California, Berkeley
Department of Statistics, University of California, Berkeley
Ragon Institute of MGH, MIT and Harvard
Chan-Zuckerberg Biohub"
655d9ba828eeff47c600240e0327c3102b9aba7c,Kernel pooled local subspaces for classification,"IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART B: CYBERNETICS, VOL. 35, NO. 3, JUNE 2005
Kernel Pooled Local Subspaces for Classification
Peng Zhang, Student Member, IEEE, Jing Peng, Member, IEEE, and Carlotta Domeniconi"
656a59954de3c9fcf82ffcef926af6ade2f3fdb5,Convolutional Network Representation for Visual Recognition,"Convolutional Network Representation
for Visual Recognition
ALI SHARIF RAZAVIAN
Doctoral Thesis
Stockholm, Sweden, 2017"
656aeb92e4f0e280576cbac57d4abbfe6f9439ea,Use of Image Enhancement Techniques for Improving Real Time Face Recognition Efficiency on Wearable Gadgets,"Journal of Engineering Science and Technology
Vol. 12, No. 1 (2017) 155 - 167
© School of Engineering, Taylor’s University
USE OF IMAGE ENHANCEMENT TECHNIQUES
FOR IMPROVING REAL TIME FACE RECOGNITION EFFICIENCY
ON WEARABLE GADGETS
MUHAMMAD EHSAN RANA1,*, AHMAD AFZAL ZADEH2,
AHMAD MOHAMMAD MAHMOOD ALQURNEH3
, 3Asia Pacific University of Technology & Innovation, Kuala Lumpur 57000, Malaysia
Staffordshire University, Beaconside Stafford ST18 0AB, United Kingdom
*Corresponding Author:"
656f05741c402ba43bb1b9a58bcc5f7ce2403d9a,Supervised Learning Approaches for Automatic Structuring of Videos. (Méthodes d'apprentissage supervisé pour la structuration automatique de vidéos),"THÈSEPour obtenir le grade deDOCTEUR DE L’UNIVERSITÉ GRENOBLE ALPESSpécialité : Mathématiques et InformatiqueArrêté ministériel : 7 août 2006Présentée parDanila POTAPOVThèse dirigée par Cordelia SCHMID et codirigée par Zaid HARCHAOUIpréparée au sein de Inria Grenoble Rhône-Alpesdans l'École Doctorale Mathématiques, Sciences et technologies de l'information, InformatiqueSupervised Learning Approaches for Automatic Structuring of VideosThèse soutenue publiquement le « 22 Juillet 2015 »,devant le jury composé de : Prof. Cordelia SCHMID Inria Grenoble Rhône-Alpes, France, Directeur de thèseDr. Zaid HARCHAOUIInria Grenoble Rhône-Alpes, France, Co-encadrant de thèse Prof. Patrick PEREZTechnicolor Rennes, France, RapporteurProf. Ivan LAPTEVInria Paris Rocquencourt, France, Rapporteur, PrésidentDr. Florent PERRONNINFacebook AI Research, Paris, France, ExaminateurDr. Matthijs DOUZEInria Grenoble Rhône-Alpes, France, Examinateur"
65817963194702f059bae07eadbf6486f18f4a0a,WhittleSearch: Interactive Image Search with Relative Attribute Feedback,"http://dx.doi.org/10.1007/s11263-015-0814-0
WhittleSearch: Interactive Image Search with Relative Attribute
Feedback
Adriana Kovashka · Devi Parikh · Kristen Grauman
Received: date / Accepted: date"
6581c5b17db7006f4cc3575d04bfc6546854a785,Contextual Person Identification in Multimedia Data,"Contextual Person Identification
in Multimedia Data
zur Erlangung des akademischen Grades eines
Doktors der Ingenieurwissenschaften
der Fakultät für Informatik
des Karlsruher Instituts für Technologie (KIT)
genehmigte
Dissertation
Dipl.-Inform. Martin Bäuml
us Erlangen
Tag der mündlichen Prüfung:
8. November 2014
Hauptreferent:
Korreferent:
Prof. Dr. Rainer Stiefelhagen
Karlsruher Institut für Technologie
Prof. Dr. Gerhard Rigoll
Technische Universität München
KIT – Universität des Landes Baden-Württemberg und nationales Forschungszentrum in der Helmholtz-Gemeinschaft
www.kit.edu"
653d19e64bd75648cdb149f755d59e583b8367e3,"Decoupling ""when to update"" from ""how to update""","Decoupling “when to update” from “how to
update”
Eran Malach and Shai Shalev-Shwartz
School of Computer Science, The Hebrew University, Israel"
65babb10e727382b31ca5479b452ee725917c739,Label Distribution Learning,"Label Distribution Learning
Xin Geng*, Member, IEEE"
62dccab9ab715f33761a5315746ed02e48eed2a0,A Short Note about Kinetics-600,"A Short Note about Kinetics-600
Jo˜ao Carreira
Eric Noland
Andras Banki-Horvath
Chloe Hillier
Andrew Zisserman"
62d1a31b8acd2141d3a994f2d2ec7a3baf0e6dc4,Noise-resistant network: a deep-learning method for face recognition under noise,"Ding et al. EURASIP Journal on Image and Video Processing  (2017) 2017:43
DOI 10.1186/s13640-017-0188-z
EURASIP Journal on Image
nd Video Processing
R ES EAR CH
Noise-resistant network: a deep-learning
method for face recognition under noise
Yuanyuan Ding1,2, Yongbo Cheng1,2, Xiaoliu Cheng1, Baoqing Li1*, Xing You1 and Xiaobing Yuan1
Open Access"
62694828c716af44c300f9ec0c3236e98770d7cf,Identification of Action Units Related to Affective States in a Tutoring System for Mathematics,"Padrón-Rivera, G., Rebolledo-Mendez, G., Parra, P. P., & Huerta-Pacheco, N. S. (2016). Identification of Action Units Related to
Identification of  Action Units Related to Affective States in a Tutoring System
Gustavo Padrón-Rivera1, Genaro Rebolledo-Mendez1*, Pilar Pozos Parra2 and N. Sofia
Facultad de Estadística e Informática, Universidad Veracruzana, Mexico // 2Universidad Juárez Autónoma de
Tabasco, Mexico // // // //
for Mathematics
Huerta-Pacheco1
*Corresponding author"
62f0d8446adee6a5e8102053a63a61af07ac4098,Facial point detection using convolutional neural network transferred from a heterogeneous task,"FACIAL POINT DETECTION USING CONVOLUTIONAL NEURAL NETWORK
TRANSFERRED FROM A HETEROGENEOUS TASK
Takayoshi Yamashita* Taro Watasue** Yuji Yamauchi* Hironobu Fujiyoshi*
**Tome R&D
*Chubu University,
200, Matsumoto-cho, Kasugai, AICHI"
62374b9e0e814e672db75c2c00f0023f58ef442c,Frontal face authentication using discriminating,"Frontalfaceauthenticationusingdiscriminatinggridswith
morphologicalfeaturevectors
A.Tefas
C.Kotropoulos
I.Pitas
DepartmentofInformatics,AristotleUniversityofThessaloniki
Box
,Thessaloniki,GREECE
EDICSnumbers:-KNOWContentRecognitionandUnderstanding
-MODAMultimodalandMultimediaEnvironments
Anovelelasticgraphmatchingprocedurebasedonmultiscalemorphologicaloperations,thesocalled
morphologicaldynamiclinkarchitecture,isdevelopedforfrontalfaceauthentication.Fastalgorithms
forimplementingmathematicalmorphologyoperationsarepresented.Featureselectionbyemploying
linearprojectionalgorithmsisproposed.Discriminatorypowercoe(cid:14)cientsthatweighthematching
errorateachgridnodearederived.Theperformanceofmorphologicaldynamiclinkarchitecturein
frontalfaceauthenticationisevaluatedintermsofthereceiveroperatingcharacteristicontheMVTS
faceimagedatabase.Preliminaryresultsforfacerecognitionusingtheproposedtechniquearealso
presented.
Correspondingauthor:I.Pitas
DRAFT
September,
"
626859fe8cafd25da13b19d44d8d9eb6f0918647,Activity Recognition Based on a Magnitude-Orientation Stream Network,"Activity Recognition based on a
Magnitude-Orientation Stream Network
Carlos Caetano, Victor H. C. de Melo, Jefersson A. dos Santos, William Robson Schwartz
Smart Surveillance Interest Group, Department of Computer Science
Universidade Federal de Minas Gerais, Belo Horizonte, Brazil"
62007c30f148334fb4d8975f80afe76e5aef8c7f,Eye In-Painting with Exemplar Generative Adversarial Networks,"Eye In-Painting with Exemplar Generative Adversarial Networks
Brian Dolhansky, Cristian Canton Ferrer
Facebook Inc.
Hacker Way, Menlo Park (CA), USA
{bdol,"
62a30f1b149843860938de6dd6d1874954de24b7,Fast Algorithm for Updating the Discriminant Vectors of Dual-Space LDA,"Fast Algorithm for Updating the Discriminant Vectors
of Dual-Space LDA
Wenming Zheng, Member, IEEE, and Xiaoou Tang, Fellow, IEEE"
62e0380a86e92709fe2c64e6a71ed94d152c6643,Facial emotion recognition with expression energy,"Facial Emotion Recognition With Expression Energy
Albert Cruz
Center for Research in
Intelligent Systems
16 Winston Chung Hall
Bir Bhanu
Center for Research in
Intelligent Systems
16 Winston Chung Hall
Ninad Thakoor
Center for Research in
Intelligent Systems
16 Winston Chung Hall
Riverside, CA, 92521-0425,
Riverside, CA, 92521-0425,
Riverside, CA, 92521-0425,"
9626bcb3fc7c7df2c5a423ae8d0a046b2f69180c,A deep learning approach for action classification in American football video sequences,"UPTEC STS 17033
Examensarbete 30 hp
November 2017
A deep learning approach for
ction classification in American
football video sequences
Jacob Westerberg"
9696b172d66e402a2e9d0a8d2b3f204ad8b98cc4,Region-Based Facial Expression Recognition in Still Images,"J Inf Process Syst, Vol.9, No.1, March 2013
pISSN 1976-913X
eISSN 2092-805X
Region-Based Facial Expression Recognition in
Still Images
Gawed M. Nagi*, Rahmita Rahmat*, Fatimah Khalid* and Muhamad Taufik*"
96f4a1dd1146064d1586ebe86293d02e8480d181,Comparative Analysis of Reranking Techniques for Web Image Search,"COMPARATIVE ANALYSIS OF RERANKING
TECHNIQUES FOR WEB IMAGE SEARCH
Suvarna V. Jadhav1, A.M.Bagade2
,2Department of Information Technology, Pune Institute of Computer Technology, Pune,( India)"
9606b1c88b891d433927b1f841dce44b8d3af066,Principal Component Analysis with Tensor Train Subspace,"Principal Component Analysis with Tensor Train
Subspace
Wenqi Wang, Vaneet Aggarwal, and Shuchin Aeron"
96b1000031c53cd4c1c154013bb722ffd87fa7da,ContextVP: Fully Context-Aware Video Prediction,"ContextVP: Fully Context-Aware Video
Prediction
Wonmin Byeon1,2,3,4, Qin Wang2,
Rupesh Kumar Srivastava4, and Petros Koumoutsakos2
NVIDIA, Santa Clara, CA, USA
ETH Zurich, Zurich, Switzerland
The Swiss AI Lab IDSIA, Manno, Switzerland
NNAISENSE, Lugano, Switzerland"
968f472477a8afbadb5d92ff1b9c7fdc89f0c009,Firefly-based Facial Expression Recognition,Firefly-based Facial Expression Recognition
9686dcf40e6fdc4152f38bd12b929bcd4f3bbbcc,Emotion Based Music Player,"International Journal of Engineering Research and General Science Volume 3, Issue 1,  January-February, 2015
ISSN 2091-2730
Emotion Based Music Player
Hafeez Kabani1, Sharik Khan2, Omar Khan3, Shabana Tadvi4
Department of Computer Science and Engineering
Department of Computer Science and Engineering
Department of Computer Science and Engineering
Asst. Professor, Department of Computer Science and Engineering
M.H Saboo Siddik College of Engineering, University of Mumbai, India"
3a2fc58222870d8bed62442c00341e8c0a39ec87,Probabilistic Local Variation Segmentation,"Probabilistic Local Variation
Segmentation
Michael Baltaxe
Technion - Computer Science Department - M.Sc. Thesis  MSC-2014-02 - 2014"
3a804cbf004f6d4e0b041873290ac8e07082b61f,A Corpus-Guided Framework for Robotic Visual Perception,"Language-Action Tools for Cognitive Artificial Agents: Papers from the 2011 AAAI Workshop (WS-11-14)
A Corpus-Guided Framework for Robotic Visual Perception
Ching L. Teo, Yezhou Yang, Hal Daum´e III, Cornelia Ferm¨uller, Yiannis Aloimonos
University of Maryland Institute for Advanced Computer Studies, College Park, MD 20742-3275
{cteo, yzyang, hal, fer,"
3abc833f4d689f37cc8a28f47fb42e32deaa4b17,Large Scale Retrieval and Generation of Image Descriptions,"Noname manuscript No.
(will be inserted by the editor)
Large Scale Retrieval and Generation of Image Descriptions
Vicente Ordonez · Xufeng Han · Polina Kuznetsova · Girish Kulkarni ·
Margaret Mitchell · Kota Yamaguchi · Karl Stratos · Amit Goyal ·
Jesse Dodge · Alyssa Mensch · Hal Daum´e III · Alexander C. Berg ·
Yejin Choi · Tamara L. Berg
Received: date / Accepted: date"
3acb6b3e3f09f528c88d5dd765fee6131de931ea,Novel representation for driver emotion recognition in motor vehicle videos,"(cid:49)(cid:50)(cid:57)(cid:40)(cid:47)(cid:3)(cid:53)(cid:40)(cid:51)(cid:53)(cid:40)(cid:54)(cid:40)(cid:49)(cid:55)(cid:36)(cid:55)(cid:44)(cid:50)(cid:49)(cid:3)(cid:41)(cid:50)(cid:53)(cid:3)(cid:39)(cid:53)(cid:44)(cid:57)(cid:40)(cid:53)(cid:3)(cid:40)(cid:48)(cid:50)(cid:55)(cid:44)(cid:50)(cid:49)(cid:3)(cid:53)(cid:40)(cid:38)(cid:50)(cid:42)(cid:49)(cid:44)(cid:55)(cid:44)(cid:50)(cid:49)(cid:3)(cid:3)
(cid:44)(cid:49)(cid:3)(cid:48)(cid:50)(cid:55)(cid:50)(cid:53)(cid:3)(cid:57)(cid:40)(cid:43)(cid:44)(cid:38)(cid:47)(cid:40)(cid:3)(cid:57)(cid:44)(cid:39)(cid:40)(cid:50)(cid:54)(cid:3)
(cid:53)(cid:68)(cid:77)(cid:78)(cid:88)(cid:80)(cid:68)(cid:85)(cid:3)(cid:55)(cid:75)(cid:72)(cid:68)(cid:74)(cid:68)(cid:85)(cid:68)(cid:77)(cid:68)(cid:81)(cid:13)(cid:15)(cid:3)(cid:37)(cid:76)(cid:85)(cid:3)(cid:37)(cid:75)(cid:68)(cid:81)(cid:88)(cid:13)(cid:15)(cid:3)(cid:36)(cid:79)(cid:69)(cid:72)(cid:85)(cid:87)(cid:3)(cid:38)(cid:85)(cid:88)(cid:93)(cid:130)(cid:15)(cid:3)(cid:37)(cid:72)(cid:79)(cid:76)(cid:81)(cid:71)(cid:68)(cid:3)(cid:47)(cid:72)(cid:13)(cid:15)(cid:3)(cid:36)(cid:86)(cid:82)(cid:81)(cid:74)(cid:88)(cid:3)(cid:55)(cid:68)(cid:80)(cid:69)(cid:82)(cid:13)(cid:3)
(cid:3)
*Center for Research in Intelligent Systems, University of California, Riverside, CA 92521, USA
(cid:130) Computer Perception Lab, California State University, Bakersfield, CA 93311, USA
(cid:36)(cid:37)(cid:54)(cid:55)(cid:53)(cid:36)(cid:38)(cid:55)(cid:3)
the  background
(cid:3)
A  novel  feature  representation  of  human  facial  expressions
for  emotion  recognition  is  developed.  The  representation
leveraged
texture  removal  ability  of
Anisotropic  Inhibited  Gabor  Filtering  (AIGF)  with  the
ompact  representation  of  spatiotemporal
local  binary
patterns. The  emotion recognition  system incorporated face
detection  and registration  followed  by the proposed  feature
representation:  Local  Anisotropic  Inhibited  Binary  Patterns
in  Three  Orthogonal"
3a60678ad2b862fa7c27b11f04c93c010cc6c430,A Multimodal Database for Affect Recognition and Implicit Tagging,"JANUARY-MARCH 2012
A Multimodal Database for
Affect Recognition and Implicit Tagging
Mohammad Soleymani, Member, IEEE, Jeroen Lichtenauer,
Thierry Pun, Member, IEEE, and Maja Pantic, Fellow, IEEE"
3a591a9b5c6d4c62963d7374d58c1ae79e3a4039,Driver Cell Phone Usage Detection from HOV/HOT NIR Images,"Driver Cell Phone Usage Detection From HOV/HOT NIR Images
Yusuf Artan, Orhan Bulan, Robert P. Loce, and Peter Paul
Xerox Research Center Webster
800 Phillips Rd. Webster NY 14580"
3aa9c8c65ce63eb41580ba27d47babb1100df8a3,Differentiating Duchenne from non-Duchenne smiles using active appearance models,"Annals of the
University of North Carolina Wilmington
Master of Science in
Computer Science and Information Systems"
3a0a839012575ba455f2b84c2d043a35133285f9,Corpus-Guided Sentence Generation of Natural Images,"Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, pages 444–454,
Edinburgh, Scotland, UK, July 27–31, 2011. c(cid:13)2011 Association for Computational Linguistics"
3a9681e2e07be7b40b59c32a49a6ff4c40c962a2,"Comparing treatment means : overlapping standard errors , overlapping confidence intervals , and tests of hypothesis","Biometrics & Biostatistics International Journal
Comparing treatment means: overlapping standard
errors, overlapping confidence intervals, and tests of
hypothesis"
3a846704ef4792dd329a5c7a2cb8b330ab6b8b4e,FACE-GRAB: Face recognition with General Region Assigned to Binary operator,"in  any  current  or
future  media,
for  all  other  uses,
© 2010 IEEE. Personal use of this material is permitted. Permission from IEEE must be
obtained
including
reprinting/republishing  this  material  for  advertising  or  promotional  purposes,  creating
new  collective  works,  for  resale  or  redistribution  to  servers  or  lists,  or  reuse  of  any
opyrighted component of this work in other works.
Pre-print of article that appeared at the IEEE Computer Society Workshop on Biometrics
010.
The published article can be accessed from:
http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=5544597"
3a95eea0543cf05670e9ae28092a114e3dc3ab5c,Constructing the L2-Graph for Robust Subspace Learning and Subspace Clustering,"Constructing the L2-Graph for Robust Subspace
Learning and Subspace Clustering
Xi Peng, Zhiding Yu, Huajin Tang, Member, IEEE, and Zhang Yi, Senior Member, IEEE"
3a4f522fa9d2c37aeaed232b39fcbe1b64495134,Face Recognition and Retrieval Using Cross-Age Reference Coding With Cross-Age Celebrity Dataset,"ISSN (Online) 2321 – 2004
ISSN (Print) 2321 – 5526
INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH IN ELECTRICAL, ELECTRONICS, INSTRUMENTATION AND CONTROL ENGINEERING
Vol. 4, Issue 5, May 2016
IJIREEICE
Face Recognition and Retrieval Using Cross
Age Reference Coding
Sricharan H S1, Srinidhi K S1, Rajath D N1, Tejas J N1, Chandrakala B M2
BE, DSCE, Bangalore1
Assistant Professor, DSCE, Bangalore2"
540b39ba1b8ef06293ed793f130e0483e777e278,Biologically Inspired Emotional Expressions for Artificial Agents,"ORIGINAL RESEARCH
published: 13 July 2018
doi: 10.3389/fpsyg.2018.01191
Biologically Inspired Emotional
Expressions for Artificial Agents
Beáta Korcsok 1*, Veronika Konok 2, György Persa 3, Tamás Faragó 2, Mihoko Niitsuma 4,
Ádám Miklósi 2,5, Péter Korondi 1, Péter Baranyi 6 and Márta Gácsi 2,5
Department of Mechatronics, Optics and Engineering Informatics, Budapest University of Technology and Economics,
Budapest, Hungary, 2 Department of Ethology, Eötvös Loránd University, Budapest, Hungary, 3 Institute for Computer Science
nd Control, Hungarian Academy of Sciences, Budapest, Hungary, 4 Department of Precision Mechanics, Chuo University,
Tokyo, Japan, 5 MTA-ELTE Comparative Ethology Research Group, Budapest, Hungary, 6 Department of Telecommunications
nd Media Informatics, Budapest University of Technology and Economics, Budapest, Hungary
A special area of human-machine interaction,
the expression of emotions gains
importance with the continuous development of artificial agents such as social robots or"
543f21d81bbea89f901dfcc01f4e332a9af6682d,Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks,"Published as a conference paper at ICLR 2016
UNSUPERVISED AND SEMI-SUPERVISED LEARNING
WITH CATEGORICAL GENERATIVE ADVERSARIAL
NETWORKS
Jost Tobias Springenberg
University of Freiburg
79110 Freiburg, Germany"
54969bcd728b0f2d3285866c86ef0b4797c2a74d,Learning for Video Compression,"IEEE TRANSACTION SUBMISSION
Learning for Video Compression
Zhibo Chen, Senior Member, IEEE, Tianyu He, Xin Jin, Feng Wu, Fellow, IEEE"
5456166e3bfe78a353df988897ec0bd66cee937f,Improved Boosting Performance by Exclusion of Ambiguous Positive Examples,"Improved Boosting Performance by Exclusion
of Ambiguous Positive Examples
Miroslav Kobetski, Josephine Sullivan
Computer Vision and Active Perception, KTH, Stockholm 10800, Sweden
{kobetski,
Keywords:
Boosting, Image Classification, Algorithm Evaluation, Dataset Pruning, VOC2007."
54aacc196ffe49b3450059fccdf7cd3bb6f6f3c3,A joint learning framework for attribute models and object descriptions,"A Joint Learning Framework for Attribute Models and Object Descriptions
Dhruv Mahajan
Yahoo! Labs, Bangalore, India
Sundararajan Sellamanickam
Vinod Nair"
541bccf19086755f8b5f57fd15177dc49e77d675,A few days of a robot's life in the human's world: toward incremental individual recognition,"Computer Science and ArtificialIntelligence LaboratoryTechnical Reportmassachusetts institute of technology, cambridge, ma 02139 usa — www.csail.mit.eduMIT-CSAIL-TR-2007-022April 3, 2007A Few Days of A Robot’s Life in the Human’s World: Toward Incremental Individual RecognitionLijin Aryananda"
54756f824befa3f0c2af404db0122f5b5bbf16e0,Computer Vision — Visual Recognition,"Research Statement
Computer Vision — Visual Recognition
Alexander C. Berg
Computational visual recognition concerns identifying what is in an image, video, or other visual data, enabling
pplications such as measuring location, pose, size, activity, and identity as well as indexing for search by content.
Recent progress in making economical sensors and improvements in network, storage, and computational power
make visual recognition practical and relevant in almost all experimental sciences and commercial applications
such as image search. My work in visual recognition brings together machine learning, insights from psychology
nd physiology, computer graphics, algorithms, and a great deal of computation.
While I am best known for my work on general object category detection – creating techniques and building
systems for some of the best performing approaches to categorizing and localizing objects in images, recognizing
ction in video, and searching large collections of video and images – my research extends widely across visual
recognition including:
• Creating low-level image descriptors – procedures for converting pixel values to features that can be used
to model appearance for recognition. These include widely used descriptors for category recognition in
images [4, 2], object detection in images and video [11, 10, 2], and optical flow based descriptors for action
recognition in video [8].
• Developing models for recognition – ranging from what is becoming seminal work in recognizing human
ctions in video [8], to formulating object localization as approximate subgraph isomorphism [2], to models
for parsing architectural images [3], to a novel approach for face recognition based on high level describable"
549c719c4429812dff4d02753d2db11dd490b2ae,YouTube-BoundingBoxes: A Large High-Precision Human-Annotated Data Set for Object Detection in Video,"YouTube-BoundingBoxes: A Large High-Precision
Human-Annotated Data Set for Object Detection in Video
Esteban Real
Google Brain
Jonathon Shlens
Google Brain
Stefano Mazzocchi
Google Research
Xin Pan
Google Brain
Vincent Vanhoucke
Google Brain"
9853136dbd7d5f6a9c57dc66060cab44a86cd662,"Improving the Neural Network Training for Face Recognition using Adaptive Learning Rate , Resilient Back Propagation and Conjugate Gradient Algorithm","International Journal of Computer Applications (0975 – 8887)
Volume 34– No.2, November 2011
Improving the Neural Network Training for Face
Recognition using Adaptive Learning Rate, Resilient
Back Propagation and Conjugate Gradient Algorithm
Hamed Azami
M.Sc. Student
Department of Electrical
Engineering, Iran University
of Science and Technology,
Tehran, Iran
Saeid Sanei
Associate Professor
Department of Computing,
Faculty of Engineering and
Physical Sciences, University
of Surrey, UK
Karim Mohammadi
Professor
Department of Electrical"
988d1295ec32ce41d06e7cf928f14a3ee079a11e,Semantic Deep Learning,"Semantic Deep Learning
Hao Wang
September 29, 2015"
98c548a4be0d3b62971e75259d7514feab14f884,Deep generative-contrastive networks for facial expression recognition,"Deep generative-contrastive networks for facial expression recognition
Youngsung Kim†, ByungIn Yoo‡,†, Youngjun Kwak†, Changkyu Choi†, and Junmo Kim‡
Samsung Advanced Institute of Technology (SAIT), ‡KAIST
hangkyu"
981449cdd5b820268c0876477419cba50d5d1316,Learning Deep Features for One-Class Classification,"Learning Deep Features for One-Class
Classification
Pramuditha Perera, Student Member, IEEE, and Vishal M. Patel, Senior Member , IEEE"
9854145f2f64d52aac23c0301f4bb6657e32e562,An Improved Face Verification Approach Based on Speedup Robust Features and Pairwise Matching,"An Improved Face Verification Approach based on
Speedup Robust Features and Pairwise Matching
Eduardo Santiago Moura, Herman Martins Gomes and Jo˜ao Marques de Carvalho
Center for Electrical Engineering and Informatics (CEEI)
Federal University of Campina Grande (UFCG)
Campina Grande, Para´ıba, Brazil
Email:"
98127346920bdce9773aba6a2ffc8590b9558a4a,Efficient human action recognition using histograms of motion gradients and VLAD with descriptor shape information,"Noname manuscript No.
(will be inserted by the editor)
Ef‌f‌icient Human Action Recognition using
Histograms of Motion Gradients and
VLAD with Descriptor Shape Information
Ionut C. Duta · Jasper R.R. Uijlings ·
Bogdan Ionescu · Kiyoharu Aizawa ·
Alexander G. Hauptmann · Nicu Sebe
Received: date / Accepted: date"
98a660c15c821ea6d49a61c5061cd88e26c18c65,Face Databases for 2D and 3D Facial Recognition: A Survey,"IOSR Journal of Engineering (IOSRJEN)
e-ISSN: 2250-3021, p-ISSN: 2278-8719
Vol. 3, Issue 4 (April. 2013), ||V1 || PP 43-48
Face Databases for 2D and 3D Facial Recognition: A Survey
R.Senthilkumar1, Dr.R.K.Gnanamurthy2
Assistant Professor,  Department of Electronics and Communication Engineering, Institute of Road and
Professor and Dean , Department of Electronics and Communication Engineering, Odaiyappa College of
Transport Technology,Erode-638 316.
Engineering and Technology,Theni-625 531."
98519f3f615e7900578bc064a8fb4e5f429f3689,Dictionary-Based Domain Adaptation Methods for the Re-identification of Faces,"Dictionary-based Domain Adaptation Methods
for the Re-identification of Faces
Qiang Qiu, Jie Ni, and Rama Chellappa"
9825aa96f204c335ec23c2b872855ce0c98f9046,Face and Facial Expression Recognition in 3-d Using Masked Projection under Occlusion,"International Journal of Ethics in Engineering & Management Education
Website: www.ijeee.in (ISSN: 2348-4748, Volume 1, Issue 5, May2014)
FACE AND FACIAL EXPRESSION
RECOGNITION IN 3-D USING MASKED
PROJECTION UNDER OCCLUSION
Jyoti patil *
M.Tech (CSE)
GNDEC Bidar-585401
BIDAR, INDIA
Gouri Patil
M.Tech (CSE)
GNDEC Bidar- 585401
BIDAR, INDIA
Snehalata Patil
M.Tech (CSE)
VKIT, Bangalore- 560040
BANGALORE, INDIA"
53e081f5af505374c3b8491e9c4470fe77fe7934,Unconstrained realtime facial performance capture,"Unconstrained Realtime Facial Performance Capture
Pei-Lun Hsieh⇤
⇤ University of Southern California
Chongyang Ma⇤
Jihun Yu†
Hao Li⇤
Industrial Light & Magic
Figure 1: Calibration-free realtime facial performance capture on highly occluded subjects using an RGB-D sensor."
53c36186bf0ffbe2f39165a1824c965c6394fe0d,I Know How You Feel: Emotion Recognition with Facial Landmarks,"I Know How You Feel: Emotion Recognition with Facial Landmarks
Tooploox 2Polish-Japanese Academy of Information Technology 3Warsaw University of Technology
Ivona Tautkute1,2, Tomasz Trzcinski1,3 and Adam Bielski1"
5366573e96a1dadfcd4fd592f83017e378a0e185,"Server, server in the cloud. Who is the fairest in the crowd?","Böhlen, Chandola and Salunkhe
Server, server in the cloud.
Who is the fairest in the crowd?"
53a41c711b40e7fe3dc2b12e0790933d9c99a6e0,Recurrent Memory Addressing for Describing Videos,"Recurrent Memory Addressing for describing videos
Arnav Kumar Jain∗ Abhinav Agarwalla∗
Kumar Krishna Agrawal∗
Pabitra Mitra
{arnavkj95, abhinavagarawalla, kumarkrishna,
Indian Institute of Technology Kharagpur"
533bfb82c54f261e6a2b7ed7d31a2fd679c56d18,Unconstrained Face Recognition: Identifying a Person of Interest From a Media Collection,"Technical Report MSU-CSE-14-1
Unconstrained Face Recognition: Identifying a
Person of Interest from a Media Collection
Lacey Best-Rowden, Hu Han, Member, IEEE, Charles Otto, Brendan Klare, Member, IEEE, and
Anil K. Jain, Fellow, IEEE"
3fbd68d1268922ee50c92b28bd23ca6669ff87e5,A shape- and texture-based enhanced Fisher classifier for face recognition,"IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 10, NO. 4, APRIL 2001
A Shape- and Texture-Based Enhanced Fisher
Classifier for Face Recognition
Chengjun Liu, Member, IEEE, and Harry Wechsler, Fellow, IEEE"
3f22a4383c55ceaafe7d3cfed1b9ef910559d639,Robust Kronecker Component Analysis,"JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015
Robust Kronecker Component Analysis
Mehdi Bahri, Student Member, IEEE, Yannis Panagakis, and Stefanos Zafeiriou, Member, IEEE"
3fdcc1e2ebcf236e8bb4a6ce7baf2db817f30001,A Top-Down Approach for a Synthetic Autobiographical Memory System,"A top-down approach for a synthetic
utobiographical memory system
Andreas Damianou1,2, Carl Henrik Ek3, Luke Boorman1, Neil D. Lawrence2,
nd Tony J. Prescott1
Shef‌f‌ield Centre for Robotics (SCentRo), Univ. of Shef‌f‌ield, Shef‌f‌ield, S10 2TN, UK
Dept. of Computer Science, Univ. of Shef‌f‌ield, Shef‌f‌ield, S1 4DP, UK
CVAP Lab, KTH, Stockholm, Sweden"
3f848d6424f3d666a1b6dd405a48a35a797dd147,Is 2D Information Enough For Viewpoint Estimation?,"GHODRATI et al.: IS 2D INFORMATION ENOUGH FOR VIEWPOINT ESTIMATION?
Is 2D Information Enough For Viewpoint
Estimation?
Amir Ghodrati
Marco Pedersoli
Tinne Tuytelaars
KU Leuven, ESAT - PSI, iMinds
Leuven, Belgium"
3fa738ab3c79eacdbfafa4c9950ef74f115a3d84,DaMN - Discriminative and Mutually Nearest: Exploiting Pairwise Category Proximity for Video Action Recognition,"DaMN – Discriminative and Mutually Nearest:
Exploiting Pairwise Category Proximity
for Video Action Recognition
Rui Hou1, Amir Roshan Zamir1, Rahul Sukthankar2, and Mubarak Shah1
Center for Research in Computer Vision at UCF, Orlando, USA
Google Research, Mountain View, USA
http://crcv.ucf.edu/projects/DaMN/"
3fb98e76ffd8ba79e1c22eda4d640da0c037e98a,Convolutional Neural Networks for Crop Yield Prediction using Satellite Images,"Convolutional Neural Networks for Crop Yield Prediction using Satellite Images
H. Russello"
3f14b504c2b37a0e8119fbda0eff52efb2eb2461,Joint Facial Action Unit Detection and Feature Fusion: A Multi-Conditional Learning Approach,"Joint Facial Action Unit Detection and Feature
Fusion: A Multi-Conditional Learning Approach
Stefanos Eleftheriadis, Ognjen Rudovic, Member, IEEE, and Maja Pantic, Fellow, IEEE"
3fac7c60136a67b320fc1c132fde45205cd2ac66,Remarks on Computational Facial Expression Recognition from HOG Features Using Quaternion Multi-layer Neural Network,"Remarks on Computational Facial Expression
Recognition from HOG Features Using
Quaternion Multi-layer Neural Network
Kazuhiko Takahashi1, Sae Takahashi1, Yunduan Cui2,
nd Masafumi Hashimoto3
Information Systems Design, Doshisha University, Kyoto, Japan
Graduate School of Doshisha University, Kyoto, Japan
Intelligent Information Engineering and Science, Doshisha University, Kyoto, Japan"
3f9a7d690db82cf5c3940fbb06b827ced59ec01e,VIP: Finding important people in images,"VIP: Finding Important People in Images
Clint Solomon Mathialagan
Virginia Tech
Andrew C. Gallagher
Google Inc.
Dhruv Batra
Virginia Tech
Project: https://computing.ece.vt.edu/~mclint/vip/
Demo: http://cloudcv.org/vip/"
3fd90098551bf88c7509521adf1c0ba9b5dfeb57,Attribute-Based Classification for Zero-Shot Visual Object Categorization,"Page 1 of 21
*****For Peer Review Only*****
Attribute-Based Classification for Zero-Shot
Visual Object Categorization
Christoph H. Lampert, Hannes Nickisch and Stefan Harmeling"
3f7723ab51417b85aa909e739fc4c43c64bf3e84,Improved Performance in Facial Expression Recognition Using 32 Geometric Features,"Improved Performance in Facial Expression
Recognition Using 32 Geometric Features
Giuseppe Palestra1(B), Adriana Pettinicchio2, Marco Del Coco2,
Pierluigi Carcagn`ı2, Marco Leo2, and Cosimo Distante2
Department of Computer Science, University of Bari, Bari, Italy
National Institute of Optics, National Research Council, Arnesano, LE, Italy"
3f63f9aaec8ba1fa801d131e3680900680f14139,Facial Expression recognition using Local Binary Patterns and Kullback Leibler divergence,"Facial Expression Recognition using Local Binary
Patterns and Kullback Leibler Divergence
AnushaVupputuri, SukadevMeher
divergence."
3f0e0739677eb53a9d16feafc2d9a881b9677b63,Efficient Two-Stream Motion and Appearance 3D CNNs for Video Classification,"Efficient Two-Stream Motion and Appearance 3D CNNs for
Video Classification
Ali Diba
ESAT-KU Leuven
Ali Pazandeh
Sharif UTech
Luc Van Gool
ESAT-KU Leuven, ETH Zurich"
30b15cdb72760f20f80e04157b57be9029d8a1ab,Face Aging with Identity-Preserved Conditional Generative Adversarial Networks,"Face Aging with Identity-Preserved
Conditional Generative Adversarial Networks
Zongwei Wang
Shanghaitech University
Xu Tang
Baidu
Weixin Luo, Shenghua Gao∗
Shanghaitech University
{luowx,"
30870ef75aa57e41f54310283c0057451c8c822b,Overcoming catastrophic forgetting with hard attention to the task,"Overcoming Catastrophic Forgetting with Hard Attention to the Task
Joan Serr`a 1 D´ıdac Sur´ıs 1 2 Marius Miron 1 3 Alexandros Karatzoglou 1"
305346d01298edeb5c6dc8b55679e8f60ba97efb,Fine-Grained Face Annotation Using Deep Multi-Task CNN,"Article
Fine-Grained Face Annotation Using Deep
Multi-Task CNN
Luigi Celona *
, Simone Bianco
nd Raimondo Schettini
Department of Informatics, Systems and Communication, University of Milano-Bicocca,
viale Sarca, 336 Milano, Italy; (S.B.); (R.S.)
* Correspondence:
Received: 3 July 2018; Accepted: 13 August 2018; Published: 14 August 2018"
309e17e6223e13b1f76b5b0eaa123b96ef22f51b,Face recognition based on a 3D morphable model,"Face Recognition based on a 3D Morphable Model
Volker Blanz
University of Siegen
H¤olderlinstr. 3
57068 Siegen, Germany"
3046baea53360a8c5653f09f0a31581da384202e,Deformable Face Alignment via Local Measurements and Global Constraints,"Deformable Face Alignment via Local
Measurements and Global Constraints
Jason M. Saragih"
3028690d00bd95f20842d4aec84dc96de1db6e59,Leveraging Union of Subspace Structure to Improve Constrained Clustering,"Leveraging Union of Subspace Structure to Improve Constrained Clustering
John Lipor 1 Laura Balzano 1"
30c96cc041bafa4f480b7b1eb5c45999701fe066,Discrete Cosine Transform Locality-Sensitive Hashes for Face Retrieval,"Discrete Cosine Transform Locality-Sensitive
Hashes for Face Retrieval
Mehran Kafai, Member, IEEE, Kave Eshghi, and Bir Bhanu, Fellow, IEEE"
306957285fea4ce11a14641c3497d01b46095989,Face Recognition Under Varying Lighting Based on Derivates of Log Image,"FACE RECOGNITION UNDER VARYING LIGHTING BASED ON
DERIVATES OF LOG IMAGE
Laiyun Qing1,2, Shiguang Shan2,  Wen Gao1,2
ICT-ISVISION Joint R&D Laboratory for Face Recognition, CAS, Beijing 100080, China
Graduate School, CAS, Beijing, 100039, China"
307a810d1bf6f747b1bd697a8a642afbd649613d,An affordable contactless security system access for restricted area,"An affordable contactless security system access
for restricted area
Pierre Bonazza1, Johel Mitéran1, Barthélémy Heyrman1, Dominique Ginhac1,
Vincent Thivent2, Julien Dubois1
Laboratory Le2i
University Bourgogne Franche-Comté, France
Odalid compagny, France
Contact
Keywords – Smart Camera, Real-time Image Processing, Biometrics, Face Detection, Face Verifica-
tion, EigenFaces, Support Vector Machine,
We  present  in  this  paper  a  security  system  based  on
identity verification process and a low-cost smart cam-
era, intended to avoid unauthorized access to restricted
rea.  The  Le2i  laboratory  has  a  longstanding  experi-
ence in smart cameras implementation and design [1],
for  example in the  case of  real-time classical  face de-
tection [2] or human fall detection [3].
The principle of the system, fully thought and designed
in  our  laboratory,  is  as  follows:  the  allowed  user  pre-
sents a RFID card to the reader based on Odalid system"
302c9c105d49c1348b8f1d8cc47bead70e2acf08,Unconstrained Face Recognition Using A Set-to-Set Distance Measure,"This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TCSVT.2017.2710120, IEEE
Transactions on Circuits and Systems for Video Technology
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
Unconstrained Face Recognition Using A Set-to-Set
Distance Measure
Jiaojiao Zhao, Jungong Han, and Ling Shao, Senior Member IEEE"
301b0da87027d6472b98361729faecf6e1d5e5f6,Head Pose Estimation in Face Recognition Across Pose Scenarios,"HEAD POSE ESTIMATION IN FACE RECOGNITION ACROSS
POSE SCENARIOS
M. Saquib Sarfraz and Olaf Hellwich
Computer vision and Remote Sensing, Berlin university of Technology
Sekr. FR-3-1, Franklinstr. 28/29, D-10587, Berlin, Germany.
Keywords:
Pose estimation, facial pose, face recognition, local energy models, shape description, local features, head
pose classification."
30b103d59f8460d80bb9eac0aa09aaa56c98494f,Enhancing Human Action Recognition with Region Proposals,"Enhancing Human Action Recognition with Region Proposals
Fahimeh Rezazadegan, Sareh Shirazi, Niko Sünderhauf, Michael Milford, Ben Upcroft
Australian Centre for Robotic Vision(ACRV), School of Electrical Engineering and Computer Science
Queensland University of Technology(QUT)"
5e6f546a50ed97658be9310d5e0a67891fe8a102,Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?,"Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?
Kensho Hara, Hirokatsu Kataoka, Yutaka Satoh
National Institute of Advanced Industrial Science and Technology (AIST)
Tsukuba, Ibaraki, Japan
{kensho.hara, hirokatsu.kataoka,"
5e0eb34aeb2b58000726540336771053ecd335fc,Low-Quality Video Face Recognition with Deep Networks and Polygonal Chain Distance,"Low-Quality Video Face Recognition with Deep
Networks and Polygonal Chain Distance
Christian Herrmann∗†, Dieter Willersinn†, J¨urgen Beyerer†∗
Vision and Fusion Lab, Karlsruhe Institute of Technology KIT, Karlsruhe, Germany
Fraunhofer IOSB, Karlsruhe, Germany"
5e28673a930131b1ee50d11f69573c17db8fff3e,Descriptor Based Methods in the Wild,"Author manuscript, published in ""Workshop on Faces in 'Real-Life' Images: Detection, Alignment, and Recognition, Marseille : France
(2008)"""
5ea9063b44b56d9c1942b8484572790dff82731e,Multiclass Support Vector Machines and Metric Multidimensional Scaling for Facial Expression Recognition,"MULTICLASS SUPPORT VECTOR MACHINES AND METRIC MULTIDIMENSIONAL
SCALING FOR FACIAL EXPRESSION RECOGNITION
Irene Kotsiay, Stefanos Zafeiriouy, Nikolaos Nikolaidisy and Ioannis Pitasy
yAristotle University of Thessaloniki, Department of Informatics
Thessaloniki, Greece
email: fekotsia, dralbert, nikolaid,"
5e6ba16cddd1797853d8898de52c1f1f44a73279,Face Identification with Second-Order Pooling,"Face Identification with Second-Order Pooling
Fumin Shen, Chunhua Shen and Heng Tao Shen"
5ec94adc9e0f282597f943ea9f4502a2a34ecfc2,Leveraging the Power of Gabor Phase for Face Identification: A Block Matching Approach,"Leveraging the Power of Gabor Phase for Face
Identification: A Block Matching Approach
Yang Zhong, Haibo Li
KTH, Royal Institute of Technology"
5bfc32d9457f43d2488583167af4f3175fdcdc03,Local Gray Code Pattern (LGCP): A Robust Feature Descriptor for Facial Expression Recognition,"International Journal of Science and Research (IJSR), India Online ISSN: 2319-7064
Local Gray Code Pattern (LGCP): A Robust
Feature Descriptor for Facial Expression
Recognition
Mohammad Shahidul Islam
Atish Dipankar University of Science & Technology, School, Department of Computer Science and Engineering, Dhaka, Bangladesh."
5ba7882700718e996d576b58528f1838e5559225,Predicting Personalized Image Emotion Perceptions in Social Networks,"This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TAFFC.2016.2628787, IEEE
Transactions on Affective Computing
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING, VOL. X, NO. X, OCTOBER 2016
Predicting Personalized Image Emotion
Perceptions in Social Networks
Sicheng Zhao, Hongxun Yao, Yue Gao, Senior Member, IEEE, Guiguang Ding and Tat-Seng Chua"
5b6f0a508c1f4097dd8dced751df46230450b01a,Finding lost children,"Finding Lost Children
Ashley Michelle Eden
Electrical Engineering and Computer Sciences
University of California at Berkeley
Technical Report No. UCB/EECS-2010-174
http://www.eecs.berkeley.edu/Pubs/TechRpts/2010/EECS-2010-174.html
December 20, 2010"
5bb684dfe64171b77df06ba68997fd1e8daffbe1,One-Sided Unsupervised Domain Mapping,
5bae9822d703c585a61575dced83fa2f4dea1c6d,MOTChallenge 2015: Towards a Benchmark for Multi-Target Tracking,"MOTChallenge 2015:
Towards a Benchmark for Multi-Target Tracking
Laura Leal-Taix´e∗, Anton Milan∗, Ian Reid, Stefan Roth, and Konrad Schindler"
5babbad3daac5c26503088782fd5b62067b94fa5,Are You Sure You Want To Do That? Classification with Verification,"Are You Sure You Want To Do That?
Classification with Verification
Harris Chan∗
Atef Chaudhury∗
Kevin Shen∗"
5bb87c7462c6c1ec5d60bde169c3a785ba5ea48f,Targeting Ultimate Accuracy: Face Recognition via Deep Embedding,"Targeting Ultimate Accuracy: Face Recognition via Deep Embedding
Jingtuo Liu     Yafeng Deng     Tao Bai     Zhengping Wei     Chang Huang
Baidu Research – Institute of Deep Learning"
5b9d9f5a59c48bc8dd409a1bd5abf1d642463d65,An evolving spatio-temporal approach for gender and age group classification with Spiking Neural Networks,"Evolving Systems. manuscript No.
(will be inserted by the editor)
An evolving spatio-temporal approach for gender and age
group classification with Spiking Neural Networks
Fahad Bashir Alvi, Russel Pears, Nikola Kasabov
Received: date / Accepted: date"
5b01d4338734aefb16ee82c4c59763d3abc008e6,A Robust Face Recognition Algorithm Based on Kernel Regularized Relevance-Weighted Discriminant Analysis,"DI WU: A ROBUST FACE RECOGNITION ALGORITHM BASED ON KERNEL REGULARIZED RELEVANCE …
A Robust Face Recognition Algorithm Based on Kernel Regularized
Relevance-Weighted Discriminant Analysis
Di WU 1, 2
2 Hunan Provincial Key Laboratory of Wind Generator and Its Control, Hunan Institute of Engineering, Xiangtan, China.
College of Electrical and Information Engineering,
[e-mail:
I. INTRODUCTION
interface  and  security
recognition
their
this  paper,  we  propose  an  effective"
5b721f86f4a394f05350641e639a9d6cb2046c45,Detection under Privileged Information,"A short version of this paper is accepted to ACM Asia Conference on Computer and Communications Security (ASIACCS) 2018
Detection under Privileged Information (Full Paper)∗
Z. Berkay Celik
Pennsylvania State University
Patrick McDaniel
Pennsylvania State University
Rauf Izmailov
Vencore Labs
Nicolas Papernot,
Ryan Sheatsley, Raquel Alvarez
Pennsylvania State University
Ananthram Swami
Army Research Laboratory"
5be3cc1650c918da1c38690812f74573e66b1d32,Relative Parts: Distinctive Parts for Learning Relative Attributes,"Relative Parts: Distinctive Parts for Learning Relative Attributes
Ramachandruni N. Sandeep
Yashaswi Verma
C. V. Jawahar
Center for Visual Information Technology, IIIT Hyderabad, India - 500032"
5b6bed112e722c0629bcce778770d1b28e42fc96,Can Your Eyes Tell Me How You Think? A Gaze Directed Estimation of the Mental Activity,"FLOREA ET AL.:CANYOUREYESTELLMEHOWYOUTHINK?
Can Your Eyes Tell Me How You Think? A
Gaze Directed Estimation of the Mental
Activity
Laura Florea
http://alpha.imag.pub.ro/common/staff/lflorea
Corneliu Florea
http://alpha.imag.pub.ro/common/staff/cflorea
Ruxandra Vrânceanu
Constantin Vertan
http://alpha.imag.pub.ro/common/staff/vertan
Image Processing and Analysis
Laboratory, LAPI
University “Politehnica” of Bucharest
Bucharest, Romania"
37c8514df89337f34421dc27b86d0eb45b660a5e,Facial Landmark Tracking by Tree-Based Deformable Part Model Based Detector,"Facial Landmark Tracking by Tree-based Deformable Part Model
Based Detector
Michal Uˇriˇc´aˇr, Vojtˇech Franc, and V´aclav Hlav´aˇc
Center for Machine Perception, Department of Cybernetics
Faculty of Electrical Engineering, Czech Technical University in Prague
66 27 Prague 6, Technick´a 2, Czech Republic
{uricamic, xfrancv,"
374c7a2898180723f3f3980cbcb31c8e8eb5d7af,Facial Expression Recognition in Videos using a Novel Multi-Class Support Vector Machines Variant,"FACIAL EXPRESSION RECOGNITION IN VIDEOS USING A NOVEL MULTI-CLASS
SUPPORT VECTOR MACHINES VARIANT
Irene Kotsiay, Nikolaos Nikolaidisy and Ioannis Pitasy
yAristotle University of Thessaloniki
Department of Informatics
Box 451, 54124 Thessaloniki, Greece"
372fb32569ced35eaf3740a29890bec2be1869fa,Mu rhythm suppression is associated with the classification of emotion in faces.,"Running head: MU RHYTHM MODULATION BY CLASSIFICATION OF EMOTION  1
Mu rhythm suppression is associated with the classification of emotion in faces
Matthew R. Moore1, Elizabeth A. Franz1
Department of Psychology, University of Otago, Dunedin, New Zealand
Corresponding authors:
Matthew Moore & Liz Franz
Phone:  +64 (3) 479 5269; Fax:  +64 (3) 479 8335
Department of Psychology
University of Otago
PO Box 56
Dunedin, New Zealand"
37f2e03c7cbec9ffc35eac51578e7e8fdfee3d4e,Co-operative Pedestrians Group Tracking in Crowded Scenes Using an MST Approach,"WACV 2015 Submission #394. CONFIDENTIAL REVIEW COPY. DO NOT DISTRIBUTE.
Co-operative Pedestrians Group Tracking in Crowded Scenes using an MST
Approach
Anonymous WACV submission
Paper ID 394"
3795974e24296185d9b64454cde6f796ca235387,Finding your Lookalike: Measuring Face Similarity Rather than Face Identity,"Finding your Lookalike:
Measuring Face Similarity Rather than Face Identity
Amir Sadovnik, Wassim Gharbi, Thanh Vu
Lafayette College
Easton, PA
Andrew Gallagher
Google Research
Mountain View, CA"
37278ffce3a0fe2c2bbf6232e805dd3f5267eba3,Can we still avoid automatic face detection?,"Can we still avoid automatic face detection?
Michael J. Wilber1,2
Vitaly Shmatikov1,2
Serge Belongie1,2
Department of Computer Science, Cornell University 2 Cornell Tech"
377a1be5113f38297716c4bb951ebef7a93f949a,Facial emotion recognition with anisotropic inhibited Gabor energy histograms,"Dear Faculty, IGERT Fellows, IGERT Associates and Students,
You are cordially invited to attend a Seminar presented by Albert Cruz. Please
plan to attend.
Albert Cruz
IGERT Fellow
Electrical Engineering
Date: Friday, October 11, 2013
Location: Bourns A265
Time: 11:00am
Facial  emotion  recognition  with  anisotropic
inhibited gabor energy histograms"
370e0d9b89518a6b317a9f54f18d5398895a7046,Cross-pollination of normalisation techniques from speaker to face authentication using Gaussian mixture models,"IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. X, NO. X, XXXXXXX 20XX
Cross-pollination of normalisation techniques
from speaker to face authentication
using Gaussian mixture models
Roy Wallace, Member, IEEE, Mitchell McLaren, Member, IEEE, Christopher McCool, Member, IEEE,
nd S´ebastien Marcel, Member, IEEE"
3773e5d195f796b0b7df1fca6e0d1466ad84b5e7,UNIVERSITY OF CALIFORNIA RIVERSIDE Learning from Time Series in the Presence of Noise: Unsupervised and Semi-Supervised Approaches,"UNIVERSITY OF CALIFORNIA
RIVERSIDE
Learning from Time Series in the Presence of Noise: Unsupervised and Semi-Supervised
Approaches
A Dissertation submitted in partial satisfaction
of the requirements for the degree of
Doctor of Philosophy
Computer Science
Dragomir Dimitrov Yankov
March 2008
Dissertation Committee:
Dr. Eamonn Keogh, Chairperson
Dr. Stefano Lonardi
Dr. Vassilis Tsotras"
37eb666b7eb225ffdafc6f318639bea7f0ba9a24,"Age, Gender and Race Estimation from Unconstrained Face Images","MSU Technical Report (2014): MSU-CSE-14-5
Age, Gender and Race Estimation from
Unconstrained Face Images
Hu Han, Member, IEEE and Anil K. Jain, Fellow, IEEE"
375435fb0da220a65ac9e82275a880e1b9f0a557,From Pixels to Response Maps: Discriminative Image Filtering for Face Alignment in the Wild,"This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI
From Pixels to Response Maps: Discriminative Image
Filtering for Face Alignment in the Wild
Akshay Asthana, Stefanos Zafeiriou, Georgios Tzimiropou-
los, Shiyang Cheng and Maja Pantic"
37b6d6577541ed991435eaf899a2f82fdd72c790,Vision-based Human Gender Recognition: A Survey,"Vision-based Human Gender Recognition: A Survey
Choon Boon Ng, Yong Haur Tay, Bok Min Goi
Universiti Tunku Abdul Rahman, Kuala Lumpur, Malaysia."
372a8bf0ef757c08551d41e40cb7a485527b6cd7,Unsupervised Video Hashing by Exploiting Spatio-Temporal Feature,"Unsupervised Video Hashing by Exploiting
Spatio-Temporal Feature
Chao Ma, Yun Gu, Wei Liu, and Jie Yang(cid:63)
Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong
University, Shanghai, China."
370b5757a5379b15e30d619e4d3fb9e8e13f3256,Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments,"Labeled Faces in the Wild: A Database for Studying
Face Recognition in Unconstrained Environments
Gary B. Huang, Manu Ramesh, Tamara Berg, and Erik Learned-Miller"
08d2f655361335bdd6c1c901642981e650dff5ec,Automatic Cast Listing in Feature-Length Films with Anisotropic Manifold Space,"This is the published version:
Arandjelovic,	Ognjen	and	Cipolla,	R.	2006,	Automatic	cast	listing	in	feature‐length	films	with
Anisotropic	Manifold	Space,	in	CVPR	2006	:	Proceedings	of	the	Computer	Vision	and	Pattern
Recognition	Conference	2006,	IEEE,	Piscataway,	New	Jersey,	pp.	1513‐1520.
http://hdl.handle.net/10536/DRO/DU:30058435
Reproduced	with	the	kind	permission	of	the	copyright	owner.
Copyright	:	2006,	IEEE
Available from Deakin Research Online:"
08fbe3187f31b828a38811cc8dc7ca17933b91e9,Statistical Computations on Grassmann and Stiefel Manifolds for Image and Video-Based Recognition,"MITSUBISHI ELECTRIC RESEARCH LABORATORIES
http://www.merl.com
Statistical Computations on Grassmann and
Stiefel Manifolds for Image and Video-Based
Recognition
Turaga, P.; Veeraraghavan, A.; Srivastava, A.; Chellappa, R.
TR2011-084 April 2011"
08ae100805d7406bf56226e9c3c218d3f9774d19,Predicting the Sixteen Personality Factors (16PF) of an individual by analyzing facial features,"Gavrilescu and Vizireanu EURASIP Journal on Image and Video Processing  (2017) 2017:59
DOI 10.1186/s13640-017-0211-4
EURASIP Journal on Image
nd Video Processing
R ES EAR CH
Predicting the Sixteen Personality Factors
(16PF) of an individual by analyzing facial
features
Mihai Gavrilescu* and Nicolae Vizireanu
Open Access"
08c18b2f57c8e6a3bfe462e599a6e1ce03005876,A Least-Squares Framework for Component Analysis,"A Least-Squares Framework
for Component Analysis
Fernando De la Torre Member, IEEE,"
08ff81f3f00f8f68b8abd910248b25a126a4dfa4,Symmetric Subspace Learning for Image Analysis,"Papachristou, K., Tefas, A., & Pitas, I. (2014). Symmetric Subspace Learning
5697. DOI: 10.1109/TIP.2014.2367321
Peer reviewed version
Link to published version (if available):
0.1109/TIP.2014.2367321
Link to publication record in Explore Bristol Research
PDF-document
This is the author accepted manuscript (AAM). The final published version (version of record) is available online
via Institute of Electrical and Electronic Engineers at http://dx.doi.org/10.1109/TIP.2014.2367321. Please refer to
ny applicable terms of use of the publisher.
University of Bristol - Explore Bristol Research
General rights
This document is made available in accordance with publisher policies. Please cite only the published
version using the reference above. Full terms of use are available:
http://www.bristol.ac.uk/pure/about/ebr-terms"
0861f86fb65aa915fbfbe918b28aabf31ffba364,An Efficient Facial Annotation with Machine Learning Approach,"International Journal of Computer Trends and Technology (IJCTT) – volume 22 Number 3–April 2015
An Efficient Facial Annotation with Machine Learning Approach
A.Anusha,2R.Srinivas
Final M.Tech Student, 2Associate Professor
,2Dept of CSE ,Aditya Institute of Technology And Management, Tekkali, Srikakulam , Andhra Pradesh"
080c204edff49bf85b335d3d416c5e734a861151,CLAD: A Complex and Long Activities Dataset with Rich Crowdsourced Annotations,"CLAD: A Complex and Long Activities
Dataset with Rich Crowdsourced
Annotations
Jawad Tayyub1, Majd Hawasly2∗, David C. Hogg1 and Anthony G. Cohn1
Journal Title
XX(X):1–6
(cid:13)The Author(s) 2016
Reprints and permission:
sagepub.co.uk/journalsPermissions.nav
DOI: 10.1177/ToBeAssigned
www.sagepub.com/"
08f4832507259ded9700de81f5fd462caf0d5be8,Geometric Approach for Human Emotion Recognition using Facial Expression,"International Journal of Computer Applications (0975 – 8887)
Volume 118 – No.14, May 2015
Geometric Approach for Human Emotion
Recognition using Facial Expression
S. S. Bavkar
Assistant Professor
VPCOE Baramati
J. S. Rangole
Assistant Professor
VPCOE Baramati
V. U. Deshmukh
Assistant Professor
VPCOE Baramati"
08d40ee6e1c0060d3b706b6b627e03d4b123377a,Towards Weakly-Supervised Action Localization,"Human Action Localization
with Sparse Spatial Supervision
Philippe Weinzaepfel, Xavier Martin, and Cordelia Schmid, Fellow, IEEE"
088aabe3da627432fdccf5077969e3f6402f0a80,Classifier-to-generator Attack: Estimation,"Under review as a conference paper at ICLR 2018
CLASSIFIER-TO-GENERATOR ATTACK: ESTIMATION
OF TRAINING DATA DISTRIBUTION FROM CLASSIFIER
Anonymous authors
Paper under double-blind review"
08903bf161a1e8dec29250a752ce9e2a508a711c,Joint Dimensionality Reduction and Metric Learning: A Geometric Take,"Joint Dimensionality Reduction and Metric Learning: A Geometric Take
Mehrtash Harandi 1 2 Mathieu Salzmann 3 Richard Hartley 2 1"
08e24f9df3d55364290d626b23f3d42b4772efb6,Enhancing facial expression classification by information fusion,"ENHANCING FACIAL EXPRESSION CLASSIFICATION BY INFORMATION
FUSION
I. Buciu1, Z. Hammal 2, A. Caplier2, N. Nikolaidis 1, and I. Pitas 1
AUTH/Department of Informatics/ Aristotle University of Thessaloniki
phone: + 30(2310)99.6361, fax: + 30(2310)99.8453, email:
GR-54124, Thessaloniki, Box 451, Greece
Laboratoire des Images et des Signaux / Institut National Polytechnique de Grenoble
phone: + 33(0476)574363, fax: + 33(0476)57 47 90, email:
web: http://www.aiia.csd.auth.gr
8031 Grenoble, France
web: http://www.lis.inpg.fr"
0857281a3b6a5faba1405e2c11f4e17191d3824d,Face recognition via edge-based Gabor feature representation for plastic surgery-altered images,"Chude-Olisah et al. EURASIP Journal on Advances in Signal Processing 2014, 2014:102
http://asp.eurasipjournals.com/content/2014/1/102
R ES EAR CH
Face recognition via edge-based Gabor feature
representation for plastic surgery-altered images
Chollette C Chude-Olisah1*, Ghazali Sulong1, Uche A K Chude-Okonkwo2 and Siti Z M Hashim1
Open Access"
08f6745bc6c1b0fb68953ea61054bdcdde6d2fc7,Understanding Kin Relationships in a Photo,"Understanding Kin Relationships in a Photo
Siyu Xia, Ming Shao, Student Member, IEEE, Jiebo Luo, Fellow, IEEE, and Yun Fu, Senior Member, IEEE"
6dd052df6b0e89d394192f7f2af4a3e3b8f89875,A literature survey on Facial Expression Recognition using Global Features,"International Journal of Engineering and Advanced Technology (IJEAT)
ISSN: 2249 – 8958, Volume-2, Issue-4, April 2013
A literature survey on Facial Expression
Recognition using Global Features
Vaibhavkumar J. Mistry, Mahesh M. Goyani"
6dd5dbb6735846b214be72983e323726ef77c7a9,A Survey on Newer Prospective Biometric Authentication Modalities,"Josai Mathematical Monographs
vol. 7 (2014), pp. 25-40
A Survey on Newer Prospective
Biometric Authentication Modalities
Narishige Abe, Takashi Shinzaki"
6d10beb027fd7213dd4bccf2427e223662e20b7d,User Adaptive and Context-Aware Smart Home Using Pervasive and Semantic Technologies,"Publishing CorporationJournal of Electrical and Computer EngineeringVolume 2016, Article ID 4789803, 20 pageshttp://dx.doi.org/10.1155/2016/4789803"
6dddf1440617bf7acda40d4d75c7fb4bf9517dbb,"Beyond Counting: Comparisons of Density Maps for Crowd Analysis Tasks - Counting, Detection, and Tracking","JOURNAL OF LATEX CLASS FILES, VOL. XX, NO. X, MM YY
Beyond Counting: Comparisons of Density Maps for Crowd
Analysis Tasks - Counting, Detection, and Tracking
Di Kang, Zheng Ma, Member, IEEE, Antoni B. Chan Senior Member, IEEE,"
6d4b5444c45880517213a2fdcdb6f17064b3fa91,Harvesting Image Databases from The Web,"Journal of Information Engineering and Applications
ISSN 2224-5782 (print) ISSN 2225-0506 (online)
Vol 2, No.3, 2012
www.iiste.org
Harvesting Image Databases from The Web
Snehal M. Gaikwad
G.H.Raisoni College of Engg. & Mgmt.,Pune,India
Snehal S. Pathare
G.H.Raisoni College of Engg. & Mgmt.,Pune,India
Trupti A. Jachak
G.H.Raisoni College of Engg. & Mgmt.,Pune,India"
6d8c9a1759e7204eacb4eeb06567ad0ef4229f93,"Face Alignment Robust to Pose, Expressions and Occlusions","Face Alignment Robust to Pose, Expressions and
Occlusions
Vishnu Naresh Boddeti†, Myung-Cheol Roh†, Jongju Shin, Takaharu Oguri, Takeo Kanade"
6d618657fa5a584d805b562302fe1090957194ba,Human Facial Expression Recognition based on Principal Component Analysis and Artificial Neural Network,"Full Paper
NNGT Int. J. of Artificial Intelligence , Vol. 1, July 2014
Human Facial Expression Recognition based
on Principal Component Analysis and
Artificial Neural Network
Laboratory of Automatic and Signals Annaba (LASA) , Department of electronics, Faculty of Engineering,
Zermi.Narima, Ramdani.M, Saaidia.M
Badji-Mokhtar University, P.O.Box 12, Annaba-23000, Algeria.
E-Mail :"
6d66c98009018ac1512047e6bdfb525c35683b16,Face Recognition Based on Fitting a 3D Morphable Model,"IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 25, NO. 9, SEPTEMBER 2003
Face Recognition Based on
Fitting a 3D Morphable Model
Volker Blanz and Thomas Vetter, Member, IEEE"
0172867f4c712b33168d9da79c6d3859b198ed4c,Expression and illumination invariant preprocessing technique for Face Recognition,"Technique for Face Recognition
Computer and System Engineering Department, Faculty of Engineering, Ain Shams University, Cairo, Egypt
A. Abbas, M. I. Khalil, S. Abdel-Hay, H. M. Fahmy
Expression and Illumination Invariant Preprocessing"
0145dc4505041bf39efa70ea6d95cf392cfe7f19,Human action segmentation with hierarchical supervoxel consistency,"Human Action Segmentation with Hierarchical Supervoxel Consistency
Jiasen Lu1, Ran Xu1 Jason J. Corso2
Department of Computer Science and Engineering, SUNY at Buffalo. 2Department of EECS, University of Michigan.
Detailed analysis of human action, such as classification, detection and lo-
alization has received increasing attention from the community; datasets
like J-HMDB [1] have made it plausible to conduct studies analyzing the
impact that such deeper information has on the greater action understanding
problem. However, detailed automatic segmentation of human action has
omparatively been unexplored. In this paper, we introduce a hierarchical
MRF model to automatically segment human action boundaries in videos
“in-the-wild” (see Fig. 1).
We first propose a human motion saliency representation which incor-
porates two parts: foreground motion and human appearance information.
For foreground motion estimation, we propose a new motion saliency fea-
ture by using long-term trajectories to build a camera motion model, and
then measure the motion saliency via the deviation from the camera model.
For human appearance information, we use a DPM person detector trained
on PASCAL VOC 2007 and construct a saliency map by averaging the nor-
malized detection score of all the scale and all components.
Then, to segment the human action, we start by applying hierarchical"
01bef320b83ac4405b3fc5b1cff788c124109fb9,Translating Head Motion into Attention - Towards Processing of Student's Body-Language,"de Lausanne
RLC D1 740, CH-1015
Lausanne
de Lausanne
RLC D1 740, CH-1015
Lausanne
de Lausanne
RLC D1 740, CH-1015
Lausanne
Translating Head Motion into Attention - Towards
Processing of Student’s Body-Language
Mirko Raca
CHILI Laboratory
Łukasz Kidzi´nski
CHILI Laboratory
Pierre Dillenbourg
CHILI Laboratory
École polytechnique fédérale
École polytechnique fédérale
École polytechnique fédérale"
01c8d7a3460422412fba04e7ee14c4f6cdff9ad7,Rule Based System for Recognizing Emotions Using Multimodal Approach,"(IJACSA) International Journal of Advanced Computer Science and Applications,
Vol. 4, No. 7, 2013
Rule Based System for Recognizing Emotions Using
Multimodal Approach
Preeti Khanna
Information System
SBM, SVKM’s NMIMS
Mumbai, India"
0163d847307fae508d8f40ad193ee542c1e051b4,Classemes and Other Classifier-Based Features for Efficient Object Categorization,"JOURNAL OF LATEX CLASS FILES, VOL. 6, NO. 1, JANUARY 2007
Classemes and Other Classifier-based
Features for Efficient Object Categorization
- Supplementary material -
Alessandro Bergamo, and Lorenzo Torresani, Member, IEEE
LOW-LEVEL FEATURES
We extract the SIFT [1] features for our descriptor
ccording to the following pipeline. We first convert
each image to gray-scale, then we normalize the con-
trast by forcing the 0.01% of lightest and darkest pixels
to be mapped to white and black respectively, and
linearly rescaling the values in between. All images
exceeding 786,432 pixels of resolution are downsized
to this maximum value while keeping the aspect ratio.
The 128-dimensional SIFT descriptors are computed
from the interest points returned by a DoG detec-
tor [2]. We finally compute a Bag-Of-Word histogram
of these descriptors, using a K-means vocabulary of
500 words.
CLASSEMES"
01c4cf9c7c08f0ad3f386d88725da564f3c54679,Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV),"Interpretability Beyond Feature Attribution:
Quantitative Testing with Concept Activation Vectors (TCAV)
Been Kim Martin Wattenberg Justin Gilmer Carrie Cai James Wexler
Fernanda Viegas Rory Sayres"
017ce398e1eb9f2eed82d0b22fb1c21d3bcf9637,Face Recognition with Harmonic De-lighting,"FACE RECOGNITION WITH HARMONIC DE-LIGHTING
Laiyun Qing1,2, Shiguang Shan2, Wen Gao1,2
ICT-ISVISION Joint R&D Laboratory for Face Recognition, CAS, Beijing, China, 100080
Graduate School, CAS, Beijing, China, 100080
Emails: {lyqing, sgshan, wgao}jdl.ac.cn"
014e3d0fa5248e6f4634dc237e2398160294edce,What does 2D geometric information really tell us about 3D face shape?,"Int J Comput Vis manuscript No.
(will be inserted by the editor)
What does 2D geometric information really tell us about
D face shape?
Anil Bas1 · William A. P. Smith1
Received: date / Accepted: date"
011e6146995d5d63c852bd776f782cc6f6e11b7b,Fast Training of Triplet-Based Deep Binary Embedding Networks,"Fast Training of Triplet-based Deep Binary Embedding Networks
Bohan Zhuang, Guosheng Lin, Chunhua Shen∗, Ian Reid
The University of Adelaide; and Australian Centre for Robotic Vision"
0181fec8e42d82bfb03dc8b82381bb329de00631,Discriminative Subspace Clustering,"Discriminative Subspace Clustering
Vasileios Zografos∗1, Liam Ellis†1, and Rudolf Mester‡1 2
CVL, Dept. of Electrical Engineering, Link¨oping University, Link¨oping, Sweden
VSI Lab, Computer Science Department, Goethe University, Frankfurt, Germany"
0113b302a49de15a1d41ca4750191979ad756d2f,Matching Faces with Textual Cues in Soccer Videos,"­4244­0367­7/06/$20.00 ©2006 IEEE
ICME 2006"
0601416ade6707c689b44a5bb67dab58d5c27814,Feature Selection in Face Recognition: A Sparse Representation Perspective,"Feature Selection in Face Recognition: A Sparse
Representation Perspective
Allan Y. Yang
John Wright
Yi Ma
S. Shankar Sastry
Electrical Engineering and Computer Sciences
University of California at Berkeley
Technical Report No. UCB/EECS-2007-99
http://www.eecs.berkeley.edu/Pubs/TechRpts/2007/EECS-2007-99.html
August 14, 2007"
064b797aa1da2000640e437cacb97256444dee82,Coarse-to-fine Face Alignment with Multi-Scale Local Patch Regression,"Coarse-to-fine Face Alignment with Multi-Scale Local Patch Regression
Zhiao Huang
Megvii Inc.
Erjin Zhou
Megvii Inc.
Zhimin Cao
Megvii Inc."
06f146dfcde10915d6284981b6b84b85da75acd4,Scalable Face Image Retrieval Using Attribute-Enhanced Sparse Codewords,"Scalable Face Image Retrieval using
Attribute-Enhanced Sparse Codewords
Bor-Chun Chen, Yan-Ying Chen, Yin-Hsi Kuo, Winston H. Hsu"
0697bd81844d54064d992d3229162fe8afcd82cb,User-driven mobile robot storyboarding: Learning image interest and saliency from pairwise image comparisons,"User-driven mobile robot storyboarding: Learning image interest and
saliency from pairwise image comparisons
Michael Burke1"
06262d6beeccf2784e4e36a995d5ee2ff73c8d11,Recognize Actions by Disentangling Components of Dynamics,"Recognize Actions by Disentangling Components of Dynamics
Yue Zhao1, Yuanjun Xiong1,2, and Dahua Lin1
CUHK - SenseTime Joint Lab, The Chinese University of Hong Kong 2Amazon Rekognition"
06d93a40365da90f30a624f15bf22a90d9cfe6bb,Learning from Candidate Labeling Sets,"Learning from Candidate Labeling Sets
Idiap Research Institute and EPF Lausanne
Luo Jie
Francesco Orabona
DSI, Universit`a degli Studi di Milano"
06e7e99c1fdb1da60bc3ec0e2a5563d05b63fe32,WhittleSearch: Image search with relative attribute feedback,"WhittleSearch: Image Search with Relative Attribute Feedback
Adriana Kovashka, Devi Parikh and Kristen Grauman
(Supplementary Material)
Comparative Qualitative Search Results
We present three qualitative search results for human-generated feedback, in addition to those
shown in the paper. Each example shows one search iteration, where the 20 reference images are
randomly selected (rather than ones that match a keyword search, as the image examples in the
main paper illustrate). For each result, the first figure shows our method and the second figure
shows the binary feedback result for the corresponding target image. Note that for our method,
“more/less X” (where X is an attribute) means that the target image is more/less X than the
reference image which is shown.
Figures 1 and 2 show results for human-generated relative attribute and binary feedback, re-
spectively, when both methods are used to target the same “mental image” of a shoe shown in the
top left bubble. The top right grid of 20 images are the reference images displayed to the user, and
those outlined and annotated with constraints are the ones chosen by the user to give feedback.
The bottom row of images in either figure shows the top-ranked images after integrating the user’s
feedback into the scoring function, revealing the two methods’ respective performance. We see that
while both methods retrieve high-heeled shoes, only our method retrieves images that are as “open”
s the target image. This is because using the proposed approach, the user was able to comment
explicitly on the desired openness property."
066d71fcd997033dce4ca58df924397dfe0b5fd1,Iranian Face Database and Evaluation with a New Detection Algorithm,"(cid:1)(cid:2)(cid:3)(cid:4)(cid:5)(cid:3)(cid:4)(cid:6)(cid:7)(cid:3)(cid:8)(cid:9)(cid:6)(cid:10)(cid:3)(cid:11)(cid:3)(cid:12)(cid:3)(cid:13)(cid:9)
(cid:3)(cid:4)(cid:14)(cid:6)(cid:15)(cid:16)(cid:3)(cid:17)(cid:18)(cid:3)(cid:11)(cid:5)(cid:19)(cid:4) (cid:20)(cid:5)(cid:11)(cid:21)(cid:6)(cid:3)(cid:6)(cid:22)(cid:9)(cid:20)(cid:6)(cid:10)(cid:9)(cid:11)(cid:9)(cid:8)(cid:11)(cid:5)(cid:19)(cid:4)(cid:6)(cid:23)(cid:17)(cid:24)(cid:19)(cid:2)(cid:5)(cid:11)(cid:21)(cid:25)
(cid:26)(cid:11)(cid:5)(cid:8)(cid:17)(cid:6)(cid:27)(cid:1)(cid:9)(cid:22)(cid:8)(cid:18)(cid:1)(cid:28)(cid:12)(cid:6)(cid:29)(cid:4)(cid:20)(cid:11)(cid:6)(cid:24)(cid:30)(cid:1)(cid:15)(cid:25)(cid:1)(cid:31)(cid:8)(cid:20)(cid:8) (cid:14)(cid:1)!(cid:8) (cid:8)(cid:6)(cid:4)(cid:1)""(cid:16)(cid:8)(cid:16)(cid:20)(cid:14)(cid:1)(cid:3)(cid:15)(cid:8)(cid:22)(cid:4)(cid:12)(cid:1)(cid:23)(cid:5)(cid:29)(cid:18)(cid:14)(cid:1)(cid:31)(cid:8)(cid:20)(cid:8) (cid:14)(cid:1)(cid:26)!(cid:9)(cid:13)(cid:14)(cid:1)#(cid:17)(cid:8)(cid:6)(cid:5)$(cid:1)(cid:17)(cid:4)(cid:5)%(cid:8)(cid:10)(cid:8)(cid:11)(cid:6)(cid:8)(cid:12)&(cid:30)(cid:8)(cid:16)(cid:15)(cid:15)(cid:21)(cid:27)(cid:15)(cid:17)
(cid:3)(cid:4)(cid:5)(cid:6)(cid:7)(cid:8)(cid:1)(cid:9)(cid:10)(cid:10)(cid:8)(cid:11)(cid:6)(cid:8)(cid:12)(cid:1)(cid:13)(cid:6)(cid:7)(cid:14) (cid:3)(cid:15)(cid:16)(cid:8)(cid:17)(cid:17)(cid:8)(cid:18)(cid:1)(cid:3)(cid:8)(cid:16)(cid:18)(cid:6)(cid:1)(cid:19)(cid:4)(cid:16)(cid:11)(cid:16)(cid:6)(cid:10)(cid:6)(cid:14)(cid:1)(cid:19)(cid:20)(cid:21)(cid:1)(cid:9)(cid:22)(cid:8)(cid:17)(cid:1)(cid:23)(cid:8)(cid:11)(cid:24)(cid:8)(cid:12)(cid:25)(cid:8)(cid:20)(cid:18)
(cid:23)(cid:12)(cid:13)(cid:11)(cid:2)(cid:3)(cid:8)(cid:11)$(cid:1)’(cid:16)(cid:6)(cid:11) ((cid:8)((cid:4)(cid:20)(cid:1)(cid:6)(cid:12)(cid:24)(cid:20)(cid:15)(cid:18))(cid:27)(cid:4)(cid:11)(cid:1)(cid:8)(cid:1)(cid:18)(cid:8)(cid:24)(cid:8)(cid:10)(cid:8)(cid:11)(cid:4)(cid:1)(cid:15)(cid:25)(cid:1)(cid:15)(cid:29)(cid:4)(cid:20)(cid:1)*(cid:14)+,,(cid:1)(cid:27)(cid:15)(cid:5)(cid:15)(cid:20)(cid:1)(cid:6)(cid:17)(cid:8)-(cid:4)(cid:11)(cid:1).(cid:4)(cid:1)(cid:27)(cid:15)(cid:5)(cid:5)(cid:4)(cid:27)(cid:24)(cid:4)(cid:18)(cid:1)(cid:25)(cid:20)(cid:15)(cid:17)(cid:1)+(cid:2)+(cid:1)(cid:18)(cid:6)(cid:25)(cid:25)(cid:4)(cid:20)(cid:4)(cid:12)(cid:24)(cid:1)(cid:16))(cid:17)(cid:8)(cid:12)(cid:1)(cid:25)(cid:8)(cid:27)(cid:4)(cid:11) (cid:6)(cid:12)(cid:1)(cid:8)-(cid:4)(cid:11)(cid:1)(cid:10)(cid:4)(cid:24).(cid:4)(cid:4)(cid:12)(cid:1)/
(cid:8)(cid:12)(cid:18) 01(cid:21)(cid:1)2(cid:4)(cid:1)(cid:12)(cid:8)(cid:17)(cid:4)(cid:18)(cid:1)(cid:24)(cid:16)(cid:6)(cid:11)(cid:1)(cid:18)(cid:8)(cid:24)(cid:8)(cid:10)(cid:8)(cid:11)(cid:4)(cid:1)(cid:24)(cid:16)(cid:4)(cid:1)(cid:26)(cid:20)(cid:8)(cid:12)(cid:6)(cid:8)(cid:12)(cid:1)3(cid:8)(cid:27)(cid:4)(cid:1)(cid:19)(cid:8)(cid:24)(cid:8)(cid:10)(cid:8)(cid:11)(cid:4)(cid:1)4(cid:26)3(cid:19)(cid:23)5(cid:21)(cid:1)’(cid:15)(cid:1)(cid:4)(cid:29)(cid:8)(cid:5))(cid:8)(cid:24)(cid:4)(cid:1)(cid:24)(cid:16)(cid:4)(cid:1)(cid:18)(cid:8)(cid:24)(cid:8)(cid:10)(cid:8)(cid:11)(cid:4)(cid:1)(cid:24)(cid:16)(cid:4)(cid:1)(cid:4)6((cid:4)(cid:20)(cid:6)(cid:17)(cid:4)(cid:12)(cid:24)(cid:8)(cid:5)(cid:1)(cid:20)(cid:4)(cid:11))(cid:5)(cid:24)(cid:1)(cid:15)(cid:25)(cid:1)(cid:8)(cid:1)(cid:12)(cid:4).(cid:1)(cid:25)(cid:8)(cid:27)(cid:6)(cid:8)(cid:5)(cid:1)
(cid:25)(cid:4)(cid:8)(cid:24))(cid:20)(cid:4)(cid:1)(cid:18)(cid:4)(cid:24)(cid:4)(cid:27)(cid:24)(cid:6)(cid:15)(cid:12)(cid:1)(cid:8)(cid:5)-(cid:15)(cid:20)(cid:6)(cid:24)(cid:16)(cid:17)(cid:1)(cid:6)(cid:11)(cid:1)(cid:20)(cid:4)((cid:15)(cid:20)(cid:24)(cid:4)(cid:18)(cid:21)
(cid:26)(cid:9)(cid:27) (cid:28)(cid:19)(cid:2)(cid:14)(cid:13)$(cid:1)3(cid:8)(cid:27)(cid:4)(cid:1)(cid:26)(cid:17)(cid:8)-(cid:4)(cid:1)(cid:19)(cid:8)(cid:24)(cid:8)(cid:10)(cid:8)(cid:11)(cid:4)(cid:14)(cid:1)3(cid:8)(cid:27)(cid:6)(cid:8)(cid:5)(cid:1)3(cid:4)(cid:8)(cid:24))(cid:20)(cid:4)(cid:1)(cid:19)(cid:4)(cid:24)(cid:4)(cid:27)(cid:24)(cid:6)(cid:15)(cid:12)(cid:1)(cid:9)(cid:5)-(cid:15)(cid:20)(cid:6)(cid:24)(cid:16)(cid:17)(cid:11)(cid:14)(cid:1)(cid:9)-(cid:4)(cid:1)7(cid:5)(cid:8)(cid:11)(cid:11)(cid:6)(cid:25)(cid:6)(cid:27)(cid:8)(cid:24)(cid:6)(cid:15)(cid:12)(cid:21)
(cid:29) (cid:1)(cid:4)(cid:11)(cid:2)(cid:19)(cid:14)(cid:18)(cid:8)(cid:11)(cid:5)(cid:19)(cid:4)
8)(cid:17)(cid:8)(cid:12)(cid:1) (cid:25)(cid:8)(cid:27)(cid:4)(cid:1) (cid:6)(cid:11)(cid:1) (cid:24)(cid:16)(cid:4)(cid:1) (cid:17)(cid:15)(cid:11)(cid:24)(cid:1) (cid:27)(cid:15)(cid:17)(cid:17)(cid:15)(cid:12)(cid:1) (cid:8)(cid:12)(cid:18)(cid:1) )(cid:11)(cid:4)(cid:25))(cid:5)(cid:1) (cid:7)(cid:4)(cid:30)(cid:1) (cid:24)(cid:15)(cid:1) (cid:8)(cid:1)
((cid:4)(cid:20)(cid:11)(cid:15)(cid:12)9(cid:11)(cid:1) (cid:6)(cid:18)(cid:4)(cid:12)(cid:24)(cid:6)(cid:24)(cid:30)(cid:21)(cid:1) (cid:9)(cid:11)(cid:1) (cid:16))(cid:17)(cid:8)(cid:12)(cid:11)(cid:14)(cid:1) .(cid:4)(cid:1) (cid:8)(cid:20)(cid:4)(cid:1) (cid:8)(cid:10)(cid:5)(cid:4)(cid:1) (cid:24)(cid:15)(cid:1) (cid:27)(cid:8)(cid:24)(cid:4)-(cid:15)(cid:20)(cid:6)(cid:22)(cid:4)(cid:1) (cid:8)(cid:1)
((cid:4)(cid:20)(cid:11)(cid:15)(cid:12):(cid:11)(cid:1)(cid:8)-(cid:4)(cid:1)-(cid:20)(cid:15))((cid:1)(cid:25)(cid:20)(cid:15)(cid:17)(cid:1)(cid:8)(cid:1)((cid:4)(cid:20)(cid:11)(cid:15)(cid:12):(cid:11)(cid:1)(cid:25)(cid:8)(cid:27)(cid:4)(cid:1)(cid:6)(cid:17)(cid:8)-(cid:4)(cid:1)(cid:8)(cid:12)(cid:18)(cid:1)(cid:8)(cid:20)(cid:4)(cid:1)(cid:15)(cid:25)(cid:24)(cid:4)(cid:12)(cid:1)
(cid:8)(cid:10)(cid:5)(cid:4)(cid:1)(cid:24)(cid:15)(cid:1)(cid:10)(cid:4)(cid:1);)(cid:6)(cid:24)(cid:4)(cid:1)((cid:20)(cid:4)(cid:27)(cid:6)(cid:11)(cid:4)(cid:1)(cid:6)(cid:12)(cid:1)(cid:24)(cid:16)(cid:6)(cid:11)(cid:1)(cid:4)(cid:11)(cid:24)(cid:6)(cid:17)(cid:8)(cid:24)(cid:6)(cid:15)(cid:12)(cid:1)<(cid:2)=(cid:21)(cid:1)(cid:26)(cid:12)(cid:1)(cid:20)(cid:4)(cid:27)(cid:4)(cid:12)(cid:24)(cid:1)(cid:30)(cid:4)(cid:8)(cid:20)(cid:11)(cid:14)(cid:1)
(cid:25)(cid:8)(cid:27)(cid:4)(cid:1) (cid:20)(cid:4)(cid:27)(cid:15)-(cid:12)(cid:6)(cid:24)(cid:6)(cid:15)(cid:12)(cid:1) (cid:8)(cid:12)(cid:18)(cid:1) (cid:20)(cid:4)(cid:5)(cid:8)(cid:24)(cid:4)(cid:18)(cid:1) .(cid:15)(cid:20)(cid:7)(cid:11)(cid:1) (cid:16)(cid:8)(cid:29)(cid:4)(cid:1) (cid:20)(cid:4)(cid:27)(cid:4)(cid:6)(cid:29)(cid:4)(cid:18)(cid:1) (cid:11))(cid:10)(cid:11)(cid:24)(cid:8)(cid:12)(cid:24)(cid:6)(cid:8)(cid:5)(cid:1)
(cid:8)(cid:24)(cid:24)(cid:4)(cid:12)(cid:24)(cid:6)(cid:15)(cid:12)(cid:1) (cid:25)(cid:20)(cid:15)(cid:17)(cid:1) (cid:20)(cid:4)(cid:11)(cid:4)(cid:8)(cid:20)(cid:27)(cid:16)(cid:4)(cid:20)(cid:11)(cid:1) (cid:6)(cid:12)(cid:1) (cid:10)(cid:6)(cid:15)(cid:17)(cid:4)(cid:24)(cid:20)(cid:6)(cid:27)(cid:11)(cid:14)(cid:1) ((cid:8)(cid:24)(cid:24)(cid:4)(cid:20)(cid:12)(cid:1) (cid:20)(cid:4)(cid:27)(cid:15)-(cid:12)(cid:6)(cid:24)(cid:6)(cid:15)(cid:12)(cid:14)(cid:1)
(cid:8)(cid:12)(cid:18)(cid:1) (cid:27)(cid:15)(cid:17)()(cid:24)(cid:4)(cid:20) (cid:29)(cid:6)(cid:11)(cid:6)(cid:15)(cid:12)(cid:1) (cid:27)(cid:15)(cid:17)(cid:17))(cid:12)(cid:6)(cid:24)(cid:6)(cid:4)(cid:11)(cid:1) </(cid:14)(cid:1) *(cid:14)(cid:1) > (cid:8)(cid:12)(cid:18) 1=(cid:21)(cid:1) ’(cid:16)(cid:4)(cid:11)(cid:4)(cid:1)
(cid:27)(cid:15)(cid:17)(cid:17)(cid:15)(cid:12)(cid:1)(cid:6)(cid:12)(cid:24)(cid:4)(cid:20)(cid:4)(cid:11)(cid:24)(cid:11)(cid:1)(cid:8)(cid:17)(cid:15)(cid:12)-(cid:1)(cid:20)(cid:4)(cid:11)(cid:4)(cid:8)(cid:20)(cid:27)(cid:16)(cid:4)(cid:20)(cid:11)(cid:1)(cid:17)(cid:15)(cid:24)(cid:6)(cid:29)(cid:8)(cid:24)(cid:4)(cid:18)(cid:1))(cid:11)(cid:1)(cid:24)(cid:15)(cid:1)(cid:27)(cid:15)(cid:5)(cid:5)(cid:4)(cid:27)(cid:24)(cid:1)(cid:8)(cid:1)
(cid:18)(cid:8)(cid:24)(cid:8)(cid:10)(cid:8)(cid:11)(cid:4)(cid:1) (cid:15)(cid:25)(cid:1) (cid:25)(cid:8)(cid:27)(cid:6)(cid:8)(cid:5)(cid:1) (cid:6)(cid:17)(cid:8)-(cid:4)(cid:11)(cid:1) (cid:25)(cid:20)(cid:15)(cid:17)(cid:1) ((cid:4)(cid:15)((cid:5)(cid:4)(cid:1) (cid:6)(cid:12)(cid:1) (cid:18)(cid:6)(cid:25)(cid:25)(cid:4)(cid:20)(cid:4)(cid:12)(cid:24)(cid:1) (cid:8)-(cid:4)(cid:11)(cid:21) ’(cid:16)(cid:4)(cid:1)
(cid:18)(cid:8)(cid:24)(cid:8)(cid:10)(cid:8)(cid:11)(cid:4)(cid:1)(cid:6)(cid:11)(cid:1)(cid:6)(cid:12)(cid:24)(cid:4)(cid:12)(cid:18)(cid:4)(cid:18)(cid:1)(cid:25)(cid:15)(cid:20)(cid:1)(cid:18)(cid:6)(cid:11)(cid:24)(cid:20)(cid:6)(cid:10))(cid:24)(cid:6)(cid:15)(cid:12)(cid:1)(cid:24)(cid:15)(cid:1)(cid:20)(cid:4)(cid:11)(cid:4)(cid:8)(cid:20)(cid:27)(cid:16)(cid:4)(cid:20)(cid:11)(cid:21)
’(cid:16)(cid:4)(cid:20)(cid:4)(cid:1) (cid:8)(cid:20)(cid:4)(cid:1) (cid:17)(cid:8)(cid:12)(cid:30)(cid:1) ()(cid:10)(cid:5)(cid:6)(cid:27)(cid:8)(cid:5)(cid:5)(cid:30)(cid:1) (cid:8)(cid:29)(cid:8)(cid:6)(cid:5)(cid:8)(cid:10)(cid:5)(cid:4)(cid:1) (cid:18)(cid:8)(cid:24)(cid:8)(cid:10)(cid:8)(cid:11)(cid:4)(cid:11)(cid:1) (cid:25)(cid:15)(cid:20)(cid:1) (cid:25)(cid:8)(cid:27)(cid:4)(cid:1)"
06560d5721ecc487a4d70905a485e22c9542a522,Deep Facial Attribute Detection in the Wild: From General to Specific,"SUN, YU: DEEP FACIAL ATTRIBUTE DETECTION IN THE WILD
Deep Facial Attribute Detection in the Wild:
From General to Specific
Yuechuan Sun
Jun Yu
Department of Automation
University of Science and Technology
of China
Hefei, China"
06fe63b34fcc8ff68b72b5835c4245d3f9b8a016,Learning semantic representations of objects and their parts,"Mach Learn
DOI 10.1007/s10994-013-5336-9
Learning semantic representations of objects
nd their parts
Grégoire Mesnil · Antoine Bordes · Jason Weston ·
Gal Chechik · Yoshua Bengio
Received: 24 May 2012 / Accepted: 26 February 2013
© The Author(s) 2013"
06aab105d55c88bd2baa058dc51fa54580746424,Image Set-Based Collaborative Representation for Face Recognition,"Image Set based Collaborative Representation for
Face Recognition
Pengfei Zhu, Student Member, IEEE, Wangmeng Zuo, Member, IEEE, Lei Zhang, Member, IEEE, Simon C.K. Shiu,
Member, IEEE, David Zhang, Fellow, IEEE"
06262d14323f9e499b7c6e2a3dec76ad9877ba04,Real-Time Pose Estimation Piggybacked on Object Detection,"Real-Time Pose Estimation Piggybacked on Object Detection
Roman Jur´anek, Adam Herout, Mark´eta Dubsk´a, Pavel Zemˇc´ık
Brno University of Technology
Brno, Czech Republic"
062c41dad67bb68fefd9ff0c5c4d296e796004dc,Temporal Generative Adversarial Nets with Singular Value Clipping,"Temporal Generative Adversarial Nets with Singular Value Clipping
Masaki Saito∗
Eiichi Matsumoto∗
Preferred Networks inc., Japan
{msaito, matsumoto,
Shunta Saito"
0694b05cbc3ef5d1c5069a4bfb932a5a7b4d5ff0,Exploiting Local Class Information in Extreme Learning Machine,"Iosifidis, A., Tefas, A., & Pitas, I. (2014). Exploiting Local Class Information
in Extreme Learning Machine. Paper presented at International Joint
Conference on Computational Intelligence (IJCCI), Rome, Italy.
Peer reviewed version
Link to publication record in Explore Bristol Research
PDF-document
University of Bristol - Explore Bristol Research
General rights
This document is made available in accordance with publisher policies. Please cite only the published
version using the reference above. Full terms of use are available:
http://www.bristol.ac.uk/pure/about/ebr-terms"
060820f110a72cbf02c14a6d1085bd6e1d994f6a,Fine-grained classification of pedestrians in video: Benchmark and state of the art,"Fine-Grained Classification of Pedestrians in Video: Benchmark and State of the Art
David Hall and Pietro Perona
California Institute of Technology.
The dataset was labelled with bounding boxes, tracks, pose and fine-
grained labels. To achieve this, crowdsourcing, using workers from Ama-
zon’s Mechanical Turk (MTURK) was used. A summary of the dataset’s
statistics can be found in Table 1.
Number of Frames Sent to MTURK
Number of Frames with at least 1 Pedestrian
Number of Bounding Box Labels
Number of Pose Labels
Number of Tracks
8,708
0,994
2,457
7,454
,222
Table 1: Dataset Statistics
A state-of-the-art algorithm for fine-grained classification was tested us-
ing the dataset. The results are reported as a useful performance baseline."
063a3be18cc27ba825bdfb821772f9f59038c207,The development of spontaneous facial responses to others’ emotions in infancy: An EMG study,"This is a repository copy of The development of spontaneous facial responses to others’
emotions in infancy. An EMG study.
White Rose Research Online URL for this paper:
http://eprints.whiterose.ac.uk/125231/
Version: Published Version
Article:
Kaiser, Jakob, Crespo-Llado, Maria Magdalena, Turati, Chiara et al. (1 more author)
(2017) The development of spontaneous facial responses to others’ emotions in infancy.
An EMG study. Scientific Reports. ISSN 2045-2322
https://doi.org/10.1038/s41598-017-17556-y
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06ad99f19cf9cb4a40741a789e4acbf4433c19ae,SenTion: A framework for Sensing Facial Expressions,"SenTion: A framework for Sensing Facial
Expressions
Rahul Islam∗, Karan Ahuja∗, Sandip Karmakar∗, Ferdous Barbhuiya∗ ∗IIIT Guwahati
{rahul.islam, karan.ahuja, sandip,"
6c27eccf8c4b22510395baf9f0d0acc3ee547862,Using CMU PIE Human Face Database to a Convolutional Neural Network - Neocognitron,"Using CMU PIE Human Face Database to a
Convolutional Neural Network - Neocognitron
José Hiroki Saito1, Tiago Vieira de Carvalho1, Marcelo Hirakuri1, André Saunite1,
Alessandro Noriaki Ide2 and Sandra Abib1
- Federal University of São Carlos - Computer Science Department - GAPIS
Rodovia Washington Luis, Km 235, São Carlos – SP - Brazil
- University of Genoa - Department of Informatics, Systems and Telematics - Neurolab
Via Opera Pia, 13 – I-16145 – Genoa - Italy"
6cefb70f4668ee6c0bf0c18ea36fd49dd60e8365,Privacy-Preserving Deep Inference for Rich User Data on The Cloud,"Privacy-Preserving Deep Inference for Rich User
Data on The Cloud
Seyed Ali Osia ♯, Ali Shahin Shamsabadi ♯, Ali Taheri ♯, Kleomenis Katevas ⋆,
Hamid R. Rabiee ♯, Nicholas D. Lane †, Hamed Haddadi ⋆
♯ Sharif University of Technology
⋆ Queen Mary University of London
Nokia Bell Labs & University of Oxford"
6c304f3b9c3a711a0cca5c62ce221fb098dccff0,Attentive Semantic Video Generation Using Captions,"Attentive Semantic Video Generation using Captions
Tanya Marwah∗
IIT Hyderabad
Gaurav Mittal∗
Vineeth N. Balasubramanian
IIT Hyderabad"
6cb7648465ba7757ecc9c222ac1ab6402933d983,Visual Forecasting by Imitating Dynamics in Natural Sequences,"Visual Forecasting by Imitating Dynamics in Natural Sequences
Kuo-Hao Zeng†‡ William B. Shen† De-An Huang† Min Sun‡ Juan Carlos Niebles†
{khzeng, bshen88, dahuang,
Stanford University ‡National Tsing Hua University"
6c2b392b32b2fd0fe364b20c496fcf869eac0a98,Fully automatic face recognition framework based on local and global features,"DOI 10.1007/s00138-012-0423-7
ORIGINAL PAPER
Fully automatic face recognition framework based
on local and global features
Cong Geng · Xudong Jiang
Received: 30 May 2011 / Revised: 21 February 2012 / Accepted: 29 February 2012 / Published online: 22 March 2012
© Springer-Verlag 2012"
6cddc7e24c0581c50adef92d01bb3c73d8b80b41,Face Verification Using the LARK Representation,"Face Verification Using the LARK
Representation
Hae Jong Seo, Student Member, IEEE, Peyman Milanfar, Fellow, IEEE,"
6cfc337069868568148f65732c52cbcef963f79d,Audio-Visual Speaker Localization via Weighted Clustering Israel -,"Audio-Visual Speaker Localization via Weighted
Clustering
Israel-Dejene Gebru, Xavier Alameda-Pineda, Radu Horaud, Florence Forbes
To cite this version:
Israel-Dejene Gebru, Xavier Alameda-Pineda, Radu Horaud, Florence Forbes. Audio-Visual Speaker
Localization via Weighted Clustering. IEEE Workshop on Machine Learning for Signal Processing,
Sep 2014, Reims, France. pp.1-6, 2014, <10.1109/MLSP.2014.6958874>. <hal-01053732>
HAL Id: hal-01053732
https://hal.archives-ouvertes.fr/hal-01053732
Submitted on 11 Aug 2014
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d’enseignement et de"
6cd96f2b63c6b6f33f15c0ea366e6003f512a951,A New Approach in Solving Illumination and Facial Expression Problems for Face Recognition,"A New Approach in Solving Illumination and Facial Expression Problems
for Face Recognition
Yee Wan Wong, Kah Phooi Seng, Li-Minn Ang
The University of Nottingham Malaysia Campus
Tel : 03-89248358, Fax : 03-89248017
E-mail :
Jalan Broga
3500 Semenyih, Selangor"
6c8c7065d1041146a3604cbe15c6207f486021ba,Attention Modeling for Face Recognition via Deep Learning,"Attention Modeling for Face Recognition via Deep Learning
Sheng-hua Zhong
Department of Computing, Hung Hom, Kowloon
Hong Kong, 999077 CHINA
Yan Liu
Department of Computing, Hung Hom, Kowloon
Hong Kong, 99907 CHINA
Yao Zhang
Department of Computing, Hung Hom, Kowloon
Hong Kong, 99907 CHINA
Fu-lai Chung
Department of Computing, Hung Hom, Kowloon
Hong Kong, 99907 CHINA"
390f3d7cdf1ce127ecca65afa2e24c563e9db93b,Learning Deep Representation for Face Alignment with Auxiliary Attributes,"Learning Deep Representation for Face
Alignment with Auxiliary Attributes
Zhanpeng Zhang, Ping Luo, Chen Change Loy, Member, IEEE and Xiaoou Tang, Fellow, IEEE"
39ed31ced75e6151dde41944a47b4bdf324f922b,Pose-Guided Photorealistic Face Rotation,"Pose-Guided Photorealistic Face Rotation
Yibo Hu1,2, Xiang Wu1, Bing Yu3, Ran He1,2 ∗, Zhenan Sun1,2
CRIPAC & NLPR & CEBSIT, CASIA 2University of Chinese Academy of Sciences
Noah’s Ark Laboratory, Huawei Technologies Co., Ltd.
{yibo.hu, {rhe,"
3918b425bb9259ddff9eca33e5d47bde46bd40aa,Learning Language from Ambiguous Perceptual Context,"Copyright
David Lieh-Chiang Chen"
3998c5aa6be58cce8cb65a64cb168864093a9a3e,Understanding head and hand activities and coordination in naturalistic driving videos,Intelligent Vehicles Symposium 2014
39dc2ce4cce737e78010642048b6ed1b71e8ac2f,Recognition of six basic facial expressions by feature-points tracking using RBF neural network and fuzzy inference system,"Recognition of Six Basic Facial Expressions by Feature-Points Tracking using
RBF Neural Network and Fuzzy Inference System
Hadi Seyedarabi*, Ali Aghagolzadeh **, Sohrab Khanmohammadi **
*Islamic Azad University of AHAR
**Elect. Eng. Faculty, Tabriz University, Tabriz, Iran"
397085122a5cade71ef6c19f657c609f0a4f7473,Using Segmentation to Predict the Absence of Occluded Parts,"GHIASI, FOWLKES: USING SEGMENTATION TO DETECT OCCLUSION
Using Segmentation to Predict the Absence
of Occluded Parts
Golnaz Ghiasi
Charless C. Fowlkes
Dept. of Computer Science
University of California
Irvine, CA"
39c8b34c1b678235b60b648d0b11d241a34c8e32,Learning to Deblur Images with Exemplars,"Learning to Deblur Images with Exemplars
Jinshan Pan∗, Wenqi Ren∗, Zhe Hu∗, and Ming-Hsuan Yang"
3986161c20c08fb4b9b791b57198b012519ea58b,An Efficient Method for Face Recognition based on Fusion of Global and Local Feature Extraction,"International Journal of Soft Computing and Engineering (IJSCE)
ISSN: 2231-2307, Volume-4 Issue-4, September 2014
An Efficient Method for Face Recognition based on
Fusion of Global and Local Feature Extraction
E. Gomathi, K. Baskaran"
392c3cabe516c0108b478152902a9eee94f4c81e,Tiny images,"Computer Science and Artificial Intelligence Laboratory
Technical Report
MIT-CSAIL-TR-2007-024
April 23, 2007
Tiny images
Antonio Torralba, Rob Fergus, and William T. Freeman
m a s s a c h u s e t t s   i n s t i t u t e   o f   t e c h n o l o g y,   c a m b r i d g e ,   m a   0 213 9   u s a   —   w w w. c s a i l . m i t . e d u"
3933e323653ff27e68c3458d245b47e3e37f52fd,Evaluation of a 3 D-aided Pose Invariant 2 D Face Recognition System,"Evaluation of a 3D-aided Pose Invariant 2D Face Recognition System
Xiang Xu, Ha A. Le, Pengfei Dou, Yuhang Wu, Ioannis A. Kakadiaris
{xxu18, hale4, pdou, ywu35,
Computational Biomedicine Lab
800 Calhoun Rd. Houston, TX, USA"
3958db5769c927cfc2a9e4d1ee33ecfba86fe054,Describable Visual Attributes for Face Verification and Image Search,"Describable Visual Attributes for
Face Verification and Image Search
Neeraj Kumar, Student Member, IEEE, Alexander C. Berg, Member, IEEE,
Peter N. Belhumeur, and Shree K. Nayar, Member, IEEE"
99ced8f36d66dce20d121f3a29f52d8b27a1da6c,Organizing Multimedia Data in Video Surveillance Systems Based on Face Verification with Convolutional Neural Networks,"Organizing Multimedia Data in Video
Surveillance Systems Based on Face Verification
with Convolutional Neural Networks
Anastasiia D. Sokolova, Angelina S. Kharchevnikova, Andrey V. Savchenko
National Research University Higher School of Economics, Nizhny Novgorod, Russian
Federation"
994f7c469219ccce59c89badf93c0661aae34264,Model Based Face Recognition Across Facial Expressions,"Model Based Face Recognition Across Facial
Expressions
Zahid Riaz, Christoph Mayer, Matthias Wimmer, and Bernd Radig, Senior Member, IEEE
screens,  embedded  into  mobiles  and  installed  into  everyday
living  and  working  environments  they  become  valuable  tools
for human system interaction. A particular important aspect of
this  interaction  is  detection  and  recognition  of  faces  and
interpretation  of  facial  expressions.  These  capabilities  are
deeply  rooted  in  the  human  visual  system  and  a  crucial
uilding  block  for  social  interaction.  Consequently,  these
apabilities  are  an  important  step  towards  the  acceptance  of
many technical systems.
trees  as  a  classifier
lies  not  only"
9949ac42f39aeb7534b3478a21a31bc37fe2ffe3,Parametric Stereo for Multi-pose Face Recognition and 3D-Face Modeling,"Parametric Stereo for Multi-Pose Face Recognition and
D-Face Modeling
Rik Fransens, Christoph Strecha, Luc Van Gool
PSI ESAT-KUL
Leuven, Belgium"
9958942a0b7832e0774708a832d8b7d1a5d287ae,The Sparse Matrix Transform for Covariance Estimation and Analysis of High Dimensional Signals,"The Sparse Matrix Transform for Covariance
Estimation and Analysis of High Dimensional
Signals
Guangzhi Cao*, Member, IEEE, Leonardo R. Bachega, and Charles A. Bouman, Fellow, IEEE"
99726ad232cef837f37914b63de70d8c5101f4e2,Facial Expression Recognition Using PCA & Distance Classifier,"International Journal of Scientific & Engineering Research, Volume 5, Issue 5, May-2014                                                                                                      570
ISSN 2229-5518
Facial Expression Recognition Using PCA & Distance Classifier
AlpeshKumar Dauda*
Dept. of Electronics & Telecomm. Engg.
Ph.D Scholar,VSSUT
BURLA, ODISHA, INDIA
Nilamani Bhoi
Reader in Dept. of Electronics & Telecomm. Engg.
VEER SURENDRA SAI UNIVERSITY OF
TECHNOLOGY
BURLA, ODISHA, INDIA"
9993f1a7cfb5b0078f339b9a6bfa341da76a3168,"A Simple, Fast and Highly-Accurate Algorithm to Recover 3D Shape from 2D Landmarks on a Single Image","JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015
A Simple, Fast and Highly-Accurate Algorithm to
Recover 3D Shape from 2D Landmarks on a Single
Image
Ruiqi Zhao, Yan Wang, Aleix M. Martinez"
992ebd81eb448d1eef846bfc416fc929beb7d28b,Exemplar-Based Face Parsing Supplementary Material,"Exemplar-Based Face Parsing
Supplementary Material
Brandon M. Smith Li Zhang
Jonathan Brandt Zhe Lin Jianchao Yang
University of Wisconsin–Madison
Adobe Research
http://www.cs.wisc.edu/~lizhang/projects/face-parsing/
. Additional Selected Results
Figures 1 and 2 supplement Figure 4 in our paper. In all cases, the input images come from our Helen [1] test set. We note
that our algorithm generally produces accurate results, as shown in Figures 1. However, our algorithm is not perfect and makes
mistakes on especially challenging input images, as shown in Figure 2.
In our view, the mouth is the most challenging region of the face to segment: the shape and appearance of the lips vary
widely from subject to subject, mouths deform significantly, and the overall appearance of the mouth region changes depending
on whether the inside of the mouth is visible or not. Unusual mouth expressions, like those shown in Figure 2, are not repre-
sented well in the exemplar images, which results in poor label transfer from the top exemplars to the test image. Despite these
hallenges, our algorithm generally performs well on the mouth, with large segmentation errors occurring infrequently.
. Comparisons with Liu et al. [2]
The scene parsing approach by Liu et al. [2] shares sevaral similarities with our work. Like our approach, they propose a
nonparametric system that transfers labels from exemplars in a database to annotate a test image. This begs the question, Why
not simply apply the approach from Liu et al. to face images?"
99c20eb5433ed27e70881d026d1dbe378a12b342,Semi-Supervised and Unsupervised Data Extraction Targeting Speakers: From Speaker Roles to Fame?,"ISCA Archive
http://www.isca-speech.org/archive
First Workshop on Speech, Language
nd Audio in Multimedia
Marseille, France
August 22-23, 2013
Proceedings of the First Workshop on Speech, Language and Audio in Multimedia (SLAM), Marseille, France, August 22-23, 2013."
9990e0b05f34b586ffccdc89de2f8b0e5d427067,Auto - Optimized Multimodal Expression Recognition Framework Using 3 D Kinect Data for ASD Therapeutic Aid,"International Journal of Modeling and Optimization, Vol. 3, No. 2, April 2013
Auto-Optimized Multimodal Expression Recognition
Framework Using 3D Kinect Data for ASD Therapeutic
Amira E. Youssef, Sherin F. Aly, Ahmed S. Ibrahim, and A. Lynn Abbott
regarding
emotion
recognize"
99d7678039ad96ee29ab520ff114bb8021222a91,Political image analysis with deep neural networks,"Political image analysis with deep neural
networks
L. Jason Anastasopoulos∗
Shiry Ginosar§.
Dhruvil Badani†
Jake Ryland Williams¶
Crystal Lee‡
November 28, 2017"
52012b4ecb78f6b4b9ea496be98bcfe0944353cd,Using Support Vector Machine and Local Binary Pattern for Facial Expression Recognition,"JOURNAL OF COMPUTATION IN BIOSCIENCES AND ENGINEERING
Journal homepage: http://scienceq.org/Journals/JCLS.php
Research Article
Using  Support  Vector  Machine  and  Local  Binary  Pattern  for  Facial  Expression
Recognition
Open Access
Ayeni Olaniyi Abiodun 1, Alese Boniface Kayode1, Dada Olabisi Matemilayo2
1. Department of Computer Science, Federal University Technology Akure, PMB 704, Akure, Nigeria.
. Department of computer science, Kwara state polytechnic Ilorin, Kwara-State, Nigeria.
. *Corresponding author:  Ayeni Olaniyi Abiodun  Mail Id:
Received:    September 22, 2015, Accepted: December 14, 2015, Published: December 14, 2015."
529e2ce6fb362bfce02d6d9a9e5de635bde81191,Normalization of Face Illumination Based on Large-and Small-Scale Features,"This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.
> TIP-05732-2009<
Normalization of Face Illumination Based
on Large- and Small- Scale Features
Xiaohua Xie, Wei-Shi Zheng, Member, IEEE, Jianhuang Lai*, Member, IEEE
Pong C. Yuen, Member, IEEE, Ching Y. Suen, IEEE Fellow"
52887969107956d59e1218abb84a1f834a314578,Travel Recommendation by Mining People Attributes and Travel Group Types From Community-Contributed Photos,"Travel Recommendation by Mining People
Attributes and Travel Group Types From
Community-Contributed Photos
Yan-Ying Chen, An-Jung Cheng, and Winston H. Hsu, Senior Member, IEEE"
52258ec5ec73ce30ca8bc215539c017d279517cf,Recognizing Faces with Expressions: Within-class Space and Between-class Space,"Recognizing Faces with Expressions: Within-class Space and Between-class Space
Department of Computer Science and Engineering, Zhejang University, Hangzhou 310027,P.R.China
Email:
Yu  Bing      Chen  Ping      Jin  Lianfu"
529baf1a79cca813f8c9966ceaa9b3e42748c058,Triangle wise Mapping Technique to Transform one Face Image into Another Face Image,"Triangle Wise Mapping Technique to Transform one Face Image into Another Face Image
{tag}                                                                           {/tag}
International Journal of Computer Applications
© 2014 by IJCA Journal
Volume 87 - Number 6
Year of Publication: 2014
Authors:
Rustam Ali Ahmed
Bhogeswar Borah
10.5120/15209-3714
{bibtex}pxc3893714.bib{/bibtex}"
5239001571bc64de3e61be0be8985860f08d7e7e,Deep Appearance Models: A Deep Boltzmann Machine Approach for Face Modeling,"SUBMITTED TO IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, JUNE 2016
Deep Appearance Models: A Deep Boltzmann
Machine Approach for Face Modeling
Chi Nhan Duong, Student, IEEE, Khoa Luu, Member, IEEE,
Kha Gia Quach, Student, IEEE, Tien D. Bui, Senior Member, IEEE"
55ea0c775b25d9d04b5886e322db852e86a556cd,DOCK: Detecting Objects by transferring Common-sense Knowledge,"DOCK: Detecting Objects
y transferring Common-sense Knowledge
Santosh Divvala2,3[0000−0003−4042−5874], Ali Farhadi2,3[0000−0001−7249−2380], and
Krishna Kumar Singh1,3[0000−0002−8066−6835],
Yong Jae Lee1[0000−0001−9863−1270]
University of California, Davis 2University of Washington 3Allen Institute for AI
https://dock-project.github.io"
554b9478fd285f2317214396e0ccd81309963efd,Spatio-Temporal Action Localization For Human Action Recognition in Large Dataset,"Spatio-Temporal Action Localization For Human Action
Recognition in Large Dataset
Sameh MEGRHI1, Marwa JMAL 2, Azeddine BEGHDADI1 and Wided Mseddi1,2
L2TI, Institut Galil´ee, Universit´e Paris 13, France;
SERCOM, Ecole Polytechnique de Tunisie"
55c68c1237166679d2cb65f266f496d1ecd4bec6,Learning to score the figure skating sports videos,"Learning to Score Figure Skating Sport Videos
Chengming Xu, Yanwei Fu, Zitian Chen,Bing Zhang, Yu-Gang Jiang, Xiangyang Xue"
5502dfe47ac26e60e0fb25fc0f810cae6f5173c0,Affordance Prediction via Learned Object Attributes,"Affordance Prediction via Learned Object Attributes
Tucker Hermans
James M. Rehg
Aaron Bobick"
55a158f4e7c38fe281d06ae45eb456e05516af50,Simile Classifiers for Face Classification,"The 22nd International Conference on Computer Graphics and Vision
GraphiCon’2012"
5550a6df1b118a80c00a2459bae216a7e8e3966c,A perusal on Facial Emotion Recognition System ( FERS ),"ISSN: 0974-2115
www.jchps.com                                                                       Journal of Chemical and Pharmaceutical Sciences
A perusal on Facial Emotion Recognition System (FERS)
School of Information Technology and Engineering, VIT University, Vellore, 632014, India
Krithika L.B
*Corresponding author: E-Mail:"
55079a93b7d1eb789193d7fcdcf614e6829fad0f,Efficient and Robust Inverse Lighting of a Single Face Image Using Compressive Sensing,"Efficient and Robust Inverse Lighting of a Single Face Image using Compressive
Sensing
Miguel Heredia Conde†, Davoud Shahlaei#, Volker Blanz# and Otmar Loffeld†
Center for Sensor Systems† (ZESS) and Institute for Vision and Graphics#, University of Siegen
57076 Siegen, Germany"
551fa37e8d6d03b89d195a5c00c74cc52ff1c67a,GeThR-Net: A Generalized Temporally Hybrid Recurrent Neural Network for Multimodal Information Fusion,"GeThR-Net: A Generalized Temporally Hybrid
Recurrent Neural Network for Multimodal
Information Fusion
Ankit Gandhi1 ∗, Arjun Sharma1 ∗ , Arijit Biswas2, and Om Deshmukh1
Xerox Research Centre India; 2 Amazon Development Center India
(*-equal contribution)"
55c40cbcf49a0225e72d911d762c27bb1c2d14aa,Indian Face Age Database : A Database for Face Recognition with Age Variation,"Indian Face Age Database: A Database for Face Recognition with Age Variation
{tag}                                                                  {/tag}
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 126
Number 5
Year of Publication: 2015
Authors:
Reecha Sharma, M.S. Patterh
10.5120/ijca2015906055
{bibtex}2015906055.bib{/bibtex}"
973e3d9bc0879210c9fad145a902afca07370b86,From Emotion Recognition to Website Customizations,"(IJACSA) International Journal of Advanced Computer Science and Applications,
Vol. 7, No. 7, 2016
From Emotion Recognition to Website
Customizations
O.B.  Efremides
School  of  Web  Media
Bahrain  Polytechnic
Isa  Town,  Kingdom  of  Bahrain"
97b8249914e6b4f8757d22da51e8347995a40637,"Large-Scale Vehicle Detection, Indexing, and Search in Urban Surveillance Videos","Large-Scale Vehicle Detection, Indexing,
nd Search in Urban Surveillance Videos
Rogerio Schmidt Feris, Associate Member, IEEE, Behjat Siddiquie, James Petterson,
Yun Zhai, Associate Member, IEEE, Ankur Datta, Lisa M. Brown, Senior Member, IEEE, and
Sharath Pankanti, Fellow, IEEE"
972ef9ddd9059079bdec17abc8b33039ed25c99c,A Novel on understanding How IRIS Recognition works,"International Journal of Innovations in Engineering and Technology (IJIET)
A Novel on understanding How IRIS
Recognition works
Vijay Shinde
Dept. of Comp. Science
M.P.M. College, Bhopal, India
Prof. Prakash Tanwar
Asst. Professor CSE
M.P.M. College, Bhopal, India"
97032b13f1371c8a813802ade7558e816d25c73f,Total Recall Final Report,"Total Recall Final Report
Peter Collingbourne, Nakul Durve, Khilan Gudka, Steve Lovegrove, Jiefei Ma, Sadegh Shahrbaf
Supervisor: Professor Duncan Gillies
January 11, 2006"
97f9c3bdb4668f3e140ded2da33fe704fc81f3ea,An Experimental Comparison of Appearance and Geometric Model Based Recognition,"AnExperimentalComparisonofAppearance
ndGeometricModelBasedRecognition
J.Mundy
,A.Liu
,N.Pillow,A.Zisserman,S.Abdallah,S.Utcke,
S.NayarandC.Rothwell

GeneralElectricCorporateResearchandDevelopment,Schenectady,NY,USA
RoboticsResearchGroup,UniversityofOxford,Oxford,UK
Dept.ofComputerScience,ColumbiaUniversity,NY,USA
INRIA,SophiaAntipolis,France"
97cf04eaf1fc0ac4de0f5ad4a510d57ce12544f5,"Deep Affect Prediction in-the-wild: Aff-Wild Database and Challenge, Deep Architectures, and Beyond","manuscript No.
(will be inserted by the editor)
Deep Affect Prediction in-the-wild: Aff-Wild Database and Challenge,
Deep Architectures, and Beyond
Dimitrios Kollias (cid:63) · Panagiotis Tzirakis † · Mihalis A. Nicolaou ∗ · Athanasios
Papaioannou(cid:107) · Guoying Zhao1 · Bj¨orn Schuller2 · Irene Kotsia3 · Stefanos
Zafeiriou4"
97d1d561362a8b6beb0fdbee28f3862fb48f1380,Age Synthesis and Estimation via Faces: A Survey,"Age Synthesis and Estimation via Faces:
A Survey
Yun Fu, Member, IEEE, Guodong Guo, Senior Member, IEEE, and
Thomas S. Huang, Fellow, IEEE"
97865d31b5e771cf4162bc9eae7de6991ceb8bbf,Face and Gender Classification in Crowd Video,"Face and Gender Classification in Crowd Video
Priyanka Verma
IIIT-D-MTech-CS-GEN-13-100
July 16, 2015
Indraprastha Institute of Information Technology
New Delhi
Thesis Advisors
Dr. Richa Singh
Dr. Mayank Vatsa
Submitted in partial fulfillment of the requirements
for the Degree of M.Tech. in Computer Science
(cid:13) Verma, 2015
Keywords : Face Recognition, Gender Classification, Crowd database"
9755554b13103df634f9b1ef50a147dd02eab02f,How Transferable Are CNN-Based Features for Age and Gender Classification?,"How Transferable are CNN-based Features for
Age and Gender Classification?
Gökhan Özbulak1, Yusuf Aytar2 and Hazım Kemal Ekenel1"
63cf5fc2ee05eb9c6613043f585dba48c5561192,Prototype Selection for Classification in Standard and Generalized Dissimilarity Spaces Prototype Selection for Classification in Standard and Generalized Dissimilarity Spaces,"Prototype Selection for
Classification in Standard
nd Generalized
Dissimilarity Spaces"
63c109946ffd401ee1195ed28f2fb87c2159e63d,Robust Facial Feature Localization Using Improved Active Shape Model and Gabor Filter,"MVA2011 IAPR Conference on Machine Vision Applications, June 13-15, 2011, Nara, JAPAN
Robust Facial Feature Localization using Improved Active Shape
Model and Gabor Filter
Hui-Yu Huang
Engineering, National Formosa University,
Taiwan
E-mail:"
631483c15641c3652377f66c8380ff684f3e365c,Sync-DRAW: Automatic GIF Generation using Deep Recurrent Attentive Architectures,"Sync-DRAW: Automatic Video Generation using Deep Recurrent
A(cid:130)entive Architectures
Gaurav Mi(cid:138)al∗
Tanya Marwah∗
IIT Hyderabad
Vineeth N Balasubramanian
IIT Hyderabad"
632fa986bed53862d83918c2b71ab953fd70d6cc,What Face and Body Shapes Can Tell About Height,"GÜNEL ET AL.: WHAT FACE AND BODY SHAPES CAN TELL ABOUT HEIGHT
What Face and Body Shapes Can Tell
About Height
Semih Günel
Helge Rhodin
Pascal Fua
CVLab
EPFL,
Lausanne, Switzerland"
63340c00896d76f4b728dbef85674d7ea8d5ab26,Discriminant Subspace Analysis: A Fukunaga-Koontz Approach,"Discriminant Subspace Analysis:
A Fukunaga-Koontz Approach
Sheng Zhang, Member, IEEE, and Terence Sim, Member, IEEE"
634541661d976c4b82d590ef6d1f3457d2857b19,Advanced Techniques for Face Recognition under Challenging Environments,"AAllmmaa  MMaatteerr  SSttuuddiioorruumm  ––  UUnniivveerrssiittàà  ddii  BBoollooggnnaa
in cotutela con Università di Sassari
DOTTORATO DI RICERCA IN
INGEGNERIA ELETTRONICA, INFORMATICA E DELLE
TELECOMUNICAZIONI
Ciclo XXVI
Settore Concorsuale di afferenza: 09/H1
Settore Scientifico disciplinare: ING-INF/05
ADVANCED TECHNIQUES FOR FACE RECOGNITION
UNDER CHALLENGING ENVIRONMENTS
TITOLO TESI
YUNLIAN SUN
Presentata da:
Coordinatore Dottorato
ALESSANDRO VANELLI-CORALLI
Relatore
DAVIDE MALTONI
Relatore
MASSIMO TISTARELLI
Esame finale anno 2014"
6332a99e1680db72ae1145d65fa0cccb37256828,MASTER IN COMPUTER VISION AND ARTIFICIAL INTELLIGENCE REPORT OF THE RESEARCH PROJECT OPTION: COMPUTER VISION Pose and Face Recovery via Spatio-temporal GrabCut Human Segmentation,"MASTER IN COMPUTER VISION AND ARTIFICIAL INTELLIGENCE
REPORT OF THE RESEARCH PROJECT
OPTION: COMPUTER VISION
Pose and Face Recovery via
Spatio-temporal GrabCut Human
Segmentation
Author: Antonio Hernández Vela
Date: 13/07/2010
Advisor: Sergio Escalera Guerrero"
63488398f397b55552f484409b86d812dacde99a,Learning Universal Multi-view Age Estimator by Video Contexts,"Learning Universal Multi-view Age Estimator by Video Contexts
Zheng Song1, Bingbing Ni3, Dong Guo4, Terence Sim2, Shuicheng Yan1
Department of Electrical and Computer Engineering, 2 School of Computing, National University of Singapore;
{zheng.s,
Advanced Digital Sciences Center, Singapore; 4 Facebook"
63c022198cf9f084fe4a94aa6b240687f21d8b41,Consensus Message Passing for Layered Graphical Models,
0f65c91d0ed218eaa7137a0f6ad2f2d731cf8dab,Multi-Directional Multi-Level Dual-Cross Patterns for Robust Face Recognition,"Multi-Directional Multi-Level Dual-Cross
Patterns for Robust Face Recognition
Changxing Ding, Jonghyun Choi, Dacheng Tao, Senior Member, IEEE, and Larry S. Davis, Fellow, IEEE"
0f112e49240f67a2bd5aaf46f74a924129f03912,Age-Invariant Face Recognition,"Age-Invariant Face Recognition
Unsang Park, Member, IEEE,
Yiying Tong, Member, IEEE, and
Anil K. Jain, Fellow, IEEE"
0f4cfcaca8d61b1f895aa8c508d34ad89456948e,Local appearance based face recognition using discrete cosine transform,"LOCAL APPEARANCE BASED FACE RECOGNITION USING
DISCRETE COSINE TRANSFORM  (WedPmPO4)
Author(s) :"
0fdcfb4197136ced766d538b9f505729a15f0daf,Multiple pattern classification by sparse subspace decomposition,"Multiple Pattern Classification by Sparse Subspace Decomposition
Institute of Media and Information Technology, Chiba University
Tomoya Sakai
-33 Yayoi, Inage, Chiba, Japan"
0fad544edfc2cd2a127436a2126bab7ad31ec333,Decorrelating Semantic Visual Attributes by Resisting the Urge to Share,"Decorrelating Semantic Visual Attributes by Resisting the Urge to Share
Dinesh Jayaraman
UT Austin
Fei Sha
Kristen Grauman
UT Austin"
0fd1715da386d454b3d6571cf6d06477479f54fc,A Survey of Autonomous Human Affect Detection Methods for Social Robots Engaged in Natural HRI,"J Intell Robot Syst (2016) 82:101–133
DOI 10.1007/s10846-015-0259-2
A Survey of Autonomous Human Affect Detection Methods
for Social Robots Engaged in Natural HRI
Derek McColl · Alexander Hong ·
Naoaki Hatakeyama · Goldie Nejat ·
Beno Benhabib
Received: 10 December 2014 / Accepted: 11 August 2015 / Published online: 23 August 2015
© Springer Science+Business Media Dordrecht 2015"
0f92e9121e9c0addc35eedbbd25d0a1faf3ab529,MORPH-II: A Proposed Subsetting Scheme,"MORPH-II: A Proposed Subsetting Scheme
Participants: K. Kempfert, J. Fabish, K. Park, and R. Towner
Mentors: Y. Wang, C. Chen, and T. Kling
NSF-REU Site at UNC Wilmington, Summer 2017"
0ff23392e1cb62a600d10bb462d7a1f171f579d0,Toward Sparse Coding on Cosine Distance,"Toward	Sparse	Coding	on	Cosine
Distance
Jonghyun	Choi,	Hyunjong	Cho,	Jungsuk	Kwak#,
Larry	S.	Davis
UMIACS	|	University	of	Maryland,	College	Park
#Stanford	University"
0f395a49ff6cbc7e796656040dbf446a40e300aa,The Change of Expression Configuration Affects Identity-Dependent Expression Aftereffect but Not Identity-Independent Expression Aftereffect,"ORIGINAL RESEARCH
published: 22 December 2015
doi: 10.3389/fpsyg.2015.01937
The Change of Expression
Configuration Affects
Identity-Dependent Expression
Aftereffect but Not
Identity-Independent Expression
Aftereffect
Miao Song 1, 2*, Keizo Shinomori 2, Qian Qian 3, Jun Yin 1 and Weiming Zeng 1
College of Information Engineering, Shanghai Maritime University, Shanghai, China, 2 School of Information, Kochi University
of Technology, Kochi, Japan, 3 Yunnan Key Laboratory of Computer Technology Applications, Kunming University of Science
nd Technology, Kunming, China
The present study examined the influence of expression configuration on cross-identity
expression aftereffect. The expression configuration refers to the spatial arrangement
of facial features in a face for conveying an emotion, e.g., an open-mouth smile vs.
closed-mouth smile. In the first of two experiments, the expression aftereffect is
measured using a cross-identity/cross-expression configuration factorial design. The
facial
identities of test faces were the same or different from the adaptor, while"
0fd1bffb171699a968c700f206665b2f8837d953,Weakly Supervised Object Localization with Multi-Fold Multiple Instance Learning,"Weakly Supervised Object Localization with
Multi-fold Multiple Instance Learning
Ramazan Gokberk Cinbis, Jakob Verbeek, and Cordelia Schmid, Fellow, IEEE"
0a6d344112b5af7d1abbd712f83c0d70105211d0,Constrained Local Neural Fields for Robust Facial Landmark Detection in the Wild,"Constrained Local Neural Fields for robust facial landmark detection in the wild
Tadas Baltruˇsaitis
Peter Robinson
University of Cambridge Computer Laboratory
USC Institute for Creative Technologies
5 JJ Thomson Avenue
Louis-Philippe Morency
2015 Waterfront Drive"
0a3863a0915256082aee613ba6dab6ede962cdcd,Early and Reliable Event Detection Using Proximity Space Representation,"Early and Reliable Event Detection Using Proximity Space Representation
Maxime Sangnier
LTCI, CNRS, T´el´ecom ParisTech, Universit´e Paris-Saclay, 75013, Paris, France
J´erˆome Gauthier
LADIS, CEA, LIST, 91191, Gif-sur-Yvette, France
Alain Rakotomamonjy
Normandie Universit´e, UR, LITIS EA 4108, Avenue de l’universit´e, 76801, Saint-Etienne-du-Rouvray, France"
0a60d9d62620e4f9bb3596ab7bb37afef0a90a4f,Chimpanzee Faces in the Wild: Log-Euclidean CNNs for Predicting Identities and Attributes of Primates,"Chimpanzee Faces in the Wild: Log-Euclidean CNNs for Predicting Identities and Attributes of Primates. GCPR 2016
(cid:13) Copyright by Springer. The final publication will be available at link.springer.com
A. Freytag, E. Rodner, M. Simon, A. Loos, H. K¨uhl and J. Denzler
Chimpanzee Faces in the Wild:
Log-Euclidean CNNs for Predicting Identities
nd Attributes of Primates
Alexander Freytag1,2, Erik Rodner1,2, Marcel Simon1, Alexander Loos3,
Hjalmar S. K¨uhl4,5, and Joachim Denzler1,2,5
Computer Vision Group, Friedrich Schiller University Jena, Germany
Michael Stifel Center Jena, Germany
Fraunhofer Institute for Digital Media Technology, Germany
Max Planck Institute for Evolutionary Anthropology, Germany
5German Centre for Integrative Biodiversity Research (iDiv), Germany"
0aa9872daf2876db8d8e5d6197c1ce0f8efee4b7,Timing is everything : a spatio-temporal approach to the analysis of facial actions,"Imperial College of Science, Technology and Medicine
Department of Computing
Timing is everything
A spatio-temporal approach to the analysis of facial
ctions
Michel Fran¸cois Valstar
Submitted in part fulfilment of the requirements for the degree of
Doctor of Philosophy in Computing of Imperial College, February 2008"
0a87d781fe2ae2e700237ddd00314dbc10b1429c,Multi-scale HOG Prescreening Algorithm for Detection of Buried Explosive Hazards in FL-IR and FL-GPR Data,"Distribution Statement A:  Approved for public release; distribution unlimited.
Multi-scale HOG Prescreening Algorithm for Detection of Buried
Explosive Hazards in FL-IR and FL-GPR Data
*University of Missouri, Electrical and Computer Engineering Department, Columbia, MO
K. Stone*, J. M. Keller*, D. Shaw*"
0af48a45e723f99b712a8ce97d7826002fe4d5a5,Toward Wide-Angle Microvision Sensors,"Toward Wide-Angle Microvision Sensors
Sanjeev J. Koppal, Member, IEEE, Ioannis Gkioulekas, Student Member, IEEE,
Travis Young, Member, IEEE, Hyunsung Park, Student Member, IEEE,
Kenneth B. Crozier, Member, IEEE, Geoffrey L. Barrows, Member, IEEE, and
Todd Zickler, Member, IEEE"
0aa8a0203e5f406feb1815f9b3dd49907f5fd05b,Mixture Subclass Discriminant Analysis,"Mixture subclass discriminant analysis
Nikolaos Gkalelis, Vasileios Mezaris, Ioannis Kompatsiaris"
0a7309147d777c2f20f780a696efe743520aa2db,Stories for Images-in-Sequence by using Visual and Narrative Components,"Stories for Images-in-Sequence by using Visual
nd Narrative Components (cid:63)
Marko Smilevski1,2, Ilija Lalkovski2, and Gjorgji Madjarov1,3
Ss. Cyril and Methodius University, Skopje, Macedonia
Pendulibrium, Skopje, Macedonia
Elevate Global, Skopje, Macedonia"
0a1138276c52c734b67b30de0bf3f76b0351f097,Discriminant Incoherent Component Analysis,"This is the author's version of an article that has been published in this journal. Changes were made to this version by the publisher prior to publication.
The final version of record is available at
http://dx.doi.org/10.1109/TIP.2016.2539502
Discriminant Incoherent Component Analysis
Christos Georgakis, Student Member, IEEE, Yannis Panagakis, Member, IEEE, and Maja Pantic, Fellow, IEEE"
0a6a25ee84fc0bf7284f41eaa6fefaa58b5b329a,Neural Networks Regularization Through Representation Learning,"THÈSEPour obtenir le diplôme de doctorat Spécialité Informatique Préparée au sein de « l'INSA Rouen Normandie » Présentée et soutenue parSoufiane BELHARBIThèse dirigée par Sébastien ADAM, laboratoire LITIS Neural Networks Regularization Through Representation LearningThèse soutenue publiquement le 06 Juillet 2018 devant le jury composé deSébastien ADAMProfesseur à l'Université de Rouen NormandieDirecteur de thèseClément CHATELAINMaître de conférence à l'INSA  Rouen NormandieEncadrant de thèseRomain HÉRAULTMaître de conférence à l'INSA  Rouen NormandieEncadrant de thèseElisa FROMONTProfesseur à l'Université de Rennes 1Rapporteur de thèseThierry ARTIÈRESProfesseur à l'École Centrale MarseilleRapporteur de thèseJohn LEEProfesseur à l'Université Catholique de LouvainExaminateur de thèseDavid PICARDMaître de conférences à l'École Nationale Supérieure de l'Électronique et de ses ApplicationsExaminateur de thèseFrédéric JURIEProfesseur à l' Université de Caen NormandieInvité"
0ae9cc6a06cfd03d95eee4eca9ed77b818b59cb7,"Multi-task, multi-label and multi-domain learning with residual convolutional networks for emotion recognition","Noname manuscript No.
(will be inserted by the editor)
Multi-task, multi-label and multi-domain learning with
residual convolutional networks for emotion recognition
Gerard Pons · David Masip
Received: date / Accepted: date"
0acf23485ded5cb9cd249d1e4972119239227ddb,Dual coordinate solvers for large-scale structural SVMs,"Dual coordinate solvers for large-scale structural SVMs
Deva Ramanan
UC Irvine
This manuscript describes a method for training linear SVMs (including binary SVMs, SVM regression,
nd structural SVMs) from large, out-of-core training datasets. Current strategies for large-scale learning fall
into one of two camps; batch algorithms which solve the learning problem given a finite datasets, and online
lgorithms which can process out-of-core datasets. The former typically requires datasets small enough to fit
in memory. The latter is often phrased as a stochastic optimization problem [4, 15]; such algorithms enjoy
strong theoretical properties but often require manual tuned annealing schedules, and may converge slowly
for problems with large output spaces (e.g., structural SVMs). We discuss an algorithm for an “intermediate”
regime in which the data is too large to fit in memory, but the active constraints (support vectors) are small
enough to remain in memory.
In this case, one can design rather ef‌f‌icient learning algorithms that are
s stable as batch algorithms, but capable of processing out-of-core datasets. We have developed such a
MATLAB-based solver and used it to train a series of recognition systems [19, 7, 21, 12] for articulated pose
estimation, facial analysis, 3D object recognition, and action classification, all with publicly-available code.
This writeup describes the solver in detail.
Approach: Our approach is closely based on data-subsampling algorithms for collecting hard exam-
ples [9, 10, 6], combined with the dual coordinate quadratic programming (QP) solver described in liblinear
[8]. The latter appears to be current fastest method for learning linear SVMs. We make two extensions (1)"
6412d8bbcc01f595a2982d6141e4b93e7e982d0f,"Deep Convolutional Neural Network Using Triplets of Faces, Deep Ensemble, and Score-Level Fusion for Face Recognition","Deep Convolutional Neural Network using Triplets of Faces, Deep Ensemble, and
Score-level Fusion for Face Recognition
Bong-Nam Kang, Student Member, IEEE1, Yonghyun Kim, Student Member, IEEE2, and
Daijin Kim, Member, IEEE2
Department of Creative IT Engineering, POSTECH, Korea
Department of Computer Science and Engineering, POSTECH, Korea
{bnkang, gkyh0805,"
641f0989b87bf7db67a64900dcc9568767b7b50f,Reconstructing faces from their signatures using RBF regression,"Reconstructing Faces from their Signatures using RBF
Regression
Alexis Mignon, Fr´ed´eric Jurie
To cite this version:
Alexis Mignon, Fr´ed´eric Jurie. Reconstructing Faces from their Signatures using RBF Regres-
sion. British Machine Vision Conference 2013, Sep 2013, Bristol, United Kingdom. pp.103.1–
03.12, 2013, <10.5244/C.27.103>. <hal-00943426>
HAL Id: hal-00943426
https://hal.archives-ouvertes.fr/hal-00943426
Submitted on 13 Feb 2014
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destin´ee au d´epˆot et `a la diffusion de documents
scientifiques de niveau recherche, publi´es ou non,
´emanant des ´etablissements d’enseignement et de"
64153df77fe137b7c6f820a58f0bdb4b3b1a879b,Shape Invariant Recognition of Segmented Human Faces using Eigenfaces,"Shape Invariant Recognition of Segmented Human
Faces using Eigenfaces
Zahid Riaz, Michael Beetz, Bernd Radig
Department of Informatics
Technical University of Munich, Germany"
649eb674fc963ce25e4e8ce53ac7ee20500fb0e3,Toward correlating and solving abstract tasks using convolutional neural networks,
645de797f936cb19c1b8dba3b862543645510544,Deep Temporal Linear Encoding Networks,"Deep Temporal Linear Encoding Networks
Ali Diba1,(cid:63), Vivek Sharma1,(cid:63), and Luc Van Gool1,2
ESAT-PSI, KU Leuven, 2CVL, ETH Z¨urich"
90d735cffd84e8f2ae4d0c9493590f3a7d99daf1,Recognition of Faces using Efficient Multiscale Local Binary Pattern and Kernel Discriminant Analysis in Varying Environment,"Original Research Paper
American Journal of Engineering and Applied Sciences
Recognition of Faces using Efficient Multiscale Local Binary
Pattern and Kernel Discriminant Analysis in Varying
Environment
Sujata G. Bhele and
V.H. Mankar
Department of Electronics Engg, Priyadarshini College of Engg, Nagpur, India
Department of Electronics Engg, Government Polytechnic, Nagpur, India
Article history
Received: 20-06-2017
Revised: 18-07-2017
Accepted: 21-08-2017
Corresponding Author:
Sujata G. Bhele
Department of Electronics
Engg, Priyadarshini College of
Engg, Nagpur, India
Email:"
90fb58eeb32f15f795030c112f5a9b1655ba3624,Face and Iris Recognition in a Video Sequence Using Dbpnn and Adaptive Hamming Distance,"INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND ROBOTICS
www.ijrcar.com
Vol.4 Issue 6, Pg.: 12-27
June 2016
INTERNATIONAL JOURNAL OF
RESEARCH IN COMPUTER
APPLICATIONS AND ROBOTICS
ISSN 2320-7345
FACE AND IRIS RECOGNITION IN A
VIDEO SEQUENCE USING DBPNN AND
ADAPTIVE HAMMING DISTANCE
S. Revathy, 2Mr. L. Ramasethu
PG Scholar, Hindusthan College of Engineering and Technology, Coimbatore, India.
Assistant Professor, Hindusthan College of Engineering and Technology, Coimbatore, India.
Email id:"
902114feaf33deac209225c210bbdecbd9ef33b1,Side-Information based Linear Discriminant Analysis for Face Recognition,"KAN et al.: SIDE-INFORMATION BASED LDA FOR FACE RECOGNITION
Side-Information based Linear
Discriminant Analysis for Face
Recognition
Meina Kan1,2,3
Shiguang Shan1,2
Dong Xu3
Xilin Chen1,2
Digital Media Research Center,
Institute of Computing
Technology, CAS, Beijing, China
Key Laboratory of Intelligent
Information Processing, Chinese
Academy of Sciences, Beijing,
China
School of Computer Engineering,
Nanyang Technological
University, Singapore"
90cb074a19c5e7d92a1c0d328a1ade1295f4f311,Fully Automatic Upper Facial Action Recognition,"MIT. Media Laboratory Affective Computing Technical Report #571
Appears in IEEE International Workshop on Analysis and Modeling of Faces and Gestures , Oct 2003
Fully Automatic Upper Facial Action Recognition
Ashish Kapoor Yuan Qi Rosalind W. Picard
MIT Media Laboratory
Cambridge, MA 02139"
907475a4febf3f1d4089a3e775ea018fbec895fe,Statistical modeling for facial expression analysis and synthesis,"STATISTICAL MODELING FOR FACIAL EXPRESSION ANALYSIS AND SYNTHESIS
Bouchra Abboud, Franck Davoine, Mˆo Dang
Heudiasyc Laboratory, CNRS, University of Technology of Compi`egne.
BP 20529, 60205 COMPIEGNE Cedex, FRANCE.
E-mail:"
9028fbbd1727215010a5e09bc5758492211dec19,Solving the Uncalibrated Photometric Stereo Problem Using Total Variation,"Solving the Uncalibrated Photometric Stereo
Problem using Total Variation
Yvain Qu´eau1, Fran¸cois Lauze2, and Jean-Denis Durou1
IRIT, UMR CNRS 5505, Toulouse, France
Dept. of Computer Science, Univ. of Copenhagen, Denmark"
bff77a3b80f40cefe79550bf9e220fb82a74c084,Facial Expression Recognition Based on Local Binary Patterns and Local Fisher Discriminant Analysis,"Facial Expression Recognition Based on Local Binary Patterns and
Local Fisher Discriminant Analysis
SHIQING ZHANG 1, XIAOMING ZHAO 2, BICHENG LEI 1
School of Physics and Electronic Engineering
Taizhou University
Taizhou 318000
CHINA
2Department of Computer Science
Taizhou University
Taizhou 318000
CHINA"
bf1e0279a13903e1d43f8562aaf41444afca4fdc,Different Viewpoints of Recognizing Fleeting Facial Expressions with DWT,"International Research Journal of Engineering and Technology (IRJET)       e-ISSN: 2395-0056
Volume: 04 Issue: 10 | Oct -2017                     www.irjet.net                                                                 p-ISSN: 2395-0072
Different Viewpoints of Recognizing Fleeting Facial Expressions with
VAIBHAV SHUBHAM1, MR. SANJEEV SHRIVASTAVA2, DR. MOHIT GANGWAR3
information
to  get  desired
information
Introduction
---------------------------------------------------------------------***---------------------------------------------------------------------"
bf4825474673246ae855979034c8ffdb12c80a98,"UNIVERSITY OF CALIFORNIA RIVERSIDE Active Learning in Multi-Camera Networks, With Applications in Person Re-Identification A Dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Electrical Engineering","UNIVERSITY OF CALIFORNIA
RIVERSIDE
Active Learning in Multi-Camera Networks, With Applications in Person
Re-Identification
A Dissertation submitted in partial satisfaction
of the requirements for the degree of
Doctor of Philosophy
Electrical Engineering
Abir Das
December 2015
Dissertation Committee:
Professor Amit K. Roy-Chowdhury, Chairperson
Professor Anastasios Mourikis
Professor Walid Najjar"
bf5940d57f97ed20c50278a81e901ae4656f0f2c,Query-Free Clothing Retrieval via Implicit Relevance Feedback,"Query-free Clothing Retrieval via Implicit
Relevance Feedback
Zhuoxiang Chen, Zhe Xu, Ya Zhang, Member, IEEE, and Xiao Gu"
bfb98423941e51e3cd067cb085ebfa3087f3bfbe,Sparseness helps: Sparsity Augmented Collaborative Representation for Classification,"Sparseness helps: Sparsity Augmented
Collaborative Representation for Classification
Naveed Akhtar, Faisal Shafait, and Ajmal Mian"
d3b73e06d19da6b457924269bb208878160059da,Implementation of an Automated Smart Home Control for Detecting Human Emotions via Facial Detection,"Proceedings of the 5th International Conference on Computing and Informatics, ICOCI 2015
1-13 August, 2015 Istanbul, Turkey. Universiti Utara Malaysia (http://www.uum.edu.my )
Paper No.
IMPLEMENTATION OF AN AUTOMATED SMART HOME
CONTROL FOR DETECTING HUMAN EMOTIONS VIA FACIAL
DETECTION
Lim Teck Boon1, Mohd Heikal Husin2, Zarul Fitri Zaaba3 and Mohd Azam
Osman4
Universiti Sains Malaysia, Malaysia,
Universiti Sains Malaysia, Malaysia,
Universiti Sains Malaysia, Malaysia,
Universiti Sains Malaysia, Malaysia,"
d3d71a110f26872c69cf25df70043f7615edcf92,Learning Compact Feature Descriptor and Adaptive Matching Framework for Face Recognition,"Learning Compact Feature Descriptor and Adaptive
Matching Framework for Face Recognition
Zhifeng Li, Senior Member, IEEE, Dihong Gong, Xuelong Li, Fellow, IEEE, and Dacheng Tao, Fellow, IEEE
improvements"
d3b18ba0d9b247bfa2fb95543d172ef888dfff95,Learning and Using the Arrow of Time,"Learning and Using the Arrow of Time
Donglai Wei1, Joseph Lim2, Andrew Zisserman3 and William T. Freeman4,5
Harvard University 2University of Southern California
University of Oxford 4Massachusetts Institute of Technology 5Google Research
Figure 1: Seeing these ordered frames from videos, can you tell whether each video is playing forward or backward? (answer
elow1). Depending on the video, solving the task may require (a) low-level understanding (e.g. physics), (b) high-level
reasoning (e.g. semantics), or (c) familiarity with very subtle effects or with (d) camera conventions. In this work, we learn
nd exploit several types of knowledge to predict the arrow of time automatically with neural network models trained on
large-scale video datasets."
d309e414f0d6e56e7ba45736d28ee58ae2bad478,Efficient Two-Stream Motion and Appearance 3 D CNNs for Video Classification,"Efficient Two-Stream Motion and Appearance 3D CNNs for
Video Classification
Ali Diba
ESAT-KU Leuven
Ali Pazandeh
Sharif UTech
Luc Van Gool
ESAT-KU Leuven, ETH Zurich"
d3f5a1848b0028d8ab51d0b0673732cad2e3c8c9,STAIR Actions: A Video Dataset of Everyday Home Actions,
d3c004125c71942846a9b32ae565c5216c068d1e,Recognizing Age-Separated Face Images: Humans and Machines,"RESEARCH ARTICLE
Recognizing Age-Separated Face Images:
Humans and Machines
Daksha Yadav1, Richa Singh2, Mayank Vatsa2*, Afzel Noore1
. West Virginia University, Morgantown, West Virginia, United States of America, 2. IIIT Delhi, New Delhi,
Delhi, India"
d350a9390f0818703f886138da27bf8967fe8f51,Lighting design for portraits with a virtual light stage,"LIGHTING DESIGN FOR PORTRAITS WITH A VIRTUAL LIGHT STAGE
Davoud Shahlaei, Marcel Piotraschke, Volker Blanz
Institute for Vision and Graphics, University of Siegen, Germany"
d33fcdaf2c0bd0100ec94b2c437dccdacec66476,Neurons With Paraboloid Decision Boundaries for Improved Neural Network Classification Performance.,"Neurons with Paraboloid Decision Boundaries for
Improved Neural Network Classification
Performance
Nikolaos Tsapanos, Anastasios Tefas, Member, IEEE, Nikolaos Nikolaidis, Member, IEEE, and
Ioannis Pitas, Fellow, IEEE"
d46b790d22cb59df87f9486da28386b0f99339d3,Learning Face Deblurring Fast and Wide,"Learning Face Deblurring Fast and Wide
Meiguang Jin
University of Bern
Switzerland
Michael Hirsch†
Amazon Research
Germany
Paolo Favaro
University of Bern
Switzerland"
d41c11ebcb06c82b7055e2964914b9af417abfb2,CDI-Type I: Unsupervised and Weakly-Supervised Discovery of Facial Events,"CDI-Type I: Unsupervised and Weakly-Supervised
Introduction
Discovery of Facial Events
The face is one of the most powerful channels of nonverbal communication. Facial expression has been a
focus of emotion research for over a hundred years [12]. It is central to several leading theories of emotion
[18, 31, 54] and has been the focus of at times heated debate about issues in emotion science [19, 24, 50].
Facial expression figures prominently in research on almost every aspect of emotion, including psychophys-
iology [40], neural correlates [20], development [11], perception [4], addiction [26], social processes [30],
depression [49] and other emotion disorders [55], to name a few. In general, facial expression provides cues
bout emotional response, regulates interpersonal behavior, and communicates aspects of psychopathology.
Because of its importance to behavioral science and the emerging fields of computational behavior
science, perceptual computing, and human-robot interaction, significant efforts have been applied toward
developing algorithms that automatically detect facial expression. With few exceptions, previous work on
facial expression relies on supervised approaches to learning (i.e. event categories are defined in advance
in labeled training data). While supervised learning has important advantages, two critical limitations may
e noted. One, because labeling facial expression is highly labor intensive, progress in automated facial
expression recognition and analysis is slowed. For the most detailed and comprehensive labeling or coding
systems, such as Facial Action Coding System (FACS), three to four months is typically required to train
coder (’coding’ refers to the labeling of video using behavioral descriptors). Once trained, each minute
of video may require 1 hour or more to code [9]. No wonder relatively few databases are yet available,"
d444368421f456baf8c3cb089244e017f8d32c41,CNN for IMU assisted odometry estimation using velodyne LiDAR,"CNN for IMU Assisted Odometry Estimation using Velodyne LiDAR
Martin Velas, Michal Spanel, Michal Hradis, and Adam Herout"
d4885ca24189b4414031ca048a8b7eb2c9ac646c,"Efficient Facial Representations for Age, Gender and Identity Recognition in Organizing Photo Albums using Multi-output CNN","Ef‌f‌icient Facial Representations for Age, Gender
nd Identity Recognition in Organizing Photo
Albums using Multi-output CNN
Andrey V. Savchenko
Samsung-PDMI Joint AI Center, St. Petersburg Department of Steklov Institute of
Mathematics
National Research University Higher School of Economics
Nizhny Novgorod, Russia"
d4001826cc6171c821281e2771af3a36dd01ffc0,Modélisation de contextes pour l'annotation sémantique de vidéos. (Context based modeling for video semantic annotation),"Modélisation de contextes pour l’annotation sémantique
de vidéos
Nicolas Ballas
To cite this version:
Nicolas Ballas. Modélisation de contextes pour l’annotation sémantique de vidéos. Autre [cs.OH].
Ecole Nationale Supérieure des Mines de Paris, 2013. Français. <NNT : 2013ENMP0051>. <pastel-
00958135>
HAL Id: pastel-00958135
https://pastel.archives-ouvertes.fr/pastel-00958135
Submitted on 11 Mar 2014
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d’enseignement et de"
d458c49a5e34263c95b3393386b5d76ba770e497,A Comparative Analysis of Gender Classification Techniques,"Middle-East Journal of Scientific Research 20 (1): 01-13, 2014
ISSN 1990-9233
© IDOSI Publications, 2014
DOI: 10.5829/idosi.mejsr.2014.20.01.11434
A Comparative Analysis of Gender Classification Techniques
Sajid Ali Khan, Maqsood Ahmad, Muhammad Nazir and Naveed Riaz
Shaheed Zulfikar Ali Bhutto Institute of Science and Technology, Islamabad, Pakistan"
d4e669d5d35fa0ca9f8d9a193c82d4153f5ffc4e,A Lightened CNN for Deep Face Representation,"A Lightened CNN for Deep Face Representation
Xiang Wu
School of Computer and Communication Engineering
University of Science and Technology Beijing, Beijing, China
Ran He, Zhenan Sun
National Laboratory of Pattern Recognition
Institute of Automation Chinese Academy of Sciences, Beijing, China
{rhe,"
d4b88be6ce77164f5eea1ed2b16b985c0670463a,A Survey of Different 3D Face Reconstruction Methods,"TECHNICAL REPORT JAN.15.2016
A Survey of Different 3D Face Reconstruction
Methods
Amin Jourabloo
Department of Computer Science and Engineering"
d44ca9e7690b88e813021e67b855d871cdb5022f,"Selecting, Optimizing and Fusing 'Salient' Gabor Features for Facial Expression Recognition","QUT Digital Repository:
http://eprints.qut.edu.au/
Zhang, Ligang and Tjondronegoro, Dian W. (2009) Selecting, optimizing and
fusing ‘salient’ Gabor features for facial expression recognition. In: Neural
Information Processing (Lecture Notes in Computer Science), 1-5 December
009, Hotel Windsor Suites Bangkok, Bangkok.
©  Copyright 2009 Springer-Verlag GmbH Berlin Heidelberg"
bafb8812817db7445fe0e1362410a372578ec1fc,Image-Quality-Based Adaptive Face Recognition,"Image-Quality-Based Adaptive Face Recognition
Harin Sellahewa and Sabah A. Jassim"
ba99c37a9220e08e1186f21cab11956d3f4fccc2,A Fast Factorization-Based Approach to Robust PCA,"A Fast Factorization-based Approach to Robust PCA
Department of Computer Science, Southern Illinois University,Carbondale, IL 62901 USA
Chong Peng, Zhao Kang, and Qiang Cheng
Email:"
ba816806adad2030e1939450226c8647105e101c,MindLAB at the THUMOS Challenge,"MindLAB at the THUMOS Challenge
Fabi´an P´aez
Jorge A. Vanegas
Fabio A. Gonz´alez
MindLAB Research Group
MindLAB Research Group
MindLAB Research Group
Bogot´a, Colombia
Bogot´a, Colombia
Bogot´a, Colombia"
badcd992266c6813063c153c41b87babc0ba36a3,Recent Advances in Object Detection in the Age of Deep Convolutional Neural Networks,"Recent Advances in Object Detection in the Age
of Deep Convolutional Neural Networks
Shivang Agarwal(∗
,1), Jean Ogier du Terrail(∗
,1,2), Fr´ed´eric Jurie(1)
(∗) equal contribution
(1)Normandie Univ, UNICAEN, ENSICAEN, CNRS
(2)Safran Electronics and Defense
September 11, 2018"
ba8a99d35aee2c4e5e8a40abfdd37813bfdd0906,Uporaba emotivno pogojenega računalništva v priporočilnih sistemih,"ELEKTROTEHNI ˇSKI VESTNIK 78(1-2): 12–17, 2011
EXISTING SEPARATE ENGLISH EDITION
Uporaba emotivno pogojenega raˇcunalniˇstva v
priporoˇcilnih sistemih
Marko Tkalˇciˇc, Andrej Koˇsir, Jurij Tasiˇc
Univerza v Ljubljani, Fakulteta za elektrotehniko, Trˇzaˇska 25, 1000 Ljubljana, Slovenija
Univerza v Ljubljani, Fakulteta za raˇcunalniˇstvo in informatiko, Trˇzaˇska 25, 1000 Ljubljana, Slovenija
E-poˇsta:
Povzetek. V ˇclanku predstavljamo rezultate treh raziskav, vezanih na izboljˇsanje delovanja multimedijskih
priporoˇcilnih sistemov s pomoˇcjo metod emotivno pogojenega raˇcunalniˇstva (ang. affective computing).
Vsebinski priporoˇcilni sistem smo izboljˇsali s pomoˇcjo metapodatkov, ki opisujejo emotivne odzive uporabnikov.
Pri skupinskem priporoˇcilnem sistemu smo dosegli znaˇcilno izboljˇsanje v obmoˇcju hladnega zagona z uvedbo
nove mere podobnosti, ki temelji na osebnostnem modelu velikih pet (ang. five factor model). Razvili smo tudi
sistem za neinvazivno oznaˇcevanje vsebin z emotivnimi parametri, ki pa ˇse ni zrel za uporabo v priporoˇcilnih
sistemih.
Kljuˇcne besede: priporoˇcilni sistemi, emotivno pogojeno raˇcunalniˇstvo, strojno uˇcenje, uporabniˇski profil,
emocije
Uporaba emotivnega raˇcunalniˇstva v priporoˇcilnih
sistemih
In this paper we present the results of three investigations of"
badd371a49d2c4126df95120902a34f4bee01b00,Parallel Separable 3D Convolution for Video and Volumetric Data Understanding,"GONDA, WEI, PARAG, PFISTER: PARALLEL SEPARABLE 3D CONVOLUTION
Parallel Separable 3D Convolution for Video
nd Volumetric Data Understanding
Harvard John A. Paulson School of
Engineering and Applied Sciences
Camabridge MA, USA
Felix Gonda
Donglai Wei
Toufiq Parag
Hanspeter Pfister"
a0f94e9400938cbd05c4b60b06d9ed58c3458303,Value-Directed Human Behavior Analysis from Video Using Partially Observable Markov Decision Processes,"Value-Directed Human Behavior Analysis
from Video Using Partially Observable
Markov Decision Processes
Jesse Hoey and James J. Little, Member, IEEE"
a022eff5470c3446aca683eae9c18319fd2406d5,Deep learning for semantic description of visual human traits. (Apprentissage profond pour la description sémantique des traits visuels humains),"017-ENST-0071
EDITE - ED 130
Doctorat ParisTech
T H È S E
pour obtenir le grade de docteur délivré par
TÉLÉCOM ParisTech
Spécialité « SIGNAL et IMAGES »
présentée et soutenue publiquement par
Grigory ANTIPOV
le 15 décembre 2017
Apprentissage Profond pour la Description Sémantique des Traits
Visuels Humains
Directeur de thèse : Jean-Luc DUGELAY
Co-encadrement de la thèse : Moez BACCOUCHE
Mme Bernadette DORIZZI, PRU, Télécom SudParis
Mme Jenny BENOIS-PINEAU, PRU, Université de Bordeaux
M. Christian WOLF, MC/HDR, INSA de Lyon
M. Patrick PEREZ, Chercheur/HDR, Technicolor Rennes
M. Moez BACCOUCHE, Chercheur/Docteur, Orange Labs Rennes
M. Jean-Luc DUGELAY, PRU, Eurecom Sophia Antipolis"
a0c37f07710184597befaa7e6cf2f0893ff440e9,Fast Retinomorphic Event Stream for Video Recognition and Reinforcement Learning,
a0fd85b3400c7b3e11122f44dc5870ae2de9009a,Learning Deep Representation for Face Alignment with Auxiliary Attributes,"Learning Deep Representation for Face
Alignment with Auxiliary Attributes
Zhanpeng Zhang, Ping Luo, Chen Change Loy, Member, IEEE and Xiaoou Tang, Fellow, IEEE"
a0dfb8aae58bd757b801e2dcb717a094013bc178,Reconocimiento de expresiones faciales con base en la dinámica de puntos de referencia faciales,"Reconocimiento de expresiones faciales con base
en la din´amica de puntos de referencia faciales
E. Morales-Vargas, C.A. Reyes-Garcia, Hayde Peregrina-Barreto
Instituto Nacional de Astrof´ısica ´Optica y Electr´onica,
Divisi´on de Ciencias Computacionales, Tonantzintla, Puebla,
M´exico
Resumen. Las expresiones faciales permiten a las personas comunicar
emociones, y es pr´acticamente lo primero que observamos al interactuar
on alguien. En el ´area de computaci´on, el reconocimiento de expresiones
faciales es importante debido a que su an´alisis tiene aplicaci´on directa en
´areas como psicolog´ıa, medicina, educaci´on, entre otras. En este articulo
se presenta el proceso de dise˜no de un sistema para el reconocimiento de
expresiones faciales utilizando la din´amica de puntos de referencia ubi-
ados en el rostro, su implementaci´on, experimentos realizados y algunos
de los resultados obtenidos hasta el momento.
Palabras clave: Expresiones faciales, clasificaci´on, m´aquinas de soporte
vectorial,modelos activos de apariencia.
Facial Expressions Recognition Based on Facial
Landmarks Dynamics"
a03cfd5c0059825c87d51f5dbf12f8a76fe9ff60,Simultaneous Learning and Alignment: Multi-Instance and Multi-Pose Learning,"Simultaneous Learning and Alignment:
Multi-Instance and Multi-Pose Learning?
Boris Babenko1 Piotr Doll´ar1,2
Zhuowen Tu3
Serge Belongie1,2
Comp. Science & Eng.
Univ. of CA, San Diego
Electrical Engineering
California Inst. of Tech.
Lab of Neuro Imaging
Univ. of CA, Los Angeles"
a090d61bfb2c3f380c01c0774ea17929998e0c96,On the dimensionality of video bricks under varying illumination,"On the Dimensionality of Video Bricks under Varying Illumination
Beijing Lab of Intelligent Information Technology, School of Computer Science,
Youdong Zhao, Xi Song, Yunde Jia
Beijing Institute of Technology, Beijing 100081, PR China
{zyd458, songxi,"
a000149e83b09d17e18ed9184155be140ae1266e,Action Recognition in Realistic Sports Videos,"Chapter 9
Action Recognition in Realistic
Sports Videos
Khurram Soomro and Amir R. Zamir"
a01f9461bc8cf8fe40c26d223ab1abea5d8e2812,Facial Age Estimation Through the Fusion of Texture and Local Appearance Descriptors,"Facial Age Estimation Through the Fusion of Texture
nd local appearance Descriptors
Ivan Huerta1, Carles Fern´andez2, and Andrea Prati1
DPDCE, University IUAV, Santa Croce 1957, 30135 Venice, Italy
Herta Security, Pau Claris 165 4-B, 08037 Barcelona, Spain"
a702fc36f0644a958c08de169b763b9927c175eb,Facial expression recognition using Hough forest,"FACIAL EXPRESSION RECOGNITION USING HOUGH FOREST
Chi-Ting Hsu1, Shih-Chung Hsu1, and Chung-Lin Huang1,2
.  Department of Electrical Engineering, National Tsing-Hua University, Hsin-Chu, Taiwan
Email:
.  Department of Applied Informatics and Multimedia, Asia University, Taichung, Taiwan"
a7267bc781a4e3e79213bb9c4925dd551ea1f5c4,Proceedings of eNTERFACE 2015 Workshop on Intelligent Interfaces,"Proceedings of eNTERFACE’15
The 11th Summer Workshop
on Multimodal Interfaces
August 10th - September 4th, 2015
Numediart Institute, University of Mons
Mons, Belgium"
a784a0d1cea26f18626682ab108ce2c9221d1e53,Anchored Regression Networks Applied to Age Estimation and Super Resolution,"Anchored Regression Networks applied to Age Estimation and Super Resolution
Eirikur Agustsson
D-ITET, ETH Zurich
Switzerland
Radu Timofte
D-ITET, ETH Zurich
Merantix GmbH
Luc Van Gool
D-ITET, ETH Zurich
ESAT, KU Leuven"
a77e9f0bd205a7733431a6d1028f09f57f9f73b0,Multimodal feature fusion for CNN-based gait recognition: an empirical comparison,"Multimodal feature fusion for CNN-based gait recognition: an
empirical comparison
F.M. Castroa,, M.J. Mar´ın-Jim´enezb, N. Guila, N. P´erez de la Blancac
Department of Computer Architecture, University of Malaga, Spain, 29071
Department of Computing and Numerical Analysis, University of Cordoba, Spain, 14071
Department of Computer Science and Artificial Intelligence, University of Granada, Spain, 18071"
a7d23c699a5ae4ad9b8a5cbb8c38e5c3b5f5fb51,A Summary of literature review : Face Recognition,"Postgraduate Annual Research Seminar 2007 (3-4 July 2007)
A Summary of literature review : Face Recognition
Kittikhun Meethongjan & Dzulkifli Mohamad
Faculty of Computer Science & Information System,
University Technology of Malaysia, 81310 Skudai, Johor, Malaysia."
a7664247a37a89c74d0e1a1606a99119cffc41d4,Modal Consistency based Pre-Trained Multi-Model Reuse,Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17)
a7a6eb53bee5e2224f2ecd56a14e3a5a717e55b9,Face Recognition Using Multi-viewpoint Patterns for Robot Vision,"1th International Symposium of Robotics Research (ISRR2003), pp.192-201, 2003
Face Recognition Using Multi-viewpoint Patterns for
Robot Vision
Kazuhiro Fukui and Osamu Yamaguchi
Corporate Research and Development Center, TOSHIBA Corporation
, KomukaiToshiba-cho, Saiwai-ku, Kawasaki 212-8582 Japan"
a758b744a6d6962f1ddce6f0d04292a0b5cf8e07,"Study on Human Face Recognition under Invariant Pose, Illumination and Expression using LBP, LoG and SVM","ISSN XXXX XXXX © 2017 IJESC
Research Article                                                                                                                              Volume 7 Issue No.4
Study on Human Face Recognition under Invariant Pose, Illumination
nd Expression using LBP, LoG and SVM
Amrutha
Depart ment of Co mputer Science & Engineering
Mangalore Institute of Technology & Engineering , Moodabidri, Mangalore, India
INTRODUCTION
RELATED WORK
Abstrac t:
Face  recognition  system  uses  human  face  for  the  identification  of  the   user.  Face  recognition  is  a  difficu lt  task  there  is  no  unique
method  that  provide  accurate  an  accurate  and  effic ient  solution  in  all  the  situations  like  the  face  image  with  differen t  pose ,
illu mination  and  exp ression.  Local  Binary  Pattern  (LBP)  and  Laplac ian  of  Gaussian  (Lo G)  operators.  Support  Vector  Machine
lassifier  is  used  to  recognize  the  human  face.  The  Lo G  algorith m  is  used  to preprocess the  image  to  detect  the  edges of  the  face
image to get the image information. The  LBP operator divides the face  image into several blocks to generate the features informat ion
on  pixe l  level  by  creating  LBP  labels  for  all  the  blocks  of  image  is  obtained  by  concatenating  all  the  individual  local  histo grams.
Support Vector Machine classifier  (SVM )  is used to classify t he image. The a lgorith m performances is verified under the constraints
like illu mination, e xp ression and pose variation
Ke ywor ds:  Face  Recognition,  Local  Binary  Pattern,  Laplac ian  of  Gaussian,  histogram,  illu mination,  pose  angle,  exp ression
variations, SVM ."
a75ee7f4c4130ef36d21582d5758f953dba03a01,Human face attributes prediction with Deep Learning,"DD2427 Final Project Report
Mohamed Abdulaziz Ali Haseeb
DD2427 Final Project Report
Human face attributes prediction with Deep
Learning
Mohamed Abdulaziz Ali Haseeb"
a775da3e6e6ea64bffab7f9baf665528644c7ed3,Human Face Pose Estimation based on Feature Extraction Points,"International Journal of Computer Applications (0975 – 8887)
Volume 142 – No.9, May 2016
Human Face Pose Estimation based on Feature
Extraction Points
Guneet Bhullar
Research scholar,
Department of ECE
SBSSTC, Moga Road,
Ferozepur, Punjab, India"
a703d51c200724517f099ee10885286ddbd8b587,Fuzzy neural networks(FNN)-based approach for personalized facial expression recognition with novel feature selection method,"Fuzzy Neural Networks(FNN)-based Approach for
Personalized Facial Expression Recognition with
Novel Feature Selection Method
Dae-Jin Kim and Zeungnam Bien
Div. of EE, Dept. of EECS, KAIST
73-1 Guseong-dong, Yuseong-gu, Daejeon 305-701, Korea
Kwang-Hyun Park
Human-friendly Welfare Robotic System Engineering Research Center, KAIST
73-1 Guseong-dong, Yuseong-gu, Daejeon 305-701, Korea"
b871d1b8495025ff8a6255514ed39f7765415935,Application of Completed Local Binary Pattern for Facial Expression Recognition on Gabor Filtered Facial Images,"Application of Completed Local Binary Pattern for Facial Expression
Recognition on Gabor Filtered Facial Images
Tanveer Ahsan, 2Rifat Shahriar, *3Uipil Chong
Dept. of Electrical and Computer Engineering, University of Ulsan, Ulsan, Republic of Korea"
b8dba0504d6b4b557d51a6cf4de5507141db60cf,Comparing Performances of Big Data Stream Processing Platforms with RAM3S,"Comparing Performances of Big Data Stream
Processing Platforms with RAM3S"
b89862f38fff416d2fcda389f5c59daba56241db,A Web Survey for Facial Expressions Evaluation,"A Web Survey for Facial Expressions Evaluation
Matteo Sorci
Gianluca Antonini
Jean-Philippe Thiran
Ecole Polytechnique Federale de Lausanne
Signal Processing Institute
Ecublens, 1015 Lausanne, Switzerland
Ecole Polytechnique Federale de Lausanne, Operation Research Group
Michel Bierlaire
Ecublens, 1015 Lausanne, Switzerland
June 9, 2008"
b8f3f6d8f188f65ca8ea2725b248397c7d1e662d,Selfie Detection by Synergy-Constraint Based Convolutional Neural Network,"Selfie Detection by Synergy-Constriant Based
Convolutional Neural Network
Yashas Annadani, Vijaykrishna Naganoor, Akshay Kumar Jagadish and Krishnan Chemmangat
Electrical and Electronics Engineering, NITK-Surathkal, India."
b85580ff2d8d8be0a2c40863f04269df4cd766d9,HCMUS team at the Multimodal Person Discovery in Broadcast TV Task of MediaEval 2016,"HCMUS team at the Multimodal Person Discovery in
Broadcast TV Task of MediaEval 2016
Vinh-Tiep Nguyen, Manh-Tien H. Nguyen, Quoc-Huu Che, Van-Tu Ninh,
Tu-Khiem Le, Thanh-An Nguyen, Minh-Triet Tran
Faculty of Information Technology
University of Science, Vietnam National University-Ho Chi Minh city
{nhmtien, cqhuu, nvtu,"
b8a829b30381106b806066d40dd372045d49178d,A Probabilistic Framework for Joint Pedestrian Head and Body Orientation Estimation,"A Probabilistic Framework for Joint Pedestrian Head
nd Body Orientation Estimation
Fabian Flohr, Madalin Dumitru-Guzu, Julian F. P. Kooij, and Dariu M. Gavrila"
b1d89015f9b16515735d4140c84b0bacbbef19ac,Too Far to See? Not Really!—Pedestrian Detection With Scale-Aware Localization Policy,"Too Far to See? Not Really!
— Pedestrian Detection with Scale-aware
Localization Policy
Xiaowei Zhang, Li Cheng, Bo Li, and Hai-Miao Hu"
b14b672e09b5b2d984295dfafb05604492bfaec5,Apprentissage de Modèles pour la Classification et la Recherche d ’ Images Learning Image Classification and Retrieval Models,LearningImageClassificationandRetrievalModelsThomasMensink
b1a3b19700b8738b4510eecf78a35ff38406df22,Automatic Analysis of Facial Actions: A Survey,"This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TAFFC.2017.2731763, IEEE
Transactions on Affective Computing
JOURNAL OF LATEX CLASS FILES, VOL. 13, NO. 9, SEPTEMBER 2014
Automatic Analysis of Facial Actions: A Survey
Brais Martinez, Member, IEEE, Michel F. Valstar, Senior Member, IEEE, Bihan Jiang,
nd Maja Pantic, Fellow, IEEE"
b166ce267ddb705e6ed855c6b679ec699d62e9cb,Sample group and misplaced atom dictionary learning for face recognition,"Turk J Elec Eng & Comp Sci
(2017) 25: 4421 { 4430
⃝ T (cid:127)UB_ITAK
doi:10.3906/elk-1702-49
Sample group and misplaced atom dictionary learning for face recognition
Meng WANG1;2, Zhengping HU1;(cid:3)
, Zhe Sun1, Mei ZHU2, Mei SUN2
Department of Information Science & Engineering, Faculty of Electronics & Communication, Yanshan University,
Department of Physics & Electronics Engineering, Faculty of Electronics & Communication, Taishan University,
Qinhuangdao, P.R. China
Tai’an, P.R. China
Received: 04.02.2017
(cid:15)
Accepted/Published Online: 01.06.2017
(cid:15)
Final Version: 05.10.2017"
b15a06d701f0a7f508e3355a09d0016de3d92a6d,Facial contrast is a cue for perceiving health from the face.,"Running head:  FACIAL CONTRAST LOOKS HEALTHY
Facial contrast is a cue for perceiving health from the face
Richard Russell1, Aurélie Porcheron2,3, Jennifer R. Sweda1, Alex L. Jones1, Emmanuelle
Mauger2, Frederique Morizot2
Gettysburg College, Gettysburg, PA, USA
CHANEL Recherche et Technologie, Chanel PB
Université Grenoble Alpes
Author Note
Richard Russell, Jennifer R. Sweda, and Alex L. Jones, Department of Psychology,
Gettysburg College.  Aurélie Porcheron, Emmanuelle Mauger, and Frederique Morizot,
CHANEL Recherche et Technologie, Chanel PB.  Aurélie Porcheron, Laboratoire de
Psychologie et NeuroCognition, Université Grenoble Alpes.
Corresponding author:  Richard Russell, Department of Psychology, Box 407, Gettysburg
College, Gettysburg, PA 17325, USA.  Email:
This is a prepublication copy.  This article may not exactly replicate the authoritative document
published in the APA journal. It is not the copy of record.  The authoritative document can be
found through this DOI: http://psycnet.apa.org/doi/10.1037/xhp0000219"
b1444b3bf15eec84f6d9a2ade7989bb980ea7bd1,Local Directional Relation Pattern for Unconstrained and Robust Face Retrieval,"LOCAL DIRECTIONAL RELATION PATTERN
Local Directional Relation Pattern for
Unconstrained and Robust Face Retrieval
Shiv Ram Dubey, Member, IEEE"
b1451721864e836069fa299a64595d1655793757,Criteria Sliders: Learning Continuous Database Criteria via Interactive Ranking,"Criteria Sliders: Learning Continuous
Database Criteria via Interactive Ranking
James Tompkin,1∗ Kwang In Kim,2∗ Hanspeter Pfister,3 and Christian Theobalt4
Brown University 2University of Bath
Harvard University 4Max Planck Institute for Informatics"
b19e83eda4a602abc5a8ef57467c5f47f493848d,Heat Kernel Based Local Binary Pattern for Face Representation,"JOURNAL OF LATEX CLASS FILES
Heat Kernel Based Local Binary Pattern for
Face Representation
Xi Li†, Weiming Hu†, Zhongfei Zhang‡, Hanzi Wang§"
dde5125baefa1141f1ed50479a3fd67c528a965f,Synthesizing Normalized Faces from Facial Identity Features,"Synthesizing Normalized Faces from Facial Identity Features
Forrester Cole1 David Belanger1,2 Dilip Krishnan1 Aaron Sarna1 Inbar Mosseri1 William T. Freeman1,3
Google, Inc. 2University of Massachusetts Amherst 3MIT CSAIL
{fcole, dbelanger, dilipkay, sarna, inbarm,"
dd8084b2878ca95d8f14bae73e1072922f0cc5da,"Model Distillation with Knowledge Transfer in Face Classification, Alignment and Verification","Model Distillation with Knowledge Transfer from
Face Classification to Alignment and Verification
Chong Wang∗, Xipeng Lan and Yangang Zhang
Beijing Orion Star Technology Co., Ltd. Beijing, China
{chongwang.nlpr, xipeng.lan,"
ddf55fc9cf57dabf4eccbf9daab52108df5b69aa,Methodology and Performance Analysis of 3-D Facial Expression Recognition Using Statistical Shape Representation,"International Journal of Grid and Distributed Computing
Vol. 4, No. 3, September, 2011
Methodology and Performance Analysis of 3-D Facial Expression
Recognition Using Statistical Shape Representation
Wei Quan, Bogdan J. Matuszewski, Lik-Kwan Shark
ADSIP Research Centre, University of Central Lancashire
{WQuan, BMatuszewski1,
Charlie Frowd
School of Psychology, University of Central Lancashire"
ddea3c352f5041fb34433b635399711a90fde0e8,Facial Expression Classification using Visual Cues and Language,"Facial Expression Classification using Visual Cues and Language
Abhishek Kar
Advisor: Dr. Amitabha Mukerjee
Department of Computer Science and Engineering, IIT Kanpur"
ddbd24a73ba3d74028596f393bb07a6b87a469c0,Multi-region Two-Stream R-CNN for Action Detection,"Multi-region two-stream R-CNN
for action detection
Xiaojiang Peng, Cordelia Schmid
Inria(cid:63)"
ddf099f0e0631da4a6396a17829160301796151c,Learning Face Image Quality from Human Assessments,"IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
Learning Face Image Quality from
Human Assessments
Lacey Best-Rowden, Member, IEEE, and Anil K. Jain, Life Fellow, IEEE"
dd0a334b767e0065c730873a95312a89ef7d1c03,Eigenexpressions: Emotion Recognition Using Multiple Eigenspaces,"Eigenexpressions: Emotion Recognition using Multiple
Eigenspaces
Luis Marco-Gim´enez1, Miguel Arevalillo-Herr´aez1, and Cristina Cuhna-P´erez2
University of Valencia. Computing Department,
Burjassot. Valencia 46100, Spain,
Universidad Cat´olica San Vicente M´artir de Valencia (UCV),
Burjassot. Valencia. Spain"
dd8d53e67668067fd290eb500d7dfab5b6f730dd,A Parameter-Free Framework for General Supervised Subspace Learning,"A Parameter-Free Framework for General
Supervised Subspace Learning
Shuicheng Yan, Member, IEEE, Jianzhuang Liu, Senior Member, IEEE, Xiaoou Tang, Senior Member, IEEE,
nd Thomas S. Huang, Life Fellow, IEEE"
ddbb6e0913ac127004be73e2d4097513a8f02d37,Face Detection Using Quantized Skin Color Regions Merging and Wavelet Packet Analysis,"IEEE TRANSACTIONS ON MULTIMEDIA, VOL. 1, NO. 3, SEPTEMBER 1999
Face Detection Using Quantized Skin Color
Regions Merging and Wavelet Packet Analysis
Christophe Garcia and Georgios Tziritas, Member, IEEE"
dc550f361ae82ec6e1a0cf67edf6a0138163382e,Emotion Based Music Player,"ISSN XXXX XXXX © 2018 IJESC
Research Article                                                                                                                           Volume 8 Issue No.3
Vijay Chakole1, Aniket Choudhary2, Kalyani Trivedi3, Kshitija Bhoyar4, Ruchita Bodele5, Sayali Karmore6
Emotion Based Music Player
Professor1, UG Student2, 3, 4, 5, 6
Department of Electronics Engineering
K.D.K. College of Engineering Nagpur, India"
dcb44fc19c1949b1eda9abe998935d567498467d,Ordinal Zero-Shot Learning,"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17)
labelunseen labelFigure1:Supervisionintensityfordifferentlabels.Greenrepre-sentsseenlabelsandredrepresentsunseenlabels.Thegroundtruthlabelofthisinstanceis“Good”,soithasthestrongestsupervisionintensity.Although“Common”isanunseenlabel,itstillhascertainsupervisioninformationbecauseitiscloselyrelatedto“Good”.classifier;[ZhangandSaligrama,2016]learnsajointlatentspaceusingstructuredlearning.Thedifficultyinobtainingthesideinformationorusingothertechniquestoprocessthesideinformationarethemostseriousissuesformanyexistingzero-shotlearningmethods.Fortheattribute-basedmethods,humanexpertsareneededtolabelattributesandthisisverytime-consumingandnoteasytoobtainthediscriminativecategory-levelattributes.Somemethodsdiscoverattributesinteractively[ParikhandGrau-man,2011][Bransonetal.,2010],butthisalsorequiresla-borioushumanparticipation.Althoughmanyalgorithmscandiscoverattribute-relatedconceptsontheWeb[Rohrbachetal.,2010][Bergetal.,2010],theycanalsobebiasedorlackinformationthatiscriticaltoaparticulartask[ParikhandGrauman,2011].Forthetextcorpora-basedmethods,theyfirstrequirealargelanguagecorpora,suchasWikipedia,andthenneedtolearnwordrepresentation[Socheretal.,2013]orusestandardNaturalLanguageProcessing(NLP)techniquestoproduceclassdescriptions[Elhoseinyetal.,2013].Itishardtoguaranteethecorrectnessofsuchclassdescriptionsforzero-shotlearning.Conclusively,althoughsideinforma-tionishelpfulforzero-shotlearning,ithasmanydisadvan-tages.Generatingthesesideinformationisverytediousandsometimeswecannotknowwhichsideinformationistrulywanted.IfwedependonhumanlabororNLPtechniques,noisysideinformationwillbecomealmostinevitableandin-fluencethefinalperformance.Toavoidtheseproblems,itisimportanttosolvezero-shotlearninginwhateverpossiblecasesthathavesomepropertieswecanutilizetoavoidusingsideinformation."
dc2e805d0038f9d1b3d1bc79192f1d90f6091ecb,Face Recognition and Facial Attribute Analysis from Unconstrained Visual Data,
dc974c31201b6da32f48ef81ae5a9042512705fe,Am I Done? Predicting Action Progress in Videos,"Am I done? Predicting Action Progress in Video
Federico Becattini1, Tiberio Uricchio1, Lorenzo Seidenari1,
Alberto Del Bimbo1, and Lamberto Ballan2
Media Integration and Communication Center, Univ. of Florence, Italy
Department of Mathematics “Tullio Levi-Civita”, Univ. of Padova, Italy"
b613b30a7cbe76700855479a8d25164fa7b6b9f1,Identifying User-Specific Facial Affects from Spontaneous Expressions with Minimal Annotation,"Identifying User-Specific Facial Affects from
Spontaneous Expressions with Minimal Annotation
Michael Xuelin Huang, Grace Ngai, Kien A. Hua, Fellow, IEEE, Stephen C.F. Chan, Member, IEEE
nd Hong Va Leong, Member, IEEE Computer Society"
b6f682648418422e992e3ef78a6965773550d36b,"CBMM Memo No . 061 February 8 , 2017 Full interpretation of minimal images","February 8, 2017"
a9791544baa14520379d47afd02e2e7353df87e5,The Need for Careful Data Collection for Pattern Recognition in Digital Pathology,"Technical Note
The Need for Careful Data Collection for Pattern Recognition in
Digital Pathology
Raphaël Marée1
Department of Electrical Engineering and Computer Science, Montefiore Institute, University of Liège, 4000 Liège, Belgium
Received: 08 December 2016
Accepted: 15 March 2017
Published: 10 April 2017"
a9eb6e436cfcbded5a9f4b82f6b914c7f390adbd,A Model for Facial Emotion Inference Based on Planar Dynamic Emotional Surfaces,"(IJARAI) International Journal of Advanced Research in Artificial Intelligence,
Vol. 5, No.6, 2016
A Model for Facial Emotion Inference Based on
Planar Dynamic Emotional Surfaces
Ruivo,  J.  P.  P.
Escola  Polit´ecnica
Negreiros,  T.
Escola  Polit´ecnica
Barretto,  M.  R.  P.
Escola  Polit´ecnica
Tinen,  B.
Escola  Polit´ecnica
Universidade de S˜ao Paulo
Universidade de S˜ao Paulo
Universidade de S˜ao Paulo
Universidade de S˜ao Paulo
S˜ao Paulo, Brazil
S˜ao Paulo, Brazil
S˜ao Paulo, Brazil
S˜ao Paulo, Brazil"
a955033ca6716bf9957b362b77092592461664b4,Video Based Face Recognition Using Artificial Neural Network,"ISSN(Online): 2320-9801
ISSN (Print):  2320-9798
International Journal of Innovative Research in Computer
nd Communication Engineering
(An ISO 3297: 2007 Certified Organization)
Video Based Face Recognition Using Artificial
Vol. 3, Issue 6, June 2015
Neural Network
Santhy Mol T, Neethu Susan Jacob
Pursuing M.Tech, Dept. of CSE, Caarmel Engineering College, MG University, Kerala, India
Assistant Professor, Dept of CSE, Caarmel Engineering College, MG University, Kerala, India"
a956ff50ca958a3619b476d16525c6c3d17ca264,A novel bidirectional neural network for face recognition,"A Novel Bidirectional Neural Network for Face Recognition
JalilMazloum, Ali Jalali and Javad Amiryan
Electrical and Computer Engineering Department
ShahidBeheshti University
Tehran, Iran"
a98316980b126f90514f33214dde51813693fe0d,Collaborations on YouTube: From Unsupervised Detection to the Impact on Video and Channel Popularity,"Collaborations on YouTube: From Unsupervised Detection to the
Impact on Video and Channel Popularity
Christian Koch, Moritz Lode, Denny Stohr, Amr Rizk, Ralf Steinmetz
Multimedia Communications Lab (KOM), Technische Universität Darmstadt, Germany
E-Mail: {Christian.Koch | Denny.Stohr | Amr.Rizk |"
a93781e6db8c03668f277676d901905ef44ae49f,Recent Data Sets on Object Manipulation: A Survey.,"Recent Datasets on Object Manipulation: A Survey
Yongqiang Huang, Matteo Bianchi, Minas Liarokapis and Yu Sun"
a9adb6dcccab2d45828e11a6f152530ba8066de6,Aydınlanma Alt-uzaylarına dayalı Gürbüz Yüz Tanıma Illumination Subspaces based Robust Face Recognition,"Aydınlanma Alt-uzaylarına dayalı Gürbüz Yüz Tanıma
Illumination Subspaces based Robust Face Recognition
D. Kern, H.K. Ekenel, R. Stiefelhagen
Interactive Systems Labs, Universität Karlsruhe (TH)
76131 Karlsruhe, Almanya
web: http://isl.ira.uka.de/face_recognition
Özetçe
yönlerine
ydınlanma
kaynaklanan
sonra,  yüz  uzayı
Bu çalışmada aydınlanma alt-uzaylarına dayalı bir yüz tanıma
sistemi  sunulmuştur.  Bu  sistemde,
ilk  olarak,  baskın
ydınlanma yönleri, bir topaklandırma algoritması kullanılarak
öğrenilmiştir.  Topaklandırma  algoritması  sonucu  önden,  sağ
ve  sol  yanlardan  olmak  üzere  üç  baskın  aydınlanma  yönü
gözlemlenmiştir.  Baskın
karar
-yüzün  görünümündeki"
a95dc0c4a9d882a903ce8c70e80399f38d2dcc89,Review and Implementation of High-Dimensional Local Binary Patterns and Its Application to Face Recognition,"TR-IIS-14-003
Review and Implementation of
High-Dimensional Local Binary
Patterns and Its Application to
Face Recognition
Bor-Chun Chen, Chu-Song Chen, Winston Hsu
July. 24,    2014    ||    Technical Report No. TR-IIS-14-003
http://www.iis.sinica.edu.tw/page/library/TechReport/tr2014/tr14.html"
a9286519e12675302b1d7d2fe0ca3cc4dc7d17f6,Learning to Succeed while Teaching to Fail: Privacy in Closed Machine Learning Systems,"Learning to Succeed while Teaching to Fail:
Privacy in Closed Machine Learning Systems
Jure Sokoli´c, Qiang Qiu, Miguel R. D. Rodrigues, and Guillermo Sapiro"
a949b8700ca6ba96ee40f75dfee1410c5bbdb3db,Instance-Weighted Transfer Learning of Active Appearance Models,"Instance-weighted Transfer Learning of Active Appearance Models
Daniel Haase, Erik Rodner, and Joachim Denzler
Computer Vision Group, Friedrich Schiller University of Jena, Germany
Ernst-Abbe-Platz 2-4, 07743 Jena, Germany"
a92b5234b8b73e06709dd48ec5f0ec357c1aabed,Disjoint Multi-task Learning Between Heterogeneous Human-Centric Tasks,
d50c6d22449cc9170ab868b42f8c72f8d31f9b6c,Dynamic Multi-Task Learning with Convolutional Neural Network,Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17)
d522c162bd03e935b1417f2e564d1357e98826d2,Weakly supervised object extraction with iterative contour prior for remote sensing images,"He et al. EURASIP Journal on Advances in Signal Processing 2013, 2013:19
http://asp.eurasipjournals.com/content/2013/1/19
RESEARCH
Open Access
Weakly supervised object extraction with
iterative contour prior for remote sensing
images
Chu He1,2*, Yu Zhang1, Bo Shi1, Xin Su3, Xin Xu1 and Mingsheng Liao2"
d59f18fcb07648381aa5232842eabba1db52383e,Robust Facial Expression Recognition Using Spatially Localized Geometric Model,"International Conference on Systemics, Cybernetics and Informatics, February 12–15, 2004
ROBUST FACIAL EXPRESSION RECOGNITION USING SPATIALLY
LOCALIZED GEOMETRIC MODEL
Department of Electrical Engineering
Dept. of Computer Sc. and Engg.
Ashutosh Saxena
IIT Kanpur
Kanpur 208016, India
Kanpur 208016, India
Ankit Anand
IIT Kanpur
Prof. Amitabha Mukerjee
Dept. of Computer Sc. and Engg.
IIT Kanpur
Kanpur 208016, India
While  approaches  based  on  3D  deformable  facial  model  have
chieved expression recognition rates of as high as 98% [2], they
re  computationally  inefficient  and  require  considerable  apriori
training  based  on  3D  information,  which  is  often  unavailable.
Recognition  from  2D  images  remains  a  difficult  yet  important"
d588dd4f305cdea37add2e9bb3d769df98efe880,Audio - Visual Authentication System over the Internet Protocol,"Audio-Visual Authentication System over the
Internet Protocol
Yee Wan Wong, Kah Phooi Seng, and Li-Minn Ang
bandoned.
illumination  based
is  developed  with  the  objective  to"
d5444f9475253bbcfef85c351ea9dab56793b9ea,BoxCars: Improving Fine-Grained Recognition of Vehicles using 3-D Bounding Boxes in Traffic Surveillance,"IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
BoxCars: Improving Fine-Grained Recognition
of Vehicles using 3D Bounding Boxes
in Traffic Surveillance
Jakub Sochor, Jakub ˇSpaˇnhel, Adam Herout
in contrast"
d5ab6aa15dad26a6ace5ab83ce62b7467a18a88e,Optimized Structure for Facial Action Unit Relationship Using Bayesian Network,"World Journal of Computer Application and Technology 2(7): 133-138, 2014
DOI: 10.13189/wjcat.2014.020701
http://www.hrpub.org
Optimized Structure for Facial Action Unit Relationship
Using Bayesian Network
Yee Koon Loh*, Shahrel A. Suandi
Intelligent Biometric Group, School of Electrical and Electronic Engineering, Engineering Campus, Universiti Sains Malaysia, Pulau
*Corresponding Author:
Pinang, Malaysia
Copyright © 2014 Horizon Research Publishing All rights reserved."
d56fe69cbfd08525f20679ffc50707b738b88031,Training of multiple classifier systems utilizing partially labeled sequential data sets,"Training of multiple classifier systems utilizing
partially labelled sequences
Martin Schels, Patrick Schillinger, and Friedhelm Schwenker
Ulm University - Department of Neural Information Processing
89069 Ulm - Germany"
d5de42d37ee84c86b8f9a054f90ddb4566990ec0,Asynchronous Temporal Fields for Action Recognition,"Asynchronous Temporal Fields for Action Recognition
Gunnar A. Sigurdsson1∗ Santosh Divvala2,3 Ali Farhadi2,3 Abhinav Gupta1,3
Carnegie Mellon University 2University of Washington 3Allen Institute for Artificial Intelligence
github.com/gsig/temporal-fields/"
d50a40f2d24363809a9ac57cf7fbb630644af0e5,End-to-end Trained CNN Encode-Decoder Networks for Image Steganography,"END-TO-END TRAINED CNN ENCODER-DECODER NETWORKS FOR IMAGE
STEGANOGRAPHY
Atique ur Rehman, Rafia Rahim, Shahroz Nadeem, Sibt ul Hussain
National University of Computer & Emerging Sciences (NUCES-FAST), Islamabad, Pakistan.
Reveal.ai (Recognition, Vision & Learning) Lab"
d5b5c63c5611d7b911bc1f7e161a0863a34d44ea,Extracting Scene-Dependent Discriminant Features for Enhancing Face Recognition under Severe Conditions,"Extracting Scene-dependent Discriminant
Features for Enhancing Face Recognition
under Severe Conditions
Rui Ishiyama and Nobuyuki Yasukawa
Information and Media Processing Research Laboratories, NEC Corporation
753, Shimonumabe, Nakahara-Ku, Kawasaki 211-8666 Japan"
d59404354f84ad98fa809fd1295608bf3d658bdc,Face Synthesis from Visual Attributes via Sketch using Conditional VAEs and GANs,"International Journal of Computer Vision manuscript No.
(will be inserted by the editor)
Face Synthesis from Visual Attributes via Sketch using
Conditional VAEs and GANs
Xing Di · Vishal M. Patel
Received: date / Accepted: date"
d231a81b38fde73bdbf13cfec57d6652f8546c3c,SUPERRESOLUTION TECHNIQUES FOR FACE RECOGNITION FROM VIDEO by Osman,"SUPERRESOLUTION TECHNIQUES
FOR FACE RECOGNITION FROM VIDEO
Osman Gökhan Sezer
B.S., E.E., Boğaziçi University, 2003
Submitted to the Graduate School of Engineering
and Natural Sciences in partially fulfillment of
the requirement for the degree of
Master of Science
Graduate Program in Electronics Engineering and Computer Science
Sabancı University
Spring 2005"
d22785eae6b7503cb16402514fd5bd9571511654,Evaluating Facial Expressions with Different Occlusion around Image Sequence,"Evaluating Facial Expressions with Different
Occlusion around Image Sequence
Ankita Vyas, Ramchand Hablani
Department of Computer Science
Sanghvi Institute of Management & Science
Indore (MP), India
local
INTRODUCTION"
d2eb1079552fb736e3ba5e494543e67620832c52,DeSTNet: Densely Fused Spatial Transformer Networks,"ANNUNZIATA, SAGONAS, CALÌ: DENSELY FUSED SPATIAL TRANSFORMER NETWORKS1
DeSTNet: Densely Fused Spatial
Transformer Networks1
Roberto Annunziata
Christos Sagonas
Jacques Calì
Onfido Research
Finsbury Avenue
London, UK"
d24dafe10ec43ac8fb98715b0e0bd8e479985260,"Effects of Social Anxiety on Emotional Mimicry and Contagion: Feeling Negative, but Smiling Politely","J Nonverbal Behav (2018) 42:81–99
https://doi.org/10.1007/s10919-017-0266-z
O R I G I N A L P A P E R
Effects of Social Anxiety on Emotional Mimicry
nd Contagion: Feeling Negative, but Smiling Politely
Corine Dijk1
Charlotte van Eeuwijk4
• Gerben A. van Kleef2
• Agneta H. Fischer2
• Nexhmedin Morina3
Published online: 25 September 2017
Ó The Author(s) 2017. This article is an open access publication"
d278e020be85a1ccd90aa366b70c43884dd3f798,Learning From Less Data: Diversified Subset Selection and Active Learning in Image Classification Tasks,"Learning From Less Data: Diversified Subset Selection and
Active Learning in Image Classification Tasks
Vishal Kaushal
IIT Bombay
Mumbai, Maharashtra, India
Khoshrav Doctor
AITOE Labs
Mumbai, Maharashtra, India
Suyash Shetty
AITOE Labs
Mumbai, Maharashtra, India
Rishabh Iyer
AITOE Labs
Seattle, Washington, USA
Anurag Sahoo
AITOE Labs
Seattle, Washington, USA
Narsimha Raju
IIT Bombay
Mumbai, Maharashtra, India"
aae742779e8b754da7973949992d258d6ca26216,Robust facial expression classification using shape and appearance features,"Robust Facial Expression Classification Using Shape
nd Appearance Features
S L Happy and Aurobinda Routray
Department of Electrical Engineering,
Indian Institute of Technology Kharagpur, India"
aa52910c8f95e91e9fc96a1aefd406ffa66d797d,Face Recognition System Based on 2dfld and Pca,"FACE RECOGNITION SYSTEM BASED
ON 2DFLD AND PCA
Dr. Sachin D. Ruikar
E&TC Department
Sinhgad Academy of Engineering
Pune, India
Mr. Hulle Rohit Rajiv
ME E&TC [Digital System]
Sinhgad Academy of Engineering
Pune, India"
aafb8dc8fda3b13a64ec3f1ca7911df01707c453,Excitation Backprop for RNNs,"Excitation Backprop for RNNs
Sarah Adel Bargal∗1, Andrea Zunino∗ 2, Donghyun Kim1, Jianming Zhang3,
Vittorio Murino2,4, Stan Sclaroff1
Department of Computer Science, Boston University 2Pattern Analysis & Computer Vision (PAVIS),
Istituto Italiano di Tecnologia 3Adobe Research 4Computer Science Department, Universit`a di Verona
Figure 1: Our proposed framework spatiotemporally highlights/grounds the evidence that an RNN model used in producing a class label
or caption for a given input video. In this example, by using our proposed back-propagation method, the evidence for the activity class
CliffDiving is highlighted in a video that contains CliffDiving and HorseRiding. Our model employs a single backward pass to produce
saliency maps that highlight the evidence that a given RNN used in generating its outputs."
aadfcaf601630bdc2af11c00eb34220da59b7559,Multi-view Hybrid Embedding: A Divide-and-Conquer Approach,"Multi-view Hybrid Embedding:
A Divide-and-Conquer Approach
Jiamiao Xu∗, Shujian Yu∗, Xinge You†, Senior Member, IEEE, Mengjun Leng,
Xiao-Yuan Jing, and C. L. Philip Chen, Fellow, IEEE"
aaa4c625f5f9b65c7f3df5c7bfe8a6595d0195a5,Biometrics in ambient intelligence,"Biometrics in Ambient Intelligence
Massimo Tistarelli§ and Ben Schouten§§"
aae0e417bbfba701a1183d3d92cc7ad550ee59c3,A Statistical Method for 2-D Facial Landmarking,"A Statistical Method for 2-D Facial Landmarking
Hamdi Dibeklio˘glu, Student Member, IEEE, Albert Ali Salah, Member, IEEE, and Theo Gevers, Member, IEEE"
aa577652ce4dad3ca3dde44f881972ae6e1acce7,Deep Attribute Networks,"Deep Attribute Networks
Junyoung Chung
Department of EE, KAIST
Daejeon, South Korea
Donghoon Lee
Department of EE, KAIST
Daejeon, South Korea
Youngjoo Seo
Department of EE, KAIST
Daejeon, South Korea
Chang D. Yoo
Department of EE, KAIST
Daejeon, South Korea"
aa94f214bb3e14842e4056fdef834a51aecef39c,Reconhecimento de padrões faciais: Um estudo,"Reconhecimento de padrões faciais: Um estudo
Alex Lima Silva, Marcos Evandro Cintra
Universidade Federal
Rural do Semi-Árido
Departamento de Ciências Naturais
Mossoró, RN - 59625-900
Email:
Resumo—O reconhecimento facial tem sido utilizado em di-
versas áreas para identificação e autenticação de usuários. Um
dos principais mercados está relacionado a segurança, porém há
uma grande variedade de aplicações relacionadas ao uso pessoal,
onveniência, aumento de produtividade, etc. O rosto humano
possui um conjunto de padrões complexos e mutáveis. Para
reconhecer esses padrões, são necessárias técnicas avançadas de
reconhecimento de padrões capazes, não apenas de reconhecer,
mas de se adaptar às mudanças constantes das faces das pessoas.
Este documento apresenta um método de reconhecimento facial
proposto a partir da análise comparativa de trabalhos encontra-
dos na literatura.
iométrica é o uso da biometria para reconhecimento, identi-"
aac101dd321e6d2199d8c0b48c543b541c181b66,Using Context to Enhance the Understanding of Face Images,"USING CONTEXT TO ENHANCE THE
UNDERSTANDING OF FACE IMAGES
A Dissertation Presented
VIDIT JAIN
Submitted to the Graduate School of the
University of Massachusetts Amherst in partial fulfillment
of the requirements for the degree of
DOCTOR OF PHILOSOPHY
September 2010
Department of Computer Science"
af6e351d58dba0962d6eb1baf4c9a776eb73533f,How to Train Your Deep Neural Network with Dictionary Learning,"How to Train Your Deep Neural Network with
Dictionary Learning
Vanika Singhal*, Shikha Singh+ and Angshul Majumdar#
*IIIT Delhi
Okhla Phase 3
Delhi, 110020, India
+IIIT Delhi
Okhla Phase 3
#IIIT Delhi
Okhla Phase 3
Delhi, 110020, India
Delhi, 110020, India"
af62621816fbbe7582a7d237ebae1a4d68fcf97d,Active Shape Model Based Recognition Of Facial Expression,"International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622
International Conference on Humming Bird ( 01st March 2014)
RESEARCH ARTICLE
OPEN ACCESS
Active Shape Model Based Recognition Of Facial Expression
AncyRija V  , Gayathri. S2
AncyRijaV,Author is currently pursuing M.E (Software Engineering) in Vins Christian College of
Engineering,
e-mail:
Gayathri.S, M.E., Asst.Prof.,Department of Information Technology , Vins Christian college of Engineering."
afa57e50570a6599508ee2d50a7b8ca6be04834a,Motion in action : optical flow estimation and action localization in videos. (Le mouvement en action : estimation du flot optique et localisation d'actions dans les vidéos),"Motion in action : optical flow estimation and action
localization in videos
Philippe Weinzaepfel
To cite this version:
Philippe Weinzaepfel. Motion in action : optical flow estimation and action localization in videos.
Computer Vision and Pattern Recognition [cs.CV]. Université Grenoble Alpes, 2016. English. <NNT :
016GREAM013>. <tel-01407258>
HAL Id: tel-01407258
https://tel.archives-ouvertes.fr/tel-01407258
Submitted on 1 Dec 2016
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d’enseignement et de"
afc7092987f0d05f5685e9332d83c4b27612f964,Person-independent facial expression detection using Constrained Local Models,"Person-Independent Facial Expression Detection using Constrained
Local Models
Sien. W. Chew, Patrick Lucey, Simon Lucey, Jason Saragih, Jeffrey F. Cohn and Sridha Sridharan"
b730908bc1f80b711c031f3ea459e4de09a3d324,Active Orientation Models for Face Alignment In-the-Wild,"Active Orientation Models for Face
Alignment In-the-Wild
Georgios Tzimiropoulos, Joan Alabort-i-Medina, Student Member, IEEE,
Stefanos P. Zafeiriou, Member, IEEE, and Maja Pantic, Fellow, IEEE"
b7cf7bb574b2369f4d7ebc3866b461634147041a,From NLDA to LDA/GSVD: a modified NLDA algorithm,"Neural Comput & Applic (2012) 21:1575–1583
DOI 10.1007/s00521-011-0728-x
O R I G I N A L A R T I C L E
From NLDA to LDA/GSVD: a modified NLDA algorithm
Jun Yin • Zhong Jin
Received: 2 August 2010 / Accepted: 3 August 2011 / Published online: 19 August 2011
Ó Springer-Verlag London Limited 2011"
b7894c1f805ffd90ab4ab06002c70de68d6982ab,A comprehensive age estimation on face images using hybrid filter based feature extraction,"Biomedical Research 2017; Special Issue: S610-S618
ISSN 0970-938X
www.biomedres.info
A comprehensive age estimation on face images using hybrid filter based
feature extraction.
Karthikeyan D1*, Balakrishnan G2
Department of ECE, Srinivasan Engineering College, Perambalur, India
Department of Computer Science and Engineering, Indra Ganesan College of Engineering, Trichy, India"
b7eead8586ffe069edd190956bd338d82c69f880,A Video Database for Facial Behavior Understanding,"A VIDEO DATABASE FOR FACIAL
BEHAVIOR UNDERSTANDING
D. Freire-Obreg´on and M. Castrill´on-Santana.
SIANI, Universidad de Las Palmas de Gran Canaria, Spain"
b7774c096dc18bb0be2acef07ff5887a22c2a848,Distance metric learning for image and webpage comparison. (Apprentissage de distance pour la comparaison d'images et de pages Web),"Distance metric learning for image and webpage
omparison
Marc Teva Law
To cite this version:
Marc Teva Law. Distance metric learning for image and webpage comparison. Image Processing. Uni-
versité Pierre et Marie Curie - Paris VI, 2015. English. <NNT : 2015PA066019>. <tel-01135698v2>
HAL Id: tel-01135698
https://tel.archives-ouvertes.fr/tel-01135698v2
Submitted on 18 Mar 2015
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d’enseignement et de
recherche français ou étrangers, des laboratoires"
b7f05d0771da64192f73bdb2535925b0e238d233,Robust Active Shape Model using AdaBoosted Histogram Classifiers,"MVA2005  IAPR  Conference on Machine VIsion Applications, May 16-18, 2005 Tsukuba Science City, Japan
Robust Active Shape Model using AdaBoosted Histogram Classifiers
Yuanzhong Li
W ataru Ito
Imaging Software Technology Center
Imaging Software Technology Center
FUJI PHOTO FILM  CO., LTD.
fujifilm.co.jp
FUJI PHOTO FILM  CO., LTD.
fujifilm.co.jp"
b755505bdd5af078e06427d34b6ac2530ba69b12,NFRAD: Near-Infrared Face Recognition at a Distance,"To appear in the International Joint Conf. Biometrics, Washington D.C., October, 2011
NFRAD: Near-Infrared Face Recognition at a Distance
Hyunju Maenga, Hyun-Cheol Choia, Unsang Parkb, Seong-Whan Leea and Anil K. Jaina,b
Dept. of Brain and Cognitive Eng. Korea Univ., Seoul, Korea
Dept. of Comp. Sci. & Eng. Michigan State Univ., E. Lansing, MI, USA 48824
{hjmaeng, ,"
b7b461f82c911f2596b310e2b18dd0da1d5d4491,K-mappings and Regression trees,"014 IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP)
978-1-4799-2893-4/14/$31.00 ©2014 IEEE
K-MAPPINGS AND REGRESSION TREES
SAMSI and Duke University
. INTRODUCTION
rgminM1,...,MK
P1,...PK
Arthur Szlam†
.1. Partitioning Y
K(cid:2)
(cid:2)
(cid:3)
(cid:4)"
b73fdae232270404f96754329a1a18768974d3f6,Local Relation Map : A Novel Illumination Invariant Face Recognition Approach Regular Paper,
b76af8fcf9a3ebc421b075b689defb6dc4282670,Face Mask Extraction in Video Sequence,"Face Mask Extraction in Video Sequence
Yujiang Wang 1 · Bingnan Luo 1 · Jie Shen 1 · Maja Pantic 1"
b7f7a4df251ff26aca83d66d6b479f1dc6cd1085,Handling missing weak classifiers in boosted cascade: application to multiview and occluded face detection,"Bouges et al. EURASIP Journal on Image and Video Processing 2013, 2013:55
http://jivp.eurasipjournals.com/content/2013/1/55
RESEARCH
Open Access
Handling missing weak classifiers in boosted
ascade: application to multiview and
occluded face detection
Pierre Bouges1*, Thierry Chateau1*, Christophe Blanc1 and Gaëlle Loosli2"
db227f72bb13a5acca549fab0dc76bce1fb3b948,Characteristic Based Image Search Using Re-Ranking Method,"International Refereed Journal of Engineering and Science (IRJES)
ISSN (Online) 2319-183X, (Print) 2319-1821
Volume 4, Issue 6 (June 2015), PP.169-169-174
Characteristic Based Image Search using Re-Ranking method
Chitti Babu, 2Yasmeen Jaweed, 3G.Vijay Kumar
,2,3Computer Science Engineering Dept, Sree Dattha Institute of Engineering & Science"
dbaf89ca98dda2c99157c46abd136ace5bdc33b3,Nonlinear Cross-View Sample Enrichment for Action Recognition,"Nonlinear Cross-View Sample Enrichment for
Action Recognition
Ling Wang, Hichem Sahbi
Institut Mines-T´el´ecom; T´el´ecom ParisTech; CNRS LTCI"
dbe255d3d2a5d960daaaba71cb0da292e0af36a7,Evolutionary Cost-Sensitive Extreme Learning Machine,"Evolutionary Cost-sensitive Extreme Learning
Machine
Lei Zhang, Member, IEEE, and David Zhang, Fellow, IEEE"
dbb0a527612c828d43bcb9a9c41f1bf7110b1dc8,Machine Learning Techniques for Face Analysis,"Chapter 7
Machine Learning Techniques
for Face Analysis
Roberto Valenti, Nicu Sebe, Theo Gevers, and Ira Cohen"
dbb7f37fb9b41d1aa862aaf2d2e721a470fd2c57,Face image analysis with convolutional neural networks,"Face Image Analysis With
Convolutional Neural Networks
Dissertation
Zur Erlangung des Doktorgrades
der Fakult¨at f¨ur Angewandte Wissenschaften
n der Albert-Ludwigs-Universit¨at Freiburg im Breisgau
Stefan Duffner"
dbd5e9691cab2c515b50dda3d0832bea6eef79f2,Image - based Face Recognition : Issues and Methods 1,"Image-basedFaceRecognition:IssuesandMethods

WenYiZhao
RamaChellappa
Sarno(cid:11)Corporation
CenterforAutomationResearch

WashingtonRoad
UniversityofMaryland
Princeton,NJ
CollegePark,MD-"
db67edbaeb78e1dd734784cfaaa720ba86ceb6d2,SPECFACE — A dataset of human faces wearing spectacles,"SPECFACE - A Dataset of Human Faces Wearing Spectacles
Anirban Dasgupta, Shubhobrata Bhattacharya and Aurobinda Routray
Indian Institute of Technology Kharagpur
India"
a83fc450c124b7e640adc762e95e3bb6b423b310,Deep Face Feature for Face Alignment and Reconstruction,"Deep Face Feature for Face Alignment
Boyi Jiang, Juyong Zhang, Bailin Deng, Yudong Guo and Ligang Liu"
a85e9e11db5665c89b057a124547377d3e1c27ef,Dynamics of Driver's Gaze: Explorations in Behavior Modeling and Maneuver Prediction,"Dynamics of Driver’s Gaze: Explorations in
Behavior Modeling & Maneuver Prediction
Sujitha Martin, Member, IEEE, Sourabh Vora, Kevan Yuen, and Mohan M. Trivedi, Fellow, IEEE"
a8117a4733cce9148c35fb6888962f665ae65b1e,A Good Practice Towards Top Performance of Face Recognition: Transferred Deep Feature Fusion,"IEEE TRANSACTIONS ON XXXX, VOL. XX, NO. XX, XX 201X
A Good Practice Towards Top Performance of Face
Recognition: Transferred Deep Feature Fusion
Lin Xiong1∗†, Jayashree Karlekar1∗, Jian Zhao2∗†, Jiashi Feng2, Member, IEEE, Sugiri Pranata1, and
Shengmei Shen1"
a87ab836771164adb95d6744027e62e05f47fd96,Understanding human-human interactions: a survey,"Understanding human-human interactions: a survey
Alexandros Stergiou
Department of Information and Computing Sciences, Utrecht University,Buys Ballotgebouw, Princetonplein 5, Utrecht, 3584CC, Netherlands
Department of Information and Computing Sciences, Utrecht University,Buys Ballotgebouw, Princetonplein 5, Utrecht, 3584CC, Netherlands
Ronald Poppe1"
a88640045d13fc0207ac816b0bb532e42bcccf36,Simultaneously Learning Neighborship and Projection Matrix for Supervised Dimensionality Reduction,"ARXIV VERSION
Simultaneously Learning Neighborship and
Projection Matrix for Supervised
Dimensionality Reduction
Yanwei Pang, Senior Member, IEEE, Bo Zhou, and Feiping Nie, Senior Member, IEEE"
a8a30a8c50d9c4bb8e6d2dd84bc5b8b7f2c84dd8,This is a repository copy of Modelling of Orthogonal Craniofacial Profiles,"This is a repository copy of Modelling of Orthogonal Craniofacial Profiles.
White Rose Research Online URL for this paper:
http://eprints.whiterose.ac.uk/131767/
Version: Published Version
Article:
Dai, Hang, Pears, Nicholas Edwin orcid.org/0000-0001-9513-5634 and Duncan, Christian
(2017) Modelling of Orthogonal Craniofacial Profiles. Journal of Imaging. ISSN 2313-433X
https://doi.org/10.3390/jimaging3040055
Reuse
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If you consider content in White Rose Research Online to be in breach of UK law, please notify us by
emailing including the URL of the record and the reason for the withdrawal request.
https://eprints.whiterose.ac.uk/"
a8638a07465fe388ae5da0e8a68e62a4ee322d68,How to predict the global instantaneous feeling induced by a facial picture?,"How to predict the global instantaneous feeling induced
y a facial picture?
Arnaud Lienhard, Patricia Ladret, Alice Caplier
To cite this version:
Arnaud Lienhard, Patricia Ladret, Alice Caplier. How to predict the global instantaneous
feeling induced by a facial picture?. Signal Processing: Image Communication, Elsevier, 2015,
pp.1-30. .
HAL Id: hal-01198718
https://hal.archives-ouvertes.fr/hal-01198718
Submitted on 14 Sep 2015
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
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broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destin´ee au d´epˆot et `a la diffusion de documents
scientifiques de niveau recherche, publi´es ou non,
´emanant des ´etablissements d’enseignement et de"
a8e75978a5335fd3deb04572bb6ca43dbfad4738,Sparse Graphical Representation based Discriminant Analysis for Heterogeneous Face Recognition,"Sparse Graphical Representation based Discriminant
Analysis for Heterogeneous Face Recognition
Chunlei Peng, Xinbo Gao, Senior Member, IEEE, Nannan Wang, Member, IEEE, and Jie Li"
ded968b97bd59465d5ccda4f1e441f24bac7ede5,Large scale 3 D Morphable Models,"Noname manuscript No.
(will be inserted by the editor)
Large scale 3D Morphable Models
James Booth · Anastasios Roussos · Allan Ponniah · David Dunaway · Stefanos
Zafeiriou
Received: date / Accepted: date"
de0eb358b890d92e8f67592c6e23f0e3b2ba3f66,Inference-Based Similarity Search in Randomized Montgomery Domains for Privacy-Preserving Biometric Identification,"ACCEPTED BY IEEE TRANS. PATTERN ANAL. AND MACH. INTELL.
Inference-Based Similarity Search in
Randomized Montgomery Domains for
Privacy-Preserving Biometric Identification
Yi Wang, Jianwu Wan, Jun Guo, Yiu-Ming Cheung, and Pong C Yuen"
dee406a7aaa0f4c9d64b7550e633d81bc66ff451,Content-Adaptive Sketch Portrait Generation by Decompositional Representation Learning,"Content-Adaptive Sketch Portrait Generation by
Decompositional Representation Learning
Dongyu Zhang, Liang Lin, Tianshui Chen, Xian Wu, Wenwei Tan, and Ebroul Izquierdo"
dedabf9afe2ae4a1ace1279150e5f1d495e565da,Robust Face Recognition With Structurally Incoherent Low-Rank Matrix Decomposition,"Robust Face Recognition With Structurally
Incoherent Low-Rank Matrix Decomposition
Chia-Po Wei, Chih-Fan Chen, and Yu-Chiang Frank Wang"
de398bd8b7b57a3362c0c677ba8bf9f1d8ade583,Hierarchical Bayesian Theme Models for Multipose Facial Expression Recognition,"Hierarchical Bayesian Theme Models for
Multi-pose Facial Expression Recognition
Qirong Mao, Member, IEEE, Qiyu Rao, Yongbin Yu, and Ming Dong*, Member, IEEE"
defa8774d3c6ad46d4db4959d8510b44751361d8,FEBEI - Face Expression Based Emoticon Identification CS - B657 Computer Vision,"FEBEI - Face Expression Based Emoticon Identification
CS - B657 Computer Vision
Nethra Chandrasekaran Sashikar - necsashi
Prashanth Kumar Murali - prmurali
Robert J Henderson - rojahend"
b08203fca1af7b95fda8aa3d29dcacd182375385,Object and Text-guided Semantics for CNN-based Activity Recognition,"OBJECT AND TEXT-GUIDED SEMANTICS FOR CNN-BASED ACTIVITY RECOGNITION
(cid:63)Sungmin Eum †§, (cid:63)Christopher Reale †, Heesung Kwon†, Claire Bonial †, Clare Voss†
U.S. Army Research Laboratory, Adelphi, MD, USA
§Booz Allen Hamilton Inc., McLean, VA, USA"
b09b693708f412823053508578df289b8403100a,Two-Stream SR-CNNs for Action Recognition in Videos,"WANG et al.: TWO-STREAM SR-CNNS FOR ACTION RECOGNITION IN VIDEOS
Two-Stream SR-CNNs for Action
Recognition in Videos
Yifan Wang1
Jie Song1
Limin Wang2
Luc Van Gool2
Otmar Hilliges1
Advanced Interactive Technologies Lab
ETH Zurich
Zurich, Switzerland
Computer Vision Lab
ETH Zurich
Zurich, Switzerland"
b07582d1a59a9c6f029d0d8328414c7bef64dca0,Employing Fusion of Learned and Handcrafted Features for Unconstrained Ear Recognition,"Employing Fusion of Learned and Handcrafted
Features for Unconstrained Ear Recognition
Maur´ıcio Pamplona Segundo∗†
Earnest E. Hansley∗
Sudeep Sarkar∗‡
October 24, 2017"
b0c1615ebcad516b5a26d45be58068673e2ff217,How Image Degradations Affect Deep CNN-Based Face Recognition?,"How Image Degradations Affect Deep CNN-based Face
Recognition?
S¸amil Karahan1 Merve Kılınc¸ Yıldırım1 Kadir Kırtac¸1 Ferhat S¸ ¨ukr¨u Rende1
G¨ultekin B¨ut¨un1Hazım Kemal Ekenel2"
b0de0892d2092c8c70aa22500fed31aa7eb4dd3f,A Robust and Efficient Video Representation for Action Recognition,"(will be inserted by the editor)
A robust and efficient video representation for action recognition
Heng Wang · Dan Oneata · Jakob Verbeek · Cordelia Schmid
Received: date / Accepted: date"
b073313325b6482e22032e259d7311fb9615356c,Robust and accurate cancer classification with gene expression profiling,"Robust and Accurate Cancer Classification with Gene Expression Profiling
Haifeng Li
Keshu Zhang
Tao Jiang
Dept. of Computer Science
Human Interaction Research Lab
Dept. of Computer Science
University of California
Riverside, CA 92521
Motorola, Inc.
Tempe, AZ 85282
University of California
Riverside, CA 92521"
a66d89357ada66d98d242c124e1e8d96ac9b37a0,Failure Detection for Facial Landmark Detectors,"Failure Detection for Facial Landmark Detectors
Andreas Steger, Radu Timofte, and Luc Van Gool
Computer Vision Lab, D-ITET, ETH Zurich, Switzerland
{radu.timofte,"
a608c5f8fd42af6e9bd332ab516c8c2af7063c61,Age Estimation via Grouping and Decision Fusion,"Age Estimation via Grouping and Decision Fusion
Kuan-Hsien Liu, Member, IEEE, Shuicheng Yan, Senior Member, IEEE,
nd C.-C. Jay Kuo, Fellow, IEEE"
a6eb6ad9142130406fb4ffd4d60e8348c2442c29,"Video Description: A Survey of Methods, Datasets and Evaluation Metrics","Video Description: A Survey of Methods,
Datasets and Evaluation Metrics
Nayyer Aafaq, Syed Zulqarnain Gilani, Wei Liu, and Ajmal Mian"
a6590c49e44aa4975b2b0152ee21ac8af3097d80,3D Interpreter Networks for Viewer-Centered Wireframe Modeling,"https://doi.org/10.1007/s11263-018-1074-6
D Interpreter Networks for Viewer-Centered Wireframe Modeling
Jiajun Wu1 · Tianfan Xue2 · Joseph J. Lim3 · Yuandong Tian4 ·
Joshua B. Tenenbaum1 · Antonio Torralba1 · William T. Freeman1,5
Received: date / Accepted: date"
a694180a683f7f4361042c61648aa97d222602db,Face recognition using scattering wavelet under Illicit Drug Abuse variations,"Face Recognition using Scattering Wavelet under Illicit Drug Abuse Variations
Prateekshit Pandey, Richa Singh, Mayank Vatsa
fprateekshit12078, rsingh,
IIIT-Delhi India"
a6ce2f0795839d9c2543d64a08e043695887e0eb,Driver Gaze Region Estimation Without Using Eye Movement,"Driver Gaze Region Estimation
Without Using Eye Movement
Lex Fridman, Philipp Langhans, Joonbum Lee, and Bryan Reimer
Massachusetts Institute of Technology (MIT)"
a6ebe013b639f0f79def4c219f585b8a012be04f,Facial Expression Recognition Based on Hybrid Approach,"Facial Expression Recognition Based on Hybrid
Approach
Md. Abdul Mannan, Antony Lam, Yoshinori Kobayashi, and Yoshinori Kuno
Graduate School of Science and Engineering, Saitama University,
55 Shimo-Okubo, Sakura-ku, Saitama-shi, Saitama 338-8570, Japan
E-mail"
b97f694c2a111b5b1724eefd63c8d64c8e19f6c9,Group Affect Prediction Using Multimodal Distributions,"Group Affect Prediction Using Multimodal Distributions
Saqib Nizam Shamsi
Aspiring Minds
Bhanu Pratap Singh
Univeristy of Massachusetts, Amherst
Manya Wadhwa
Johns Hopkins University"
b9d0774b0321a5cfc75471b62c8c5ef6c15527f5,Fishy Faces: Crafting Adversarial Images to Poison Face Authentication,"Fishy Faces: Crafting Adversarial Images to Poison Face Authentication
Giuseppe Garofalo
Vera Rimmer
Tim Van hamme
imec-DistriNet, KU Leuven
imec-DistriNet, KU Leuven
imec-DistriNet, KU Leuven
Davy Preuveneers
Wouter Joosen
imec-DistriNet, KU Leuven
imec-DistriNet, KU Leuven"
b9cad920a00fc0e997fc24396872e03f13c0bb9c,Face liveness detection under bad illumination conditions,"FACE LIVENESS DETECTION UNDER BAD ILLUMINATION CONDITIONS
Bruno Peixoto, Carolina Michelassi, and Anderson Rocha
University of Campinas (Unicamp)
Campinas, SP, Brazil"
b908edadad58c604a1e4b431f69ac8ded350589a,Deep Face Feature for Face Alignment,"Deep Face Feature for Face Alignment
Boyi Jiang, Juyong Zhang, Bailin Deng, Yudong Guo and Ligang Liu"
b9f2a755940353549e55690437eb7e13ea226bbf,Unsupervised Feature Learning from Videos for Discovering and Recognizing Actions,"Unsupervised Feature Learning from Videos for Discovering and Recognizing Actions
Carolina Redondo-Cabrera
Roberto J. López-Sastre"
b9cedd1960d5c025be55ade0a0aa81b75a6efa61,Inexact Krylov Subspace Algorithms for Large Matrix Exponential Eigenproblem from Dimensionality Reduction,"INEXACT KRYLOV SUBSPACE ALGORITHMS FOR LARGE
MATRIX EXPONENTIAL EIGENPROBLEM FROM
DIMENSIONALITY REDUCTION
GANG WU∗, TING-TING FENG† , LI-JIA ZHANG‡ , AND MENG YANG§"
b971266b29fcecf1d5efe1c4dcdc2355cb188ab0,On the Reconstruction of Face Images from Deep Face Templates.,"MAI et al.: ON THE RECONSTRUCTION OF FACE IMAGES FROM DEEP FACE TEMPLATES
On the Reconstruction of Face Images from
Deep Face Templates
Guangcan Mai, Kai Cao, Pong C. Yuen∗, Senior Member, IEEE, and Anil K. Jain, Life Fellow, IEEE"
a158c1e2993ac90a90326881dd5cb0996c20d4f3,Symmetry as an Intrinsically Dynamic Feature,"OPEN ACCESS
ISSN 2073-8994
Article
Vito Di Gesu 1,2,†, Marco E. Tabacchi 1,3,* and Bertrand Zavidovique 4
DMA, Università degli Studi di Palermo, via Archirafi 34, 90123 Palermo, Italy
CITC, Università degli Studi di Palermo, via Archirafi 34, 90123 Palermo, Itlay
Istituto Nazionale di Ricerche Demopolis, via Col. Romey 7, 91100 Trapani, Italy
IEF, Université Paris IX–Orsay, Paris, France; E-Mail: (B.Z.)
Deceased on 15 March 2009.
* Author to whom correspondence should be addressed; E-Mail:
Received: 4 March 2010; in revised form: 23 March 2010 / Accepted: 29 March 2010 /
Published: 1 April 2010"
a15d9d2ed035f21e13b688a78412cb7b5a04c469,Object Detection Using Strongly-Supervised Deformable Part Models,"Object Detection Using
Strongly-Supervised Deformable Part Models
Hossein Azizpour1 and Ivan Laptev2
Computer Vision and Active Perception Laboratory (CVAP), KTH, Sweden
INRIA, WILLOW, Laboratoire d’Informatique de l’Ecole Normale Superieure"
a1b1442198f29072e907ed8cb02a064493737158,Crowdsourcing Facial Responses to Online Videos,"Crowdsourcing Facial Responses
to Online Videos
Daniel McDuff, Student Member, IEEE, Rana El Kaliouby, Member, IEEE, and
Rosalind W. Picard, Fellow, IEEE"
a15c728d008801f5ffc7898568097bbeac8270a4,ForgetIT Deliverable Template,"www.forgetit-project.eu
ForgetIT
Concise Preservation by Combining Managed Forgetting
nd Contextualized Remembering
Grant Agreement No. 600826
Deliverable D4.4
Work-package
Deliverable
Deliverable Leader
Quality Assessor
Dissemination level
Delivery date in Annex I
Actual delivery date
Revisions
Status
Keywords
Information Consolidation and Con-
entration
D4.4:
Information analysis, consolidation"
a1132e2638a8abd08bdf7fc4884804dd6654fa63,Real-Time Video Face Recognition for Embedded Devices,"Real-Time Video Face Recognition
for Embedded Devices
Gabriel Costache, Sathish Mangapuram, Alexandru
Drimbarean, Petronel Bigioi and Peter Corcoran
Tessera, Galway,
Ireland
. Introduction
This  chapter  will  address  the  challenges  of  real-time  video  face  recognition  systems
implemented  in  embedded  devices.  Topics  to  be  covered  include:  the  importance  and
hallenges of video face recognition in real life scenarios, describing a general architecture of
generic  video  face  recognition  system  and  a  working  solution  suitable  for  recognizing
faces  in  real-time  using  low  complexity  devices.  Each  component  of  the  system  will  be
described  together  with  the  system’s  performance  on  a  database  of  video  samples  that
resembles real life conditions.
. Video face recognition
Face recognition remains a very active topic in computer vision and receives attention from
large  community  of  researchers  in  that  discipline.  Many  reasons  feed  this  interest;  the
main being  the wide range of commercial, law enforcement and security applications that
require  authentication.  The  progress  made  in  recent  years  on  the  methods  and  algorithms
for data processing as well as the availability of new technologies makes it easier to study"
a14ae81609d09fed217aa12a4df9466553db4859,Face Identification Using Large Feature Sets,"REVISED VERSION, JUNE 2011
Face Identification Using Large Feature Sets
William Robson Schwartz, Huimin Guo, Jonghyun Choi, and Larry S. Davis, Fellow, IEEE"
a1f1120653bb1bd8bd4bc9616f85fdc97f8ce892,Latent Embeddings for Zero-Shot Classification,"Latent Embeddings for Zero-shot Classification
Yongqin Xian1, Zeynep Akata1, Gaurav Sharma1,2,∗, Quynh Nguyen3, Matthias Hein3 and Bernt Schiele1
MPI for Informatics
IIT Kanpur
Saarland University"
a1e97c4043d5cc9896dc60ae7ca135782d89e5fc,"Re-identification of Humans in Crowds using Personal, Social and Environmental Constraints","IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
Re-identification of Humans in Crowds using
Personal, Social and Environmental Constraints
Shayan Modiri Assari, Member, IEEE, Haroon Idrees, Member, IEEE, and Mubarak Shah, Fellow, IEEE"
ef940b76e40e18f329c43a3f545dc41080f68748,A Face Recognition and Spoofing Detection Adapted to Visually- Impaired People,"Research Article                                                                                                                              Volume 7 Issue No.3
ISSN XXXX XXXX © 2017 IJESC
A Face Recognition and Spoofing Detection Adapted to Visually-
Impaired People
Rutuja R. Dengale1, Bhagyashri S. Deshmukh 2, Anuja R. Mahangade3, Shivani V. Ujja inkar4
K.K Wagh Institute of Engineering and Education Research, Nashik, India
Depart ment of Co mputer Engineering
Abstrac t:
According to estimates by the world Health organization, about 285 million people suffer fro m so me kind of v isual disabilit ies of
which 39 million are  blind, resulting in  0.7 of the word population. As many v isual impaired peoples in  the word they are  unable
to recognize the people who is standing in front of them and some peoples who have problem to re me mbe r na me of the person.
They  can  easily  recognize   the  person  using  this  system.  A   co mputer  vision  technique  and  image  ana lysis  can  help  v isually
the home using face identification and spoofing detection system. This system also provide feature to add newly  known people
nd keep records of all peoples visiting their ho me.
Ke ywor ds: face-recognition, spoofing detection, visually-impaired, system architecture.
INTRODUCTION
The  facia l  ana lysis  can  be  used  to  e xtract  very  useful  and
relevant  information  in   order  to  help  people  with  visual
impairment in several of  its tasks daily providing them with a
greater  degree  of  autonomy  and  security.  Facia l  recognition"
efd308393b573e5410455960fe551160e1525f49,Tracking Persons-of-Interest via Unsupervised Representation Adaptation,"Tracking Persons-of-Interest via
Unsupervised Representation Adaptation
Shun Zhang, Jia-Bin Huang, Jongwoo Lim, Yihong Gong, Jinjun Wang,
Narendra Ahuja, and Ming-Hsuan Yang"
ef230e3df720abf2983ba6b347c9d46283e4b690,QUIS-CAMPI: an annotated multi-biometrics data feed from surveillance scenarios,"Page 1 of 20
QUIS-CAMPI: An Annotated Multi-biometrics Data Feed From
Surveillance Scenarios
João Neves1,*, Juan Moreno2, Hugo Proença3
IT - Instituto de Telecomunicações, University of Beira Interior
Department of Computer Science, University of Beira Interior
IT - Instituto de Telecomunicações, University of Beira Interior"
ef4ecb76413a05c96eac4c743d2c2a3886f2ae07,Modeling the importance of faces in natural images,"Modeling the Importance of Faces in Natural Images
Jin B.a, Yildirim G.a, Lau C.a, Shaji A.a, Ortiz Segovia M.b and S¨usstrunk S.a
EPFL, Lausanne, Switzerland;
Oc´e, Paris, France"
ef032afa4bdb18b328ffcc60e2dc5229cc1939bc,Attribute-enhanced metric learning for face retrieval,"Fang and Yuan EURASIP Journal on Image and Video
Processing  (2018) 2018:44
https://doi.org/10.1186/s13640-018-0282-x
EURASIP Journal on Image
nd Video Processing
RESEARCH
Open Access
Attribute-enhanced metric learning for
face retrieval
Yuchun Fang*
nd Qiulong Yuan"
ef5531711a69ed687637c48930261769465457f0,Studio2Shop: from studio photo shoots to fashion articles,"Studio2Shop: from studio photo shoots to fashion articles
Julia Lasserre1, Katharina Rasch1 and Roland Vollgraf
Zalando Research, Muehlenstr. 25, 10243 Berlin, Germany
Keywords:
omputer vision, deep learning, fashion, item recognition, street-to-shop"
ef559d5f02e43534168fbec86707915a70cd73a0,DeepInsight: Multi-Task Multi-Scale Deep Learning for Mental Disorder Diagnosis,"DING, HUO, HU, LU: DEEPINSIGHT
DeepInsight: Multi-Task Multi-Scale Deep
Learning for Mental Disorder Diagnosis
Mingyu Ding1
Yuqi Huo2
Jun Hu2
Zhiwu Lu1
School of Information
Renmin University of China
Beijing, 100872, China
Beijing Key Laboratory
of Big Data Management
nd Analysis Methods
Beijing, 100872, China"
efa08283656714911acff2d5022f26904e451113,Active Object Localization in Visual Situations,"Active Object Localization in Visual Situations
Max H. Quinn, Anthony D. Rhodes, and Melanie Mitchell"
ef999ab2f7b37f46445a3457bf6c0f5fd7b5689d,Improving face verification in photo albums by combining facial recognition and metadata with cross-matching,"Calhoun: The NPS Institutional Archive
DSpace Repository
Theses and Dissertations
. Thesis and Dissertation Collection, all items
017-12
Improving face verification in photo albums by
ombining facial recognition and metadata
with cross-matching
Bouthour, Khoubeib
Monterey, California: Naval Postgraduate School
http://hdl.handle.net/10945/56868
Downloaded from NPS Archive: Calhoun"
c32fb755856c21a238857b77d7548f18e05f482d,Multimodal Emotion Recognition for Human-Computer Interaction: A Survey,"Multimodal Emotion Recognition for Human-
Computer Interaction: A Survey
School of Computer and Communication Engineering, University of Science and Technology Beijing, 100083 Beijing, China.
Michele Mukeshimana, Xiaojuan Ban, Nelson Karani, Ruoyi Liu"
c3beae515f38daf4bd8053a7d72f6d2ed3b05d88,ACL 2014 52nd Annual Meeting of the Association for Computational Linguistics TACL Papers,"ACL201452ndAnnualMeetingoftheAssociationforComputationalLinguisticsTACLPapersJune23-25,2014Baltimore,Maryland,USA"
c3dc4f414f5233df96a9661609557e341b71670d,Utterance independent bimodal emotion recognition in spontaneous communication,"Tao et al. EURASIP Journal on Advances in Signal Processing 2011, 2011:4
http://asp.eurasipjournals.com/content/2011/1/4
RESEARCH
Utterance independent bimodal emotion
recognition in spontaneous communication
Jianhua Tao*, Shifeng Pan, Minghao Yang, Ya Li, Kaihui Mu and Jianfeng Che
Open Access"
c3b3636080b9931ac802e2dd28b7b684d6cf4f8b,Face Recognition via Local Directional Pattern,"International Journal of Security and Its Applications
Vol. 7, No. 2, March, 2013
Face Recognition via Local Directional Pattern
Dong-Ju Kim*, Sang-Heon Lee and Myoung-Kyu Sohn
Division of IT Convergence, Daegu Gyeongbuk Institute of Science & Technology
50-1, Sang-ri, Hyeonpung-myeon, Dalseong-gun, Daegu, Korea."
c398684270543e97e3194674d9cce20acaef3db3,Comparative Face Soft Biometrics for Human Identification,"Chapter 2
Comparative Face Soft Biometrics for
Human Identification
Nawaf Yousef Almudhahka, Mark S. Nixon and Jonathon S. Hare"
c3285a1d6ec6972156fea9e6dc9a8d88cd001617,Extreme 3D Face Reconstruction: Seeing Through Occlusions,
c3bcc4ee9e81ce9c5c0845f34e9992872a8defc0,A New Scheme for Image Recognition Using Higher-Order Local Autocorrelation and Factor Analysis,"MVA2005  IAPR  Conference on Machine VIsion Applications, May 16-18, 2005 Tsukuba Science City, Japan
A New Scheme for Image Recognition Using Higher-Order Local
Autocorrelation and Factor Analysis
Naoyuki Nomotoy, Yusuke Shinoharay, Takayoshi Shirakiy, Takumi Kobayashiy, Nobuyuki Otsuy yyy
yThe University of Tokyo
Tokyo, Japan
yyyAIST
Tukuba, Japan
f shiraki, takumi, otsug"
c32383330df27625592134edd72d69bb6b5cff5c,Intrinsic Illumination Subspace for Lighting Insensitive Face Recognition,"IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART B: CYBERNETICS, VOL. 42, NO. 2, APRIL 2012
Intrinsic Illumination Subspace for Lighting
Insensitive Face Recognition
Chia-Ping Chen and Chu-Song Chen, Member, IEEE"
c32f04ccde4f11f8717189f056209eb091075254,Analysis and Synthesis of Behavioural Specific Facial Motion,"Analysis and Synthesis of Behavioural Specific
Facial Motion
Lisa Nanette Gralewski
A dissertation submitted to the University of Bristol in accordance with the requirements
for the degree of Doctor of Philosophy in the Faculty of Engineering, Department of
Computer Science.
February 2007
71657 words"
c317181fa1de2260e956f05cd655642607520a4f,Objective Classes for Micro-Facial Expression Recognition,"Research Article
Research
Article for submission to journal
Subject Areas:
omputer vision, pattern recognition,
feature descriptor
Keywords:
micro-facial expression, expression
recognition, action unit
Moi Hoon Yap
e-mail:
Objective Classes for
Micro-Facial Expression
Recognition
Adrian K. Davison1, Walied Merghani2 and
Moi Hoon Yap3
Centre for Imaging Sciences, University of
Manchester, Manchester, United Kingdom
Sudan University of Science and Technology,
Khartoum, Sudan"
c30e4e4994b76605dcb2071954eaaea471307d80,Feature Selection for Emotion Recognition based on Random Forest,
c37a971f7a57f7345fdc479fa329d9b425ee02be,A Novice Guide towards Human Motion Analysis and Understanding,"A Novice Guide towards Human Motion Analysis and Understanding
Dr. Ahmed Nabil Mohamed"
c3fb2399eb4bcec22723715556e31c44d086e054,Face recognition based on SIGMA sets of image features,"014 IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP)
978-1-4799-2893-4/14/$31.00 ©2014 IEEE
. INTRODUCTION"
c37de914c6e9b743d90e2566723d0062bedc9e6a,Joint and Discriminative Dictionary Learning for Facial Expression Recognition,"©2016 Society for Imaging Science and Technology
DOI: 10.2352/ISSN.2470-1173.2016.11.IMAWM-455
Joint  and  Discriminative  Dictionary  Learning
Expression Recognition
for  Facial
Sriram Kumar, Behnaz Ghoraani, Andreas Savakis"
c4f1fcd0a5cdaad8b920ee8188a8557b6086c1a4,The Ignorant Led by the Blind: A Hybrid Human–Machine Vision System for Fine-Grained Categorization,"Int J Comput Vis (2014) 108:3–29
DOI 10.1007/s11263-014-0698-4
The Ignorant Led by the Blind: A Hybrid Human–Machine Vision
System for Fine-Grained Categorization
Steve Branson · Grant Van Horn · Catherine Wah ·
Pietro Perona · Serge Belongie
Received: 7 March 2013 / Accepted: 8 January 2014 / Published online: 20 February 2014
© Springer Science+Business Media New York 2014"
c43862db5eb7e43e3ef45b5eac4ab30e318f2002,Provable Self-Representation Based Outlier Detection in a Union of Subspaces,"Provable Self-Representation Based Outlier Detection in a Union of Subspaces
Chong You, Daniel P. Robinson, Ren´e Vidal
Johns Hopkins University, Baltimore, MD, 21218, USA"
c4dcf41506c23aa45c33a0a5e51b5b9f8990e8ad,Understanding Activity: Learning the Language of Action,"Understanding Activity: Learning the Language of Action
Randal Nelson and Yiannis Aloimonos
Univ. of Rochester and Maryland
.1 Overview
Understanding observed activity is an important
problem, both from the standpoint of practical applications,
nd as a central issue in attempting to describe the
phenomenon of intelligence. On the practical side, there are a
large number of applications that would benefit from
improved machine ability to analyze activity. The most
prominent are various surveillance scenarios. The current
emphasis on homeland security has brought this issue to the
forefront, and resulted in considerable work on mostly low-
level detection schemes. There are also applications in
medical diagnosis and household assistants that, in the long
run, may be even more important. In addition, there are
numerous scientific projects, ranging from monitoring of
weather conditions to observation of animal behavior that
would be facilitated by automatic understanding of activity.
From a scientific standpoint, understanding activity"
c42a8969cd76e9f54d43f7f4dd8f9b08da566c5f,Towards Unconstrained Face Recognition Using 3D Face Model,"Towards Unconstrained Face Recognition
Using 3D Face Model
Zahid Riaz1, M. Saquib Sarfraz2 and Michael Beetz1
Intelligent Autonomous Systems (IAS), Technical University of Munich, Garching
Computer Vision Research Group, COMSATS Institute of Information
Technology, Lahore
Germany
Pakistan
. Introduction
Over the last couple of decades, many commercial systems are available to identify human
faces. However, face recognition is still an outstanding challenge against different kinds of
real world variations especially facial poses, non-uniform lightings and facial expressions.
Meanwhile the face recognition technology has extended its role from biometrics and security
pplications to human robot interaction (HRI). Person identity is one of the key tasks while
interacting with intelligent machines/robots, exploiting the non intrusive system security
nd authentication of the human interacting with the system. This capability further helps
machines to learn person dependent traits and interaction behavior to utilize this knowledge
for tasks manipulation. In such scenarios acquired face images contain large variations which
demands an unconstrained face recognition system.
Fig. 1. Biometric analysis of past few years has been shown in figure showing the"
eac6aee477446a67d491ef7c95abb21867cf71fc,A Survey of Sparse Representation: Algorithms and Applications,"JOURNAL
A survey of sparse representation: algorithms and
pplications
Zheng Zhang, Student Member, IEEE, Yong Xu, Senior Member, IEEE,
Jian Yang, Member, IEEE, Xuelong Li, Fellow, IEEE, and David Zhang, Fellow, IEEE"
ea079334121a0ba89452036e5d7f8e18f6851519,Unsupervised incremental learning of deep descriptors from video streams,"UNSUPERVISED INCREMENTAL LEARNING OF DEEP DESCRIPTORS
FROM VIDEO STREAMS
Federico Pernici and Alberto Del Bimbo
MICC – University of Florence"
eac1b644492c10546a50f3e125a1f790ec46365f,"Chained Multi-stream Networks Exploiting Pose, Motion, and Appearance for Action Classification and Detection","Chained Multi-stream Networks Exploiting Pose, Motion, and Appearance for
Action Classification and Detection
Mohammadreza Zolfaghari , Gabriel L. Oliveira, Nima Sedaghat, and Thomas Brox
University of Freiburg
Freiburg im Breisgau, Germany"
ea482bf1e2b5b44c520fc77eab288caf8b3f367a,Flexible Orthogonal Neighborhood Preserving Embedding,Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17)
ea6f5c8e12513dbaca6bbdff495ef2975b8001bd,Applying a Set of Gabor Filter to 2D-Retinal Fundus Image to Detect the Optic Nerve Head (ONH),"Applying a Set of Gabor Filter to 2D-Retinal Fundus Image
to Detect the Optic Nerve Head (ONH)
Rached Belgacem1,2*, Hédi Trabelsi2, Ines Malek3, Imed Jabri1
Higher National School of engineering of Tunis, ENSIT, Laboratory LATICE (Information Technology and Communication and
Electrical Engineering LR11ESO4), University of Tunis EL Manar. Adress: ENSIT 5, Avenue Taha Hussein, B. P. : 56, Bab
Menara, 1008 Tunis; 2University of Tunis El-Manar, Tunis with expertise in Mechanic, Optics, Biophysics, Conference Master
ISTMT, Laboratory of Research in Biophysics and Medical Technologies LRBTM Higher Institute of Medical Technologies of Tunis
ISTMT, University of Tunis El Manar Address: 9, Rue Docteur Zouheïr Safi – 1006; 3Faculty of Medicine of Tunis; Address: 15
Rue Djebel Lakhdhar. La Rabta. 1007, Tunis - Tunisia
Corresponding author:
Rached Belgacem,
High Institute of Medical Technologies
of Tunis, ISTMT, and High National
School Engineering of Tunis,
Information Technology and
Communication Technology and
Electrical Engineering, University of
Tunis El-Manar, ENSIT 5, Avenue Taha
Hussein, B. P.: 56, Bab Menara, 1008
Tunis, Tunisia,"
eafda8a94e410f1ad53b3e193ec124e80d57d095,Observer-Based Measurement of Facial Expression With the Facial Action Coding System,"Jeffrey F. Cohn
Zara Ambadar
Paul Ekman
Observer-Based Measurement of Facial Expression
With the Facial Action Coding System
Facial expression has been a focus of emotion research for over
hundred years (Darwin, 1872/1998). It is central to several
leading theories of emotion (Ekman, 1992; Izard, 1977;
Tomkins, 1962) and has been the focus of at times heated
debate about issues in emotion science (Ekman, 1973, 1993;
Fridlund, 1992; Russell, 1994). Facial expression figures
prominently in research on almost every aspect of emotion,
including psychophysiology (Levenson, Ekman, & Friesen,
990), neural bases (Calder et al., 1996; Davidson, Ekman,
Saron, Senulis, & Friesen, 1990), development (Malatesta,
Culver, Tesman, & Shephard, 1989; Matias & Cohn, 1993),
perception (Ambadar, Schooler, & Cohn, 2005), social pro-
esses (Hatfield, Cacioppo, & Rapson, 1992; Hess & Kirouac,
000), and emotion disorder (Kaiser, 2002; Sloan, Straussa,
Quirka, & Sajatovic, 1997), to name a few."
ea85378a6549bb9eb9bcc13e31aa6a61b655a9af,Template Protection for PCA - LDA - based 3 D Face Recognition System,"Diplomarbeit
Template Protection for PCA-LDA-based 3D
Face Recognition System
Daniel Hartung
Technische Universität Darmstadt
Fachbereich Informatik
Fachgebiet Graphisch-Interaktive Systeme
Fraunhoferstraße 5
64283 Darmstadt
Betreuer: Dipl.-Ing. Xuebing Zhou
Prüfer: Prof. Dr. techn. Dieter W. Fellner"
ea2ee5c53747878f30f6d9c576fd09d388ab0e2b,Viola-Jones Based Detectors: How Much Affects the Training Set?,"Viola-Jones based Detectors: How much affects
the Training Set?
Modesto Castrill´on-Santana, Daniel Hern´andez-Sosa, Javier Lorenzo-Navarro
SIANI
Edif. Central del Parque Cient´ıfico Tecnol´ogico
Universidad de Las Palmas de Gran Canaria
5017 - Spain"
e1f790bbedcba3134277f545e56946bc6ffce48d,Image Retrieval Using Attribute Enhanced Sparse Code Words,"International Journal of Innovative Research in Science,
Engineering and Technology
(An ISO 3297: 2007 Certified Organization)
Vol. 3, Issue 5, May 2014
Sparse Code Words
ISSN: 2319-8753
Image Retrieval Using Attribute Enhanced
M.Balaganesh1, N.Arthi2
Associate Professor, Department of Computer Science and Engineering, SRV Engineering College, sembodai, india1
P.G. Student, Department of Computer Science and Engineering, SRV Engineering College, sembodai, India 2"
e19ebad4739d59f999d192bac7d596b20b887f78,Learning Gating ConvNet for Two-Stream based Methods in Action Recognition,"Learning Gating ConvNet for Two-Stream based Methods in Action
Recognition
Jiagang Zhu1,2, Wei Zou1, Zheng Zhu1,2"
e1d726d812554f2b2b92cac3a4d2bec678969368,Human Action Recognition Bases on Local Action Attributes,"J Electr Eng Technol.2015; 10(?): 30-40
http://dx.doi.org/10.5370/JEET.2015.10.2.030
ISSN(Print)
975-0102
ISSN(Online)  2093-7423
Human Action Recognition Bases on Local Action Attributes
Jing  Zhang*, Hong Liu*, Weizhi Nie† Lekha Chaisorn**, Yongkang Wong**
nd Mohan S Kankanhalli**"
e1e6e6792e92f7110e26e27e80e0c30ec36ac9c2,Ranking with Adaptive Neighbors,"TSINGHUA SCIENCE AND TECHNOLOGY
ISSNll1007-0214
0?/?? pp???–???
DOI: 10.26599/TST.2018.9010000
Volume 1, Number 1, Septembelr 2018
Ranking with Adaptive Neighbors
Muge Li, Liangyue Li, and Feiping Nie∗"
cd9666858f6c211e13aa80589d75373fd06f6246,A Novel Time Series Kernel for Sequences Generated by LTI Systems,"A Novel Time Series Kernel for
Sequences Generated by LTI Systems
Liliana Lo Presti, Marco La Cascia
V.le delle Scienze Ed.6, DIID, Universit´a degli studi di Palermo, Italy"
cd444ee7f165032b97ee76b21b9ff58c10750570,Table of Contents.,"UNIVERSITY OF CALIFORNIA,
IRVINE
Relational Models for Human-Object Interactions and Object Affordances
DISSERTATION
submitted in partial satisfaction of the requirements
for the degree of
DOCTOR OF PHILOSOPHY
in Computer Science
Chaitanya Desai
Dissertation Committee:
Professor Deva Ramanan, Chair
Professor Charless Fowlkes
Professor Padhraic Smyth
Professor Serge Belongie"
cd596a2682d74bdfa7b7160dd070b598975e89d9,Mood Detection: Implementing a facial expression recognition system,"Mood Detection: Implementing a facial
expression recognition system
Neeraj Agrawal, Rob Cosgriff and Ritvik Mudur
. Introduction
Facial  expressions  play  a  significant  role  in  human  dialogue.  As  a  result,  there  has  been
onsiderable work done on the recognition of emotional expressions and the  application of this
research  will  be  beneficial  in  improving  human-machine  dialogue.  One  can  imagine  the
improvements  to  computer  interfaces,  automated  clinical  (psychological)  research  or  even
interactions between humans and autonomous robots.
Unfortunately,  a  lot  of  the  literature  does  not  focus  on  trying  to  achieve  high  recognition  rates
cross  multiple  databases.  In  this  project  we  develop  our  own  mood  detection  system  that
ddresses  this  challenge.  The  system  involves  pre-processing  image  data  by  normalizing  and
pplying a simple mask, extracting certain (facial) features using PCA and Gabor filters and then
using SVMs for classification and recognition of expressions. Eigenfaces for each class are used
to  determine  class-specific  masks  which  are  then  applied  to  the  image  data  and  used  to  train
multiple,  one  against  the  rest,  SVMs.  We  find  that  simply  using  normalized  pixel  intensities
works well with such an approach.
Figure 1 – Overview of our system design
. Image pre-processing
We performed pre-processing on the images used to train and test our algorithms as follows:"
cda4fb9df653b5721ad4fe8b4a88468a410e55ec,Gabor wavelet transform and its application,"Gabor wavelet transform and its application
Wei-lun Chao R98942073"
cd3005753012409361aba17f3f766e33e3a7320d,Multilinear Biased Discriminant Analysis: A Novel Method for Facial Action Unit Representation,"Multilinear Biased Discriminant Analysis: A Novel Method for Facial
Action Unit Representation
Mahmoud Khademi†, Mehran Safayani†and Mohammad T. Manzuri-Shalmani†
: Sharif University of Tech., DSP Lab,"
cd687ddbd89a832f51d5510c478942800a3e6854,A game to crowdsource data for affective computing,"A Game to Crowdsource Data for Affective Computing
Chek Tien Tan
Hemanta Sapkota
Daniel Rosser
Yusuf Pisan
Games Studio, Faculty of Engineering and IT, University of Technology, Sydney"
cd7a7be3804fd217e9f10682e0c0bfd9583a08db,Women also Snowboard: Overcoming Bias in Captioning Models,"Women also Snowboard:
Overcoming Bias in Captioning Models
Lisa Anne Hendricks * 1 Kaylee Burns * 1 Kate Saenko 2 Trevor Darrell 1 Anna Rohrbach 1"
ccfcbf0eda6df876f0170bdb4d7b4ab4e7676f18,A Dynamic Appearance Descriptor Approach to Facial Actions Temporal Modeling,"JOURNAL OF LATEX CLASS FILES, VOL. 6, NO. 1, JUNE 2011
A Dynamic Appearance Descriptor Approach to
Facial Actions Temporal Modelling
Bihan Jiang, Student Member, IEEE, Michel Valstar, Member, IEEE, Brais Martinez, Member, IEEE, and
Maja Pantic, Fellow, IEEE"
cc3c273bb213240515147e8be68c50f7ea22777c,Gaining Insight Into Films Via Topic Modeling & Visualization,"Gaining Insight Into Films
Via Topic Modeling & Visualization
MISHA RABINOVICH, MFA
YOGESH GIRDHAR, PHD
KEYWORDS Collaboration, computer vision, cultural
nalytics, economy of abundance, interactive data
visualization
We moved beyond misuse when the software actually
ecame useful for film analysis with the addition of audio
nalysis, subtitle analysis, facial recognition, and topic
modeling. Using multiple types of visualizations and
back-and-fourth workflow between people and AI
we arrived at an approach for cultural analytics that
an be used to review and develop film criticism. Finally,
we present ways to apply these techniques to Database
Cinema and other aspects of film and video creation.
PROJECT DATE 2014
URL http://misharabinovich.com/soyummy.html"
cc8e378fd05152a81c2810f682a78c5057c8a735,Expression Invariant Face Recognition System based on Topographic Independent Component Analysis and Inner Product Classifier,"International Journal of Computer Sciences and Engineering    Open Access
Research Paper                                          Volume-5, Issue-12                                          E-ISSN: 2347-2693
Expression Invariant Face Recognition System based on Topographic
Independent Component Analysis and Inner Product Classifier
Aruna Bhat
Department of Electrical Engineering, IIT Delhi, New Delhi, India
*Corresponding Author:
Available online at: www.ijcseonline.org
Received: 07/Nov/2017, Revised: 22/Nov/2017, Accepted: 14/Dec/2017, Published: 31/Dec/2017"
ccf43c62e4bf76b6a48ff588ef7ed51e87ddf50b,Nutraceuticals and Cosmeceuticals for Human Beings–An Overview,"American Journal of Food Science and Health
Vol. 2, No. 2, 2016, pp. 7-17
http://www.aiscience.org/journal/ajfsh
ISSN: 2381-7216 (Print); ISSN: 2381-7224 (Online)
Nutraceuticals and Cosmeceuticals for Human
Beings–An Overview
R. Ramasubramania Raja*
Department of Pharmacognosy, Narayana Pharmacy College, Nellore, India"
cc31db984282bb70946f6881bab741aa841d3a7c,Learning Grimaces by Watching TV,"ALBANIE, VEDALDI: LEARNING GRIMACES BY WATCHING TV
Learning Grimaces by Watching TV
Samuel Albanie
http://www.robots.ox.ac.uk/~albanie
Andrea Vedaldi
http://www.robots.ox.ac.uk/~vedaldi
Engineering Science Department
Univeristy of Oxford
Oxford, UK"
cc91001f9d299ad70deb6453d55b2c0b967f8c0d,Performance Enhancement of Face Recognition in Smart TV Using Symmetrical Fuzzy-Based Quality Assessment,"OPEN ACCESS
ISSN 2073-8994
Article
Performance Enhancement of Face Recognition in Smart TV
Using Symmetrical Fuzzy-Based Quality Assessment
Yeong Gon Kim, Won Oh Lee, Ki Wan Kim, Hyung Gil Hong and Kang Ryoung Park *
Division of Electronics and Electrical Engineering, Dongguk University, 26 Pil-dong 3-ga, Jung-gu,
Seoul 100-715, Korea; E-Mails: (Y.G.K.); (W.O.L.);
(K.W.K.); (H.G.H.)
*  Author to whom correspondence should be addressed; E-Mail:
Tel.: +82-10-3111-7022; Fax: +82-2-2277-8735.
Academic Editor: Christopher Tyler
Received: 31 March 2015 / Accepted: 21 August 2015 / Published: 25 August 2015"
cc96eab1e55e771e417b758119ce5d7ef1722b43,An Empirical Study of Recent Face Alignment Methods,"An Empirical Study of Recent
Face Alignment Methods
Heng Yang, Xuhui Jia, Chen Change Loy and Peter Robinson"
e64b683e32525643a9ddb6b6af8b0472ef5b6a37,Face Recognition and Retrieval in Video,"Face Recognition and Retrieval in Video
Caifeng Shan"
e6b45d5a86092bbfdcd6c3c54cda3d6c3ac6b227,Pairwise Relational Networks for Face Recognition,"Pairwise Relational Networks for Face
Recognition
Bong-Nam Kang1[0000−0002−6818−7532], Yonghyun Kim2[0000−0003−0038−7850],
nd Daijin Kim1,2[0000−0002−8046−8521]
Department of Creative IT Engineering, POSTECH, Korea
Department of Computer Science and Engineering, POSTECH, Korea"
e6865b000cf4d4e84c3fe895b7ddfc65a9c4aaec,"Tobias Siebenlist , Kathrin Knautz Chapter 15 . The critical role of the cold - start problem and incentive systems in emotional Web 2 . 0 services","Tobias Siebenlist, Kathrin Knautz
Chapter 15. The critical role of the
old-start problem and incentive systems
in emotional Web 2.0 services"
e6d689054e87ad3b8fbbb70714d48712ad84dc1c,Robust Facial Feature Tracking,"Robust Facial Feature Tracking
Fabrice Bourel, Claude C. Chibelushi, Adrian A. Low
School of Computing, Staffordshire University
Stafford ST18 0DG"
e6dc1200a31defda100b2e5ddb27fb7ecbbd4acd,Flexible Manifold Embedding: A Framework for Semi-Supervised and Unsupervised Dimension Reduction,"Flexible Manifold Embedding: A Framework
for Semi-Supervised and Unsupervised
Dimension Reduction
Feiping Nie, Dong Xu, Member, IEEE, Ivor Wai-Hung Tsang, and Changshui Zhang, Member, IEEE
, the linear regression function ("
e6e5a6090016810fb902b51d5baa2469ae28b8a1,Energy-Efficient Deep In-memory Architecture for NAND Flash Memories,"Title
Energy-Efficient Deep In-memory Architecture for NAND
Flash Memories
Archived version
Accepted manuscript: the content is same as the published
paper but without the final typesetting by the publisher
Published version
Published paper
Authors (contact)
0.1109/ISCAS.2018.8351458"
e6178de1ef15a6a973aad2791ce5fbabc2cb8ae5,Improving Facial Landmark Detection via a Super-Resolution Inception Network,"Improving Facial Landmark Detection via a
Super-Resolution Inception Network
Martin Knoche, Daniel Merget, Gerhard Rigoll
Institute for Human-Machine Communication
Technical University of Munich, Germany"
f9784db8ff805439f0a6b6e15aeaf892dba47ca0,"Comparing the performance of Emotion-Recognition Implementations in OpenCV, Cognitive Services, and Google Vision APIs","Comparing the performance of Emotion-Recognition Implementations
in OpenCV, Cognitive Services, and Google Vision APIs
LUIS ANTONIO BELTRÁN PRIETO, ZUZANA KOMÍNKOVÁ OPLATKOVÁ
Department of Informatics and Artificial Intelligence
Tomas Bata University in Zlín
Nad Stráněmi 4511, 76005, Zlín
CZECH REPUBLIC"
f935225e7811858fe9ef6b5fd3fdd59aec9abd1a,Spatiotemporal dynamics and connectivity pattern differences between centrally and peripherally presented faces.,"www.elsevier.com/locate/ynimg
Spatiotemporal dynamics and connectivity pattern differences
etween centrally and peripherally presented faces
Lichan Liu and Andreas A. Ioannides*
Laboratory for Human Brain Dynamics, RIKEN Brain Science Institute (BSI), 2-1 Hirosawa, Wakoshi, Saitama, 351-0198, Japan
Received 4 May 2005; revised 26 January 2006; accepted 6 February 2006
Available online 24 March 2006
Most neuroimaging studies on face processing used centrally presented
images with a relatively large visual field. Images presented in this way
ctivate widespread striate and extrastriate areas and make it difficult
to study spatiotemporal dynamics and connectivity pattern differences
from various parts of the visual field. Here we studied magneto-
encephalographic responses in humans to centrally and peripherally
presented faces for testing the hypothesis that processing of visual
stimuli with facial expressions of emotions depends on where the
stimuli are presented in the visual field. Using our tomographic and
statistical parametric mapping analyses, we identified occipitotemporal
reas activated by face stimuli more than by control conditions. V1/V2
ctivity was significantly stronger for lower than central and upper
visual field presentation. Fusiform activity, however, was significantly"
f93606d362fcbe62550d0bf1b3edeb7be684b000,Nearest Neighbor Classifier Based on Nearest Feature Decisions,"The Computer Journal Advance Access published February 1, 2012
© The Author 2012. Published by Oxford University Press on behalf of The British Computer Society. All rights reserved.
For Permissions, please email:
doi:10.1093/comjnl/bxs001
Nearest Neighbor Classifier Based
on Nearest Feature Decisions
Alex Pappachen James1,∗ and Sima Dimitrijev2
Machine Intelligence Group, School of Computer Science, Indian Institute of Information Technology and
Queensland Micro- and Nanotechnology Centre and Griffith School of Engineering, Griffith University,
Management, Kerala, India
Nathan, Australia
Corresponding author:
High feature dimensionality of realistic datasets adversely affects the recognition accuracy of nearest
neighbor (NN) classifiers. To address this issue, we introduce a nearest feature classifier that shifts
the NN concept from the global-decision level to the level of individual features. Performance
omparisons with 12 instance-based classifiers on 13 benchmark University of California Irvine
lassification datasets show average improvements of 6 and 3.5% in recognition accuracy and
rea under curve performance measures, respectively. The statistical significance of the observed
performance improvements is verified by the Friedman test and by the post hoc Bonferroni–Dunn
test. In addition, the application of the classifier is demonstrated on face recognition databases, a"
f997a71f1e54d044184240b38d9dc680b3bbbbc0,Deep Cross Modal Learning for Caricature Verification and Identification(CaVINet),"Deep Cross Modal Learning for Caricature Verification and
Identification(CaVINet)
https://lsaiml.github.io/CaVINet/
Jatin Garg∗
Indian Institute of Technology Ropar
Himanshu Tolani∗
Indian Institute of Technology Ropar
Skand Vishwanath Peri∗
Indian Institute of Technology Ropar
Narayanan C Krishnan
Indian Institute of Technology Ropar"
f9d1f12070e5267afc60828002137af949ff1544,Maximum Entropy Binary Encoding for Face Template Protection,"Maximum Entropy Binary Encoding for Face Template Protection
Rohit Kumar Pandey
Yingbo Zhou
Bhargava Urala Kota
Venu Govindaraju
University at Buffalo, SUNY
{rpandey, yingbozh, buralako,"
f0cee87e9ecedeb927664b8da44b8649050e1c86,Image Ordinal Classification and Understanding: Grid Dropout with Masking Label,"label:(1, 0, 1, 0, 1, 1, 1, 1, 1)Masking label:(0, 1, 1, 1, 0, 1, 1, 1, 1)Entire imageInput imageNeuron dropout’s gradCAMGrid dropout’s gradCAMFig.1.Above:imageordinalclassificationwithrandomlyblackoutpatches.Itiseasyforhumantorecognizetheageregardlessofthemissingpatches.Themaskinglabelisalsousefultoimageclassification.Bottom:griddropout’sgrad-CAMisbetterthanthatofneurondropout.Thatistosay,griddropoutcanhelplearningfeaturerepresentation.problem[1].Withtheproliferationofconvolutionalneuralnetwork(CNN),workshavebeencarriedoutonordinalclas-sificationwithCNN[1][2][3].Thoughgoodperformanceshavebeenloggedwithmoderndeeplearningapproaches,therearetwoproblemsinimageordinalclassification.Ononehand,theamountofordinaltrainingdataisverylim-itedwhichprohibitstrainingcomplexmodelsproperly,andtomakemattersworse,collectinglargetrainingdatasetwithordinallabelisdifficult,evenharderthanlabellinggenericdataset.Therefore,insufficienttrainingdataincreasestheriskofoverfitting.Ontheotherhand,lessstudiesareconductedtounderstandwhatdeepmodelshavelearnedonordinaldata978-1-5386-1737-3/18/$31.00c(cid:13)2018IEEE"
f0f4f16d5b5f9efe304369120651fa688a03d495,Temporal Generative Adversarial Nets,"Temporal Generative Adversarial Nets
Masaki Saito∗
Eiichi Matsumoto∗
Preferred Networks inc., Japan
{msaito,"
f0ae807627f81acb63eb5837c75a1e895a92c376,Facial Landmark Detection using Ensemble of Cascaded Regressions,"International Journal of Emerging Engineering Research and Technology
Volume 3, Issue 12, December 2015, PP 128-133
ISSN 2349-4395 (Print) & ISSN 2349-4409 (Online)
Facial  Landmark  Detection  using  Ensemble  of  Cascaded
Regressions
Martin Penev1*, Ognian Boumbarov2
Faculty of Telecommunications, Technical University, Sofia, Bulgaria
Faculty of Telecommunications, Technical University, Sofia, Bulgaria"
f0a3f12469fa55ad0d40c21212d18c02be0d1264,Sparsity Sharing Embedding for Face Verification,"Sparsity Sharing Embedding for Face
Verification
Donghoon Lee, Hyunsin Park, Junyoung Chung,
Youngook Song, and Chang D. Yoo
Department of Electrical Engineering, KAIST, Daejeon, Korea"
f740bac1484f2f2c70777db6d2a11cf4280081d6,Soft Locality Preserving Map (SLPM) for Facial Expression Recognition,"Soft Locality Preserving Map (SLPM) for Facial Expression
Recognition
Cigdem Turana,*, Kin-Man Lama, Xiangjian Heb
Centre for Signal Processing, Department of Electronic and Information Engineering, The Hong
Kong Polytechnic University, Kowloon, Hong Kong
Computer Science, School of Electrical and Data Engineering, University of Technology, Sydney,
Australia
E-mail addresses: (C. Turan), (K.-M. Lam),
(X. He)"
f79c97e7c3f9a98cf6f4a5d2431f149ffacae48f,Title On color texture normalization for active appearance models,"Provided by the author(s) and NUI Galway in accordance with publisher policies. Please cite the published
version when available.
Title
On color texture normalization for active appearance models
Author(s)
Ionita, Mircea C.; Corcoran, Peter M.; Buzuloiu, Vasile
Publication
009-05-12
Publication
Information
Ionita, M. C., Corcoran, P., & Buzuloiu, V. (2009). On Color
Texture Normalization for Active Appearance Models. Image
Processing, IEEE Transactions on, 18(6), 1372-1378.
Publisher
Link to
publisher's
version
http://dx.doi.org/10.1109/TIP.2009.2017163
Item record
http://hdl.handle.net/10379/1350"
f7dcadc5288653ec6764600c7c1e2b49c305dfaa,Interactive Image Search with Attributes by,"Copyright
Adriana Ivanova Kovashka"
f7de943aa75406fe5568fdbb08133ce0f9a765d4,Biometric Identification and Surveillance1,"Project 1.5: Human Identification at a Distance - Hornak, Adjeroh, Cukic, Gautum, & Ross
Project 1.5
Biometric Identification and Surveillance1
Don Adjeroh, Bojan Cukic, Arun Ross – West Virginia University
Year 5 Deliverable
Technical Report:
Research Challenges in Biometrics
Indexed biography of relevant biometric research literature
Donald Adjeroh, Bojan Cukic, Arun Ross
April, 2014
""This research was supported by the United States Department of Homeland Security through the National Center for Border Security
nd Immigration (BORDERS) under grant number 2008-ST-061-BS0002. However, any opinions, findings, and conclusions or
recommendations in this document are those of the authors and do not necessarily reflect views of the United States Department of
Homeland Security."""
f75852386e563ca580a48b18420e446be45fcf8d,Illumination Invariant Face Recognition,"ILLUMINATION INVARIANT FACE RECOGNITION
Raghuraman Gopalan
ENEE 631: Digital Image and Video Processing
Instructor: Dr. K. J. Ray Liu
Term Project - Spring 2006
INTRODUCTION
The  performance  of  the  Face  Recognition  algorithms  is  severely  affected  by  two
important  factors:  the  change  in  Pose  and  Illumination  conditions  of  the  subjects.  The
hanges in Illumination conditions of the subjects can be so drastic that, the variation in
lighting will be of the similar order as that of the variation due to the change in subjects
[1] and this can result in misclassification.
For example, in the acquisition of the face of a person from a real time video, the
mbient  conditions  will  cause  different  lighting  variations  on  the  tracked  face.  Some
examples  of  images  with  different  illumination  conditions  are  shown  in  Fig.  1.  In  this
project, we study some algorithms that are capable of performing Illumination Invariant
Face Recognition. The performances of these algorithms were compared on the CMU-
Illumination dataset [13], by using the entire face as the input to the algorithms. Then, a
model  of  dividing  the  face  into  four  regions  is  proposed  and  the  performance  of  the
lgorithms on these new features is analyzed."
f77c9bf5beec7c975584e8087aae8d679664a1eb,Local Deep Neural Networks for Age and Gender Classification,"Local Deep Neural Networks for Age and Gender Classification
Zukang Liao, Stavros Petridis, Maja Pantic
March 27, 2017"
f7ba77d23a0eea5a3034a1833b2d2552cb42fb7a,LOTS about attacking deep features,"This is a pre-print of the original paper accepted at the International Joint Conference on Biometrics (IJCB) 2017.
LOTS about Attacking Deep Features
Andras Rozsa, Manuel G¨unther, and Terrance E. Boult
Vision and Security Technology (VAST) Lab
University of Colorado, Colorado Springs, USA"
e8686663aec64f4414eba6a0f821ab9eb9f93e38,Improving shape-based face recognition by means of a supervised discriminant Hausdorff distance,"IMPROVING SHAPE-BASED FACE RECOGNITION BY MEANS OF A SUPERVISED
DISCRIMINANT HAUSDORFF DISTANCE
J.L. Alba
, A. Pujol
, A. L´opez
nd J.J. Villanueva
Signal Theory and Communications Department, University of Vigo, Spain
Centre de Visio per Computador, Universitat Autonoma de Barcelona, Spain
Digital Pointer MVT"
e8fdacbd708feb60fd6e7843b048bf3c4387c6db,Deep Learning,"Deep Learning
Andreas Eilschou
Hinnerup Net A/S
www.hinnerup.net
July 4, 2014
Introduction
Deep learning is a topic in the field of artificial intelligence (AI) and is a relatively
new research area although based on the popular artificial neural networks (supposedly
mirroring brain function). With the development of the perceptron in the 1950s and
960s by Frank RosenBlatt, research began on artificial neural networks. To further
mimic the architectural depth of the brain, researchers wanted to train a deep multi-
layer neural network – this, however, did not happen until Geoffrey Hinton in 2006
introduced Deep Belief Networks [1].
Recently, the topic of deep learning has gained public interest. Large web companies such
s Google and Facebook have a focused research on AI and an ever increasing amount
of compute power, which has led to researchers finally being able to produce results
that are of interest to the general public. In July 2012 Google trained a deep learning
network on YouTube videos with the remarkable result that the network learned to
recognize humans as well as cats [6], and in January this year Google successfully used
deep learning on Street View images to automatically recognize house numbers with"
e8b2a98f87b7b2593b4a046464c1ec63bfd13b51,CMS-RCNN: Contextual Multi-Scale Region-based CNN for Unconstrained Face Detection,"CMS-RCNN: Contextual Multi-Scale
Region-based CNN for Unconstrained Face
Detection
Chenchen Zhu*, Student, IEEE, Yutong Zheng*, Student, IEEE,
Khoa Luu, Member, IEEE, Marios Savvides, Senior Member, IEEE"
e8d1b134d48eb0928bc999923a4e092537e106f6,Weighted Multi-region Convolutional Neural Network for Action Recognition with Low-latency Online Prediction,"WEIGHTED MULTI-REGION CONVOLUTIONAL NEURAL NETWORK FOR ACTION
RECOGNITION WITH LOW-LATENCY ONLINE PREDICTION
Yunfeng Wang(cid:63), Wengang Zhou(cid:63), Qilin Zhang†, Xiaotian Zhu(cid:63), Houqiang Li(cid:63)
(cid:63)University of Science and Technology of China, Hefei, Anhui, China
HERE Technologies, Chicago, Illinois, USA"
e8c6c3fc9b52dffb15fe115702c6f159d955d308,Linear Subspace Learning for Facial Expression Analysis,"Linear Subspace Learning for
Facial Expression Analysis
Caifeng Shan
Philips Research
The Netherlands
. Introduction
Facial  expression,  resulting  from  movements  of  the  facial  muscles,  is  one  of  the  most
powerful, natural, and immediate means for human beings to communicate their emotions
nd intentions. Some examples of facial expressions are shown in Fig. 1. Darwin (1872) was
the  first  to  describe  in  detail  the  specific  facial  expressions  associated  with  emotions  in
nimals  and  humans;  he  argued  that  all  mammals  show  emotions  reliably  in  their  faces.
Psychological  studies  (Mehrabian,  1968;  Ambady  &  Rosenthal,  1992)  indicate  that  facial
expressions, with other non-verbal cues, play a major and fundamental role in face-to-face
ommunication.
Fig. 1. Facial expressions of George W. Bush.
Machine  analysis  of  facial  expressions,  enabling  computers  to  analyze  and  interpret  facial
expressions  as  humans  do,  has  many  important  applications  including  intelligent  human-
omputer  interaction,  computer  animation,  surveillance  and  security,  medical  diagnosis,
law  enforcement,  and  awareness  system  (Shan,  2007).  Driven  by  its  potential  applications
nd  theoretical  interests  of  cognitive  and  psychological  scientists,  automatic  facial"
fab83bf8d7cab8fe069796b33d2a6bd70c8cefc6,Draft: Evaluation Guidelines for Gender Classification and Age Estimation,"Draft: Evaluation Guidelines for Gender
Classification and Age Estimation
Tobias Gehrig, Matthias Steiner, Hazım Kemal Ekenel
{tobias.gehrig,
July 1, 2011
Introduction
In previous research on gender classification and age estimation did not use a
standardised evaluation procedure. This makes comparison the different ap-
proaches dif‌f‌icult.
Thus we propose here a benchmarking and evaluation protocol for gender
lassification as well as age estimation to set a common ground for future re-
search in these two areas.
The evaluations are designed such that there is one scenario under controlled
labratory conditions and one under uncontrolled real life conditions.
The datasets were selected with the criteria of being publicly available for
research purposes.
File lists for the folds corresponding to the individual benchmarking proto-
ols will be provided over our website at http://face.cs.kit.edu/befit. We
will provide two kinds of folds for each of the tasks and conditions: one set of
folds using the whole dataset and one set of folds using a reduced dataset, which"
fa08a4da5f2fa39632d90ce3a2e1688d147ece61,Supplementary material for “ Unsupervised Creation of Parameterized Avatars ” 1 Summary of Notations,"Supplementary material for
“Unsupervised Creation of Parameterized Avatars”
Summary of Notations
Tab. 1 itemizes the symbols used in the submission. Fig. 2,3,4 of the main text illustrate many of these
symbols.
DANN results
Fig. 1 shows side by side samples of the original image and the emoji generated by the method of [1].
As can be seen, these results do not preserve the identity very well, despite considerable effort invested in
finding suitable architectures.
Multiple Images Per Person
Following [4], we evaluate the visual quality that is obtained per person and not just per image, by testing
TOS on the Facescrub dataset [3]. For each person p, we considered the set of their images Xp, and selected
the emoji that was most similar to their source image, i.e., the one for which:
||f (x) − f (e(c(G(x))))||.
rgmin
Fig. 2 depicts the results obtained by this selection method on sample images form the Facescrub dataset
(it is an extension of Fig. 7 of the main text). The figure also shows, for comparison, the DTN [4] result for
the same image.
Detailed Architecture of the Various Networks
In this section we describe the architectures of the networks used in for the emoji and avatar experiments."
fa24bf887d3b3f6f58f8305dcd076f0ccc30272a,Interval Insensitive Loss for Ordinal Classification,"JMLR: Workshop and Conference Proceedings 39:189–204, 2014
ACML 2014
Interval Insensitive Loss for Ordinal Classification
Kostiantyn Antoniuk
Vojtˇech Franc
V´aclav Hlav´aˇc
Center for Machine Perception, Department of Cybernetics, Faculty of Electrical Engineering, Czech
Technical University in Prague, Technick´a 2, 166 27 Prague 6 Czech Republic
Editor: Dinh Phung and Hang Li"
fafe69a00565895c7d57ad09ef44ce9ddd5a6caa,Gaussian Mixture Models for Human Face Recognition under Illumination Variations,"Applied Mathematics, 2012, 3, 2071-2079
http://dx.doi.org/10.4236/am.2012.312A286 Published Online December 2012 (http://www.SciRP.org/journal/am)
Gaussian Mixture Models for Human Face Recognition
under Illumination Variations
Information Systems and Decision Sciences Department, Mihaylo College of Business and Economics,
California State University, Fullerton, USA
Email:
Sinjini Mitra
Received August 18, 2012; revised September 18, 2012; accepted September 25, 2012"
faca1c97ac2df9d972c0766a296efcf101aaf969,Sympathy for the Details: Dense Trajectories and Hybrid Classification Architectures for Action Recognition,"Sympathy for the Details: Dense Trajectories and Hybrid
Classification Architectures for Action Recognition
C´esar Roberto de Souza1,2, Adrien Gaidon1, Eleonora Vig3, Antonio Manuel L´opez2
Computer Vision Group, Xerox Research Center Europe, Meylan, France
Centre de Visi´o per Computador, Universitat Aut`onoma de Barcelona, Bellaterra, Spain
German Aerospace Center, Wessling, Germany
{cesar.desouza,"
fab60b3db164327be8588bce6ce5e45d5b882db6,Maximum A Posteriori Estimation of Distances Between Deep Features in Still-to-Video Face Recognition,"Maximum A Posteriori Estimation of Distances
Between Deep Features in Still-to-Video Face
Recognition
Andrey V. Savchenko
National Research University Higher School of Economics
Laboratory of Algorithms and Technologies for Network Analysis,
6 Rodionova St., Nizhny Novgorod, Russia
Natalya S. Belova
National Research University Higher School of Economics
0 Myasnitskaya St., Moscow, Russia
September 2, 2018"
fad895771260048f58d12158a4d4d6d0623f4158,Audio-visual emotion recognition for natural human-robot interaction,"Audio-Visual Emotion
Recognition For Natural
Human-Robot Interaction
Dissertation zur Erlangung des akademischen Grades
Doktor der Ingenieurwissenschaften (Dr.-Ing.)
vorgelegt von
Ahmad Rabie
n der Technischen Fakultät der Universität Bielefeld
5. März 2010"
ff8315c1a0587563510195356c9153729b533c5b,Zapping Index:Using Smile to Measure Advertisement Zapping Likelihood,"Zapping Index:Using Smile to Measure
Advertisement Zapping Likelihood
Songfan Yang, Member, IEEE, Mehran Kafai, Member, IEEE,
Le An, Student Member, IEEE, and Bir Bhanu, Fellow, IEEE"
ff44d8938c52cfdca48c80f8e1618bbcbf91cb2a,Towards Video Captioning with Naming: A Novel Dataset and a Multi-modal Approach,"Towards Video Captioning with Naming: a
Novel Dataset and a Multi-Modal Approach
Stefano Pini, Marcella Cornia, Lorenzo Baraldi, Rita Cucchiara
Dipartimento di Ingegneria “Enzo Ferrari”
Universit`a degli Studi di Modena e Reggio Emilia"
fffefc1fb840da63e17428fd5de6e79feb726894,Fine-Grained Age Estimation in the wild with Attention LSTM Networks,"Fine-Grained Age Estimation in the wild with
Attention LSTM Networks
Ke Zhang, Member, IEEE, Na Liu, Xingfang Yuan, Student Member, IEEE, Xinyao Guo, Ce Gao,
nd Zhenbing Zhao Member, IEEE,"
ff398e7b6584d9a692e70c2170b4eecaddd78357,Title of dissertation : FACE RECOGNITION AND VERIFICATION IN UNCONSTRAINED ENVIRIONMENTS,
ffd81d784549ee51a9b0b7b8aaf20d5581031b74,Performance Analysis of Retina and DoG Filtering Applied to Face Images for Training Correlation Filters,"Performance Analysis of Retina and DoG
Filtering Applied to Face Images for Training
Correlation Filters
Everardo Santiago Ram(cid:19)(cid:16)rez1, Jos(cid:19)e (cid:19)Angel Gonz(cid:19)alez Fraga1, Omar (cid:19)Alvarez
Xochihua1, Everardo Gutierrez L(cid:19)opez1, and Sergio Omar Infante Prieto2
Facultad de Ciencias, Universidad Aut(cid:19)onoma de Baja California,
Carretera Transpeninsular Tijuana-Ensenada, N(cid:19)um. 3917, Colonia Playitas,
Ensenada, Baja California, C.P. 22860
{everardo.santiagoramirez,angel_fraga,
Facultad de Ingenier(cid:19)(cid:16)a, Arquitectura y Dise~no, Universidad Aut(cid:19)onoma de Baja
California, Carretera Transpeninsular Tijuana-Ensenada, N(cid:19)um. 3917, Colonia
Playitas, Ensenada, Baja California, C.P. 22860"
ff01bc3f49130d436fca24b987b7e3beedfa404d,Fuzzy System-Based Face Detection Robust to In-Plane Rotation Based on Symmetrical Characteristics of a Face,"Article
Fuzzy System-Based Face Detection Robust to
In-Plane Rotation Based on Symmetrical
Characteristics of a Face
Hyung Gil Hong, Won Oh Lee, Yeong Gon Kim, Ki Wan Kim, Dat Tien Nguyen and
Kang Ryoung Park *
Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu,
Seoul 100-715, Korea; (H.G.H.); (W.O.L.); (Y.G.K.);
(K.W.K.); (D.T.N.)
* Correspondence: Tel.: +82-10-3111-7022; Fax: +82-2-2277-8735
Academic Editor: Angel Garrido
Received: 15 June 2016; Accepted: 29 July 2016; Published: 3 August 2016"
ffc9d6a5f353e5aec3116a10cf685294979c63d9,Eigenphase-based face recognition: a comparison of phase- information extraction methods,"Eigenphase-based face recognition: a comparison of phase-
information extraction methods
Slobodan Ribarić, Marijo Maračić
Faculty of Electrical Engineering and Computing,
University of Zagreb, Unska 3, 10 000 Zagreb
E-mail:"
ff8ef43168b9c8dd467208a0b1b02e223b731254,BreakingNews: Article Annotation by Image and Text Processing,"BreakingNews: Article Annotation by
Image and Text Processing
Arnau Ramisa*, Fei Yan*, Francesc Moreno-Noguer,
nd Krystian Mikolajczyk"
ff9195f99a1a28ced431362f5363c9a5da47a37b,Serial dependence in the perception of attractiveness,"Journal of Vision (2016) 16(15):28, 1–8
Serial dependence in the perception of attractiveness
Ye Xia
Department of Psychology, University of California,
Berkeley, CA, USA
Allison Yamanashi Leib
Department of Psychology, University of California,
Berkeley, CA, USA
David Whitney
Department of Psychology, University of California,
Berkeley, CA, USA
Helen Wills Neuroscience Institute, University of
California, Berkeley, CA, USA
Vision Science Group, University of California,
Berkeley, CA, USA
The perception of attractiveness is essential for choices
of food, object, and mate preference. Like perception of
other visual features, perception of attractiveness is
stable despite constant changes of image properties due
to factors like occlusion, visual noise, and eye"
ffcbedb92e76fbab083bb2c57d846a2a96b5ae30,Sparse Dictionary Learning and Domain Adaptation for Face and Action Recognition,
ff7bc7a6d493e01ec8fa2b889bcaf6349101676e,Facial expression recognition with spatiotemporal local descriptors_v3.rtf,"Facial expression recognition with spatiotemporal local
descriptors
Guoying Zhao, Matti Pietikäinen
Machine  Vision  Group,  Infotech  Oulu  and  Department  of  Electrical  and
Information Engineering, P. O. Box 4500 FI-90014 University of Oulu, Finland
{gyzhao,"
ff46c41e9ea139d499dd349e78d7cc8be19f936c,A Novel Method for Movie Character Identification and its Facial Expression Recognition,"International Journal of Modern Engineering Research (IJMER)
www.ijmer.com              Vol.3, Issue.3, May-June. 2013 pp-1339-1342             ISSN: 2249-6645
A Novel Method for Movie Character Identification and its
Facial Expression Recognition
M. Dharmateja Purna, 1 N. Praveen2
M.Tech, Sri Sunflower College of Engineering & Technology, Lankapalli
Asst. Professor, Dept. of ECE, Sri Sunflower College of Engineering & Technology, Lankapalli"
ff5dd6f96e108d8233220cc262bc282229c1a582,Robust Facial Marks Detection Method Using AAM And SURF,"Ziaul Haque Choudhury, K.M. Mehata / International Journal of Engineering Research and
Applications (IJERA) ISSN: 2248-9622   www.ijera.com
Vol. 2, Issue 6, November- December 2012, pp.708-715
Robust Facial Marks Detection Method Using AAM And SURF
Ziaul Haque Choudhury, K.M. Mehata
Dept. of Information Technology, B.S. Abdur Rahman University, Chennai-48, India
Dept. of Computer Science & Engineering, B.S. Abdur Rahman University, Chennai-48, India"
c588c89a72f89eed29d42f34bfa5d4cffa530732,Attributes2Classname: A Discriminative Model for Attribute-Based Unsupervised Zero-Shot Learning,"Attributes2Classname: A discriminative model for attribute-based
unsupervised zero-shot learning
Berkan Demirel1,3, Ramazan Gokberk Cinbis2, Nazli Ikizler-Cinbis3
HAVELSAN Inc., 2Bilkent University, 3Hacettepe University"
c574c72b5ef1759b7fd41cf19a9dcd67e5473739,"COGNIMUSE: a multimodal video database annotated with saliency, events, semantics and emotion with application to summarization","Zlatintsi et al. EURASIP Journal on Image and Video Processing  (2017) 2017:54
DOI 10.1186/s13640-017-0194-1
EURASIP Journal on Image
nd Video Processing
RESEARCH
Open Access
COGNIMUSE: a multimodal video
database annotated with saliency, events,
semantics and emotion with application to
summarization
Athanasia Zlatintsi1*
Niki Efthymiou1, Katerina Pastra4, Alexandros Potamianos1 and Petros Maragos1
, Petros Koutras1, Georgios Evangelopoulos2, Nikolaos Malandrakis3,"
c5a561c662fc2b195ff80d2655cc5a13a44ffd2d,Using Language to Learn Structured Appearance Models for Image Annotation,"Using Language to Learn Structured Appearance
Models for Image Annotation
Michael Jamieson, Student Member, IEEE, Afsaneh Fazly, Suzanne Stevenson, Sven Dickinson, Member, IEEE,
Sven Wachsmuth, Member, IEEE"
c5c379a807e02cab2e57de45699ababe8d13fb6d,Facial Expression Recognition Using Sparse Representation,"Facial Expression Recognition Using Sparse Representation
SHIQING ZHANG 1, XIAOMING ZHAO 2, BICHENG LEI 1
School of Physics and Electronic Engineering
Taizhou University
Taizhou 318000
CHINA
2Department of Computer Science
Taizhou University
Taizhou 318000
CHINA"
c5ea084531212284ce3f1ca86a6209f0001de9d1,Audio-visual speech processing for multimedia localisation,"Audio-Visual Speech Processing for
Multimedia Localisation
Matthew Aaron Benatan
Submitted in accordance with the requirements
for the degree of Doctor of Philosophy
The University of Leeds
School of Computing
September 2016"
c5844de3fdf5e0069d08e235514863c8ef900eb7,A Study on Similarity Computations in Template Matching Technique for Identity Verification,"Lam S K et al. / (IJCSE) International Journal on Computer Science and Engineering
Vol. 02, No. 08, 2010, 2659-2665
A Study on Similarity Computations in Template
Matching Technique for Identity Verification
Lam, S. K., Yeong, C. Y., Yew, C. T., Chai, W. S., Suandi, S. A.
Intelligent Biometric Group, School of Electrical and Electronic Engineering
Engineering Campus, Universiti Sains Malaysia
4300 Nibong Tebal, Pulau Pinang, MALAYSIA
Email:"
c590c6c171392e9f66aab1bce337470c43b48f39,Emotion Recognition by Machine Learning Algorithms using Psychophysiological Signals,"Emotion Recognition by Machine Learning Algorithms using
Psychophysiological Signals
Eun-Hye Jang, 2Byoung-Jun Park, 3Sang-Hyeob Kim, 4Jin-Hun Sohn
, 2, 3 BT Convergence Technology Research Department, Electronics and Telecommunications
Research Institute, 138 Gajeongno, Yuseong-gu, Daejeon, 305-700, Republic of Korea,
*4Department of Psychology/Brain Research Institute, Chungnam National University 220,
Gung-dong, Yuseong-gu, Daejeon, 305-765, Republic of Korea,"
c2c3ff1778ed9c33c6e613417832505d33513c55,"Multimodal Biometric Person Authentication Using Fingerprint, Face Features","Multimodal Biometric Person Authentication
Using Fingerprint, Face Features
Tran Binh Long1, Le Hoang Thai2, and Tran Hanh1
Department of Computer Science, University of Lac Hong 10 Huynh Van Nghe,
DongNai 71000, Viet Nam
Department of Computer Science, Ho Chi Minh City University of Science
27 Nguyen Van Cu, HoChiMinh 70000, Viet Nam"
c27f64eaf48e88758f650e38fa4e043c16580d26,Title of the proposed research project: Subspace analysis using Locality Preserving Projection and its applications for image recognition,"Title of the proposed research project: Subspace analysis using Locality Preserving
Projection and its applications for image recognition
Research area: Data manifold learning for pattern recognition
Contact Details:
Name: Gitam C Shikkenawis
Email Address:
University:  Dhirubhai  Ambani  Institute  of  Information  and  Communication  Technology
(DA-IICT), Gandhinagar."
c220f457ad0b28886f8b3ef41f012dd0236cd91a,Crystal Loss and Quality Pooling for Unconstrained Face Verification and Recognition,"JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015
Crystal Loss and Quality Pooling for
Unconstrained Face Verification and Recognition
Rajeev Ranjan, Member, IEEE, Ankan Bansal, Hongyu Xu, Member, IEEE,
Swami Sankaranarayanan, Member, IEEE, Jun-Cheng Chen, Member, IEEE,
Carlos D Castillo, Member, IEEE, and Rama Chellappa, Fellow, IEEE"
c254b4c0f6d5a5a45680eb3742907ec93c3a222b,A Fusion-based Gender Recognition Method Using Facial Images,"A Fusion-based Gender Recognition Method
Using Facial Images
Benyamin Ghojogh, Saeed Bagheri Shouraki, Hoda Mohammadzade*, Ensieh Iranmehr"
c2e6daebb95c9dfc741af67464c98f1039127627,Efficient Measuring of Facial Action Unit Activation Intensities using Active Appearance Models,"MVA2013 IAPR International Conference on Machine Vision Applications, May 20-23, 2013, Kyoto, JAPAN
Ef‌f‌icient Measuring of Facial Action Unit Activation Intensities
using Active Appearance Models
Daniel Haase1, Michael Kemmler1, Orlando Guntinas-Lichius2, Joachim Denzler1
Computer Vision Group, Friedrich Schiller University of Jena, Germany
Department of Otolaryngology, University Hospital Jena, Germany"
f6f06be05981689b94809130e251f9e4bf932660,An Approach to Illumination and Expression Invariant Multiple Classifier Face Recognition,"An Approach to Illumination and Expression Invariant
International Journal of Computer Applications (0975 – 8887)
Volume 91 – No.15, April 2014
Multiple Classifier Face Recognition
Dalton Meitei Thounaojam
National Institute of Technology
Silchar
Assam: 788010
India
Hidangmayum Saxena Devi
National Institute of Technology
Silchar
Assam: 788010
India
Romesh Laishram
Manipur Institute of Technology
Imphal West: 795001
India"
f6742010372210d06e531e7df7df9c01a185e241,Dimensional Affect and Expression in Natural and Mediated Interaction,"Dimensional Affect and Expression in
Natural and Mediated Interaction
Michael J. Lyons
Ritsumeikan, University
Kyoto, Japan
October, 2007"
f6ca29516cce3fa346673a2aec550d8e671929a6,Algorithm for Face Matching Using Normalized Cross - Correlation,"International Journal of Engineering and Advanced Technology (IJEAT)
ISSN: 2249 – 8958, Volume-2, Issue-4, April 2013
Algorithm for Face Matching Using Normalized
Cross-Correlation
C. Saravanan, M. Surender"
f67a73c9dd1e05bfc51219e70536dbb49158f7bc,A Gaussian Mixture Model for Classifying the Human Age using DWT and Sammon Map,"Journal of Computer Science 10 (11): 2292-2298, 2014
ISSN: 1549-3636
© 2014 Nithyashri and Kulanthaivel, This open access article is distributed under a Creative Commons Attribution
(CC-BY) 3.0 license
A GAUSSIAN MIXTURE MODEL FOR CLASSIFYING THE
HUMAN AGE USING DWT AND SAMMON MAP
J. Nithyashri and 2G. Kulanthaivel
Department of Computer Science and Engineering, Sathyabama University, Chennai, India
Department of Electronics Engineering, NITTTR, Chennai, India
Received 2014-05-08; Revised 2014-05-23; Accepted 2014-11-28"
f6c70635241968a6d5fd5e03cde6907022091d64,Measuring Deformations and Illumination Changes in Images with Applications to Face Recognition,
f6ce34d6e4e445cc2c8a9b8ba624e971dd4144ca,Cross-Label Suppression: A Discriminative and Fast Dictionary Learning With Group Regularization,"Cross-label Suppression: A Discriminative and Fast
Dictionary Learning with Group Regularization
Xiudong Wang and Yuantao Gu∗
April 24, 2017"
f6fa97fbfa07691bc9ff28caf93d0998a767a5c1,K2-means for Fast and Accurate Large Scale Clustering,"k2-means for fast and accurate large scale clustering
Eirikur Agustsson
Computer Vision Lab
D-ITET
ETH Zurich
Radu Timofte
Computer Vision Lab
D-ITET
ETH Zurich
Luc Van Gool
ESAT, KU Leuven
D-ITET, ETH Zurich"
f6cf2108ec9d0f59124454d88045173aa328bd2e,Robust User Identification Based on Facial Action Units Unaffected by Users' Emotions,"Robust user identification based on facial action units
unaffected by users’ emotions
Ricardo Buettner
Aalen University, Germany"
f68f20868a6c46c2150ca70f412dc4b53e6a03c2,Differential Evolution to Optimize Hidden Markov Models Training: Application to Facial Expression Recognition,"Differential Evolution to Optimize
Hidden Markov Models Training:
Application to Facial Expression
Recognition
Khadoudja Ghanem, Amer Draa, Elvis Vyumvuhore and
Ars`ene Simbabawe
MISC Laboratory, Constantine 2 University, Constantine, Algeria
The base system in this paper uses Hidden Markov
Models (HMMs) to model dynamic relationships among
facial features in facial behavior interpretation and un-
derstanding field. The input of HMMs is a new set
of derived features from geometrical distances obtained
from detected and automatically tracked facial points.
Numerical data representation which is in the form of
multi-time series is transformed to a symbolic repre-
sentation in order to reduce dimensionality, extract the
most pertinent information and give a meaningful repre-
sentation to humans. The main problem of the use of
HMMs is that the training is generally trapped in local
minima, so we used the Differential Evolution (DE)"
e9ed17fd8bf1f3d343198e206a4a7e0561ad7e66,Cognitive Learning for Social Robot through Facial Expression from Video Input,"International Journal of Enhanced Research in Science Technology & Engineering, ISSN: 2319-7463
Vol. 3 Issue 1, January-2014, pp: (362-365), Impact Factor: 1.252, Available online at: www.erpublications.com
Cognitive Learning for Social Robot through
Facial Expression from Video Input
Neeraj Rai1, Deepak Rai2
Department of Automation & Robotics, 2Department of Computer Science & Engg.
,2Ajay Kumar Garg Engineering College, Ghaziabad, UP, India"
e988be047b28ba3b2f1e4cdba3e8c94026139fcf,Multi-Task Convolutional Neural Network for Pose-Invariant Face Recognition,"Multi-Task Convolutional Neural Network for
Pose-Invariant Face Recognition
Xi Yin and Xiaoming Liu Member, IEEE,"
e9d43231a403b4409633594fa6ccc518f035a135,Deformable Part Models with CNN Features,"Deformable Part Models with CNN Features
Pierre-Andr´e Savalle1, Stavros Tsogkas1,2, George Papandreou3, Iasonas
Kokkinos1,2
Ecole Centrale Paris,2 INRIA, 3TTI-Chicago (cid:63)"
e9fcd15bcb0f65565138dda292e0c71ef25ea8bb,Analysing Facial Regions for Face Recognition Using Forensic Protocols,"Repositorio Institucional de la Universidad Autónoma de Madrid
https://repositorio.uam.es
Esta es la versión de autor de la comunicación de congreso publicada en:
This is an author produced version of a paper published in:
Highlights on Practical Applications of Agents and Multi-Agent Systems:
International Workshops of PAAMS. Communications in Computer and
Information Science, Volumen 365. Springer, 2013. 223-230
DOI:    http://dx.doi.org/10.1007/978-3-642-38061-7_22
Copyright:  © 2013 Springer-Verlag
El acceso a la versión del editor puede requerir la suscripción del recurso
Access to the published version may require subscription"
e9363f4368b04aeaa6d6617db0a574844fc59338,BenchIP: Benchmarking Intelligence Processors,"BENCHIP: Benchmarking Intelligence
Processors
Jinhua Tao1, Zidong Du1,2, Qi Guo1,2, Huiying Lan1, Lei Zhang1
Shengyuan Zhou1, Lingjie Xu3, Cong Liu4, Haifeng Liu5, Shan Tang6
Allen Rush7,Willian Chen7, Shaoli Liu1,2, Yunji Chen1, Tianshi Chen1,2
ICT CAS,2Cambricon,3Alibaba Infrastructure Service, Alibaba Group
IFLYTEK,5JD,6RDA Microelectronics,7AMD"
f16a605abb5857c39a10709bd9f9d14cdaa7918f,Fast greyscale road sign model matching and recognition,"Fast greyscale road sign model matching
nd recognition
Sergio Escalera and Petia Radeva
Centre de Visió per Computador
Edifici O – Campus UAB, 08193 Bellaterra, Barcelona, Catalonia, Spain"
f1aa120fb720f6cfaab13aea4b8379275e6d40a2,InverseFaceNet: Deep Single-Shot Inverse Face Rendering From A Single Image,"InverseFaceNet: Deep Single-Shot Inverse Face Rendering From A Single Image
Hyeongwoo Kim1
Justus Thies2
Max-Planck-Institute for Informatics
Michael Zollhöfer1
Christian Richardt3
University of Erlangen-Nuremberg 3 University of Bath
Christian Theobalt1
Ayush Tewari1
Figure 1. Our single-shot deep inverse face renderer InverseFaceNet obtains a high-quality geometry, reflectance and illumination estimate
from just a single input image. We jointly recover the face pose, shape, expression, reflectance and incident scene illumination. From left to
right: input photo, our estimated face model, its geometry, and the pointwise Euclidean error compared to Garrido et al. [14]."
f1ba2fe3491c715ded9677862fea966b32ca81f0,Face Tracking and Recognition in Videos : HMM Vs KNN,"ISSN: 2321-7782 (Online)
Volume 1, Issue 7, December 2013
International Journal of Advance Research in
Computer Science and Management Studies
Research Paper
Available online at: www.ijarcsms.com
Face Tracking and Recognition in Videos:
HMM Vs KNN
Madhumita R. Baviskar
Assistant Professor
Department of Computer Engineering
MIT College of Engineering (Pune University)
Pune - India"
f1d090fcea63d9f9e835c49352a3cd576ec899c1,Single-hidden Layer Feedforward Neual network training using class geometric information,"Iosifidis, A., Tefas, A., & Pitas, I. (2015). Single-Hidden Layer Feedforward
Neual Network Training Using Class Geometric Information. In . J. J.
Merelo, A. Rosa, J. M. Cadenas, A. Dourado, K. Madani, & J. Filipe (Eds.),
Computational Intelligence: International Joint Conference, IJCCI 2014
Rome, Italy, October 22-24, 2014 Revised Selected Papers. (Vol. III, pp.
51-364). (Studies in Computational Intelligence; Vol. 620). Springer. DOI:
0.1007/978-3-319-26393-9_21
Peer reviewed version
Link to published version (if available):
0.1007/978-3-319-26393-9_21
Link to publication record in Explore Bristol Research
PDF-document
University of Bristol - Explore Bristol Research
General rights
This document is made available in accordance with publisher policies. Please cite only the published
version using the reference above. Full terms of use are available:
http://www.bristol.ac.uk/pure/about/ebr-terms.html"
f113aed343bcac1021dc3e57ba6cc0647a8f5ce1,A Survey on Mining of Weakly Labeled Web Facial Images and Annotation,"International Journal of Science and Research (IJSR)
ISSN (Online): 2319-7064
Index Copernicus Value (2013): 6.14 | Impact Factor (2014): 5.611
A Survey on Mining of Weakly Labeled Web Facial
Images and Annotation
Tarang Boharupi1, Pranjali Joshi2
Pune Institute of Computer Technology, Pune, India
Professor, Pune Institute of Computer Technology, Pune, India
the  proposed  system  which"
f19777e37321f79e34462fc4c416bd56772031bf,Literature Review of Image Compression Algorithm,"International Journal of Scientific & Engineering Research, Volume 3, Issue 6, June-2012                                                                                         1
ISSN 2229-5518
Literature Review of Image Compression Algorithm
Dr. B. Chandrasekhar
Padmaja.V.K
email:                                                           email::
Jawaharlal Technological University, Anantapur"
f19ab817dd1ef64ee94e94689b0daae0f686e849,Blickrichtungsunabhängige Erkennung von Personen in Bild- und Tiefendaten,"TECHNISCHE UNIVERSIT¨AT M ¨UNCHEN
Lehrstuhl f¨ur Mensch-Maschine-Kommunikation
Blickrichtungsunabh¨angige Erkennung von
Personen in Bild- und Tiefendaten
Andre St¨ormer
Vollst¨andiger Abdruck der von der Fakult¨at f¨ur Elektrotechnik und Informationstechnik
der Technischen Universit¨at M¨unchen zur Erlangung des akademischen Grades eines
Doktor-Ingenieurs (Dr.-Ing.)
genehmigten Dissertation.
Vorsitzender:
Univ.-Prof. Dr.-Ing. Thomas Eibert
Pr¨ufer der Dissertation:
. Univ.-Prof. Dr.-Ing. habil. Gerhard Rigoll
. Univ.-Prof. Dr.-Ing. Horst-Michael Groß,
Technische Universit¨at Ilmenau
Die Dissertation wurde am 16.06.2009 bei der Technischen Universit¨at M¨unchen einge-
reicht und durch die Fakult¨at f¨ur Elektrotechnik und Informationstechnik am 30.10.2009
ngenommen."
e76798bddd0f12ae03de26b7c7743c008d505215,Joint Max Margin and Semantic Features for Continuous Event Detection in Complex Scenes,
e7cac91da51b78eb4a28e194d3f599f95742e2a2,"Positive Feeling, Negative Meaning: Visualizing the Mental Representations of In-Group and Out-Group Smiles","RESEARCH ARTICLE
Positive Feeling, Negative Meaning:
Visualizing the Mental Representations of In-
Group and Out-Group Smiles
Andrea Paulus1☯*, Michaela Rohr1☯, Ron Dotsch2,3, Dirk Wentura1
Saarland University, Saarbrücken, Germany, 2 Utrecht University, Utrecht, the Netherlands,
Behavioural Science Institute, Radboud University, Nijmegen, the Netherlands
☯ These authors contributed equally to this work."
e78394213ae07b682ce40dc600352f674aa4cb05,Expression-invariant three-dimensional face recognition,"Expression-invariant three-dimensional face recognition
Alexander M. Bronstein
Email:
Michael M. Bronstein
Ron Kimmel
Computer Science Department,
Technion – Israel Institute of Technology,
Haifa 32000, Israel
One of the hardest problems in face recognition is dealing with facial expressions. Finding an
expression-invariant representation of the face could be a remedy for this problem. We suggest
treating faces as deformable surfaces in the context of Riemannian geometry, and propose to ap-
proximate facial expressions as isometries of the facial surface. This way, we can define geometric
invariants of a given face under different expressions. One such invariant is constructed by iso-
metrically embedding the facial surface structure into a low-dimensional flat space. Based on this
pproach, we built an accurate three-dimensional face recognition system that is able to distinguish
etween identical twins under various facial expressions. In this chapter we show how under the
near-isometric model assumption, the dif‌f‌icult problem of face recognition in the presence of facial
expressions can be solved in a relatively simple way.
0.1 Introduction
It is well-known that some characteristics or behavior patterns of the human body are strictly"
e7b6887cd06d0c1aa4902335f7893d7640aef823,Modelling of Facial Aging and Kinship: A Survey,"Modelling of Facial Aging and Kinship: A Survey
Markos Georgopoulos, Yannis Panagakis, and Maja Pantic,"
cbca355c5467f501d37b919d8b2a17dcb39d3ef9,Super-resolution of Very Low Resolution Faces from Videos,"CANSIZOGLU, JONES: SUPER-RESOLUTION OF VERY LR FACES FROM VIDEOS
Super-resolution of Very Low-Resolution
Faces from Videos
Esra Ataer-Cansizoglu
Michael Jones
Mitsubishi Electric Research Labs
(MERL)
Cambridge, MA, USA"
cbcf5da9f09b12f53d656446fd43bc6df4b2fa48,Face Recognition using Gray level Co-occurrence Matrix and Snap Shot Method of the Eigen Face,"ISSN: 2277-3754
ISO 9001:2008 Certified
International Journal of Engineering and Innovative Technology (IJEIT)
Volume 2, Issue 6, December 2012
Face Recognition using Gray level Co-occurrence
Matrix and Snap Shot Method of the Eigen Face
Sri Chandrasekharendra Saraswathi Viswa Mahavidyalaya University, Kanchipuram, India
M. Madhu, R. Amutha
SSN College of Engineering, Chennai, India"
cb004e9706f12d1de83b88c209ac948b137caae0,Face Aging Effect Simulation Using Hidden Factor Analysis Joint Sparse Representation,"Face Aging Effect Simulation using Hidden Factor
Analysis Joint Sparse Representation
Hongyu Yang, Student Member, IEEE, Di Huang, Member, IEEE, Yunhong Wang, Member, IEEE, Heng Wang,
nd Yuanyan Tang, Fellow, IEEE"
cb08f679f2cb29c7aa972d66fe9e9996c8dfae00,Action Understanding with Multiple Classes of Actors,"JOURNAL OF LATEX CLASS FILES, VOL. 13, NO. 9, SEPTEMBER 2014
Action Understanding
with Multiple Classes of Actors
Chenliang Xu, Member, IEEE, Caiming Xiong, and Jason J. Corso, Senior Member, IEEE"
cb84229e005645e8623a866d3d7956c197f85e11,Disambiguating Visual Verbs,"IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. X, NO. X, MONTH 201X
Disambiguating Visual Verbs
Spandana Gella, Frank Keller, and Mirella Lapata"
cbe859d151466315a050a6925d54a8d3dbad591f,Gaze shifts as dynamical random sampling,"GAZE SHIFTS AS DYNAMICAL RANDOM SAMPLING
Giuseppe Boccignone
Mario Ferraro
Dipartimento di Scienze dell’Informazione
Universit´a di Milano
Via Comelico 39/41
0135 Milano, Italy"
f842b13bd494be1bbc1161dc6df244340b28a47f,An Improved Face Recognition Technique Based on Modular Multi-directional Two-dimensional Principle Component Analysis Approach,"An Improved Face Recognition Technique Based
on Modular Multi-directional Two-dimensional
Principle Component Analysis Approach
Department of    Physics and Electronic Engineering, Hanshan Normal University, Chaozhou, 521041, China
Xiaoqing Dong
Department of    Physics and Electronic Engineering, Hanshan Normal University, Chaozhou, 521041, China
Email:
Hongcai Chen
Email:"
f8c94afd478821681a1565d463fc305337b02779,Design and Implementation of Robust Face Recognition System for Uncontrolled Pose and Illumination Changes,"www.semargroup.org,
www.ijsetr.com
ISSN 2319-8885
Vol.03,Issue.25
September-2014,
Pages:5079-5085
Design and Implementation of Robust Face Recognition System for
Uncontrolled Pose and Illumination Changes
VIJAYA BHASKAR TALARI
, VENKATESWARLU PRATTI
PG Scholar, Dept of ECE, LITAM, JNTUK, Andhrapradesh, India, Email:
Assistant Professor, Dept of ECE, LITAM, JNTUK, Andhrapradesh, India, Email:"
f8ec92f6d009b588ddfbb47a518dd5e73855547d,Extreme Learning Machine Ensemble Using Bagging for Facial Expression Recognition,"J Inf Process Syst, Vol.10, No.3, pp.443~458, September 2014
ISSN 1976-913X (Print)
ISSN 2092-805X (Electronic)
Extreme Learning Machine Ensemble Using
Bagging for Facial Expression Recognition
Deepak Ghimire* and Joonwhoan Lee*"
f8ed5f2c71e1a647a82677df24e70cc46d2f12a8,Artificial Neural Network Design and Parameter Optimization for Facial Expressions Recognition,"International Journal of Scientific & Engineering Research, Volume 2, Issue 12, December-2011                                                                                         1
ISSN 2229-5518
Artificial Neural Network Design and Parameter
Optimization for Facial Expressions Recognition
Ammar A. Alzaydi"
f8f872044be2918de442ba26a30336d80d200c42,Facial Emotion Recognition Techniques : A Survey,"IJSRD - International Journal for Scientific Research & Development| Vol. 3, Issue 03, 2015 | ISSN (online): 2321-0613
Facial Emotion Recognition Techniques: A Survey
Namita Rathore1 Rohit Miri2
,2Department of Computer Science and Engineering
,2Dr C V Raman Institute of Science and Technology
defense
systems,
surveillance"
f8a5bc2bd26790d474a1f6cc246b2ba0bcde9464,"KDEF-PT: Valence, Emotional Intensity, Familiarity and Attractiveness Ratings of Angry, Neutral, and Happy Faces","ORIGINAL RESEARCH
published: 19 December 2017
doi: 10.3389/fpsyg.2017.02181
KDEF-PT: Valence, Emotional
Intensity, Familiarity and
Attractiveness Ratings of Angry,
Neutral, and Happy Faces
Margarida V. Garrido* and Marília Prada
Instituto Universitário de Lisboa (ISCTE-IUL), CIS – IUL, Lisboa, Portugal
The Karolinska Directed Emotional Faces (KDEF)
is one of the most widely used
human facial expressions database. Almost a decade after the original validation study
(Goeleven et al., 2008), we present subjective rating norms for a sub-set of 210 pictures
which depict 70 models (half female) each displaying an angry, happy and neutral facial
expressions. Our main goals were to provide an additional and updated validation
to this database, using a sample from a different nationality (N = 155 Portuguese
students, M = 23.73 years old, SD = 7.24) and to extend the number of subjective
dimensions used to evaluate each image. Specifically, participants reported emotional
labeling (forced-choice task) and evaluated the emotional intensity and valence of the
expression, as well as the attractiveness and familiarity of the model (7-points rating"
ce85d953086294d989c09ae5c41af795d098d5b2,Bilinear Analysis for Kernel Selection and Nonlinear Feature Extraction,"This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination.
Bilinear Analysis for Kernel Selection and
Nonlinear Feature Extraction
Shu Yang, Shuicheng Yan, Member, IEEE, Chao Zhang, and Xiaoou Tang, Senior Member, IEEE"
ceaa5eb51f761b5f84bd88b58c8f484fcd2a22d6,UC San Diego UC San Diego Electronic Theses and Dissertations Title Interactive learning and prediction algorithms for computer vision applications,"UC San Diego
UC San Diego Electronic Theses and Dissertations
Title
Inhibitions of ascorbate fatty acid derivatives on three rabbit muscle glycolytic enzymes
Permalink
https://escholarship.org/uc/item/8x33n1gj
Author
Pham, Duyen-Anh
Publication Date
011-01-01
Peer reviewed|Thesis/dissertation
eScholarship.org
Powered by the California Digital Library
University of California"
ce9a61bcba6decba72f91497085807bface02daf,Eigen-harmonics faces: face recognition under generic lighting,"Eigen-Harmonics Faces: Face Recognition under Generic Lighting
Laiyun Qing1,2, Shiguang Shan2, Wen Gao1,2
Graduate School, CAS, Beijing, China, 100080
ICT-ISVISION Joint R&D Laboratory for Face Recognition, CAS, Beijing, China, 100080
Emails: {lyqing, sgshan, wgao}jdl.ac.cn"
cef6cffd7ad15e7fa5632269ef154d32eaf057af,Emotion Detection Through Facial Feature Recognition,"Emotion Detection Through Facial Feature
Recognition
James Pao
through  consistent"
cebfafea92ed51b74a8d27c730efdacd65572c40,Matching 2.5D face scans to 3D models,"JANUARY 2006
Matching 2.5D Face Scans to 3D Models
Xiaoguang Lu, Student Member, IEEE, Anil K. Jain, Fellow, IEEE, and
Dirk Colbry, Student Member, IEEE"
ce54e891e956d5b502a834ad131616786897dc91,Face Recognition Using LTP Algorithm,"International Journal of Science and Research (IJSR)
ISSN (Online): 2319-7064
Index Copernicus Value (2013): 6.14 | Impact Factor (2014): 5.611
Face Recognition Using LTP Algorithm
Richa Sharma1, Rohit Arora2
ECE & KUK
Assistant Professor (ECE)
Volume 4 Issue 12, December 2015
Licensed Under Creative Commons Attribution CC BY
www.ijsr.net
  Variation  in  luminance:  Third  main  challenge  that
ppears in face recognition process is the luminance. Due
to variation in the luminance the representation get varied
from  the  original  image.  The  person  with  same  poses
expression and seen from same viewpoint can be appear
very different due to variation in lightening."
ce6f459462ea9419ca5adcc549d1d10e616c0213,A Survey on Face Identification Methodologies in Videos,"A Survey on Face Identification Methodologies in
Videos
Student, M.Tech  CSE ,Department of Computer Science
& Engineering ,G.H.Raisoni College of Engineering &
Technology for Women, Nagpur, Maharashtra, India.
Deepti Yadav"
ce933821661a0139a329e6c8243e335bfa1022b1,Temporal Modeling Approaches for Large-scale Youtube-8M Video Understanding,"Temporal Modeling Approaches for Large-scale
Youtube-8M Video Understanding
Fu Li, Chuang Gan, Xiao Liu, Yunlong Bian, Xiang Long, Yandong Li, Zhichao Li, Jie Zhou, Shilei Wen
Baidu IDL & Tsinghua University"
e0dedb6fc4d370f4399bf7d67e234dc44deb4333,Supplementary Material: Multi-Task Video Captioning with Video and Entailment Generation,"Supplementary Material: Multi-Task Video Captioning with Video and
Entailment Generation
Ramakanth Pasunuru and Mohit Bansal
UNC Chapel Hill
{ram,
Experimental Setup
.1 Datasets
.1.1 Video Captioning Datasets
YouTube2Text or MSVD The Microsoft Re-
search Video Description Corpus (MSVD) or
YouTube2Text (Chen and Dolan, 2011) is used
for our primary video captioning experiments. It
has 1970 YouTube videos in the wild with many
diverse captions in multiple languages for each
video. Caption annotations to these videos are
ollected using Amazon Mechanical Turk (AMT).
All our experiments use only English captions. On
verage, each video has 40 captions, and the over-
ll dataset has about 80, 000 unique video-caption
pairs. The average clip duration is roughly 10 sec-"
e096b11b3988441c0995c13742ad188a80f2b461,DeepProposals: Hunting Objects and Actions by Cascading Deep Convolutional Layers,"Noname manuscript No.
(will be inserted by the editor)
DeepProposals: Hunting Objects and Actions by Cascading
Deep Convolutional Layers
Amir Ghodrati · Ali Diba · Marco Pedersoli · Tinne Tuytelaars · Luc
Van Gool
Received: date / Accepted: date"
e0939b4518a5ad649ba04194f74f3413c793f28e,Mind-reading machines : automated inference of complex mental states Rana,"Technical Report
UCAM-CL-TR-636
ISSN 1476-2986
Number 636
Computer Laboratory
Mind-reading machines:
utomated inference
of complex mental states
Rana Ayman el Kaliouby
July 2005
5 JJ Thomson Avenue
Cambridge CB3 0FD
United Kingdom
phone +44 1223 763500
http://www.cl.cam.ac.uk/"
e0ed0e2d189ff73701ec72e167d44df4eb6e864d,Recognition of static and dynamic facial expressions: a study review,"Recognition of static and dynamic facial expressions: a study review
Estudos de Psicologia, 18(1), janeiro-março/2013, 125-130
Nelson Torro Alves
Federal University of Paraíba"
e065a2cb4534492ccf46d0afc81b9ad8b420c5ec,SFace: An Efficient Network for Face Detection in Large Scale Variations,"SFace: An Ef‌f‌icient Network for Face Detection
in Large Scale Variations
Jianfeng Wang12∗, Ye Yuan 1†, Boxun Li†, Gang Yu† and Sun Jian†
College of Software, Beihang University∗
Megvii Inc. (Face++)†"
e013c650c7c6b480a1b692bedb663947cd9d260f,Robust Image Analysis With Sparse Representation on Quantized Visual Features,"Robust Image Analysis With Sparse Representation
on Quantized Visual Features
Bing-Kun Bao, Guangyu Zhu, Jialie Shen, and Shuicheng Yan, Senior Member, IEEE"
46a4551a6d53a3cd10474ef3945f546f45ef76ee,Robust and continuous estimation of driver gaze zone by dynamic analysis of multiple face videos,"014 IEEE Intelligent Vehicles Symposium (IV)
June 8-11, 2014. Dearborn, Michigan, USA
978-1-4799-3637-3/14/$31.00 ©2014 IEEE"
4686bdcee01520ed6a769943f112b2471e436208,Fast search based on generalized similarity measure,"Utsumi et al. IPSJ Transactions on Computer Vision and
Applications  (2017) 9:11
DOI 10.1186/s41074-017-0024-5
IPSJ Transactions on Computer
Vision and Applications
EXPRESS PAPER
Open Access
Fast search based on generalized
similarity measure
Yuzuko Utsumi*†, Tomoya Mizuno†, Masakazu Iwamura and Koichi Kise"
4688787d064e59023a304f7c9af950d192ddd33e,Investigating the Discriminative Power of Keystroke Sound,"Investigating the Discriminative Power of Keystroke
Sound
Joseph Roth Student Member, IEEE,, Xiaoming Liu, Member, IEEE, Arun Ross, Senior Member, IEEE,
nd Dimitris Metaxas, Member, IEEE"
46f2611dc4a9302e0ac00a79456fa162461a8c80,Spatio-Temporal Channel Correlation Networks for Action Classification,"for Action Classification
Ali Diba1,4,(cid:63), Mohsen Fayyaz3,(cid:63), Vivek Sharma2, M.Mahdi Arzani4, Rahman
Yousefzadeh4, Juergen Gall3, Luc Van Gool1,4
ESAT-PSI, KU Leuven, 2CV:HCI, KIT, Karlsruhe, 3University of Bonn, 4Sensifai"
466a5add15bb5f91e0cfd29a55f5fb159a7980e5,Video Repeat Recognition and Mining by Visual Features,"Video Repeat Recognition and Mining by Visual
Features
Xianfeng Yang1and Qi Tian"
46f3b113838e4680caa5fc8bda6e9ae0d35a038c,Automated Dermoscopy Image Analysis of Pigmented Skin Lesions,"Cancers 2010, 2, 262-273; doi:10.3390/cancers2020262
OPEN ACCESS
ancers
ISSN 2072-6694
www.mdpi.com/journal/cancers
Review
Automated Dermoscopy Image Analysis of Pigmented Skin
Lesions
Alfonso Baldi 1,2,*, Marco Quartulli 3, Raffaele Murace 2, Emanuele Dragonetti 2,
Mario Manganaro 3, Oscar Guerra 3 and Stefano Bizzi 3
Department of Biochemistry, Section of Pathology, Second University of Naples, Via L. Armanni
5, 80138 Naples, Italy
Futura-onlus, Via Pordenone 2, 00182 Rome, Italy; E-Mail:
ACS, Advanced Computer Systems, Via della Bufalotta 378, 00139 Rome, Italy
*  Author to whom correspondence should be addressed; E-Mail:
Fax: +390815569693.
Received: 23 February 2010; in revised form: 15 March 2010 / Accepted: 25 March 2010 /
Published: 26 March 2010"
46538b0d841654a0934e4c75ccd659f6c5309b72,A Novel Approach to Generate Face Biometric Template Using Binary Discriminating Analysis,"Signal & Image Processing : An International Journal (SIPIJ) Vol.5, No.1, February 2014
A NOVEL APPROACH TO GENERATE FACE
BIOMETRIC TEMPLATE USING BINARY
DISCRIMINATING ANALYSIS
Shraddha S. Shinde1 and Prof. Anagha P. Khedkar2
P.G. Student, Department of Computer Engineering, MCERC, Nashik (M.S.), India.
Associate Professor, Department of Computer Engineering,
MCERC, Nashik (M.S.), India"
469ee1b00f7bbfe17c698ccded6f48be398f2a44,SURVEy: Techniques for Aging Problems in Face Recognition,"MIT International Journal of Computer Science and Information Technology, Vol. 4, No. 2, August 2014, pp. 82-88
ISSN 2230-7621©MIT Publications
SURVEy: Techniques for
Aging Problems in Face Recognition
Aashmi
Sakshi Sahni
Sakshi Saxena
Scholar, Computer Science Engg. Dept.
Moradabad Institute of Technology
Scholar, Computer Science Engg. Dept.
Moradabad Institute of Technology
Scholar, Computer Science Engg. Dept.
Moradabad Institute of Technology
Moradabad, U.P., INDIA
Moradabad, U.P., INDIA
Moradabad, U.P., INDIA
E-mail:
E-mail:
E-mail:"
4682fee7dc045aea7177d7f3bfe344aabf153bd5,Tabula rasa: Model transfer for object category detection,"Tabula Rasa: Model Transfer for
Object Category Detection
Yusuf Aytar & Andrew Zisserman,
Department of Engineering Science
Oxford
(Presented by Elad Liebman)"
2c8743089d9c7df04883405a31b5fbe494f175b4,Real-time full-body human gender recognition in (RGB)-D data,"Washington State Convention Center
Seattle, Washington, May 26-30, 2015
978-1-4799-6922-7/15/$31.00 ©2015 IEEE"
2c93c8da5dfe5c50119949881f90ac5a0a4f39fe,Advanced local motion patterns for macro and micro facial expression recognition,"Advanced local motion patterns for macro and micro facial
expression recognition
B. Allaerta,∗, IM. Bilascoa, C. Djerabaa
Univ. Lille, CNRS, Centrale Lille, UMR 9189 - CRIStAL -
Centre de Recherche en Informatique Signal et Automatique de Lille, F-59000 Lille, France"
2c34bf897bad780e124d5539099405c28f3279ac,Robust Face Recognition via Block Sparse Bayesian Learning,"Robust Face Recognition via Block Sparse Bayesian Learning
Taiyong Li1,2, Zhilin Zhang3,4,∗
School of Financial Information Engineering, Southwestern University of Finance and Economics, Chengdu 610074,
China
Institute of Chinese Payment System, Southwestern University of Finance and Economics, Chengdu 610074, China
Department of Electrical and Computer Engineering, University of California at San Diego, La Jolla, CA 92093-0407,
Samsung R&D Institute America - Dallas, 1301 East Lookout Drive, Richardson, TX 75082, USA"
2cc4ae2e864321cdab13c90144d4810464b24275,Face Recognition Using Optimized 3D Information from Stereo Images,"Face Recognition Using Optimized 3D
Information from Stereo Images
Changhan Park1 and Joonki Paik2
Advanced Technology R&D Center, Samsung Thales Co., Ltd., 2Graduate School of
Advanced Imaging Science, Multimedia, and Film Chung-Ang University, Seoul
Korea
. Introduction
Human  biometric  characteristics  are  unique,  so  it  can  not  be  easily  duplicated  [1].  Such
information
includes;  facial,  hands,  torso,  fingerprints,  etc.  Potential  applications,
economical  efficiency,  and  user  convenience  make  the  face  detection  and  recognition
technique an important commodity compared to other biometric features [2], [3]. It can also
use a low-cost personal computer (PC) camera instead of expensive equipments, and require
minimal user interface. Recently, extensive research using 3D face data has been carried out
in  order  to  overcome  the  limits  of  2D  face  detection  and  feature  extraction  [2],  which
includes  PCA  [3],  neural  networks  (NN)  [4],  support  vector  machines  (SVM)  [5],  hidden
markov models (HMM) [6], and linear discriminant analysis (LDA) [7]. Among them, PCA
nd  LDA  methods  with  self-learning  method  are  most  widely  used  [3].  The  frontal  face
image  database  provides  fairly  high  recognition  rate.  However,  if  the  view  data  of  facial
rotation,  illumination  and  pose  change  is  not  acquired,  the  correct  recognition  rate"
2cac8ab4088e2bdd32dcb276b86459427355085c,A Face-to-Face Neural Conversation Model,"A Face-to-Face Neural Conversation Model
Hang Chu1
Daiqing Li1 Sanja Fidler1
University of Toronto 2Vector Institute
{chuhang1122, daiqing,"
2c2786ea6386f2d611fc9dbf209362699b104f83,1)local Feature Representations for Facial Expression Recognition Based on Differences of Gray Color Values of Neighboring Pixels,1)LOCAL FEATURE REPRESENTATIONS FOR FACIAL EXPRESSION RECOGNITION BASED ON DIFFERENCES OF GRAY COLOR VALUES OF NEIGHBORING PIXELS        Mohammad Shahidul Islam          A Dissertation Submitted in Partial  Fulfillment of the Requirement for the Degree of  Doctor of Philosophy (Computer Science and Information Systems) School of Applied Statistics National Institute of Development Administration 2013
2c92839418a64728438c351a42f6dc5ad0c6e686,Pose-Aware Face Recognition in the Wild,"Pose-Aware Face Recognition in the Wild
Iacopo Masi1
Prem Natarajan2
USC Institute for Robotics and Intelligent Systems (IRIS), Los Angeles, CA
G´erard Medioni1
Stephen Rawls2
USC Information Sciences Institute (ISI), Marina Del Rey, CA"
2c848cc514293414d916c0e5931baf1e8583eabc,An automatic facial expression recognition system evaluated by different classifiers,"An automatic facial expression recognition system
evaluated by different classifiers
Caroline Silva∗, Andrews Sobral∗ and Raissa Tavares Vieira†
Programa de P´os-Graduac¸˜ao em Mecatrˆonica
Universidade Federal da Bahia,
Email:
Email:
Department of Electrical Engineering - EESC/USP
Email:"
2c883977e4292806739041cf8409b2f6df171aee,Are Haar-Like Rectangular Features for Biometric Recognition Reducible?,"Aalborg Universitet
Are Haar-like Rectangular Features for Biometric Recognition Reducible?
Nasrollahi, Kamal; Moeslund, Thomas B.
Published in:
Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
DOI (link to publication from Publisher):
0.1007/978-3-642-41827-3_42
Publication date:
Document Version
Early version, also known as pre-print
Link to publication from Aalborg University
Citation for published version (APA):
Nasrollahi, K., & Moeslund, T. B. (2013). Are Haar-like Rectangular Features for Biometric Recognition
Reducible? In J. Ruiz-Shulcloper, & G. Sanniti di Baja (Eds.), Progress in Pattern Recognition, Image Analysis,
Computer Vision, and Applications (Vol. 8259, pp. 334-341). Springer Berlin Heidelberg: Springer Publishing
Company.  Lecture Notes in Computer Science, DOI: 10.1007/978-3-642-41827-3_42
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? Users may download and print one copy of any publication from the public portal for the purpose of private study or research."
2cdd9e445e7259117b995516025fcfc02fa7eebb,Temporal Exemplar-Based Bayesian Networks for Facial Expression Recognition,"Title
Temporal Exemplar-based Bayesian Networks for facial
expression recognition
Author(s)
Shang, L; Chan, KP
Citation
Proceedings - 7Th International Conference On Machine
Learning And Applications, Icmla 2008, 2008, p. 16-22
Issued Date
http://hdl.handle.net/10722/61208
Rights
This work is licensed under a Creative Commons Attribution-
NonCommercial-NoDerivatives 4.0 International License.;
International Conference on Machine Learning and Applications
Proceedings. Copyright © IEEE.; ©2008 IEEE. Personal use of
this material is permitted. However, permission to
reprint/republish this material for advertising or promotional
purposes or for creating new collective works for resale or
redistribution to servers or lists, or to reuse any copyrighted
omponent of this work in other works must be obtained from"
2c5d1e0719f3ad7f66e1763685ae536806f0c23b,AENet: Learning Deep Audio Features for Video Analysis,"AENet: Learning Deep Audio Features for Video
Analysis
Naoya Takahashi, Member, IEEE, Michael Gygli, Member, IEEE, and Luc Van Gool, Member, IEEE"
2c8f24f859bbbc4193d4d83645ef467bcf25adc2,Classification in the Presence of Label Noise: A Survey,"Classification in the Presence of
Label Noise: a Survey
Benoît Frénay and Michel Verleysen, Member, IEEE"
2cdd5b50a67e4615cb0892beaac12664ec53b81f,Mirror mirror: crowdsourcing better portraits,"To appear in ACM TOG 33(6).
Mirror Mirror: Crowdsourcing Better Portraits
Jun-Yan Zhu1
Aseem Agarwala2
Alexei A. Efros1
Eli Shechtman2
Jue Wang2
University of California, Berkeley1 Adobe2
Figure 1: We collect thousands of portraits by capturing video of a subject while they watch movie clips designed to elicit a range of positive
emotions. We use crowdsourcing and machine learning to train models that can predict attractiveness scores of different expressions. These
models can be used to select a subject’s best expressions across a range of emotions, from more serious professional portraits to big smiles."
2cdde47c27a8ecd391cbb6b2dea64b73282c7491,Order-aware Convolutional Pooling for Video Based Action Recognition,"ORDER-AWARE CONVOLUTIONAL POOLING FOR VIDEO BASED ACTION RECOGNITION
Order-aware Convolutional Pooling for Video Based
Action Recognition
Peng Wang, Lingqiao Liu, Chunhua Shen, and Heng Tao Shen"
2cf5f2091f9c2d9ab97086756c47cd11522a6ef3,MPIIGaze: Real-World Dataset and Deep Appearance-Based Gaze Estimation,"MPIIGaze: Real-World Dataset and Deep
Appearance-Based Gaze Estimation
Xucong Zhang, Yusuke Sugano∗, Mario Fritz, Andreas Bulling"
2c4b96f6c1a520e75eb37c6ee8b844332bc0435c,Automatic Emotion Recognition in Robot-Children Interaction for ASD Treatment,"Automatic Emotion Recognition in Robot-Children Interaction for ASD
Treatment
Marco Leo, Marco Del Coco, Pierluigi Carcagn`ı, Cosimo Distante
ISASI UOS Lecce
Campus Universitario via Monteroni sn, 73100 Lecce Italy
Massimo Bernava, Giovanni Pioggia
ISASI UOS Messina
Giuseppe Palestra
Univerisita’ di Bari
Marine Institute, via Torre Bianca, 98164 Messina Italy
Via Orabona 4, 70126 Bari, Italy"
790aa543151312aef3f7102d64ea699a1d15cb29,Confidence-Weighted Local Expression Predictions for Occlusion Handling in Expression Recognition and Action Unit Detection,"Confidence-Weighted Local Expression Predictions for
Occlusion Handling in Expression Recognition and Action
Unit detection
Arnaud Dapogny1
Kevin Bailly1
Séverine Dubuisson1
Sorbonne Universités, UPMC Univ Paris 06, CNRS, ISIR UMR 7222
place Jussieu 75005 Paris"
79f6a8f777a11fd626185ab549079236629431ac,Pradeep RavikumarDiscriminative Object Categorization with External Semantic Knowledge,"Copyright
Sung Ju Hwang"
79b669abf65c2ca323098cf3f19fa7bdd837ff31,Efficient tensor based face recognition,"Deakin Research Online
This is the published version:
Rana, Santu, Liu, Wanquan, Lazarescu, Mihai and Venkatesh, Svetha 2008, Efficient tensor
ased face recognition, in ICPR 2008 : Proceedings of the 19th International Conference on
Pattern Recognition, IEEE, Washington, D. C., pp. 1-4.
Available from Deakin Research Online:
http://hdl.handle.net/10536/DRO/DU:30044585
Reproduced with the kind permissions of the copyright owner.
Personal use of this material is permitted. However, permission to reprint/republish this
material for advertising or promotional purposes or for creating new collective works for
resale or redistribution to servers or lists, or to reuse any copyrighted component of this work
in other works must be obtained from the IEEE.
Copyright : 2008, IEEE"
79dd787b2877cf9ce08762d702589543bda373be,Face detection using SURF cascade,"Face Detection Using SURF Cascade
Jianguo Li, Tao Wang, Yimin Zhang
Intel Labs China"
2d294c58b2afb529b26c49d3c92293431f5f98d0,Maximum Margin Projection Subspace Learning for Visual Data Analysis,"Maximum Margin Projection Subspace Learning
for Visual Data Analysis
Symeon Nikitidis, Anastasios Tefas, Member, IEEE, and Ioannis Pitas, Fellow, IEEE"
2d88e7922d9f046ace0234f9f96f570ee848a5b5,Detection under Privileged Information,"Building Better Detection with Privileged Information
Z. Berkay Celik
Department of CSE
The Pennsylvania State
University
Patrick McDaniel
Department of CSE
The Pennsylvania State
University
Rauf Izmailov
Applied Communication
Sciences
Basking Ridge, NJ, US
Nicolas Papernot
Department of CSE
The Pennsylvania State
University
Ananthram Swami
Army Research
Laboratory"
2d05e768c64628c034db858b7154c6cbd580b2d5,FACIAL EXPRESSION RECOGNITION : Machine Learning using C #,"Neda Firoz et al, International Journal of Computer Science and Mobile Computing, Vol.4 Issue.8, August- 2015, pg. 431-446
Available Online at www.ijcsmc.com
International Journal of Computer Science and Mobile Computing
A Monthly Journal of Computer Science and Information Technology
IJCSMC, Vol. 4, Issue. 8, August 2015, pg.431 – 446
RESEARCH ARTICLE
ISSN 2320–088X
FACIAL EXPRESSION RECOGNITION:
Machine Learning using C#
Author: Neda Firoz
Advisor: Dr. Prashant Ankur Jain"
2d072cd43de8d17ce3198fae4469c498f97c6277,Random Cascaded-Regression Copse for Robust Facial Landmark Detection,"Random Cascaded-Regression Copse for Robust
Facial Landmark Detection
Zhen-Hua Feng, Student Member, IEEE, Patrik Huber, Josef Kittler, Life Member, IEEE, William Christmas,
nd Xiao-Jun Wu"
2d71e0464a55ef2f424017ce91a6bcc6fd83f6c3,A Survey on:Image Process using Two-Stage Crawler,"International Journal of Computer Applications (0975 – 8887)
National Conference on Advancements in Computer & Information Technology (NCACIT-2016)
A Survey on: Image Process using Two- Stage Crawler
Nilesh Wani
Assistant Professor
SPPU, Pune
Department of Computer Engg
Department of Computer Engg
Department of Computer Engg
Dipak Bodade
BE Student
SPPU, Pune
Savita Gunjal
BE Student
SPPU, Pune
Varsha Mahadik
BE Student
Department of Computer Engg
SPPU, Pune
dditional"
2d84c0d96332bb4fbd8acced98e726aabbf15591,UNIVERSITY OF CALIFORNIA RIVERSIDE Investigating the Role of Saliency for Face Recognition A Dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Electrical Engineering,"UNIVERSITY OF CALIFORNIA
RIVERSIDE
Investigating the Role of Saliency for Face Recognition
A Dissertation submitted in partial satisfaction
of the requirements for the degree of
Doctor of Philosophy
Electrical Engineering
Ramya Malur Srinivasan
March 2015
Dissertation Committee:
Professor Amit K Roy-Chowdhury, Chairperson
Professor Ertem Tuncel
Professor Conrad Rudolph
Professor Tamar Shinar"
2d8d089d368f2982748fde93a959cf5944873673,Visually Guided Spatial Relation Extraction from Text,"Proceedings of NAACL-HLT 2018, pages 788–794
New Orleans, Louisiana, June 1 - 6, 2018. c(cid:13)2018 Association for Computational Linguistics"
2df4d05119fe3fbf1f8112b3ad901c33728b498a,Multi-task Learning for Structured Output Prediction,"Facial landmark detection using structured output deep
neural networks
Soufiane Belharbi ∗1, Cl´ement Chatelain∗1, Romain H´erault∗1, and S´ebastien
Adam∗2
LITIS EA 4108, INSA de Rouen, Saint ´Etienne du Rouvray 76800, France
LITIS EA 4108, UFR des Sciences, Universit´e de Rouen, France.
September 24, 2015"
4188bd3ef976ea0dec24a2512b44d7673fd4ad26,Nonlinear Non-Negative Component Analysis Algorithms,"Nonlinear Non-Negative Component
Analysis Algorithms
Stefanos Zafeiriou, Member, IEEE, and Maria Petrou, Senior Member, IEEE"
41000c3a3344676513ef4bfcd392d14c7a9a7599,A Novel Approach For Generating Face Template Using Bda,"A NOVEL APPROACH FOR GENERATING FACE
TEMPLATE USING BDA
Shraddha S. Shinde1 and Prof. Anagha P. Khedkar2
P.G. Student, Department of Computer Engineering, MCERC, Nashik (M.S.), India.
Associate Professor, Department of Computer Engineering, MCERC, Nashik (M.S.),
India"
414715421e01e8c8b5743c5330e6d2553a08c16d,PoTion : Pose MoTion Representation for Action Recognition,"PoTion: Pose MoTion Representation for Action Recognition
Philippe Weinzaepfel2
Inria∗
NAVER LABS Europe
J´erˆome Revaud2 Cordelia Schmid1
Vasileios Choutas1,2"
41ab4939db641fa4d327071ae9bb0df4a612dc89,Interpreting Face Images by Fitting a Fast Illumination-Based 3D Active Appearance Model,"Interpreting Face Images by Fitting a Fast
Illumination-Based 3D Active Appearance
Model
Salvador E. Ayala-Raggi, Leopoldo Altamirano-Robles, Janeth Cruz-Enriquez
Instituto Nacional de Astrof´ısica, ´Optica y Electr´onica,
Luis Enrique Erro #1, 72840 Sta Ma. Tonantzintla. Pue., M´exico
Coordinaci´on de Ciencias Computacionales
{saraggi, robles,"
41a6196f88beced105d8bc48dd54d5494cc156fb,Using facial images for the diagnosis of genetic syndromes: A survey,"015 International Conference on
Communications, Signal
Processing, and their Applications
(ICCSPA 2015)
Sharjah, United Arab Emirates
7-19 February 2015
IEEE Catalog Number:
ISBN:
CFP1574T-POD
978-1-4799-6533-5"
41de109bca9343691f1d5720df864cdbeeecd9d0,Facial Emotion Recognition: A Survey and Real-World User Experiences in Mixed Reality,"Article
Facial Emotion Recognition: A Survey and
Real-World User Experiences in Mixed Reality
Dhwani Mehta, Mohammad Faridul Haque Siddiqui and Ahmad Y. Javaid * ID
EECS Department, The University of Toledo, Toledo, OH 43606, USA; (D.M.);
(M.F.H.S.)
* Correspondence: Tel.: +1-419-530-8260
Received: 10 December 2017; Accepted: 26 January 2018; Published: 1 Febuary 2018"
41d9a240b711ff76c5448d4bf4df840cc5dad5fc,Image Similarity Using Sparse Representation and Compression Distance,"JOURNAL DRAFT, VOL. X, NO. X, APR 2013
Image Similarity Using Sparse Representation
nd Compression Distance
Tanaya Guha, Student Member, IEEE, and Rabab K Ward, Fellow, IEEE"
419a6fca4c8d73a1e43003edc3f6b610174c41d2,A component based approach improves classification of discrete facial expressions over a holistic approach,"A Component Based Approach Improves Classification of Discrete
Facial Expressions Over a Holistic Approach
Kenny Hong, and Stephan K. Chalup, Senior Member, IEEE and Robert A.R. King"
4180978dbcd09162d166f7449136cb0b320adf1f,Real-time head pose classification in uncontrolled environments with Spatio-Temporal Active Appearance Models,"Real-time head pose classification in uncontrolled environments
with Spatio-Temporal Active Appearance Models
Miguel Reyes∗ and Sergio Escalera+ and Petia Radeva +
Matematica Aplicada i Analisi ,Universitat de Barcelona, Barcelona, Spain
+ Matematica Aplicada i Analisi, Universitat de Barcelona, Barcelona, Spain
+ Matematica Aplicada i Analisi, Universitat de Barcelona, Barcelona, Spain"
413a184b584dc2b669fbe731ace1e48b22945443,Human Pose Co-Estimation and Applications,"Human Pose Co-Estimation and Applications
Marcin Eichner and Vittorio Ferrari"
83b7578e2d9fa60d33d9336be334f6f2cc4f218f,The S-HOCK dataset: Analyzing crowds at the stadium,"The S-HOCK Dataset: Analyzing Crowds at the Stadium
Davide Conigliaro1,3, Paolo Rota2, Francesco Setti3, Chiara Bassetti3, Nicola Conci4, Nicu Sebe4, Marco Cristani1,
University of Verona. 2Vienna Institute of Technology. 3ISTC–CNR (Trento). 4University of Trento.
The topic of crowd modeling in computer vision usually assumes a sin-
gle generic typology of crowd, which is very simplistic. In this paper we
dopt a taxonomy that is widely accepted in sociology, focusing on a partic-
ular category, the spectator crowd, which is formed by people “interested in
watching something specific that they came to see” [1]. This can be found
t the stadiums, amphitheaters, cinema, etc.
In particular, we propose a
novel dataset, the Spectators Hockey (S-HOCK), which deals with 4 hockey
matches during an international tournament.
The dataset is unique in the crowd literature, and in general in the
surveillance realm. The dataset analyzes the crowd at different levels of
detail. At the highest level, it models the network of social connections
mong the public (who knows whom in the neighborhood), what is the sup-
ported team and what has been the best action in the match; all of this has
een obtained by interviews at the stadium. At a medium level, spectators
re localized, and information regarding the pose of their heads and body is
given. Finally, at a lowest level, a fine grained specification of all the actions"
83ca4cca9b28ae58f461b5a192e08dffdc1c76f3,Detecting emotional stress from facial expressions for driving safety,"DETECTING EMOTIONAL STRESS FROM FACIAL EXPRESSIONS FOR DRIVING SAFETY
Hua Gao, Anil Y¨uce, Jean-Philippe Thiran
Signal Processing Laboratory (LTS5),
´Ecole Polytechnique F´ed´erale de Lausanne, Switzerland"
831fbef657cc5e1bbf298ce6aad6b62f00a5b5d9,Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning,
832e1d128059dd5ed5fa5a0b0f021a025903f9d5,Pairwise Conditional Random Forests for Facial Expression Recognition,"Pairwise Conditional Random Forests for Facial Expression Recognition
Arnaud Dapogny1
Kevin Bailly1
S´everine Dubuisson1
Sorbonne Universit´es, UPMC Univ Paris 06, CNRS, ISIR UMR 7222, 4 place Jussieu 75005 Paris"
83e093a07efcf795db5e3aa3576531d61557dd0d,Facial Landmark Localization Using Robust Relationship Priors and Approximative Gibbs Sampling,"Facial Landmark Localization using Robust
Relationship Priors and Approximative Gibbs
Sampling
Karsten Vogt, Oliver M¨uller and J¨orn Ostermann
Institut f¨ur Informationsverarbeitung (tnt)
Leibniz Universit¨at Hannover, Germany
{vogt, omueller,"
83b4899d2899dd6a8d956eda3c4b89f27f1cd308,A Robust Approach for Eye Localization Under Variable Illuminations,"-4244-1437-7/07/$20.00 ©2007 IEEE
I - 377
ICIP 2007"
8323af714efe9a3cadb31b309fcc2c36c8acba8f,Automatic Real-Time Facial Expression Recognition for Signed Language Translation,"Automatic Real-Time
Facial Expression Recognition
for Signed Language Translation
Jacob Richard Whitehill
A thesis submitted in partial fulfillment of the requirements for the de-
gree of Magister Scientiae in the Department of Computer Science,
University of the Western Cape.
May 2006"
83fd5c23204147844a0528c21e645b757edd7af9,USDOT number localization and recognition from vehicle side-view NIR images,"USDOT Number Localization and Recognition From Vehicle Side-View NIR
Images
Orhan Bulan, Safwan Wshah, Ramesh Palghat, Vladimir Kozitsky and Aaron Burry
Palo Alto Research Center (PARC)
800 Phillips Rd. Webster NY 14580"
8395cf3535a6628c3bdc9b8d0171568d551f5ff0,Entropy Non-increasing Games for the Improvement of Dataflow Programming,"Entropy Non-increasing Games for the
Improvement of Dataflow Programming
Norbert B´atfai, Ren´at´o Besenczi, Gerg˝o Bogacsovics,
Fanny Monori∗
February 16, 2017"
834f5ab0cb374b13a6e19198d550e7a32901a4b2,Face Translation between Images and Videos using Identity-aware CycleGAN,"Face Translation between Images and Videos using Identity-aware CycleGAN
Zhiwu Huang†, Bernhard Kratzwald†, Danda Pani Paudel†, Jiqing Wu†, Luc Van Gool†‡
Computer Vision Lab, ETH Zurich, Switzerland
VISICS, KU Leuven, Belgium
{zhiwu.huang, paudel, jwu,"
8320dbdd3e4712cca813451cd94a909527652d63,Ear Biometrics,"EAR BIOMETRICS
Mark Burge
nd Wilhelm Burger
Johannes Kepler University(cid:1) Institute of Systems Science(cid:1) A(cid:2) Linz(cid:1) Austria(cid:1)
urge(cid:1)cast(cid:2)uni(cid:3)linz(cid:2)ac(cid:2)at"
837e99301e00c2244023a8a48ff98d7b521c93ac,Local Feature Evaluation for a Constrained Local Model Framework,"Local Feature Evaluation for a Constrained
Local Model Framework
Maiya Hori(B), Shogo Kawai, Hiroki Yoshimura, and Yoshio Iwai
Graduate School of Engineering, Tottori University,
01 Minami 4-chome, Koyama-cho, Tottori 680-8550, Japan"
834b15762f97b4da11a2d851840123dbeee51d33,Landmark-free smile intensity estimation,"Landmark-free smile intensity estimation
J´ulio C´esar Batista, Olga R. P. Bellon and Luciano Silva
IMAGO Research Group - Universidade Federal do Paran´a
Fig. 1. Overview of our method for smile intensity estimation"
833f6ab858f26b848f0d747de502127406f06417,Learning weighted similarity measurements for unconstrained face recognition,"978-1-4244-5654-3/09/$26.00 ©2009 IEEE
ICIP 2009"
8309e8f27f3fb6f2ac1b4343a4ad7db09fb8f0ff,Generic versus Salient Region-Based Partitioning for Local Appearance Face Recognition,"Generic versus Salient Region-based Partitioning
for Local Appearance Face Recognition
Hazım Kemal Ekenel and Rainer Stiefelhagen
Computer Science Depatment, Universit¨at Karlsruhe (TH)
Am Fasanengarten 5, Karlsruhe 76131, Germany
http://isl.ira.uka.de/cvhci"
1b02b9413b730b96b91d16dcd61b2420aef97414,Détection de marqueurs affectifs et attentionnels de personnes âgées en interaction avec un robot. (Audio-visual detection of emotional (laugh and smile) and attentional markers for elderly people in social interaction with a robot),"Détection de marqueurs affectifs et attentionnels de
personnes âgées en interaction avec un robot
Fan Yang
To cite this version:
Fan Yang. Détection de marqueurs affectifs et attentionnels de personnes âgées en interaction
vec un robot.
Intelligence artificielle [cs.AI]. Université Paris-Saclay, 2015. Français. <NNT :
015SACLS081>. <tel-01280505>
HAL Id: tel-01280505
https://tel.archives-ouvertes.fr/tel-01280505
Submitted on 29 Feb 2016
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,"
1b55c4e804d1298cbbb9c507497177014a923d22,Incremental Class Representation Learning for Face Recognition,"Incremental Class Representation
Learning for Face Recognition
Degree’s Thesis
Audiovisual Systems Engineering
Author:
Advisors: Elisa Sayrol, Josep Ramon Morros
Eric Presas Valga
Universitat Politècnica de Catalunya (UPC)
016 - 2017"
1b6394178dbc31d0867f0b44686d224a19d61cf4,EPML: Expanded Parts Based Metric Learning for Occlusion Robust Face Verification,"EPML: Expanded Parts based Metric Learning for
Occlusion Robust Face Verification
Gaurav Sharma, Fr´ed´eric Jurie, Patrick P´erez
To cite this version:
Gaurav Sharma, Fr´ed´eric Jurie, Patrick P´erez. EPML: Expanded Parts based Metric Learning
for Occlusion Robust Face Verification. Asian Conference on Computer Vision, Nov 2014, -,
Singapore. pp.1-15, 2014. <hal-01070657>
HAL Id: hal-01070657
https://hal.archives-ouvertes.fr/hal-01070657
Submitted on 2 Oct 2014
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destin´ee au d´epˆot et `a la diffusion de documents
scientifiques de niveau recherche, publi´es ou non,
´emanant des ´etablissements d’enseignement et de"
1bdef21f093c41df2682a07f05f3548717c7a3d1,Towards Automated Classification of Emotional Facial Expressions,"Towards Automated Classification of Emotional Facial Expressions
Lewis J. Baker Vanessa LoBue
Elizabeth Bonawitz & Patrick Shafto
Department of Mathematics and Computer Science, 2Department of Psychology
Rutgers University – Newark, 101 Warren St., Newark, NJ, 07102 USA"
1b150248d856f95da8316da868532a4286b9d58e,Analyzing 3D Objects in Cluttered Images,"Analyzing 3D Objects in Cluttered Images
Mohsen Hejrati
UC Irvine
Deva Ramanan
UC Irvine"
1be498d4bbc30c3bfd0029114c784bc2114d67c0,Age and Gender Estimation of Unfiltered Faces,"Age and Gender Estimation of Unfiltered Faces
Eran Eidinger, Roee Enbar, Tal Hassner*"
1bbec7190ac3ba34ca91d28f145e356a11418b67,Explorer Action Recognition with Dynamic Image Networks,"Action Recognition with Dynamic Image Networks
Citation for published version:
Bilen, H, Fernando, B, Gravves, E & Vedaldi, A 2017, 'Action Recognition with Dynamic Image Networks'
IEEE Transactions on Pattern Analysis and Machine Intelligence. DOI: 10.1109/TPAMI.2017.2769085
Digital Object Identifier (DOI):
0.1109/TPAMI.2017.2769085
Link:
Link to publication record in Edinburgh Research Explorer
Document Version:
Peer reviewed version
Published In:
IEEE Transactions on Pattern Analysis and Machine Intelligence
General rights
Copyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s)
nd / or other copyright owners and it is a condition of accessing these publications that users recognise and
bide by the legal requirements associated with these rights.
Take down policy
The University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorer
ontent complies with UK legislation. If you believe that the public display of this file breaches copyright please
ontact providing details, and we will remove access to the work immediately and"
1b3587363d37dd197b6adbcfa79d49b5486f27d8,Multimodal Grounding for Language Processing,"Multimodal Grounding for Language Processing
Lisa Beinborn◦∗3
Teresa Botschen∗(cid:52)
Iryna Gurevych (cid:52)
Language Technology Lab, University of Duisburg-Essen
(cid:52) Ubiquitous Knowledge Processing Lab (UKP) and Research Training Group AIPHES
Department of Computer Science, Technische Universit¨at Darmstadt
www.ukp.tu-darmstadt.de"
1b300a7858ab7870d36622a51b0549b1936572d4,Dynamic Facial Expression Recognition With Atlas Construction and Sparse Representation,"This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TIP.2016.2537215, IEEE
Transactions on Image Processing
Dynamic Facial Expression Recognition with Atlas
Construction and Sparse Representation
Yimo Guo, Guoying Zhao, Senior Member, IEEE, and Matti Pietik¨ainen, Fellow, IEEE"
1b90507f02967ff143fce993a5abbfba173b1ed0,Gradient-DCT (G-DCT) descriptors,"Image Processing Theory, Tools and Applications
Gradient-DCT (G-DCT) Descriptors
Radovan Fusek, Eduard Sojka
Technical University of Ostrava, FEECS, Department of Computer Science,
7. listopadu 15, 708 33 Ostrava-Poruba, Czech Republic
e-mail:"
1b1173a3fb33f9dfaf8d8cc36eb0bf35e364913d,Registration Invariant Representations for Expression Detection,"DICTA
DICTA 2010 Submission #147. CONFIDENTIAL REVIEW COPY. DO NOT DISTRIBUTE.
Registration Invariant Representations for Expression Detection
Anonymous DICTA submission
Paper ID 147"
1b0a071450c419138432c033f722027ec88846ea,Looking at faces in a vehicle: A deep CNN based approach and evaluation,"Windsor Oceanico Hotel, Rio de Janeiro, Brazil, November 1-4, 2016
978-1-5090-1889-5/16/$31.00 ©2016 IEEE"
1b3b01513f99d13973e631c87ffa43904cd8a821,HMM recognition of expressions in unrestrained video intervals,"HMM RECOGNITION OF EXPRESSIONS IN UNRESTRAINED VIDEO INTERVALS
José Luis Landabaso, Montse Pardàs, Antonio Bonafonte
Universitat Politècnica de Catalunya, Barcelona, Spain"
1be18a701d5af2d8088db3e6aaa5b9b1d54b6fd3,Enhancement of Fast Face Detection Algorithm Based on a Cascade of Decision Trees,"ENHANCEMENT OF FAST FACE DETECTION ALGORITHM BASED ON A CASCADE OF
DECISION TREES
V. V. Khryashchev a, *, A. A. Lebedev a, A. L. Priorov a
YSU, Yaroslavl, Russia - (vhr,
Commission II, WG II/5
KEY WORDS: Face Detection, Cascade Algorithm, Decision Trees."
1b70bbf7cdfc692873ce98dd3c0e191580a1b041,Enhancing Performance of Face Recognition System Using Independent Component Analysis,"International Research Journal of Engineering and Technology (IRJET)        e-ISSN: 2395 -0056
Volume: 03 Issue: 10 | Oct -2016                      www.irjet.net                                                                 p-ISSN: 2395-0072
Enhancing Performance of Face Recognition
System Using Independent Component Analysis
Dipti Rane1, Prof. Uday Bhave2, and Asst Prof. Manimala Mahato3
Student, Computer Science, Shah and Anchor Kuttchi Engineering College, Mumbai, India 1
Guide, HOD, Computer Science, Shah and Anchor Kuttchi Engineering College, Mumbai, India 2
Co-Guide, Assistant Prof., Computer Science, Shah and Anchor Kuttchi Engineering College, Mumbai, India 3
---------------------------------------------------------------------***---------------------------------------------------------------------
ards, tokens and keys. Biometric based methods examine"
1b71d3f30238cb6621021a95543cce3aab96a21b,Fine-grained Video Classification and Captioning,"Fine-grained Video Classification and Captioning
Farzaneh Mahdisoltani1,2, Guillaume Berger2, Waseem Gharbieh2
David Fleet1, Roland Memisevic2
{farzaneh,
University of Toronto1, Twenty Billion Neurons2"
1b4f6f73c70353869026e5eec1dd903f9e26d43f,Robust Subjective Visual Property Prediction from Crowdsourced Pairwise Labels,"Robust Subjective Visual Property Prediction
from Crowdsourced Pairwise Labels
Yanwei Fu, Timothy M. Hospedales, Tao Xiang, Jiechao Xiong,
Shaogang Gong, Yizhou Wang, and Yuan Yao"
1bc23c771688109bed9fd295ce82d7e702726327,Sparse Modeling of High - Dimensional Data for Learning and Vision,(cid:13) 2011 Jianchao Yang
1b4bc7447f500af2601c5233879afc057a5876d8,Facial Action Unit Classification with Hidden Knowledge under Incomplete Annotation,"Facial Action Unit Classification with Hidden Knowledge
under Incomplete Annotation
Jun Wang
University of Science and
Technology of China
Hefei, Anhui
Shangfei Wang
University of Science and
Technology of China
Hefei, Anhui
Rensselaer Polytechnic
Qiang Ji
Institute
Troy, NY
P.R.China, 230027
P.R.China, 230027
USA, 12180"
7711a7404f1f1ac3a0107203936e6332f50ac30c,Action Classification and Highlighting in Videos,"Action Classification and Highlighting in Videos
Atousa Torabi
Disney Research Pittsburgh
Leonid Sigal
Disney Research Pittsburgh"
778c9f88839eb26129427e1b8633caa4bd4d275e,Pose pooling kernels for sub-category recognition,"Pose Pooling Kernels for Sub-category Recognition
Ning Zhang
ICSI & UC Berkeley
Ryan Farrell
ICSI & UC Berkeley
Trever Darrell
ICSI & UC Berkeley"
7789a5d87884f8bafec8a82085292e87d4e2866f,A Unified Tensor-based Active Appearance Face Model,"A Unified Tensor-based Active Appearance Face
Model
Zhen-Hua Feng, Member, IEEE, Josef Kittler, Life Member, IEEE, William Christmas, and Xiao-Jun Wu,
Member, IEEE"
778bff335ae1b77fd7ec67404f71a1446624331b,Hough Forest-Based Facial Expression Recognition from Video Sequences,"Hough Forest-based Facial Expression Recognition from
Video Sequences
Gabriele Fanelli, Angela Yao, Pierre-Luc Noel, Juergen Gall, and Luc Van Gool
BIWI, ETH Zurich http://www.vision.ee.ethz.ch
VISICS, K.U. Leuven http://www.esat.kuleuven.be/psi/visics"
7726a6ab26a1654d34ec04c0b7b3dd80c5f84e0d,Content-aware compression using saliency-driven image retargeting,"CONTENT-AWARE COMPRESSION USING SALIENCY-DRIVEN IMAGE RETARGETING
Fabio Z¨und*†, Yael Pritch*, Alexander Sorkine-Hornung*, Stefan Mangold*, Thomas Gross†
*Disney Research Zurich
ETH Zurich"
7754b708d6258fb8279aa5667ce805e9f925dfd0,Facial Action Unit Recognition by Exploiting Their Dynamic and Semantic Relationships,"Facial Action Unit Recognition by Exploiting
Their Dynamic and Semantic Relationships
Yan Tong, Student Member, IEEE, Wenhui Liao, Member, IEEE, and Qiang Ji, Senior Member, IEEE"
77db171a523fc3d08c91cea94c9562f3edce56e1,Gauss-Laguerre wavelet textural feature fusion with geometrical information for facial expression identification,"Poursaberi et al. EURASIP Journal on Image and Video Processing 2012, 2012:17
http://jivp.eurasipjournals.com/content/2012/1/17
R ES EAR CH
Open Access
Gauss–Laguerre wavelet textural feature fusion
with geometrical information for facial expression
identification
Ahmad Poursaberi1*, Hossein Ahmadi Noubari2, Marina Gavrilova1 and Svetlana N Yanushkevich1"
77037a22c9b8169930d74d2ce6f50f1a999c1221,Robust Face Recognition With Kernelized Locality-Sensitive Group Sparsity Representation,"Robust Face Recognition With Kernelized
Locality-Sensitive Group Sparsity  Representation
Shoubiao Tan, Xi Sun, Wentao Chan, Lei Qu, and Ling    Shao"
779ad364cae60ca57af593c83851360c0f52c7bf,Steerable Pyramids Feature Based Classification Using Fisher Linear Discriminant for Face Recognition,"Steerable Pyramids Feature Based Classification Using Fisher
Linear Discriminant for Face Recognition
EL AROUSSI MOHAMED1
EL HASSOUNI MOHAMMED12
GHOUZALI SANAA1
RZIZA MOHAMMED1
ABOUTAJDINE DRISS1
GSCM-LRIT, Faculty of Sciences, Mohammed V University-Agdal, Rabat, Morocco
DESTEC, FLSHR Mohammed V University-Agdal, Rabat, Morocco
PO.Box 1014, Rabat, Morocco"
77d31d2ec25df44781d999d6ff980183093fb3de,The Multiverse Loss for Robust Transfer Learning,"The Multiverse Loss for Robust Transfer Learning
Supplementary
. Omitted proofs
for which the joint loss:
m(cid:88)
L(F r, br, D, y)
J(F 1, b1...F m, bm, D, y) =
is bounded by:
mL∗(D, y) ≤ J(F 1, b1...F m, bm, D, y)
m−1(cid:88)
≤ mL∗(D, y) +
Alλd−j+1
where [A1 . . . Am−1] are bounded parameters.
We provide proofs that were omitted from the paper for
lack of space. We follow the same theorem numbering as in
the paper.
Lemma 1. The minimizers F ∗, b∗ of L are not unique, and
it holds that for any vector v ∈ Rc and scalar s, the solu-
tions F ∗ + v1(cid:62)
Proof. denoting V = v1(cid:62)"
48186494fc7c0cc664edec16ce582b3fcb5249c0,P-CNN: Pose-Based CNN Features for Action Recognition,"P-CNN: Pose-based CNN Features for Action Recognition
Guilhem Ch´eron∗ †
Ivan Laptev∗
INRIA
Cordelia Schmid†"
48499deeaa1e31ac22c901d115b8b9867f89f952,Interim Report of Final Year Project HKU-Face : A Large Scale Dataset for Deep Face Recognition,"Interim Report of Final Year Project
HKU-Face: A Large Scale Dataset for
Deep Face Recognition
Haicheng Wang
035140108
Haoyu Li
035141841
COMP4801 Final Year Project
Project Code: 17007"
486a82f50835ea888fbc5c6babf3cf8e8b9807bc,Face Search at Scale: 80 Million Gallery,"MSU TECHNICAL REPORT MSU-CSE-15-11, JULY 24, 2015
Face Search at Scale: 80 Million Gallery
Dayong Wang, Member, IEEE, Charles Otto, Student Member, IEEE, Anil K. Jain, Fellow, IEEE"
4850af6b54391fc33c8028a0b7fafe05855a96ff,Discovering useful parts for pose estimation in sparsely annotated datasets,"Discovering Useful Parts for Pose Estimation in Sparsely Annotated Datasets
Mikhail Breslav1, Tyson L. Hedrick2, Stan Sclaroff1, and Margrit Betke1
Department of Computer Science and 2Department of Biology
Boston University and 2University of North Carolina"
48a5b6ee60475b18411a910c6084b3a32147b8cd,Pedestrian Attribute Recognition with Part-based CNN and Combined Feature Representations,"Pedestrian attribute recognition with part-based CNN
nd combined feature representations
Yiqiang Chen, Stefan Duffner, Andrei Stoian, Jean-Yves Dufour, Atilla
Baskurt
To cite this version:
Yiqiang Chen, Stefan Duffner, Andrei Stoian, Jean-Yves Dufour, Atilla Baskurt. Pedestrian attribute
recognition with part-based CNN and combined feature representations. VISAPP2018, Jan 2018,
Funchal, Portugal. <hal-01625470>
HAL Id: hal-01625470
https://hal.archives-ouvertes.fr/hal-01625470
Submitted on 21 Jun 2018
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,"
488e475eeb3bb39a145f23ede197cd3620f1d98a,Pedestrian Attribute Classification in Surveillance: Database and Evaluation,"Pedestrian Attribute Classification in Surveillance: Database and Evaluation
Jianqing Zhu, Shengcai Liao, Zhen Lei, Dong Yi, Stan Z. Li∗
Center for Biometrics and Security Research & National Laboratory of Pattern Recognition
Institute of Automation, Chinese Academy of Sciences (CASIA)
95 Zhongguancun East Road, 100190, Beijing, China
{jqzhu, scliao, zlei, dyi,"
487df616e981557c8e1201829a1d0ec1ecb7d275,Acoustic Echo Cancellation Using a Vector-Space-Based Adaptive Filtering Algorithm,"Acoustic Echo Cancellation Using a Vector-Space-Based
Adaptive Filtering Algorithm
Yu Tsao, Member IEEE, Shih-Hau Fang*, Senior Member IEEE, and Yao Shiao"
48319e611f0daaa758ed5dcf5a6496b4c6ef45f2,Non Binary Local Gradient Contours for Face Recognition,"Non Binary Local Gradient Contours for Face Recognition
Abdullah Gubbia, Mohammad Fazle Azeemb, M Sharmila Kumaric
Department of Electronics and Communication, P.A. College of Engnineering, Mangalore,
Nadupadavu, Mangalore, India, Contact:
Senior IEEE Member, Department of Electrical and Electronics Engineering, Aligarh Muslim
University, India, Contact:
Department of Computer Science and Engineering, P A College of Engineering, Nadupadavu,
Mangalore, India. Contact:
As the features from the traditional Local Binary patterns (LBP) and Local Directional Patterns (LDP) are
found to be ineffective for face recognition, we have proposed a new approach derived on the basis of Information
sets whereby the loss of information that occurs during the binarization is eliminated. The information sets
s a product. Since face is having smooth texture in a limited area, the extracted features must be highly
discernible. To limit the number of features, we consider only the non overlapping windows. By the application
of the information set theory we can reduce the number of feature of an image. The derived features are shown
to work fairly well over eigenface, fisherface and LBP methods.
Keywords: Local Binary Pattern, Local Directional Pattern, Information Sets, Gradient Contour, Support
Vector Machine, KNN, Face Recognition.
. INTRODUCTION
In face recognition, the major issue to be ad-
dressed is the extraction of features which are"
48cfc5789c246c6ad88ff841701204fc9d6577ed,Age Invariant Face Recognition Based on DCT Feature Extraction and Kernel Fisher Analysis,"J Inf Process Syst, Vol.12, No.3, pp.392~409, September 2016
ISSN 1976-913X (Print)
ISSN 2092-805X (Electronic)
Age Invariant Face Recognition Based on DCT
Feature Extraction and Kernel Fisher Analysis
Leila Boussaad*, Mohamed Benmohammed**, and Redha Benzid***"
70f189798c8b9f2b31c8b5566a5cf3107050b349,The challenge of face recognition from digital point-and-shoot cameras,"The Challenge of Face Recognition from Digital Point-and-Shoot Cameras
J. Ross Beveridge∗
Geof H. Givens§
W. Todd Scruggs¶
P. Jonathon Phillips†
Yui Man Lui∗
Kevin W. Bowyer(cid:107)
David Bolme‡
Mohammad Nayeem Teli∗
Patrick J. Flynn(cid:107)
Bruce A. Draper∗,
Hao Zhang∗
Su Cheng†"
70109c670471db2e0ede3842cbb58ba6be804561,Zero-Shot Visual Recognition via Bidirectional Latent Embedding,"Noname manuscript No.
(will be inserted by the editor)
Zero-Shot Visual Recognition via Bidirectional Latent Embedding
Qian Wang · Ke Chen
Received: date / Accepted: date"
706236308e1c8d8b8ba7749869c6b9c25fa9f957,Crowdsourced data collection of facial responses,"Crowdsourced Data Collection of Facial Responses
Daniel McDuff
MIT Media Lab
Cambridge
02139, USA
Rosalind Picard
MIT Media Lab
Cambridge
02139, USA
Rana el Kaliouby
MIT Media Lab
Cambridge
02139, USA"
706b9767a444de4fe153b2f3bff29df7674c3161,Fast Metric Learning For Deep Neural Networks,"Fast Metric Learning For Deep Neural Networks
Henry Gouk1, Bernhard Pfahringer1, and Michael Cree2
Department of Computer Science, University of Waikato, Hamilton, New Zealand
School of Engineering, University of Waikato, Hamilton, New Zealand"
70c58700eb89368e66a8f0d3fc54f32f69d423e1,In Unsupervised Spatio-temporal Feature Learning,"INCORPORATING SCALABILITY IN UNSUPERVISED SPATIO-TEMPORAL FEATURE
LEARNING
Sujoy Paul, Sourya Roy and Amit K. Roy-Chowdhury
Dept. of Electrical and Computer Engineering, University of California, Riverside, CA 92521"
70e79d7b64f5540d309465620b0dab19d9520df1,Facial Expression Recognition System Using Extreme Learning Machine,"International Journal of Scientific & Engineering Research, Volume 8, Issue 3, March-2017
ISSN 2229-5518
Facial Expression Recognition System
Using Extreme Learning Machine
Firoz Mahmud, Dr. Md. Al Mamun"
7003d903d5e88351d649b90d378f3fc5f211282b,Facial Expression Recognition using Gabor Wavelet,"International Journal of Computer Applications (0975 – 8887)
Volume 68– No.23, April 2013
Facial Expression Recognition using Gabor Wavelet
Mahesh Kumbhar
ENTC SVERI’S COE (Poly),
Pandharpur,
Solapur, India
Manasi Patil
ENTC SVERI’S COE,
Pandharpur,
Solapur, India
Ashish Jadhav
ENTC SVERI’S COE (Poly),
Pandharpur,
Solapur, India"
70bf1769d2d5737fc82de72c24adbb7882d2effd,Face Detection in Intelligent Ambiences with Colored Illumination,"Face detection in intelligent ambiences with colored illumination
Christina Katsimerou, Judith A. Redi, Ingrid Heynderickx
Department of Intelligent Systems
TU Delft
Delft, The Netherlands"
1e058b3af90d475bf53b3f977bab6f4d9269e6e8,Manifold Relevance Determination,"Manifold Relevance Determination
Andreas C. Damianou
Dept. of Computer Science & Sheffield Institute for Translational Neuroscience, University of Sheffield, UK
Carl Henrik Ek
KTH – Royal Institute of Technology, CVAP Lab, Stockholm, Sweden
Michalis K. Titsias
Wellcome Trust Centre for Human Genetics, Roosevelt Drive, Oxford OX3 7BN, UK
Neil D. Lawrence
Dept. of Computer Science & Sheffield Institute for Translational Neuroscience, University of Sheffield, UK"
1e799047e294267087ec1e2c385fac67074ee5c8,Automatic Classification of Single Facial Images,"IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 21, NO. 12, DECEMBER 1999
Short Papers___________________________________________________________________________________________________
Automatic Classification of
Single Facial Images
Michael J. Lyons, Julien Budynek, and
Shigeru Akamatsu"
1eb4ea011a3122dc7ef3447e10c1dad5b69b0642,Contextual Visual Recognition from Images and Videos,"Contextual Visual Recognition from Images and Videos
Georgia Gkioxari
Jitendra Malik
Electrical Engineering and Computer Sciences
University of California at Berkeley
Technical Report No. UCB/EECS-2016-132
http://www.eecs.berkeley.edu/Pubs/TechRpts/2016/EECS-2016-132.html
July 19, 2016"
1e7ae86a78a9b4860aa720fb0fd0bdc199b092c3,A Brief Review of Facial Emotion Recognition Based on Visual Information,"Article
A Brief Review of Facial Emotion Recognition Based
on Visual Information
Byoung Chul Ko ID
Department of Computer Engineering, Keimyung University, Daegu 42601, Korea;
Tel.: +82-10-3559-4564
Received: 6 December 2017; Accepted: 25 January 2018; Published: 30 January 2018"
1e8eee51fd3bf7a9570d6ee6aa9a09454254689d,Face Search at Scale,"This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TPAMI.2016.2582166, IEEE
Transactions on Pattern Analysis and Machine Intelligence
Face Search at Scale
Dayong Wang, Member, IEEE, Charles Otto, Student Member, IEEE, Anil K. Jain, Fellow, IEEE"
1eec03527703114d15e98ef9e55bee5d6eeba736,Automatic identification of persons in TV series,"UNIVERSITÄT KARLSRUHE (TH)
FAKULTÄT FÜR INFORMATIK
INTERACTIVE SYSTEMS LABS
Prof. Dr. A. Waibel
DIPLOMA THESIS
Automatic identification
of persons in TV series
SUBMITTED BY
Mika Fischer
MAY 2008
ADVISORS
M.Sc. Hazım Kemal Ekenel
Dr.-Ing. Rainer Stiefelhagen"
1ef1f33c48bc159881c5c8536cbbd533d31b0e9a,Identity-based Adversarial Training of Deep CNNs for Facial Action Unit Recognition,"Z. ZHANG ET AL.: ADVERSARIAL TRAINING FOR ACTION UNIT RECOGNITION
Identity-based Adversarial Training of Deep
CNNs for Facial Action Unit Recognition
Zheng Zhang
Shuangfei Zhai
Lijun Yin
Department of Computer Science
State University of New York at
Binghamton
NY, USA."
1e8394cc9fe7c2392aa36fb4878faf7e78bbf2de,Zero-Shot Object Recognition System Based on Topic Model,"TO APPEAR IN IEEE THMS
Zero-Shot Object Recognition System
ased on Topic Model
Wai Lam Hoo and Chee Seng Chan"
1ecb56e7c06a380b3ce582af3a629f6ef0104457,"A New Way of Discovery of Belief, Desire and Intention in the BDI Agent-Based Software Modeling","List of Contents Vol.8
Contents of
Journal of Advanced Computational
Intelligence and Intelligent Informatics
Volume 8
Vol.8 No.1, January 2004
Editorial:
o Special Issue on Selected Papers from Humanoid,
Papers:
o Dynamic Color Object Recognition Using Fuzzy
Nano-technology, Information Technology,
Communication and Control, Environment, and
Management (HNICEM’03).
Elmer P. Dadios
Papers:
o A New Way of Discovery of Belief, Desire and
Intention  in the BDI Agent-Based Software
Modeling .
Chang-Hyun Jo
o Integration of Distributed Robotic Systems"
1e64b2d2f0a8a608d0d9d913c4baee6973995952,Dominant and Complementary Multi-Emotional Facial Expression Recognition Using C-Support Vector Classification,"DOMINANT AND
COMPLEMENTARY MULTI-
EMOTIONAL FACIAL
EXPRESSION RECOGNITION
USING C-SUPPORT VECTOR
CLASSIFICATION
Christer Loob, Pejman Rasti, Iiris Lusi, Julio C. S. Jacques
Junior, Xavier Baro, Sergio Escalera, Tomasz Sapinski,
Dorota Kaminska and Gholamreza Anbarjafari"
1e21b925b65303ef0299af65e018ec1e1b9b8d60,Unsupervised Cross-Domain Image Generation,"Under review as a conference paper at ICLR 2017
UNSUPERVISED CROSS-DOMAIN IMAGE GENERATION
Yaniv Taigman, Adam Polyak & Lior Wolf
Facebook AI Research
Tel-Aviv, Israel"
1ee27c66fabde8ffe90bd2f4ccee5835f8dedbb9,9 Entropy Regularization,"Entropy Regularization
Yves Grandvalet
Yoshua Bengio
The problem of semi-supervised induction consists in learning a decision rule from
labeled and unlabeled data. This task can be undertaken by discriminative methods,
provided that learning criteria are adapted consequently. In this chapter, we moti-
vate the use of entropy regularization as a means to bene(cid:12)t from unlabeled data in
the framework of maximum a posteriori estimation. The learning criterion is derived
from clearly stated assumptions and can be applied to any smoothly parametrized
model of posterior probabilities. The regularization scheme favors low density sep-
ration, without any modeling of the density of input features. The contribution
of unlabeled data to the learning criterion induces local optima, but this problem
an be alleviated by deterministic annealing. For well-behaved models of posterior
probabilities, deterministic annealing EM provides a decomposition of the learning
problem in a series of concave subproblems. Other approaches to the semi-supervised
problem are shown to be close relatives or limiting cases of entropy regularization.
A series of experiments illustrates the good behavior of the algorithm in terms of
performance and robustness with respect to the violation of the postulated low den-
sity separation assumption. The minimum entropy solution bene(cid:12)ts from unlabeled
data and is able to challenge mixture models and manifold learning in a number of"
1ee3b4ba04e54bfbacba94d54bf8d05fd202931d,Celebrity Face Recognition using Deep Learning,"Indonesian Journal of Electrical Engineering and Computer Science
Vol. 12, No. 2, November 2018, pp. 476~481
ISSN: 2502-4752, DOI: 10.11591/ijeecs.v12.i2.pp476-481
      476
Celebrity Face Recognition using Deep Learning
Nur Ateqah Binti Mat Kasim1, Nur Hidayah Binti Abd Rahman2, Zaidah Ibrahim3,
Nur Nabilah Abu Mangshor4
,2,3Faculty of Computer and Mathematical Sciences, UniversitiTeknologi MARA (UiTM),
Faculty of Computer and Mathematical Sciences, UniversitiTeknologi MARA (UiTM),
Shah Alam, Selangor, Malaysia
Campus Jasin, Melaka, Malaysia
Article Info
Article history:
Received May 29, 2018
Revised Jul 30, 2018
Accepted Aug 3, 2018
Keywords:
AlexNet
Convolutional neural network
Deep learning"
1e41a3fdaac9f306c0ef0a978ae050d884d77d2a,Robust Object Recognition with Cortex-Like Mechanisms,"Robust Object Recognition with
Cortex-Like Mechanisms
Thomas Serre, Lior Wolf, Stanley Bileschi, Maximilian Riesenhuber, and
Tomaso Poggio, Member, IEEE"
1e1e66783f51a206509b0a427e68b3f6e40a27c8,Semi-supervised Estimation of Perceived Age from Face Images,"SEMI-SUPERVISED ESTIMATION OF PERCEIVED AGE
FROM FACE IMAGES
VALWAY Technology Center, NEC Soft, Ltd., Tokyo, Japan
Kazuya Ueki
Masashi Sugiyama
Keywords:"
1efaa128378f988965841eb3f49d1319a102dc36,Hierarchical binary CNNs for landmark localization with limited resources,"JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015
Hierarchical binary CNNs for landmark
localization with limited resources
Adrian Bulat and Georgios Tzimiropoulos"
8451bf3dd6bcd946be14b1a75af8bbb65a42d4b2,Consensual and Privacy-Preserving Sharing of Multi-Subject and Interdependent Data,"Consensual and Privacy-Preserving Sharing of
Multi-Subject and Interdependent Data
Alexandra-Mihaela Olteanu
EPFL, UNIL–HEC Lausanne
K´evin Huguenin
UNIL–HEC Lausanne
Italo Dacosta
Jean-Pierre Hubaux"
84e4b7469f9c4b6c9e73733fa28788730fd30379,Projective complex matrix factorization for facial expression recognition,"Duong et al. EURASIP Journal on Advances in Signal Processing  (2018) 2018:10
DOI 10.1186/s13634-017-0521-9
EURASIP Journal on Advances
in Signal Processing
R ES EAR CH
Projective complex matrix factorization for
facial expression recognition
Viet-Hang Duong1, Yuan-Shan Lee1, Jian-Jiun Ding2, Bach-Tung Pham1, Manh-Quan Bui1, Pham The Bao2
nd Jia-Ching Wang1,3*
Open Access"
84fa126cb19d569d2f0147bf6f9e26b54c9ad4f1,Improved Boosting Performance by Explicit Handling of Ambiguous Positive Examples,"Improved Boosting Performance by Explicit
Handling of Ambiguous Positive Examples
Miroslav Kobetski and Josephine Sullivan"
84508e846af3ac509f7e1d74b37709107ba48bde,Use of the Septum as a Reference Point in a Neurophysiologic Approach to Facial Expression Recognition,"Use of the Septum as a Reference Point in a Neurophysiologic Approach to
Facial Expression Recognition
Igor Stankovic and Montri Karnjanadecha
Department of Computer Engineering, Faculty of Engineering,
Prince of Songkla University, Hat Yai, Songkhla, 90112 Thailand
Telephone: (66)080-7045015, (66)074-287-357
E-mail:"
841a5de1d71a0b51957d9be9d9bebed33fb5d9fa,PCANet: A Simple Deep Learning Baseline for Image Classification?,"PCANet: A Simple Deep Learning Baseline for
Image Classification?
Tsung-Han Chan, Member, IEEE, Kui Jia, Shenghua Gao, Jiwen Lu, Senior Member, IEEE,
Zinan Zeng, and Yi Ma, Fellow, IEEE"
8411fe1142935a86b819f065cd1f879f16e77401,Facial Recognition using Modified Local Binary Pattern and Random Forest,"International Journal of Artificial Intelligence & Applications (IJAIA), Vol. 4, No. 6, November 2013
Facial Recognition using Modified Local Binary
Pattern and Random Forest
Brian O’Connor and Kaushik Roy
Department of Computer Science,
North Carolina A&T State University,
Greensboro, NC 27411"
849f891973ad2b6c6f70d7d43d9ac5805f1a1a5b,ResNet Backbone Proposals Classification Loss Regression Loss Classification Loss Regression Loss RPN Classification Branch Box Regression Branch Conv Conv,"Detecting Faces Using Region-based Fully
Convolutional Networks
Yitong Wang Xing Ji Zheng Zhou Hao Wang Zhifeng Li∗
Tencent AI Lab, China"
4adca62f888226d3a16654ca499bf2a7d3d11b71,Models of Semantic Representation with Visual Attributes,"Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, pages 572–582,
Sofia, Bulgaria, August 4-9 2013. c(cid:13)2013 Association for Computational Linguistics"
4a2d54ea1da851151d43b38652b7ea30cdb6dfb2,Direct recognition of motion-blurred faces,"Direct Recognition of Motion Blurred Faces
Kaushik Mitra, Priyanka Vageeswaran and Rama Chellappa"
4ab84f203b0e752be83f7f213d7495b04b1c4c79,Concave Losses for Robust Dictionary Learning,"CONCAVE LOSSES FOR ROBUST DICTIONARY LEARNING
Rafael Will M. de Araujo, R. Hirata Jr ∗
Alain Rakotomamonjy †
University of S˜ao Paulo
Institute of Mathematics and Statistics
Rua do Mat˜ao, 1010 – 05508-090 – S˜ao Paulo-SP, Brazil
Universit´e de Rouen Normandie
LITIS EA 4108
76800 Saint- ´Etienne-du-Rouvray, France"
4a3758f283b7c484d3f164528d73bc8667eb1591,Attribute Enhanced Face Aging with Wavelet-based Generative Adversarial Networks,"Attribute Enhanced Face Aging with Wavelet-based Generative Adversarial
Networks
Yunfan Liu, Qi Li, and Zhenan Sun∗
Center for Research on Intelligent Perception and Computing, CASIA
National Laboratory of Pattern Recognition, CASIA
{qli,"
4a4da3d1bbf10f15b448577e75112bac4861620a,"Face , Expression , and Iris Recognition","FACE, EXPRESSION, AND IRIS RECOGNITION
USING LEARNING-BASED APPROACHES
Guodong Guo
A dissertation submitted in partial fulfillment of
the requirements for the degree of
Doctor of Philosophy
(Computer Sciences)
t the
UNIVERSITY OF WISCONSIN–MADISON"
4abd49538d04ea5c7e6d31701b57ea17bc349412,Recognizing Fine-Grained and Composite Activities Using Hand-Centric Features and Script Data,"Recognizing Fine-Grained and Composite Activities
using Hand-Centric Features and Script Data
Marcus Rohrbach · Anna Rohrbach · Michaela Regneri ·
Sikandar Amin · Mykhaylo Andriluka · Manfred Pinkal · Bernt Schiele"
4a0f98d7dbc31497106d4f652968c708f7da6692,Real-time eye gaze direction classification using convolutional neural network,"Real-time Eye Gaze Direction Classification Using
Convolutional Neural Network
Anjith George, Member, IEEE, and Aurobinda Routray, Member, IEEE"
4a5592ae1f5e9fa83d9fa17451c8ab49608421e4,Multi-modal social signal analysis for predicting agreement in conversation settings,"Multi-modal Social Signal Analysis for Predicting
Agreement in Conversation Settings
Víctor Ponce-López
IN3, Open University of
Catalonia, Roc Boronat, 117,
08018 Barcelona, Spain.
Dept. MAiA, University of
Barcelona, Gran Via, 585,
08007 Barcelona, Spain.
Computer Vision Center, UAB,
08193 Barcelona, Spain.
Sergio Escalera
Dept. MAiA, University of
Barcelona, Gran Via, 585,
08007 Barcelona, Spain.
Computer Vision Center, UAB,
08193 Barcelona, Spain.
Xavier Baró
EIMT, Open University of
Catalonia, Rbla. Poblenou,"
4a1a5316e85528f4ff7a5f76699dfa8c70f6cc5c,Face Recognition using Local Features based on Two-layer Block Model,"MVA2005  IAPR  Conference on Machine VIsion Applications, May 16-18, 2005 Tsukuba Science City, Japan
Face Recognition using Local Features based on Two-layer Block M odel
W onjun Hwang1          Ji-Yeun Kim        Seokcheol Kee
Computing Lab.,
Samsung Advanced Institute of Technology
ombined  by  Yang  and  etc  [7].  The  sparsification  of  LFA
helps the reduction of dimension of image in LDA scheme
nd  local  topological  property  is  more  useful  than  holistic
property of PCA in recognition, but there is still structural
problem  because  the  method  to  select  the  features  is
designed  for  minimization  of  reconstruction  error,  not  for
increasing discriminability in face model.
In  this  paper,  we  proposed  the  novel  recognition
lgorithm  to  merge LFA  and LDA  method. We do  not use
the existing sparsification method for selecting features but
dopt  the  two-layer  block  model  to  make  several  groups
with  topographic  local  features  in  similar  position.  Each
local  block,  flocked  local  features,  can  represent  its  own
local  property  and  at
time  holistic  face"
4a2062ba576ca9e9a73b6aa6e8aac07f4d9344b9,Fusing Deep Convolutional Networks for Large Scale Visual Concept Classification,"Fusing Deep Convolutional Networks for Large
Scale Visual Concept Classification
Hilal Ergun and Mustafa SertB
Department of Computer Engineering
Bas¸kent University
06810 Ankara, TURKEY"
4ac3cd8b6c50f7a26f27eefc64855134932b39be,Robust Facial Landmark Detection via a Fully-Convolutional Local-Global Context Network,"Robust Facial Landmark Detection
via a Fully-Convolutional Local-Global Context Network
Daniel Merget
Matthias Rock
Gerhard Rigoll
Technical University of Munich"
4abaebe5137d40c9fcb72711cdefdf13d9fc3e62,Dimension Reduction for Regression with Bottleneck Neural Networks,"Dimension Reduction for Regression
with Bottleneck Neural Networks
Elina Parviainen
BECS, Aalto University School of Science and Technology, Finland"
4aeb87c11fb3a8ad603311c4650040fd3c088832,Self-paced Mixture of Regressions,"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17)
SamplesSelected SamplesOutliersMoRSPMoR (ours)6361242024Figure1:Inter-componentimbalanceandintra-componentoutliersinMixtureofRegression(MoR)approaches.StandardMoRcannotlearnaccurateregressors(denotedbythedashedlines).Byintroduc-inganovelself-pacedscheme,ourSPMoRapproach(denotedbythesolidlines)selectsbalancedandconfidenttrainingsamplesfromeachcomponent,whilepreventlearningfromtheoutliersthroughoutthetrainingprocedure.theywillbeinevitablybiasedbydatadistribution:lowre-gressionerrorindenselysampledspacewhilehigherrorineverywhereelse.Foraddressingtheissuesofthedatadiscontinuityandheterogeneity,thedivide-and-conquerapproacheswerepro-posedlately.Thecoreideaistolearntocombinemultiplelocalregressors.Forinstance,thehierarchical-based[Hanetal.,2015]andtree-basedregression[HaraandChellappa,2014]makehardpartitionsrecursively,andthesubsetsofsam-plesmaynotbehomogeneousforlearninglocalregressors.WhileMixtureofRegressions(MoR)[Jacobsetal.,1991;JordanandXu,1995]distributesregressionerroramonglocalregressorsbymaximizinglikelihoodinthejointinput-outputspace.Theseapproachesreduceoverallerrorbyfittingre-gressionlocallyandreliefsthebiasbydiscontinuousdatadistribution.Unfortunately,theaforementionedapproachesstillcannotachievesatisfactoryperformancewhenapplyinginsomereal-worldapplications.Themainreasonisthattheseapproachestendtobesensitivetotheintra-componentoutliers(i.e.,thenoisytrainingdataresidingincertaincomponents)andtheinter-componentimbalance(i.e.,thedifferentamountsoftrain-"
4a3d96b2a53114da4be3880f652a6eef3f3cc035,A Dictionary Learning-Based 3D Morphable Shape Model,"A Dictionary Learning-Based
D Morphable Shape Model
Claudio Ferrari
, Giuseppe Lisanti, Stefano Berretti
, Senior Member, IEEE, and Alberto Del Bimbo"
4a6fcf714f663618657effc341ae5961784504c7,Scaling Up Class-Specific Kernel Discriminant Analysis for Large-Scale Face Verification,"Scaling up Class-Specific Kernel Discriminant
Analysis for large-scale Face Verification
Alexandros Iosifidis, Senior Member, IEEE, and Moncef Gabbouj, Fellow, IEEE"
24115d209e0733e319e39badc5411bbfd82c5133,Long-Term Recurrent Convolutional Networks for Visual Recognition and Description,"Long-term Recurrent Convolutional Networks for
Visual Recognition and Description
Jeff Donahue, Lisa Anne Hendricks, Marcus Rohrbach, Subhashini Venugopalan, Sergio Guadarrama,
Kate Saenko, Trevor Darrell"
24c442ac3f6802296d71b1a1914b5d44e48b4f29,Pose and Expression-Coherent Face Recovery in the Wild,"Pose and expression-coherent face recovery in the wild
Xavier P. Burgos-Artizzu
Joaquin Zepeda
Technicolor, Cesson-S´evign´e, France
Franc¸ois Le Clerc
Patrick P´erez"
245f8ec4373e0a6c1cae36cd6fed5a2babed1386,Lucas Kanade Optical Flow Computation from Superpixel based Intensity Region for Facial Expression Feature Extraction,"J. Appl. Environ. Biol. Sci., 7(3S)1-10, 2017
© 2017, TextRoad Publication
ISSN: 2090-4274
Journal of Applied Environmental
nd Biological Sciences
www.textroad.com
Lucas Kanade Optical Flow Computation from Superpixel based Intensity
Region for Facial Expression Feature Extraction
Halina Hassan1,2, Abduljalil Radman1, Shahrel Azmin Suandi1, Sazali Yaacob2
Intelligent Biometric Group, School of Electrical and Electronics Engineering, Universiti Sains Malaysia,
Electrical, Electronics and Automation Section, Universiti Kuala Lumpur Malaysian Spanish Institute, 09000
Engineering Campus, 14300 Nibong Tebal, Pulau Pinang, Malaysia
Kulim Hi-Tech Park, Kedah, Malaysia
Received: February 21, 2017
Accepted: May 14, 2017"
24e099e77ae7bae3df2bebdc0ee4e00acca71250,Robust Face Alignment Under Occlusion via Regional Predictive Power Estimation,"Robust face alignment under occlusion via regional predictive power
estimation.
Heng Yang; Xuming He; Xuhui Jia; Patras, I
© 2015 IEEE
For additional information about this publication click this link.
http://qmro.qmul.ac.uk/xmlui/handle/123456789/22467
Information about this research object was correct at the time of download; we occasionally
make corrections to records, please therefore check the published record when citing. For
more information contact"
2450c618cca4cbd9b8cdbdb05bb57d67e63069b1,A connexionist approach for robust and precise facial feature detection in complex scenes,"A Connexionist Approach for Robust and Precise Facial Feature Detection in
Complex Scenes
Stefan Duffner and Christophe Garcia
France Telecom Research & Development
, rue du Clos Courtel
5512 Cesson-S´evign´e, France
fstefan.duffner,"
244b57cc4a00076efd5f913cc2833138087e1258,Warped Convolutions: Efficient Invariance to Spatial Transformations,"Warped Convolutions: Efficient Invariance to Spatial Transformations
Jo˜ao F. Henriques 1 Andrea Vedaldi 1"
24869258fef8f47623b5ef43bd978a525f0af60e,Données multimodales pour l ’ analyse d ’ image,"UNIVERSITÉDEGRENOBLENoattribuéparlabibliothèqueTHÈSEpourobtenirlegradedeDOCTEURDEL’UNIVERSITÉDEGRENOBLESpécialité:MathématiquesetInformatiquepréparéeauLaboratoireJeanKuntzmanndanslecadredel’ÉcoleDoctoraleMathématiques,SciencesetTechnologiesdel’Information,InformatiqueprésentéeetsoutenuepubliquementparMatthieuGuillauminle27septembre2010ExploitingMultimodalDataforImageUnderstandingDonnéesmultimodalespourl’analysed’imageDirecteursdethèse:CordeliaSchmidetJakobVerbeekJURYM.ÉricGaussierUniversitéJosephFourierPrésidentM.AntonioTorralbaMassachusettsInstituteofTechnologyRapporteurMmeTinneTuytelaarsKatholiekeUniversiteitLeuvenRapporteurM.MarkEveringhamUniversityofLeedsExaminateurMmeCordeliaSchmidINRIAGrenobleExaminatriceM.JakobVerbeekINRIAGrenobleExaminateur"
2465fc22e03faf030e5a319479a95ef1dfc46e14,Influence of different feature selection approaches on the performance of emotion recognition methods based on SVM,"______________________________________________________PROCEEDING OF THE 20TH CONFERENCE OF FRUCT ASSOCIATION
Influence of Different Feature Selection Approaches
on the Performance of Emotion Recognition
Methods Based on SVM
Daniil Belkov, Konstantin Purtov, Vladimir Kublanov
Ural Federal University (UrFU)
Yekaterinburg, Russia
d.d.belkov,"
24ff832171cb774087a614152c21f54589bf7523,Beat-Event Detection in Action Movie Franchises,"Beat-Event Detection in Action Movie Franchises
Danila Potapov
Matthijs Douze
Jerome Revaud
Zaid Harchaoui
Cordelia Schmid"
247a6b0e97b9447850780fe8dbc4f94252251133,Facial action unit detection: 3D versus 2D modality,"Facial Action Unit Detection: 3D versus 2D Modality
Arman Savran
Electrical and Electronics Engineering
Bo˘gazic¸i University, Istanbul, Turkey
B¨ulent Sankur
Electrical and Electronics Engineering
Bo˘gazic¸i University, Istanbul, Turkey
M. Taha Bilge
Department of Psychology
Bo˘gazic¸i University, Istanbul, Turkey"
230527d37421c28b7387c54e203deda64564e1b7,Person Re-identification: System Design and Evaluation Overview,"Person Re-identification: System Design and
Evaluation Overview
Xiaogang Wang and Rui Zhao"
23172f9a397f13ae1ecb5793efd81b6aba9b4537,Defining Visually Descriptive Language,"Proceedings of the 2015 Workshop on Vision and Language (VL’15), pages 10–17,
Lisbon, Portugal, 18 September 2015. c(cid:13)2015 Association for Computational Linguistics."
236a4f38f79a4dcc2183e99b568f472cf45d27f4,Randomized Clustering Forests for Image Classification,"Randomized Clustering Forests
for Image Classification
Frank Moosmann, Student Member, IEEE, Eric Nowak, Student Member, IEEE, and
Frederic Jurie, Member, IEEE Computer Society"
230c4a30f439700355b268e5f57d15851bcbf41f,EM Algorithms for Weighted-Data Clustering with Application to Audio-Visual Scene Analysis,"EM Algorithms for Weighted-Data Clustering
with Application to Audio-Visual Scene Analysis
Israel D. Gebru, Xavier Alameda-Pineda, Florence Forbes and Radu Horaud"
237fa91c8e8098a0d44f32ce259ff0487aec02cf,Bidirectional PCA with assembled matrix distance metric for image recognition,"IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART B: CYBERNETICS, VOL. 36, NO. 4, AUGUST 2006
Bidirectional PCA With Assembled Matrix
Distance Metric for Image Recognition
Wangmeng Zuo, David Zhang, Senior Member, IEEE, and Kuanquan Wang, Member, IEEE"
2331df8ca9f29320dd3a33ce68a539953fa87ff5,Extended Isomap for Pattern Classification,"Extended Isomap for Pattern Classification
Ming-Hsuan Yang
Honda Fundamental Research Labs
Mountain View, CA 94041"
23ba9e462151a4bf9dfc3be5d8b12dbcfb7fe4c3,Determining Mood from Facial Expressions,"CS 229 Project, Fall 2014
Matthew Wang
Spencer Yee
Determining Mood from Facial Expressions
Introduction
Facial expressions play an extremely important role in human communication. As
society continues to make greater use of human-machine interactions, it is important for
machines to be able to interpret facial expressions in order to improve their
uthenticity. If machines can be trained to determine mood to a better extent than
humans can, especially for more subtle moods, then this could be useful in fields such as
ounseling. This could also be useful for gauging reactions of large audiences in various
ontexts, such as political talks.
The results of this project could also be applied to recognizing other features of facial
expressions, such as determining when people are purposefully suppressing emotions or
lying. The ability to recognize different facial expressions could also improve technology
that recognizes to whom specific faces belong. This could in turn be used to search a
large number of pictures for a specific photo, which is becoming increasingly difficult, as
storing photos digitally has been extremely common in the past decade. The possibilities
re endless.
II  Data and Features"
238fc68b2e0ef9f5ec043d081451902573992a03,Enhanced Local Gradient Order Features and Discriminant Analysis for Face Recognition,"Enhanced Local Gradient Order Features and
Discriminant Analysis for Face Recognition
Chuan-Xian Ren, Zhen Lei, Member, IEEE, Dao-Qing Dai, Member, IEEE, and Stan Z. Li, Fellow, IEEE
role in robust face recognition [5]. Many algorithms have
een proposed to deal with the effectiveness of feature design
nd extraction [6], [7]; however, the performance of many
existing methods is still highly sensitive to variations of
imaging conditions, such as outdoor illumination, exaggerated
expression, and continuous occlusion. These complex varia-
tions are significantly affecting the recognition accuracy in
recent years [8]–[10].
Appearance-based subspace learning is one of the sim-
plest approach for feature extraction, and many methods
re usually based on linear correlation of pixel intensities.
For example, Eigenface [11] uses eigen system of pixel
intensities to estimate the lower rank linear subspace of
set of training face images by minimizing the (cid:2)2 dis-
tance metric. The solution enjoys optimality properties when
noise is independent
identically distributed Gaussian only."
2322ec2f3571e0ddc593c4e2237a6a794c61251d,Four not six: Revealing culturally common facial expressions of emotion.,"Jack, R. E. , Sun, W., Delis, I., Garrod, O. G. B. and Schyns, P. G. (2016)
Four not six: revealing culturally common facial expressions of
emotion.Journal of Experimental Psychology: General, 145(6), pp. 708-
730.  (doi:10.1037/xge0000162)
This is the author’s final accepted version.
There may be differences between this version and the published version.
You are advised to consult the publisher’s version if you wish to cite from
http://eprints.gla.ac.uk/116592/
Deposited on: 20 April 2016
Enlighten – Research publications by members of the University of Glasgow
http://eprints.gla.ac.uk"
23120f9b39e59bbac4438bf4a8a7889431ae8adb,Improved RGB-D-T based face recognition,"Aalborg Universitet
Improved RGB-D-T based Face Recognition
Oliu Simon, Marc;   Corneanu, Ciprian; Nasrollahi, Kamal; Guerrero, Sergio Escalera;
Nikisins, Olegs; Sun, Yunlian; Li, Haiqing; Sun, Zhenan; Moeslund, Thomas B.; Greitans,
Modris
Published in:
DOI (link to publication from Publisher):
0.1049/iet-bmt.2015.0057
Publication date:
Document Version
Accepted manuscript, peer reviewed version
Link to publication from Aalborg University
Citation for published version (APA):
Oliu Simon, M.,   Corneanu, C., Nasrollahi, K., Guerrero, S. E., Nikisins, O., Sun, Y., ... Greitans, M. (2016).
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23d55061f7baf2ffa1c847d356d8f76d78ebc8c1,Generic and attribute-specific deep representations for maritime vessels,"Solmaz et al. IPSJ Transactions on Computer Vision and
Applications  (2017) 9:22
DOI 10.1186/s41074-017-0033-4
IPSJ Transactions on Computer
Vision and Applications
RESEARCH PAPER
Open Access
Generic and attribute-specific deep
representations for maritime vessels
Berkan Solmaz*†
, Erhan Gundogdu†, Veysel Yucesoy and Aykut Koc"
23a8d02389805854cf41c9e5fa56c66ee4160ce3,Influence of low resolution of images on reliability of face detection and recognition,"Multimed Tools Appl
DOI 10.1007/s11042-013-1568-8
Influence of low resolution of images on reliability
of face detection and recognition
Tomasz Marciniak· Agata Chmielewska·
Radoslaw Weychan· Marianna Parzych·
Adam Dabrowski
© The Author(s) 2013. This article is published with open access at SpringerLink.com"
23b37c2f803a2d4b701e2f39c5f623b2f3e14d8e,Modified Approaches on Face Recognition By using Multisensory Image,"Available Online at www.ijcsmc.com
International Journal of Computer Science and Mobile Computing
A Monthly Journal of Computer Science and Information Technology
ISSN 2320–088X
IJCSMC, Vol. 2, Issue. 4, April 2013, pg.646 – 649
RESEARCH ARTICLE
Modified Approaches on Face Recognition
By using Multisensory Image
S. Dhanarajan1, G. Michael2
Computer Science Department, Bharath University, India
Computer Science Department, Bharath University, India"
4f051022de100241e5a4ba8a7514db9167eabf6e,Face Parsing via a Fully-Convolutional Continuous CRF Neural Network,"Face Parsing via a Fully-Convolutional Continuous
CRF Neural Network
Lei Zhou, Zhi Liu, Senior Member, IEEE, Xiangjian He, Senior Member, IEEE"
4faded442b506ad0f200a608a69c039e92eaff11,İstanbul Technical University Institute of Science and Technology Face Recognition under Varying Illumination,"İSTANBUL TECHNICAL UNIVERSITY  INSTITUTE OF SCIENCE AND TECHNOLOGY
FACE RECOGNITION UNDER VARYING
ILLUMINATION
Master Thesis by
Erald VUÇINI, B.Sc.
Department :  Computer Engineering
Programme:  Computer Engineering
Supervisor: Prof. Dr. Muhittin GÖKMEN
JUNE 2006"
4fc936102e2b5247473ea2dd94c514e320375abb,Guess Where? Actor-Supervision for Spatiotemporal Action Localization,"Guess Where? Actor-Supervision for Spatiotemporal Action Localization
Victor Escorcia1∗
Cuong D. Dao1
Mihir Jain3
KAUST1, University of Amsterdam2, Qualcomm Technologies, Inc.3
Bernard Ghanem1
Cees Snoek2∗"
4f6adc53798d9da26369bea5a0d91ed5e1314df2,Online Nonnegative Matrix Factorization with General Divergences,"IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. , NO. , 2016
Online Nonnegative Matrix Factorization with
General Divergences
Renbo Zhao, Member, IEEE, Vincent Y. F. Tan, Senior Member, IEEE, Huan Xu"
4f591e243a8f38ee3152300bbf42899ac5aae0a5,Understanding Higher-Order Shape via 3D Shape Attributes,"SUBMITTED TO TPAMI
Understanding Higher-Order Shape
via 3D Shape Attributes
David F. Fouhey, Abhinav Gupta, Andrew Zisserman"
4f4f920eb43399d8d05b42808e45b56bdd36a929,A Novel Method for 3 D Image Segmentation with Fusion of Two Images using Color K-means Algorithm,"International Journal of Computer Applications (0975 – 8887)
Volume 123 – No.4, August 2015
A Novel Method for 3D Image Segmentation with Fusion
of Two Images using Color K-means Algorithm
Neelam Kushwah
Dept. of CSE
ITM Universe
Gwalior
Priusha Narwariya
Dept. of CSE
ITM Universe
Gwalior"
4f77a37753c03886ca9c9349723ec3bbfe4ee967,"Localizing Facial Keypoints with Global Descriptor Search, Neighbour Alignment and Locally Linear Models","Localizing Facial Keypoints with Global Descriptor Search,
Neighbour Alignment and Locally Linear Models
Md. Kamrul Hasan1, Christopher Pal1 and Sharon Moalem2
´Ecole Polytechnique de Montr´eal, Universit´e de Montr´eal
University of Toronto and Recognyz Systems Technologies
lso focused on emotion recognition in the wild [9]."
8de06a584955f04f399c10f09f2eed77722f6b1c,Facial Landmarks Localization Estimation by Cascaded Boosted Regression,"Author manuscript, published in ""International Conference on Computer Vision Theory and Applications (VISAPP 2013) (2013)"""
8d4f0517eae232913bf27f516101a75da3249d15,Event-based Dynamic Face Detection and Tracking Based on Activity,"ARXIV SUBMISSION, MARCH 2018
Event-based Dynamic Face Detection and
Tracking Based on Activity
Gregor Lenz, Sio-Hoi Ieng and Ryad Benosman"
8de2dbe2b03be8a99628ffa000ac78f8b66a1028,Action Recognition in Videos,"´Ecole Nationale Sup´erieure dInformatique et de Math´ematiques Appliqu´ees de Grenoble
INP Grenoble – ENSIMAG
UFR Informatique et Math´ematiques Appliqu´ees de Grenoble
Rapport de stage de Master 2 et de projet de fin d’´etudes
Effectu´e au sein de l’´equipe LEAR, I.N.R.I.A., Grenoble
Action Recognition in Videos
Gaidon Adrien
e ann´ee ENSIMAG – Option I.I.I.
M2R Informatique – sp´ecialit´e I.A.
04 f´evrier 2008 – 04 juillet 2008
LEAR,
I.N.R.I.A., Grenoble
655 avenue de l’Europe
8 334 Montbonnot
France
Responsable de stage
Mme. Cordelia Schmid
Tuteur ´ecole
M. Augustin Lux
M. Roger Mohr"
8d3fbdb9783716c1832a0b7ab1da6390c2869c14,Discriminant Subspace Analysis for Uncertain Situation in Facial Recognition,"Discriminant Subspace Analysis for Uncertain
Situation in Facial Recognition
Pohsiang Tsai, Tich Phuoc Tran, Tom Hintz and Tony Jan
School of Computing and Communications – University of Technology, Sydney
Australia
. Introduction
Facial    analysis  and  recognition  have  received  substential  attention  from  researchers  in
iometrics,  pattern  recognition,  and  computer  vision  communities.  They  have  a  large
number  of  applications,  such  as  security,  communication,  and  entertainment.  Although  a
great deal of efforts has been devoted to automated face recognition systems, it still remains
challenging uncertainty problem. This is because human facial appearance has potentially
of very large intra-subject variations of head pose, illumination, facial expression, occlusion
due to other objects or accessories, facial hair and aging. These misleading variations may
ause classifiers to degrade generalization performance.
It is important for face recognition systems to employ an effective feature extraction scheme
to  enhance  separability  between  pattern  classes  which  should  maintain  and  enhance
features of the input data that make distinct pattern classes separable (Jan, 2004). In general,
there  exist  a  number  of  different  feature  extraction  methods.  The  most  common  feature
extraction  methods  are  subspace  analysis  methods  such  as  principle  component  analysis
(PCA)  (Kirby  &  Sirovich,  1990)  (Jolliffe,  1986)  (Turk  &  Pentland,  1991b),  kernel  principle"
8d42a24d570ad8f1e869a665da855628fcb1378f,An Empirical Study of Context in Object Detection,"CVPR 2009 Submission #987. CONFIDENTIAL REVIEW COPY. DO NOT DISTRIBUTE.
An Empirical Study of Context in Object Detection
Anonymous CVPR submission
Paper ID 987"
8d8461ed57b81e05cc46be8e83260cd68a2ebb4d,Age identification of Facial Images using Neural Network,"Age identification of Facial Images using Neural
Network
Sneha Thakur, Ligendra Verma
CSE Department,CSVTU
RIT, Raipur, Chhattisgarh , INDIA"
8d384e8c45a429f5c5f6628e8ba0d73c60a51a89,Temporal Dynamic Graph LSTM for Action-Driven Video Object Detection,"Temporal Dynamic Graph LSTM for Action-driven Video Object Detection
Yuan Yuan1 Xiaodan Liang2 Xiaolong Wang2 Dit-Yan Yeung1 Abhinav Gupta2
The Hong Kong University of Science and Technology 2 Carneige Mellon University"
8d1adf0ac74e901a94f05eca2f684528129a630a,Facial Expression Recognition Using Facial Movement Features,"Facial Expression Recognition Using Facial
Movement Features"
8d646ac6e5473398d668c1e35e3daa964d9eb0f6,Memory-Efficient Global Refinement of Decision-Tree Ensembles and its Application to Face Alignment,"MEMORY-EFFICIENT GLOBAL REFINEMENT OF DECISION-TREE ENSEMBLES AND
ITS APPLICATION TO FACE ALIGNMENT
Nenad Markuˇs†
Ivan Gogi´c†
Igor S. Pandˇzi´c†
J¨orgen Ahlberg‡
University of Zagreb, Faculty of Electrical Engineering and Computing, Unska 3, 10000 Zagreb, Croatia
Computer Vision Laboratory, Dept. of Electrical Engineering, Link¨oping University, SE-581 83 Link¨oping, Sweden"
8dffbb6d75877d7d9b4dcde7665888b5675deee1,Emotion Recognition with Deep-Belief Networks,"Emotion Recognition with Deep-Belief
Networks
Tom McLaughlin, Mai Le, Naran Bayanbat
Introduction
For  our  CS229  project,  we  studied  the  problem  of
reliable  computerized  emotion  recognition  in  images  of
human
faces.  First,  we  performed  a  preliminary
exploration using SVM classifiers, and then developed an
pproach based on Deep Belief Nets. Deep Belief Nets, or
DBNs,  are  probabilistic  generative  models  composed  of
multiple  layers  of  stochastic  latent  variables,  where  each
“building block” layer is a Restricted Boltzmann Machine
(RBM).  DBNs  have  a  greedy  layer-wise  unsupervised
learning algorithm as well as a discriminative fine-tuning
procedure  for  optimizing  performance  on  classification
tasks. [1].
We  trained  our  classifier  on  three  databases:  the
Cohn-Kanade Extended Database (CK+) [2], the Japanese
Female  Facial Expression  Database (JAFFE) [3], and the"
8d5998cd984e7cce307da7d46f155f9db99c6590,ChaLearn looking at people: A review of events and resources,"ChaLearn Looking at People:
A Review of Events and Resources
Sergio Escalera1,2, Xavier Bar´o2,3, Hugo Jair Escalante4,5, Isabelle Guyon4,6,
Dept. Mathematics and Computer Science, UB, Spain,
Computer Vision Center, UAB, Barcelona, Spain,
EIMT, Open University of Catalonia, Barcelona, Spain,
ChaLearn, California, USA, 5 INAOE, Puebla, Mexico,
6 Universit´e Paris-Saclay, Paris, France,
http://chalearnlap.cvc.uab.es"
8dce38840e6cf5ab3e0d1b26e401f8143d2a6bff,Towards large scale multimedia indexing: A case study on person discovery in broadcast news,"Towards large scale multimedia indexing:
A case study on person discovery in broadcast news
Nam Le1, Hervé Bredin2, Gabriel Sargent3, Miquel India5, Paula Lopez-Otero6,
Claude Barras2, Camille Guinaudeau2, Guillaume Gravier3, Gabriel Barbosa da Fonseca4,
Izabela Lyon Freire4, Zenilton Patrocínio Jr4, Silvio Jamil F. Guimarães4, Gerard Martí5,
Josep Ramon Morros5, Javier Hernando5, Laura Docio-Fernandez6, Carmen Garcia-Mateo6,
Sylvain Meignier7, Jean-Marc Odobez1
Idiap Research Institute & EPFL, 2 LIMSI, CNRS, Univ. Paris-Sud, Université Paris-Saclay,
CNRS, Irisa & Inria Rennes, 4 PUC de Minas Gerais, Belo Horizonte,
5 Universitat Politècnica de Catalunya, 6 University of Vigo, 7 LIUM, University of Maine"
153f5ad54dd101f7f9c2ae17e96c69fe84aa9de4,Overview of algorithms for face detection and tracking,"Overview of algorithms for face detection and
tracking
Nenad Markuˇs"
15cd05baa849ab058b99a966c54d2f0bf82e7885,Structured Sparse Subspace Clustering: A unified optimization framework,"Structured Sparse Subspace Clustering: A Unified Optimization Framework
Chun-Guang Li1, René Vidal2
SICE, Beijing University of Posts and Telecommunications. 2Center for Imaging Science, Johns Hopkins University.
In many real-world applications, we need to deal with high-dimensional
datasets, such as images, videos, text, and more. In practice, such high-
dimensional datasets can be well approximated by multiple low-dimensional
subspaces corresponding to multiple classes or categories. For example, the
feature point trajectories associated with a rigidly moving object in a video
lie in an affine subspace (of dimension up to 4), and face images of a subject
under varying illumination lie in a linear subspace (of dimension up to 9).
Therefore, the task, known in the literature as subspace clustering [6], is
to segment the data into the corresponding subspaces and finds multiple
pplications in computer vision.
State of the art approaches [1, 2, 3, 4, 5, 7] for solving this problem fol-
low a two-stage approach: a) Construct an affinity matrix between points by
exploiting the ‘self-expressiveness’ property of the data, which allows any
data point to be represented as a linear (or affine) combination of the other
data points; b) Apply spectral clustering on the affinity matrix to recover
the data segmentation. Dividing the problem in two steps is, on the one
hand, appealing because the first step can be solved using convex optimiza-"
15136c2f94fd29fc1cb6bedc8c1831b7002930a6,Deep Learning Architectures for Face Recognition in Video Surveillance,"Deep Learning Architectures for Face
Recognition in Video Surveillance
Saman Bashbaghi, Eric Granger, Robert Sabourin and Mostafa Parchami"
153e5cddb79ac31154737b3e025b4fb639b3c9e7,Active Dictionary Learning in Sparse Representation Based Classification,"PREPRINT SUBMITTED TO IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
Active Dictionary Learning in Sparse
Representation Based Classification
Jin Xu, Haibo He, Senior Member, IEEE, and Hong Man, Senior Member, IEEE"
157eb982da8fe1da4c9e07b4d89f2e806ae4ceb6,Connecting the dots in multi-class classification: From nearest subspace to collaborative representation,"MITSUBISHI ELECTRIC RESEARCH LABORATORIES
http://www.merl.com
Connecting the Dots in Multi-Class Classification: From
Nearest Subspace to Collaborative Representation
Chi, Y.; Porikli, F.
TR2012-043
June 2012"
15e0b9ba3389a7394c6a1d267b6e06f8758ab82b,The OU-ISIR Gait Database comprising the Large Population Dataset with Age and performance evaluation of age estimation,"Xu et al. IPSJ Transactions on Computer Vision and
Applications  (2017) 9:24
DOI 10.1186/s41074-017-0035-2
IPSJ Transactions on Computer
Vision and Applications
TECHNICAL NOTE
Open Access
The OU-ISIR Gait Database comprising the
Large Population Dataset with Age and
performance evaluation of age estimation
Chi Xu1,2, Yasushi Makihara2*, Gakuto Ogi2, Xiang Li1,2, Yasushi Yagi2 and Jianfeng Lu1"
15aa6c457678e25f6bc0e818e5fc39e42dd8e533,Conditional Image Generation for Learning the Structure of Visual Objects,
15cf1f17aeba62cd834116b770f173b0aa614bf4,Facial Expression Recognition using Neural Network with Regularized Backpropagation Algorithm,"International Journal of Computer Applications (0975 – 8887)
Volume 77 – No.5, September 2013
Facial Expression Recognition using Neural Network with
Regularized Back-propagation Algorithm
Ashish Kumar Dogra
Research Scholar
Department of ECE,
Lovely Professional University,
Phagwara, India
Nikesh Bajaj
Assistant Professor
Department of ECE,
Lovely Professional University,
Phagwara, India
Harish Kumar Dogra
Research Scholar
Department of ECE,
Gyan Ganga Institute of
Technology & Sciences,
Jabalpur, India"
15f3d47b48a7bcbe877f596cb2cfa76e798c6452,Automatic face analysis tools for interactive digital games,"Automatic face analysis tools for interactive digital games
Anonymised for blind review
Anonymous
Anonymous
Anonymous"
15728d6fd5c9fc20b40364b733228caf63558c31,Expanding the Breadth and Detail of Object Recognition By,(cid:13) 2013 Ian N. Endres
153c8715f491272b06dc93add038fae62846f498,On Clustering Images of Objects,"(cid:13) Copyright by Jongwoo Lim, 2005"
122ee00cc25c0137cab2c510494cee98bd504e9f,The Application of Active Appearance Models to Comprehensive Face Analysis Technical Report,"The Application of
Active Appearance Models to
Comprehensive Face Analysis
Technical Report
Simon Kriegel
TU M¨unchen
April 5, 2007"
12cb3bf6abf63d190f849880b1703ccc183692fe,Guess Who?: A game to crowdsource the labeling of affective facial expressions is comparable to expert ratings,"Guess Who?: A game to crowdsource the labeling of affective facial
expressions is comparable to expert ratings.
Barry Borsboom
Graduation research project, june 2012
Supervised by: Dr. Joost Broekens
Leiden University Media Technology Department,"
12cd96a419b1bd14cc40942b94d9c4dffe5094d2,Leveraging Captions in the Wild to Improve Object Detection,"Proceedings of the 5th Workshop on Vision and Language, pages 29–38,
Berlin, Germany, August 12 2016. c(cid:13)2016 Association for Computational Linguistics"
1275852f2e78ed9afd189e8b845fdb5393413614,A Transfer Learning based Feature-Weak-Relevant Method for Image Clustering,"A Transfer Learning based Feature-Weak-Relevant Method for
Image Clustering
Bo Dong, Xinnian Wang
Dalian Maritime University
Dalian, China"
12055b8f82d5411f9ad196b60698d76fbd07ac1e,Multiview Facial Landmark Localization in RGB-D Images via Hierarchical Regression With Binary Patterns,"Multiview Facial Landmark Localization in RGB-D
Images via Hierarchical Regression
With Binary Patterns
Zhanpeng Zhang, Student Member, IEEE, Wei Zhang, Member, IEEE, Jianzhuang Liu, Senior Member, IEEE,
nd Xiaoou Tang, Fellow, IEEE"
120785f9b4952734818245cc305148676563a99b,Diagnostic automatique de l'état dépressif(Classification of depressive moods),"Diagnostic automatique de l’état dépressif
S. Cholet
H. Paugam-Moisy
Laboratoire de Mathématiques Informatique et Applications (LAMIA - EA 4540)
Université des Antilles, Campus de Fouillole - Guadeloupe
Résumé
Les troubles psychosociaux sont un problème de santé pu-
lique majeur, pouvant avoir des conséquences graves sur
le court ou le long terme, tant sur le plan professionnel que
personnel ou familial. Le diagnostic de ces troubles doit
être établi par un professionnel. Toutefois, l’IA (l’Intelli-
gence Artificielle) peut apporter une contribution en four-
nissant au praticien une aide au diagnostic, et au patient
un suivi permanent rapide et peu coûteux. Nous proposons
une approche vers une méthode de diagnostic automatique
de l’état dépressif à partir d’observations du visage en
temps réel, au moyen d’une simple webcam. A partir de
vidéos du challenge AVEC’2014, nous avons entraîné un
lassifieur neuronal à extraire des prototypes de visages
selon différentes valeurs du score de dépression de Beck"
12ebeb2176a5043ad57bc5f3218e48a96254e3e9,Traffic Road Sign Detection and Recognition for Automotive Vehicles,"International Journal of Computer Applications (0975 – 8887)
Volume 120 – No.24, June 2015
Traffic Road Sign Detection and Recognition for
Automotive Vehicles
Md. Safaet Hossain
Zakir Hyder
Department of Electrical Engineering and
Department of Electrical Engineering and
Computer Science North South University, Dhaka
Computer Science North South University, Dhaka
Bangladesh
Bangladesh"
12150d8b51a2158e574e006d4fbdd3f3d01edc93,Deep End2End Voxel2Voxel Prediction,"Deep End2End Voxel2Voxel Prediction
Du Tran, Lubomir Bourdev, Rob Fergus, Lorenzo
Torresani, Manohar Paluri
Presented by: Ahmed Osman
Ahmed Osman"
12d8730da5aab242795bdff17b30b6e0bac82998,Persistent Evidence of Local Image Properties in Generic ConvNets,"Persistent Evidence of Local Image Properties in Generic ConvNets
Ali Sharif Razavian, Hossein Azizpour,
Atsuto Maki, Josephine Sullivan, Carl Henrik Ek, and Stefan Carlsson
CVAP, KTH (Royal Institute of Technology), Stockholm, SE-10044"
8c13f2900264b5cf65591e65f11e3f4a35408b48,A Generic Face Representation Approach for Local Appearance Based Face Verification,"A GENERIC FACE REPRESENTATION APPROACH FOR
LOCAL APPEARANCE BASED FACE VERIFICATION
Hazim Kemal Ekenel, Rainer Stiefelhagen
Interactive Systems Labs, Universität Karlsruhe (TH)
76131 Karlsruhe, Germany
{ekenel,
web: http://isl.ira.uka.de/face_recognition/"
8c955f3827a27e92b6858497284a9559d2d0623a,Facial Expression Recognition under Noisy Environment Using Gabor Filters,"Buletinul Ştiinţific al Universităţii ""Politehnica"" din Timişoara
Seria ELECTRONICĂ şi TELECOMUNICAŢII
TRANSACTIONS on ELECTRONICS and COMMUNICATIONS
Tom 53(67), Fascicola 1-2, 2008
Facial Expression Recognition under Noisy Environment
Using Gabor Filters
Ioan Buciu1, I. Nafornita2, I. Pitas3"
8c7f4c11b0c9e8edf62a0f5e6cf0dd9d2da431fa,Dataset Augmentation for Pose and Lighting Invariant Face Recognition,"Dataset Augmentation for Pose and Lighting
Invariant Face Recognition
Daniel Crispell∗, Octavian Biris∗, Nate Crosswhite†, Jeffrey Byrne†, Joseph L. Mundy∗
Vision Systems, Inc.
Systems and Technology Research"
8ce9b7b52d05701d5ef4a573095db66ce60a7e1c,Structured Sparse Subspace Clustering: A Joint Affinity Learning and Subspace Clustering Framework,"Structured Sparse Subspace Clustering: A Joint
Affinity Learning and Subspace Clustering
Framework
Chun-Guang Li, Chong You, and Ren´e Vidal"
8cb6daba2cb1e208e809633133adfee0183b8dd2,Know Before You Do: Anticipating Maneuvers via Learning Temporal Driving Models,"Know Before You Do: Anticipating Maneuvers
via Learning Temporal Driving Models
Ashesh Jain, Hema S Koppula, Bharad Raghavan, Shane Soh, Ashutosh Saxena
Cornell University and Stanford University"
8c6c0783d90e4591a407a239bf6684960b72f34e,SESSION KNOWLEDGE ENGINEERING AND MANAGEMENT + KNOWLEDGE ACQUISITION Chair(s),"SESSION
KNOWLEDGE ENGINEERING AND
MANAGEMENT + KNOWLEDGE ACQUISITION
Chair(s)
Int'l Conf. Information and Knowledge Engineering | IKE'13 |1"
8cc07ae9510854ec6e79190cc150f9f1fe98a238,Using Deep Learning to Challenge Safety Standard for Highly Autonomous Machines in Agriculture,"Article
Using Deep Learning to Challenge Safety Standard
for Highly Autonomous Machines in Agriculture
Kim Arild Steen *,†, Peter Christiansen †, Henrik Karstoft and Rasmus Nyholm Jørgensen
Department of Engineering, Aarhus University, Finlandsgade 22 8200 Aarhus N, Denmark;
(P.C.); (H.K.); (R.N.J.)
* Correspondence: Tel.: +45-3116-8628
These authors contributed equally to this work.
Academic Editors: Francisco Rovira-Más and Gonzalo Pajares Martinsanz
Received: 18 December 2015; Accepted: 2 February 2016; Published: 15 February 2016"
8509abbde2f4b42dc26a45cafddcccb2d370712f,A way to improve precision of face recognition in SIPP without retrain of the deep neural network model,"Improving precision and recall of face recognition in SIPP with combination of
modified mean search and LSH
Xihua.Li"
858ddff549ae0a3094c747fb1f26aa72821374ec,"Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-Related Applications","Survey on RGB, 3D, Thermal, and Multimodal
Approaches for Facial Expression Recognition:
History, Trends, and Affect-related Applications
Ciprian A. Corneanu, Marc Oliu, Jeffrey F. Cohn, and Sergio Escalera"
85fd2bda5eb3afe68a5a78c30297064aec1361f6,"Are You Smiling, or Have I Seen You Before? Familiarity Makes Faces Look Happier.","702003 PSSXXX10.1177/0956797617702003Carr et al.Are You Smiling, or Have I Seen You Before?
research-article2017
Research Article
Are You Smiling, or Have I Seen You
Before? Familiarity Makes Faces Look
Happier
017, Vol. 28(8) 1087 –1102
© The Author(s) 2017
Reprints and permissions:
sagepub.com/journalsPermissions.nav
DOI: 10.1177/0956797617702003
https://doi.org/10.1177/0956797617702003
www.psychologicalscience.org/PS
Evan W. Carr1, Timothy F. Brady2, and Piotr Winkielman2,3,4
Columbia Business School, Columbia University; 2Psychology Department, University of California, San Diego;
Behavioural Science Group, Warwick Business School, University of Warwick; and 4Faculty of Psychology,
SWPS University of Social Sciences and Humanities"
858901405086056361f8f1839c2f3d65fc86a748,On Tensor Tucker Decomposition: the Case for an Adjustable Core Size,"ON TENSOR TUCKER DECOMPOSITION: THE CASE FOR AN
ADJUSTABLE CORE SIZE
BILIAN CHEN ∗, ZHENING LI † , AND SHUZHONG ZHANG ‡"
85188c77f3b2de3a45f7d4f709b6ea79e36bd0d9,"Combined model for detecting, localizing, interpreting and recognizing faces","Author manuscript, published in ""Workshop on Faces in 'Real-Life' Images: Detection, Alignment, and Recognition, Marseille :
France (2008)"""
858b51a8a8aa082732e9c7fbbd1ea9df9c76b013,Can Computer Vision Problems Benefit from Structured Hierarchical Classification?,"Can Computer Vision Problems Benefit from
Structured Hierarchical Classification?
Thomas Hoyoux1, Antonio J. Rodr´ıguez-S´anchez2, Justus H. Piater2, and
Sandor Szedmak2
INTELSIG, Montefiore Institute, University of Li`ege, Belgium
Intelligent and Interactive Systems, Institute of Computer Science, University of
Innsbruck, Austria"
8518b501425f2975ea6dcbf1e693d41e73d0b0af,Relative Hidden Markov Models for Evaluating Motion Skill,"Relative Hidden Markov Models for Evaluating Motion Skills
Qiang Zhang and Baoxin Li
Computer Science and Engineering
Arizona State Univerisity, Tempe, AZ 85281"
853bd61bc48a431b9b1c7cab10c603830c488e39,Learning Face Representation from Scratch,"Learning Face Representation from Scratch
Dong Yi, Zhen Lei, Shengcai Liao and Stan Z. Li
Center for Biometrics and Security Research & National Laboratory of Pattern Recognition
Institute of Automation, Chinese Academy of Sciences (CASIA)
dong.yi, zlei, scliao,"
854dbb4a0048007a49df84e3f56124d387588d99,Spatial-Temporal Recurrent Neural Network for Emotion Recognition,"JOURNAL OF LATEX CLASS FILES, VOL. 13, NO. 9, SEPTEMBER 2014
Spatial-Temporal Recurrent Neural Network for
Emotion Recognition
Tong Zhang, Wenming Zheng*, Member, IEEE, Zhen Cui*, Yuan Zong and Yang Li"
1d7ecdcb63b20efb68bcc6fd99b1c24aa6508de9,The Hidden Sides of Names&#x2014;Face Modeling with First Name Attributes,"The Hidden Sides of Names—Face Modeling
with First Name Attributes
Huizhong Chen, Student Member, IEEE, Andrew C. Gallagher, Senior Member, IEEE, and
Bernd Girod, Fellow, IEEE"
1d0dd20b9220d5c2e697888e23a8d9163c7c814b,Boosted Metric Learning for Efficient Identity-Based Face Retrieval,"NEGREL ET AL.: BOOSTED METRIC LEARNING FOR FACE RETRIEVAL
Boosted Metric Learning for Efficient
Identity-Based Face Retrieval
Romain Negrel
Alexis Lechervy
Frederic Jurie
GREYC, CNRS UMR 6072, ENSICAEN
Université de Caen Basse-Normandie
France"
1d776bfe627f1a051099997114ba04678c45f0f5,Deployment of Customized Deep Learning based Video Analytics On Surveillance Cameras,"Deployment of Customized Deep Learning based
Video Analytics On Surveillance Cameras
Pratik Dubal(cid:63), Rohan Mahadev(cid:63), Suraj Kothawade(cid:63),
Kunal Dargan, and Rishabh Iyer
AitoeLabs (www.aitoelabs.com)"
1dff919e51c262c22630955972968f38ba385d8a,Toward an Affect-Sensitive Multimodal Human–Computer Interaction,"Toward an Affect-Sensitive Multimodal
Human–Computer Interaction
MAJA PANTIC, MEMBER, IEEE, AND LEON J. M. ROTHKRANTZ
Invited Paper
The ability to recognize affective states of a person we are com-
municating with is the core of emotional intelligence. Emotional
intelligenceisa facet of human intelligence thathas been argued to be
indispensable and perhaps the most important for successful inter-
personal social interaction. This paper argues that next-generation
human–computer interaction (HCI) designs need to include the
essence of emotional intelligence—the ability to recognize a user’s
ffective states—in order to become more human-like, more effec-
tive, and more efficient. Affective arousal modulates all nonverbal
ommunicative cues (facial expressions, body movements, and vocal
nd physiological reactions). In a face-to-face interaction, humans
detect and interpret those interactive signals of their communicator
with little or no effort. Yet design and development of an automated
system that accomplishes these tasks is rather difficult. This paper
surveys the past work in solving these problems by a computer
nd provides a set of recommendations for developing the first"
1de8f38c35f14a27831130060810cf9471a62b45,A Branch-and-Bound Framework for Unsupervised Common Event Discovery,"Int J Comput Vis
DOI 10.1007/s11263-017-0989-7
A Branch-and-Bound Framework for Unsupervised Common
Event Discovery
Wen-Sheng Chu1
Jeffrey F. Cohn1,2 · Daniel S. Messinger3
· Fernando De la Torre1 ·
Received: 3 June 2016 / Accepted: 12 January 2017
© Springer Science+Business Media New York 2017"
1da83903c8d476c64c14d6851c85060411830129,Iterated Support Vector Machines for Distance Metric Learning,"Iterated Support Vector Machines for Distance
Metric Learning
Wangmeng Zuo, Member, IEEE, Faqiang Wang, David Zhang, Fellow, IEEE, Liang Lin, Member, IEEE,
Yuchi Huang, Member, IEEE, Deyu Meng, and Lei Zhang, Senior Member, IEEE"
1d58d83ee4f57351b6f3624ac7e727c944c0eb8d,Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions,"Enhanced Local Texture
Feature Sets for Face
Recognition under Difficult
Lighting Conditions
Xiaoyang Tan and Bill Triggs
INRIA & Laboratoire Jean
Kuntzmann,
655 avenue de l'Europe, Montbonnot 38330, France"
1d729693a888a460ee855040f62bdde39ae273af,Photorealistic Face De-Identification by Aggregating Donors' Face Components,"Photorealistic Face de-Identification by Aggregating
Donors’ Face Components
Saleh Mosaddegh, Lo¨ıc Simon, Fr´ed´eric Jurie
To cite this version:
Saleh Mosaddegh, Lo¨ıc Simon, Fr´ed´eric Jurie. Photorealistic Face de-Identification by Aggre-
gating Donors’ Face Components. Asian Conference on Computer Vision, Nov 2014, Singapore.
pp.1-16, 2014. <hal-01070658>
HAL Id: hal-01070658
https://hal.archives-ouvertes.fr/hal-01070658
Submitted on 2 Oct 2014
HAL is a multi-disciplinary open access
rchive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
broad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destin´ee au d´epˆot et `a la diffusion de documents
scientifiques de niveau recherche, publi´es ou non,
´emanant des ´etablissements d’enseignement et de"
1d4c25f9f8f08f5a756d6f472778ab54a7e6129d,An Innovative Mean Approach for Plastic Surgery Face Recognition,"International Journal of Science and Research (IJSR)
ISSN (Online): 2319-7064
Index Copernicus Value (2014): 6.14 | Impact Factor (2014): 4.438
An Innovative Mean Approach for Plastic Surgery
Face Recognition
Mahendra P. Randive1, Umesh W. Hore2
Student of M.E., Department of Electronics & Telecommunication Engineering,
P. R. Patil College of Engineering, Amravati Maharashtra – India
Assistant Professor, Department of Electronics & Telecommunication Engineering,
P. R. Patil College of Engineering, Amravati Maharashtra – India"
71b376dbfa43a62d19ae614c87dd0b5f1312c966,The temporal connection between smiles and blinks,"The Temporal Connection Between Smiles and Blinks
Laura C. Trutoiu, Jessica K. Hodgins, and Jeffrey F. Cohn"
71fd29c2ae9cc9e4f959268674b6b563c06d9480,End-to-end 3D shape inverse rendering of different classes of objects from a single input image,"End-to-end 3D shape inverse rendering of different classes
of objects from a single input image
Shima Kamyab1 and S. Zohreh Azimifar1
Computer Science and Engineering and Information Technology, Shiraz
university, Shiraz, Iran
November 17, 2017"
714d487571ca0d676bad75c8fa622d6f50df953b,eBear: An expressive Bear-Like robot,"eBear: An Expressive Bear-Like Robot
Xiao Zhang, Ali Mollahosseini, Amir H. Kargar B., Evan Boucher,
Richard M. Voyles, Rodney Nielsen and Mohammd H. Mahoor"
710011644006c18291ad512456b7580095d628a2,Learning Residual Images for Face Attribute Manipulation,"Learning Residual Images for Face Attribute Manipulation
Wei Shen
Rujie Liu
Fujitsu Research & Development Center, Beijing, China.
{shenwei,"
711bb5f63139ee7a9b9aef21533f959671a7d80e,Objects extraction and recognition for camera-based interaction : heuristic and statistical approaches,"Helsinki University of Technology Laboratory of Computational Engineering Publications
Teknillisen korkeakoulun Laskennallisen tekniikan laboratorion julkaisuja
Espoo 2007
REPORT B68
OBJECTS EXTRACTION AND RECOGNITION FOR
CAMERA-BASED INTERACTION: HEURISTIC AND
STATISTICAL APPROACHES
Hao Wang
TEKNILLINEN KORKEAKOULU
TEKNILLINEN KORKEAKOULU
TEKNISKA HÖGSKOLAN
TEKNISKA HÖGSKOLAN
HELSINKI UNIVERSITY OF TECHNOLOGY
HELSINKI UNIVERSITY OF TECHNOLOGY
TECHNISCHE UNIVERSITÄT HELSINKI
TECHNISCHE UNIVERSITÄT HELSINKI
UNIVERSITE DE TECHNOLOGIE D'HELSINKI
UNIVERSITE DE TECHNOLOGIE D'HELSINKI"
76fd801981fd69ff1b18319c450cb80c4bc78959,Alignment of Eye Movements and Spoken Language for Semantic Image Understanding,"Proceedings of the 11th International Conference on Computational Semantics, pages 76–81,
London, UK, April 15-17 2015. c(cid:13)2015 Association for Computational Linguistics"
76dc11b2f141314343d1601635f721fdeef86fdb,Weighted Decoding ECOC for Facial Action Unit Classification,"Weighted Decoding ECOC for Facial
Action Unit Classification
Terry Windeatt"
76673de6d81bedd6b6be68953858c5f1aa467e61,Discovering a Lexicon of Parts and Attributes,"Discovering a Lexicon of Parts and Attributes
Subhransu Maji
Toyota Technological Institute at Chicago,
Chicago, IL 60637, USA"
76cd5e43df44e389483f23cb578a9015d1483d70,Face Verification from Depth using Privileged Information,"BORGHI ET AL.: FACE VERIFICATION FROM DEPTH
Face Verification from Depth using
Privileged Information
Department of Engineering
""Enzo Ferrari""
University of Modena and Reggio
Emilia
Modena, Italy
Guido Borghi
Stefano Pini
Filippo Grazioli
Roberto Vezzani
Rita Cucchiara"
76b11c281ac47fe6d95e124673a408ee9eb568e3,Real-time Multi View Face Detection and Pose Estimation Aishwarya,"International Journal of Latest Engineering and Management Research (IJLEMR)
ISSN: 2455-4847
www.ijlemr.com || Volume 02 - Issue 03 || March 2017 || PP. 59-71
REAL-TIME MULTI VIEW FACE DETECTION AND POSE
ESTIMATION
AISHWARYA.S1 , RATHNAPRIYA.K1, SUKANYA SARGUNAR.V2
U. G STUDENTS, DEPT OF CSE, ALPHA COLLEGE OF ENGINEERING, CHENNAI,
ASST PROF.DEPARTMENT OF CSE, ALPHA COLLEGE OF ENGINEERING, CHENNAI"
76d9f5623d3a478677d3f519c6e061813e58e833,Fast Algorithms for the Generalized Foley-Sammon Discriminant Analysis,"FAST ALGORITHMS FOR THE GENERALIZED FOLEY-SAMMON
DISCRIMINANT ANALYSIS
LEI-HONG ZHANG∗, LI-ZHI LIAO† , AND MICHAEL K. NG‡"
76e2d7621019bd45a5851740bd2742afdcf62837,Real-Time Detection and Measurement of Eye Features from Color Images,"Article
Real-Time Detection and Measurement of Eye
Features from Color Images
Diana Borza 1, Adrian Sergiu Darabant 2 and Radu Danescu 1,*
Computer Science Department, Technical University of Cluj Napoca, 28 Memorandumului Street,
Cluj Napoca 400114, Romania;
Computer Science Department, Babes Bolyai University, 58-60 Teodor Mihali, C333, Cluj Napoca 400591,
Romania;
* Correspondence: Tel.: +40-740-502-223
Academic Editors: Changzhi Li, Roberto Gómez-García and José-María Muñoz-Ferreras
Received: 28 April 2016; Accepted: 14 July 2016; Published: 16 July 2016"
765b2cb322646c52e20417c3b44b81f89860ff71,PoseShop: Human Image Database Construction and Personalized Content Synthesis,"PoseShop: Human Image Database
Construction and Personalized
Content Synthesis
Tao Chen, Ping Tan, Member, IEEE, Li-Qian Ma, Ming-Ming Cheng, Member, IEEE,
Ariel Shamir, and Shi-Min Hu, Member, IEEE"
763158cef9d1e4041f24fce4cf9d6a3b7a7f08ff,Hierarchical Modeling and Applications to Recognition Tasks,"Hierarchical Modeling and
Applications to Recognition Tasks
Thesis submitted for the degree of
”Doctor of Philosophy”
Alon Zweig
Submitted to the Senate of the Hebrew University
August / 2013"
760ba44792a383acd9ca8bef45765d11c55b48d4,Class-specific classifier: avoiding the curse of dimensionality,"INTRODUCTION AND BACKGROUND
The purpose of this article is to introduce the
reader to the basic principles of  classification with
lass-specific features. It is written both for readers
interested in only the basic concepts as well as those
interested in getting started in applying the method.
For in-depth coverage, the reader is referred to a more
detailed article [l].
Class-Specific Classifier:
Avoiding the Curse of
Dimensionality
PAUL M. BAGGENSTOSS, Member. lEEE
US. Naval Undersea Warfare Center
This article describes a new probabilistic method called the
“class-specific method” (CSM). CSM has the potential to avoid
the “curse of dimensionality” which plagues most clmiiiers
which attempt to determine the decision boundaries in a
highdimensional featue space. In contrast, in CSM, it is possible
to build classifiers without a ” n o n   feature space. Separate
Law-dimensional features seta may be de6ned for each class, while"
766728bac030b169fcbc2fbafe24c6e22a58ef3c,A survey of deep facial landmark detection,"A survey of deep facial landmark detection
Yongzhe Yan1,2
Xavier Naturel2
Christophe Garcia3
Thierry Chateau1
Christophe Blanc1
Stefan Duffner3
Université Clermont Auvergne, France
Wisimage, France
Université de Lyon, CNRS, INSA Lyon, LIRIS, UMR5205, Lyon, France
Résumé
La détection de landmarks joue un rôle crucial dans de
nombreuses applications d’analyse du visage comme la
reconnaissance de l’identité, des expressions, l’animation
d’avatar, la reconstruction 3D du visage, ainsi que pour
les applications de réalité augmentée comme la pose de
masque ou de maquillage virtuel. L’avènement de l’ap-
prentissage profond a permis des progrès très importants
dans ce domaine, y compris sur les corpus non contraints
(in-the-wild). Nous présentons ici un état de l’art cen-"
7697295ee6fc817296bed816ac5cae97644c2d5b,Detecting and Recognizing Human-Object Interactions,"Detecting and Recognizing Human-Object Interactions
Georgia Gkioxari Ross Girshick
Piotr Doll´ar Kaiming He
Facebook AI Research (FAIR)"
7636f94ddce79f3dea375c56fbdaaa0f4d9854aa,Robust Facial Expression Recognition Using a Smartphone Working against Illumination Variation,"Appl. Math. Inf. Sci. 6 No. 2S pp. 403S-408S (2012)
An International Journal
© 2012 NSP
Applied Mathematics & Information Sciences
Robust Facial Expression Recognition Using
Smartphone Working against Illumination Variation
2012 NSP
Natural Sciences Publishing Cor.
Kyoung-Sic Cho1, In-Ho Choi1 and Yong-Guk Kim1
Department of Computer Engineering, Sejong University, 98 Kunja-Dong, Kwangjin-Gu, Seoul, Korea
Corresponding author: Email:
Received June 22, 2010; Revised March 21, 2011; Accepted 11 June 2011
Published online: 1 January 2012"
1c80bc91c74d4984e6422e7b0856cf3cf28df1fb,Hierarchical Adaptive Structural SVM for Domain Adaptation,"Noname manuscript No.
(will be inserted by the editor)
Hierarchical Adaptive Structural SVM for Domain Adaptation
Jiaolong Xu · Sebastian Ramos · David V´azquez · Antonio M. L´opez
Received: date / Accepted: date"
1ce3a91214c94ed05f15343490981ec7cc810016,Exploring photobios,"Exploring Photobios
Ira Kemelmacher-Shlizerman1
Eli Shechtman2
Rahul Garg1,3
Steven M. Seitz1,3
University of Washington∗
Adobe Systems†
Google Inc."
1cfe3533759bf95be1fce8ce1d1aa2aeb5bfb4cc,Recognition of Facial Gestures Based on Support Vector Machines,"Recognition of Facial Gestures based on Support
Vector Machines
Attila Fazekas and Istv(cid:19)an S(cid:19)anta
Faculty of Informatics, University of Debrecen, Hungary
H-4010 Debrecen P.O.Box 12."
1ce4587e27e2cf8ba5947d3be7a37b4d1317fbee,Deep fusion of visual signatures for client-server facial analysis,"Deep fusion of visual signatures
for client-server facial analysis
Binod Bhattarai
Normandie Univ, UNICAEN,
ENSICAEN, CNRS, GREYC
Gaurav Sharma
Computer Sc. & Engg.
IIT Kanpur, India
Frederic Jurie
Normandie Univ, UNICAEN,
ENSICAEN, CNRS, GREYC
Facial analysis is a key technology for enabling human-
machine interaction.
In this context, we present a client-
server framework, where a client transmits the signature of
face to be analyzed to the server, and, in return, the server
sends back various information describing the face e.g. is the
person male or female, is she/he bald, does he have a mus-
tache, etc. We assume that a client can compute one (or a
ombination) of visual features; from very simple and ef‌f‌i-"
1c30bb689a40a895bd089e55e0cad746e343d1e2,Learning Spatiotemporal Features with 3D Convolutional Networks,"Learning Spatiotemporal Features with 3D Convolutional Networks
Du Tran1
, Lubomir Bourdev1, Rob Fergus1, Lorenzo Torresani2, Manohar Paluri1
Facebook AI Research, 2Dartmouth College"
1c3073b57000f9b6dbf1c5681c52d17c55d60fd7,Direction de thèse:,"THÈSEprésentéepourl’obtentiondutitredeDOCTEURDEL’ÉCOLENATIONALEDESPONTSETCHAUSSÉESSpécialité:InformatiqueparCharlotteGHYSAnalyse,Reconstruction3D,&AnimationduVisageAnalysis,3DReconstruction,&AnimationofFacesSoutenancele19mai2010devantlejurycomposéde:Rapporteurs:MajaPANTICDimitrisSAMARASExaminateurs:MichelBARLAUDRenaudKERIVENDirectiondethèse:NikosPARAGIOSBénédicteBASCLE"
1c93b48abdd3ef1021599095a1a5ab5e0e020dd5,A Compositional and Dynamic Model for Face Aging,"JOURNAL OF LATEX CLASS FILES, VOL. *, NO. *, JANUARY 2009
A Compositional and Dynamic Model for Face Aging
Jinli Suo , Song-Chun Zhu , Shiguang Shan and Xilin Chen"
1c6e22516ceb5c97c3caf07a9bd5df357988ceda,Copycat CNN: Stealing Knowledge by Persuading Confession with Random Non-Labeled Data,"NetworkCNNimageslabelsFakeDatasetimages24132labelsTarget NetworkCNNimageslabelsOriginalDatasetFakeDatasetFig.1:Ontheleft,thetargetnetworkistrainedwithanoriginal(confidential)datasetandisservedpubliclyasanAPI,receivingimagesasinputandprovidingclasslabelsasoutput.Ontheright,itispresentedtheprocesstogetstolenlabelsandtocreateafakedataset:randomnaturalimagesaresenttotheAPIandthelabelsareobtained.Afterthat,thecopycatnetworkistrainedusingthisfakedataset.cloud-basedservicestocustomersallowingthemtooffertheirownmodelsasanAPI.Becauseoftheresourcesandmoneyinvestedincreatingthesemodels,itisinthebestinterestofthesecompaniestoprotectthem,i.e.,toavoidthatsomeoneelsecopythem.Someworkshavealreadyinvestigatedthepossibilityofcopyingmodelsbyqueryingthemasablack-box.In[1],forexample,theauthorsshowedhowtoperformmodelextractionattackstocopyanequivalentornear-equivalentmachinelearningmodel(decisiontree,logisticregression,SVM,andmultilayerperceptron),i.e.,onethatachievescloseto100%agreementonaninputspaceofinterest.In[2],theauthorsevaluatedtheprocessofcopyingaNaiveBayesandSVMclassifierinthecontextoftextclassification.Bothworksfocusedongeneralclassifiersandnotondeepneuralnetworksthatrequirelargeamountsofdatatobetrainedleavingthequestionofwhetherdeepmodelscanbeeasilycopied.Althoughthesecondusesdeeplearningtostealtheclassifiers,itdoesnottrytouseDNNstostealfromdeepmodels.Additionally,theseworksfocusoncopyingbyqueryingwithproblemdomaindata.Inrecentyears,researchershavebeenexploringsomeintriguingpropertiesofdeepneuralnetworks[3],[4].More©2018IEEE.Personaluseofthismaterialispermitted.PermissionfromIEEEmustbeobtainedforallotheruses,inanycurrentorfuturemedia,includingreprinting/republishingthismaterialforadvertisingorpromotionalpurposes,creatingnewcollectiveworks,forresaleorredistributiontoserversorlists,orreuseofanycopyrightedcomponentofthisworkinotherworks."
825f56ff489cdd3bcc41e76426d0070754eab1a8,Making Convolutional Networks Recurrent for Visual Sequence Learning,"Making Convolutional Networks Recurrent for Visual Sequence Learning
Xiaodong Yang Pavlo Molchanov Jan Kautz
NVIDIA"
82d2af2ffa106160a183371946e466021876870d,A Novel Space-Time Representation on the Positive Semidefinite Con for Facial Expression Recognition,"A Novel Space-Time Representation on the Positive Semidefinite Cone
for Facial Expression Recognition
Anis Kacem1, Mohamed Daoudi1, Boulbaba Ben Amor1, and Juan Carlos Alvarez-Paiva2
IMT Lille Douai, Univ. Lille, CNRS, UMR 9189 – CRIStAL –
Centre de Recherche en Informatique Signal et Automatique de Lille, F-59000 Lille, France
Univ. Lille, CNRS, UMR 8524, Laboratoire Paul Painlev´e, F-59000 Lille, France."
82eff71af91df2ca18aebb7f1153a7aed16ae7cc,MSU-AVIS dataset : Fusing Face and Voice Modalities for Biometric Recognition in Indoor Surveillance Videos,"MSU-AVIS dataset:
Fusing Face and Voice Modalities for Biometric
Recognition in Indoor Surveillance Videos
Anurag Chowdhury*, Yousef Atoum+, Luan Tran*, Xiaoming Liu*, Arun Ross*
*Michigan State University, USA
+Yarmouk University, Jordan"
82c303cf4852ad18116a2eea31e2291325bc19c3,Fusion Based FastICA Method: Facial Expression Recognition,"Journal of Image and Graphics, Volume 2, No.1, June, 2014
Fusion Based FastICA Method: Facial Expression
Recognition
Humayra B. Ali and David M W Powers
Computer Science, Engineering and Mathematics School, Flinders University, Australia
Email: {ali0041,"
8210fd10ef1de44265632589f8fc28bc439a57e6,Single Sample Face Recognition via Learning Deep Supervised Autoencoders,"Single Sample Face Recognition via Learning Deep
Supervised Auto-Encoders
Shenghua  Gao,  Yuting  Zhang,  Kui  Jia,  Jiwen  Lu,  Yingying  Zhang"
82a4a35b2bae3e5c51f4d24ea5908c52973bd5be,Real-time emotion recognition for gaming using deep convolutional network features,"Real-time emotion recognition for gaming using
deep convolutional network features
S´ebastien Ouellet"
82a610a59c210ff77cfdde7fd10c98067bd142da,Human attention and intent analysis using robust visual cues in a Bayesian framework,"UC San Diego
UC San Diego Electronic Theses and Dissertations
Title
Human attention and intent analysis using robust visual cues in a Bayesian framework
Permalink
https://escholarship.org/uc/item/1cb8d7vw
Author
McCall, Joel Curtis
Publication Date
006-01-01
Peer reviewed|Thesis/dissertation
eScholarship.org
Powered by the California Digital Library
University of California"
829f390b3f8ad5856e7ba5ae8568f10cee0c7e6a,A Robust Rotation Invariant Multiview Face Detection in Erratic Illumination Condition,"International Journal of Computer Applications (0975 – 8887)
Volume 57– No.20, November 2012
A Robust Rotation Invariant Multiview Face Detection in
Erratic Illumination Condition
G.Nirmala Priya
Associate Professor, Department of ECE
Sona College of Technology
Salem"
82f4e8f053d20be64d9318529af9fadd2e3547ef,Technical Report: Multibiometric Cryptosystems,"Technical Report:
Multibiometric Cryptosystems
Abhishek Nagar, Student Member, IEEE, Karthik Nandakumar, Member, IEEE, and Anil K. Jain, Fellow, IEEE"
82d781b7b6b7c8c992e0cb13f7ec3989c8eafb3d,Robust Facial Expression Recognition Using a State-based Model of Spatially-localized Facial,"REFERENCES
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82417d8ec8ac6406f2d55774a35af2a1b3f4b66e,Some Faces are More Equal than Others: Hierarchical Organization for Accurate and Efficient Large-Scale Identity-Based Face Retrieval,"Some faces are more equal than others:
Hierarchical organization for accurate and
ef‌f‌icient large-scale identity-based face retrieval
Binod Bhattarai1, Gaurav Sharma2, Fr´ed´eric Jurie1, Patrick P´erez2
GREYC, CNRS UMR 6072, Universit´e de Caen Basse-Normandie, France1
Technicolor, Rennes, France2"
826c66bd182b54fea3617192a242de1e4f16d020,Action-vectors: Unsupervised movement modeling for action recognition,"978-1-5090-4117-6/17/$31.00 ©2017 IEEE
ICASSP 2017"
4919663c62174a9bc0cc7f60da8f96974b397ad2,Human age estimation using enhanced bio-inspired features (EBIF),"HUMAN AGE ESTIMATION USING ENHANCED BIO-INSPIRED FEATURES (EBIF)
Mohamed Y.El Dib and Motaz El-Saban
Faculty of Computers and Information, Cairo University, Cairo, Egypt"
4967b0acc50995aa4b28e576c404dc85fefb0601,An Automatic Face Detection and Gender Classification from Color Images using Support Vector Machine,"Vol. 4, No. 1 Jan 2013                                                                                                 ISSN 2079-8407
Journal of Emerging Trends in Computing and Information Sciences
©2009-2013 CIS Journal. All rights reserved.
An Automatic Face Detection and Gender Classification from
http://www.cisjournal.org
Color Images using Support Vector Machine
Md. Hafizur Rahman, 2 Suman Chowdhury, 3 Md. Abul Bashar
, 2, 3 Department of Electrical & Electronic Engineering, International
University of Business Agriculture and Technology, Dhaka-1230, Bangladesh"
4972aadcce369a8c0029e6dc2f288dfd0241e144,Multi-target Unsupervised Domain Adaptation without Exactly Shared Categories,"Multi-target Unsupervised Domain Adaptation
without Exactly Shared Categories
Huanhuan Yu, Menglei Hu and Songcan Chen"
49e85869fa2cbb31e2fd761951d0cdfa741d95f3,Adaptive Manifold Learning,"Adaptive Manifold Learning
Zhenyue Zhang, Jing Wang, and Hongyuan Zha"
490a217a4e9a30563f3a4442a7d04f0ea34442c8,An SOM-based Automatic Facial Expression Recognition System,"International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.2, No.4, August 2013
An SOM-based Automatic Facial Expression
Recognition System
Mu-Chun Su1, Chun-Kai Yang1, Shih-Chieh Lin1,De-Yuan Huang1, Yi-Zeng
Hsieh1, andPa-Chun Wang2
Department of Computer Science &InformationEngineering,National Central
University,Taiwan, R.O.C.
Cathay General Hospital, Taiwan, R.O.C.
E-mail:"
49a7949fabcdf01bbae1c2eb38946ee99f491857,A concatenating framework of shortcut convolutional neural networks,"A CONCATENATING FRAMEWORK OF SHORTCUT
CONVOLUTIONAL NEURAL NETWORKS
Yujian Li Ting Zhang, Zhaoying Liu, Haihe Hu"
499343a2fd9421dca608d206e25e53be84489f44,Face Recognition with Name Using Local Weber‟s Law Descriptor,"Anil Kumar.C, et.al, International Journal of Technology and Engineering Science [IJTES]TM
Volume 1[9], pp: 1371-1375, December 2013
Face Recognition with Name Using Local Weber‟s
Law Descriptor
C.Anil kumar,2A.Rajani,3I.Suneetha
M.Tech Student,2Assistant Professor,3Associate Professor
Department of ECE, Annamacharya Institute of Technology and Sciences, Tirupati, India-517520
on  FERET"
498fd231d7983433dac37f3c97fb1eafcf065268,Linear Disentangled Representation Learning for Facial Actions,"LINEAR DISENTANGLED REPRESENTATION LEARNING FOR FACIAL ACTIONS
Xiang Xiang1 and Trac D. Tran2
Dept. of Computer Science
Dept. of Electrical & Computer Engineering
Johns Hopkins University, 3400 N. Charles Street, Baltimore, MD 21218, USA
Fig. 1. The separability of the neutral face yn and expression
omponent ye. We find yn is better for identity recognition
than y and ye is better for expression recognition than y."
49e1aa3ecda55465641b2c2acc6583b32f3f1fc6,Support Vector Machine for age classification,"International Journal of Emerging Technology and Advanced Engineering
Website: www.ijetae.com (ISSN 2250-2459, Volume 2, Issue 5, May 2012)
Support Vector Machine for age classification
Sangeeta Agrawal1, Rohit Raja2, Sonu Agrawal3
Assistant Professor, CSE, RSR RCET, Kohka Bhilai
,3 Sr. Assistant Professor, CSE, SSCET, Junwani Bhilai"
49df381ea2a1e7f4059346311f1f9f45dd997164,Client-Specific Anomaly Detection for Face Presentation Attack Detection,"On the Use of Client-Specific Information for Face
Presentation Attack Detection Based on Anomaly
Detection
Shervin Rahimzadeh Arashloo and Josef Kittler,"
496074fcbeefd88664b7bd945012ca22615d812e,Driver Distraction Using Visual-Based Sensors and Algorithms,"Review
Driver Distraction Using Visual-Based Sensors
nd Algorithms
Alberto Fernández 1,*, Rubén Usamentiaga 2, Juan Luis Carús 1 and Rubén Casado 2
Grupo TSK, Technological Scientific Park of Gijón, 33203 Gijón, Asturias, Spain;
Department of Computer Science and Engineering, University of Oviedo, Campus de Viesques, 33204 Gijón,
Asturias, Spain; (R.U.); (R.C.)
* Corrospondence: Tel.: +34-984-29-12-12; Fax: +34-984-39-06-12
Academic Editor: Gonzalo Pajares Martinsanz
Received: 14 July 2016; Accepted: 24 October 2016; Published: 28 October 2016"
40205181ed1406a6f101c5e38c5b4b9b583d06bc,Using Context to Recognize People in Consumer Images,"Using Context to Recognize People in Consumer Images
Andrew C. Gallagher and Tsuhan Chen"
40dab43abef32deaf875c2652133ea1e2c089223,Facial Communicative Signals: valence recognition in task-oriented human-robot Interaction,"Noname manuscript No.
(will be inserted by the editor)
Facial Communicative Signals
Valence Recognition in Task-Oriented Human-Robot Interaction
Christian Lang · Sven Wachsmuth · Marc Hanheide · Heiko Wersing
Received: date / Accepted: date"
40b0fced8bc45f548ca7f79922e62478d2043220,Do Convnets Learn Correspondence?,"Do Convnets Learn Correspondence?
Trevor Darrell
Jonathan Long
{jonlong, nzhang,
University of California – Berkeley
Ning Zhang"
405b43f4a52f70336ac1db36d5fa654600e9e643,What can we learn about CNNs from a large scale controlled object dataset?,"What can we learn about CNNs from a large scale controlled object dataset?
Ali Borji
Saeed Izadi
Laurent Itti"
40b86ce698be51e36884edcc8937998979cd02ec,Finding Faces in News Photos Using Both Face and Name Information,"Yüz ve İsim İlişkisi kullanarak Haberlerdeki Kişilerin Bulunması
Finding Faces in News Photos Using Both Face and Name Information
Derya Ozkan, Pınar Duygulu
Bilgisayar Mühendisliği Bölümü, Bilkent Üniversitesi, 06800, Ankara
Özetçe
Bu  çalışmada,  haber  fotoğraflarından  oluşan  geniş  veri
kümelerinde  kişilerin  sorgulanmasını  sağlayan  bir  yöntem
sunulmuştur.  Yöntem  isim  ve  yüzlerin  ilişkilendirilmesine
dayanmaktadır.  Haber  başlığında  kişinin  ismi  geçiyor  ise
fotoğrafta da o kişinin yüzünün bulunacağı  varsayımıyla, ilk
olarak  sorgulanan  isim  ile  ilişkilendirilmiş,  fotoğraflardaki
tüm yüzler seçilir. Bu yüzler arasında sorgu kişisine ait farklı
koşul,  poz  ve  zamanlarda  çekilmiş  pek  çok  resmin  yanında,
haberde ismi geçen başka kişilere ait yüzler ya da kullanılan
yüz  bulma  yönteminin  hatasından  kaynaklanan  yüz  olmayan
resimler de bulunabilir. Yine de, çoğu  zaman, sorgu kişisine
it resimler daha çok olup, bu resimler birbirine diğerlerine
olduğundan  daha  çok  benzeyeceklerdir.  Bu  nedenle,  yüzler
rasındaki  benzerlikler  çizgesel  olarak  betimlendiğinde  ,
irbirine en çok benzeyen yüzler bu çizgede en yoğun bileşen"
402f6db00251a15d1d92507887b17e1c50feebca,3D Facial Action Units Recognition for Emotional Expression,"D Facial Action Units Recognition for Emotional
Expression
Norhaida Hussain1, Hamimah Ujir, Irwandi Hipiny and Jacey-Lynn Minoi2
Department of Information Technology and Communication, Politeknik Kuching, Sarawak, Malaysia
Faculty of Computer Science and Information Technology, Universiti Malaysia Sarawak, Kota Samarahan, Sarawak, Malaysia
The  muscular  activities  caused  the  activation  of  certain  AUs  for  every  facial  expression  at  the  certain  duration  of  time
throughout the facial expression. This paper presents the methods to recognise facial Action Unit (AU) using facial distance
of the facial features which activates the muscles. The seven facial action units involved are AU1, AU4, AU6, AU12, AU15,
AU17 and AU25 that characterises happy and sad expression. The recognition is performed on each AU according to rules
defined based on the distance of each facial points. The facial distances chosen are extracted from twelve facial features.
Then the facial distances are trained using Support Vector Machine (SVM) and Neural Network (NN). Classification result
using SVM is presented with several different SVM kernels while result using NN is presented for each training, validation
nd testing phase.
Keywords: Facial action units recognition, 3D AU recognition, facial expression"
40fb4e8932fb6a8fef0dddfdda57a3e142c3e823,A mixed generative-discriminative framework for pedestrian classification,"A Mixed Generative-Discriminative Framework for Pedestrian Classification
Markus Enzweiler1
Dariu M. Gavrila2,3
Image & Pattern Analysis Group, Dept. of Math. and Comp. Sc., Univ. of Heidelberg, Germany
Environment Perception, Group Research, Daimler AG, Ulm, Germany
Intelligent Systems Lab, Faculty of Science, Univ. of Amsterdam, The Netherlands"
40dd2b9aace337467c6e1e269d0cb813442313d7,Localizing spatially and temporally objects and actions in videos. (Localiser spatio-temporallement des objets et des actions dans des vidéos),"This thesis has been submitted in fulfilment of the requirements for a postgraduate degree
(e.g. PhD, MPhil, DClinPsychol) at the University of Edinburgh. Please note the following
terms and conditions of use:
This work is protected by copyright and other intellectual property rights, which are
retained by the thesis author, unless otherwise stated.
A copy can be downloaded for personal non-commercial research or study, without
prior permission or charge.
This thesis cannot be reproduced or quoted extensively from without first obtaining
permission in writing from the author.
The content must not be changed in any way or sold commercially in any format or
medium without the formal permission of the author.
When referring to this work, full bibliographic details including the author, title,
warding institution and date of the thesis must be given."
40a34d4eea5e32dfbcef420ffe2ce7c1ee0f23cd,Bridging Heterogeneous Domains With Parallel Transport For Vision and Multimedia Applications,"Bridging Heterogeneous Domains With Parallel Transport For Vision and
Multimedia Applications
Raghuraman Gopalan
Dept. of Video and Multimedia Technologies Research
AT&T Labs-Research
San Francisco, CA 94108"
40389b941a6901c190fb74e95dc170166fd7639d,Automatic Facial Expression Recognition,"Automatic Facial Expression Recognition
Jacob Whitehill, Marian Stewart Bartlett, and Javier R. Movellan
Emotient
http://emotient.com
February 12, 2014
Imago animi vultus est, indices oculi. (Cicero)
Introduction
The face is innervated by two different brain systems that compete for control of its muscles:
cortical brain system related to voluntary and controllable behavior, and a sub-cortical
system responsible for involuntary expressions. The interplay between these two systems
generates a wealth of information that humans constantly use to read the emotions, inten-
tions, and interests [25] of others.
Given the critical role that facial expressions play in our daily life, technologies that can
interpret and respond to facial expressions automatically are likely to find a wide range of
pplications. For example, in pharmacology, the effect of new anti-depression drugs could
e assessed more accurately based on daily records of the patients’ facial expressions than
sking the patients to fill out a questionnaire, as it is currently done [7]. Facial expression
recognition may enable a new generation of teaching systems to adapt to the expression
of their students in the way good teachers do [61]. Expression recognition could be used
to assess the fatigue of drivers and air-pilots [58, 59]. Daily-life robots with automatic"
40273657e6919455373455bd9a5355bb46a7d614,Anonymizing k Facial Attributes via Adversarial Perturbations,"Anonymizing k-Facial Attributes via Adversarial Perturbations
Saheb Chhabra1, Richa Singh1, Mayank Vatsa1 and Gaurav Gupta2
IIIT Delhi, New Delhi, India
Ministry of Electronics and Information Technology, New Delhi, India
{sahebc, rsingh,"
40b10e330a5511a6a45f42c8b86da222504c717f,Implementing the Viola-Jones Face Detection Algorithm,"Implementing the Viola-Jones
Face Detection Algorithm
Ole Helvig Jensen
Kongens Lyngby 2008
IMM-M.Sc.-2008-93"
40ca925befa1f7e039f0cd40d57dbef6007b4416,Sampling Matters in Deep Embedding Learning,"Sampling Matters in Deep Embedding Learning
Chao-Yuan Wu∗
UT Austin
R. Manmatha
A9/Amazon
Alexander J. Smola
Amazon
Philipp Kr¨ahenb¨uhl
UT Austin"
4026dc62475d2ff2876557fc2b0445be898cd380,An Affective User Interface Based on Facial Expression Recognition and Eye-Gaze Tracking,"An Affective User Interface Based on Facial Expression
Recognition and Eye-Gaze Tracking
Soo-Mi Choi and Yong-Guk Kim
School of Computer Engineering, Sejong University, Seoul, Korea"
40f127fa4459a69a9a21884ee93d286e99b54c5f,Optimizing Apparent Display Resolution Enhancement for Arbitrary Videos,"Optimizing Apparent Display Resolution
Enhancement for Arbitrary Videos
Michael Stengel*, Member, IEEE, Martin Eisemann, Stephan Wenger,
Benjamin Hell, Marcus Magnor, Member, IEEE"
401e6b9ada571603b67377b336786801f5b54eee,Active Image Clustering: Seeking Constraints from Humans to Complement Algorithms,"Active Image Clustering: Seeking Constraints from
Humans to Complement Algorithms
November 22, 2011"
2e8e6b835e5a8f55f3b0bdd7a1ff765a0b7e1b87,Pointly-Supervised Action Localization,"International Journal of Computer Vision manuscript No.
(will be inserted by the editor)
Pointly-Supervised Action Localization
Pascal Mettes · Cees G. M. Snoek
Received: date / Accepted: date"
2eb37a3f362cffdcf5882a94a20a1212dfed25d9,Local Feature Based Face Recognition,"Local Feature Based Face Recognition
Sanjay A. Pardeshi and Sanjay N. Talbar
R.I.T., Rajaramnagar and S.G.G.S. COE &T, Nanded
India
. Introduction
A  reliable  automatic  face  recognition  (AFR)  system  is  a  need  of  time  because  in  today's
networked  world,  maintaining  the  security  of  private  information  or  physical  property  is
ecoming increasingly important and difficult as well. Most of the time criminals have been
taking  the  advantage  of  fundamental  flaws  in  the  conventional  access  control  systems  i.e.
the systems operating on credit card, ATM etc. do not grant access by ""who we are"", but by
""what  we  have”.  The  biometric  based  access  control systems  have  a  potential  to  overcome
most  of  the  deficiencies  of  conventional  access  control  systems  and  has  been  gaining  the
importance  in  recent  years.  These  systems  can  be  designed  with  biometric  traits  such  as
fingerprint,  face,  iris,  signature,  hand  geometry  etc.  But  comparison  of  different  biometric
traits shows that face is very attractive biometric because of its non-intrusiveness and social
cceptability.  It  provides  automated  methods  of  verifying  or  recognizing  the  identity  of  a
living person based on its facial characteristics.
In last decade, major advances occurred in face recognition, with many systems capable of
chieving  recognition  rates  greater  than  90%.  However  real-world  scenarios  remain  a
hallenge, because face acquisition process can undergo to a wide range of variations. Hence"
2e5cfa97f3ecc10ae8f54c1862433285281e6a7c,Generative Adversarial Networks for Improving Face Classification,"Generative Adversarial Networks for Improving Face Classification  JONAS NATTEN SUPERVISOR Morten Goodwin, PhD University of Agder, 2017 Faculty of Engineering and Science Department of ICT"
2e091b311ac48c18aaedbb5117e94213f1dbb529,Collaborative Facial Landmark Localization for Transferring Annotations Across Datasets,"Collaborative Facial Landmark Localization
for Transferring Annotations Across Datasets
Brandon M. Smith and Li Zhang
University of Wisconsin – Madison
http://www.cs.wisc.edu/~lizhang/projects/collab-face-landmarks/"
2e1415a814ae9abace5550e4893e13bd988c7ba1,Dictionary Based Face Recognition in Video Using Fuzzy Clustering and Fusion,"International Journal of Engineering Trends and Technology (IJETT) – Volume 21 Number 3 – March 2015
Dictionary Based Face Recognition in Video Using
Fuzzy Clustering and Fusion
Neeraja K.C.#1, RameshMarivendan E.#2,
#1IInd year M.E. Student, #2Assistant Professor
#1#2ECE Department, Dhanalakshmi Srinivasan College of Engineering,
Coimbatore,Tamilnadu,India.
Anna University."
2e68190ebda2db8fb690e378fa213319ca915cf8,Generating Videos with Scene Dynamics,"Generating Videos with Scene Dynamics
Carl Vondrick
Hamed Pirsiavash
Antonio Torralba"
2e0d56794379c436b2d1be63e71a215dd67eb2ca,Improving precision and recall of face recognition in SIPP with combination of modified mean search and LSH,"Improving precision and recall of face recognition in SIPP with combination of
modified mean search and LSH
Xihua.Li"
2e475f1d496456831599ce86d8bbbdada8ee57ed,Groupsourcing: Team Competition Designs for Crowdsourcing,"Groupsourcing: Team Competition Designs for
Crowdsourcing
Markus Rokicki, Sergej Zerr, Stefan Siersdorfer
L3S Research Center, Hannover, Germany"
2ef51b57c4a3743ac33e47e0dc6a40b0afcdd522,Leveraging Billions of Faces to Overcome Performance Barriers in Unconstrained Face Recognition,"Leveraging Billions of Faces to Overcome
Performance Barriers in Unconstrained Face
Recognition
Yaniv Taigman and Lior Wolf
face.com
{yaniv,"
2ed4973984b254be5cba3129371506275fe8a8eb,Victoria Ovsyannikova THE EFFECTS OF MOOD ON EMOTION RECOGNITION AND ITS RELATIONSHIP WITH THE GLOBAL VS LOCAL INFORMATION PROCESSING,"Victoria Ovsyannikova
THE EFFECTS OF MOOD ON
EMOTION RECOGNITION AND
ITS RELATIONSHIP WITH THE
GLOBAL VS LOCAL
INFORMATION PROCESSING
STYLES
BASIC RESEARCH PROGRAM
WORKING PAPERS
SERIES: PSYCHOLOGY
WP BRP 60/PSY/2016
This Working Paper is an output of a research project implemented at the National Research
University Higher School of Economics (HSE). Any opinions or claims contained in this
Working Paper do not necessarily reflect the views of HSE"
2e9c780ee8145f29bd1a000585dd99b14d1f5894,Simultaneous Adversarial Training - Learn from Others Mistakes,"Simultaneous Adversarial Training - Learn from
Others’ Mistakes
Zukang Liao
Lite-On Singapore Pte. Ltd, 2Imperial College London"
2ebc35d196cd975e1ccbc8e98694f20d7f52faf3,Towards Wide-angle Micro Vision Sensors,"This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
Towards Wide-angle Micro Vision Sensors
Sanjeev J. Koppal*
Ioannis Gkioulekas* Travis Young+ Hyunsung Park*
Kenneth B. Crozier* Geoffrey L. Barrows+ Todd Zickler*"
2ea78e128bec30fb1a623c55ad5d55bb99190bd2,Residual vs. Inception vs. Classical Networks for Low-Resolution Face Recognition,"Residual vs. Inception vs. Classical Networks for
Low-Resolution Face Recognition
Christian Herrmann1,2, Dieter Willersinn2, and J¨urgen Beyerer1,2
Vision and Fusion Lab, Karlsruhe Institute of Technology KIT, Karlsruhe, Germany
Fraunhofer IOSB, Karlsruhe, Germany
{christian.herrmann,dieter.willersinn,"
2e0f5e72ad893b049f971bc99b67ebf254e194f7,Apparel Classification with Style,"Apparel Classification with Style
Lukas Bossard1, Matthias Dantone1, Christian Leistner1,2,
Christian Wengert1,3, Till Quack3, Luc Van Gool1,4
ETH Z¨urich, Switzerland 2Microsoft, Austria 3Kooaba AG, Switzerland
KU Leuven, Belgium"
2ec7d6a04c8c72cc194d7eab7456f73dfa501c8c,A R Eview on T Exture B Ased E Motion R Ecognition from F Acial E Xpression,"International Journal of Scientific Research and Management Studies (IJSRMS)
ISSN: 2349-3771
Volume 3 Issue 4, pg: 164-169
A REVIEW ON TEXTURE BASED EMOTION RECOGNITION
FROM FACIAL EXPRESSION
Rishabh Bhardwaj, 2Amit Kumar Chanchal, 3 Shubham Kashyap,
3 Pankaj Pandey, 3Prashant Kumar
U.G. Scholars, 2Assistant Professor,
Dept. of E & C Engg., MIT Moradabad, Ram Ganga Vihar, Phase II, Moradabad, India."
2eb9f1dbea71bdc57821dedbb587ff04f3a25f07,Face for Ambient Interface,"Face for Ambient Interface
Maja Pantic
Imperial College, Computing Department, 180 Queens Gate,
London SW7 2AZ, U.K."
2e832d5657bf9e5678fd45b118fc74db07dac9da,"Recognition of Facial Expressions of Emotion: The Effects of Anxiety, Depression, and Fear of Negative Evaluation","Running head: RECOGNITION OF FACIAL EXPRESSIONS OF EMOTION
Recognition of Facial Expressions of Emotion: The Effects of Anxiety, Depression, and Fear of Negative
Evaluation
Rachel Merchak
Wittenberg University
Rachel Merchak, Psychology Department, Wittenberg University.
Author Note
This research was conducted in collaboration with Dr. Stephanie Little, Psychology Department,
Wittenberg University, and Dr. Michael Anes, Psychology Department, Wittenberg University.
Correspondence concerning this article should be addressed to Rachel Merchak, 10063 Fox
Chase Drive, Loveland, OH 45140.
E‐mail:"
2b4d092d70efc13790d0c737c916b89952d4d8c7,Robust Facial Expression Recognition using Local Haar Mean Binary Pattern,"JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 32, XXXX-XXXX (2016)
Robust Facial Expression Recognition using Local Haar
Mean Binary Pattern
MAHESH GOYANI1, NARENDRA PATEL2
,2 Department of Computer Engineering
Charotar University of Science and Technology, Changa, India
Gujarat Technological University, V.V.Nagar, India
E-mail:
In this paper, we propose a hybrid statistical feature extractor, Local Haar Mean Bina-
ry Pattern (LHMBP). It extracts level-1 haar approximation coefficients and computes Local
Mean  Binary  Pattern  (LMBP)  of  it.  LMBP  code  of  pixel  is  obtained  by  weighting  the
thresholded neighbor value of 3  3 patch on its mean. LHMBP produces highly discrimina-
tive code compared to other state of the art methods. To localize appearance features, ap-
proximation subband is divided into M  N regions. LHMBP feature descriptor is derived
y  concatenating  LMBP  distribution  of  each  region.  We  also  propose  a  novel  template
matching strategy called Histogram Normalized Absolute Difference (HNAD) for histogram
ased  feature  comparison.  Experiments  prove  the  superiority  of  HNAD  over  well-known
template  matching  techniques  such  as  L2  norm  and  Chi-Square.  We  also  investigated
LHMBP for expression recognition in low resolution. The performance of the proposed ap-
proach is tested on well-known CK, JAFFE, and SFEW facial expression datasets in diverse"
2b0ff4b82bac85c4f980c40b3dc4fde05d3cc23f,An Effective Approach for Facial Expression Recognition with Local Binary Pattern and Support Vector Machine,"An Effective Approach for Facial Expression Recognition with Local Binary
Pattern and Support Vector Machine
Cao Thi Nhan, 2Ton That Hoa An, 3Hyung Il Choi
*1School of Media, Soongsil University,
School of Media, Soongsil University,
School of Media, Soongsil University,"
2b1327a51412646fcf96aa16329f6f74b42aba89,Improving performance of recurrent neural network with relu nonlinearity,"Under review as a conference paper at ICLR 2016
IMPROVING PERFORMANCE OF RECURRENT NEURAL
NETWORK WITH RELU NONLINEARITY
Sachin S. Talathi & Aniket Vartak
Qualcomm Research
San Diego, CA 92121, USA"
2b632f090c09435d089ff76220fd31fd314838ae,Early Adaptation of Deep Priors in Age Prediction from Face Images,"Early Adaptation of Deep Priors in Age Prediction from Face Images
Mahdi Hajibabaei
Computer Vision Lab
D-ITET, ETH Zurich
Anna Volokitin
Computer Vision Lab
D-ITET, ETH Zurich
Radu Timofte
CVL, D-ITET, ETH Zurich
Merantix GmbH"
2b507f659b341ed0f23106446de8e4322f4a3f7e,Deep Identity-aware Transfer of Facial Attributes,"Deep Identity-aware Transfer of Facial Attributes
Mu Li1, Wangmeng Zuo2, David Zhang1
The Hong Kong Polytechnic University 2Harbin Institute of Technology"
2b8dfbd7cae8f412c6c943ab48c795514d53c4a7,Polynomial based texture representation for facial expression recognition,"014 IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP)
978-1-4799-2893-4/14/$31.00 ©2014 IEEE
e-mail:
e-mail:
RECOGNITION
. INTRODUCTION
(d1,d2)∈[0;d]2
d1+d2≤d"
2bae810500388dd595f4ebe992c36e1443b048d2,Analysis of Facial Expression Recognition by Event-related Potentials,"International Journal of Bioelectromagnetism
Vol. 18, No. 1, pp. 13 - 18, 2016
www.ijbem.org
Analysis of Facial Expression Recognition
y Event-related Potentials
Taichi Hayasaka and Ayumi Miyachi
Department of Information and Computer Engineering,
National Institute of Technology, Toyota College, Japan
Correspondence: Taichi Hayasaka, Department of Information and Computer Engineering, National Institute of Technology,
Toyota College, 2-1 Eisei, Toyota-shi, Aichi, 471-8525 Japan,
E-mail: phone +81 565 36 5861, fax +81 565 36 5926"
2bbbbe1873ad2800954058c749a00f30fe61ab17,Face Verification across Ages Using Self Organizing Map,"ISSN(Online): 2320-9801
ISSN (Print):  2320-9798
International Journal of Innovative Research in Computer and Communication Engineering
(An ISO 3297: 2007 Certified Organization)
Vol.2, Special Issue 1, March 2014
Proceedings of International Conference On Global Innovations In Computing Technology (ICGICT’14)
Organized by
Department of CSE, JayShriram Group of Institutions, Tirupur, Tamilnadu, India on 6th & 7th March 2014
Face Verification across Ages Using Self
Organizing Map
B.Mahalakshmi1, K.Duraiswamy2, P.Gnanasuganya3, P.Aruldhevi4, R.Sundarapandiyan5
Associate Professor, Department of CSE, K.S.Rangasamy College of Technology, Namakkal, TamilNadu, India1
Dean, K.S.Rangasamy College of Technology, Namakkal, TamilNadu, India2
B.E, Department of CSE, K.S.Rangasamy College of Technology, Namakkal, TamilNadu, India3, 4, 5"
477236563c6a6c6db922045453b74d3f9535bfa1,Attribute Based Image Search Re-Ranking Snehal,"International Journal of Science and Research (IJSR)
ISSN (Online): 2319-7064
Index Copernicus Value (2013): 6.14 | Impact Factor (2014): 5.611
Attribute Based Image Search Re-Ranking
Snehal S Patil1, Ajay Dani2
Master of Computer Engg, Savitribai Phule Pune University, G. H. Raisoni Collage of Engg and Technology, Wagholi, Pune
2Professor, Computer and Science Dept, Savitribai Phule Pune University, G. H .Raisoni Collage of Engg and Technology, Wagholi, Pune
integrating
images  by"
470dbd3238b857f349ebf0efab0d2d6e9779073a,Unsupervised Simultaneous Orthogonal basis Clustering Feature Selection,"Unsupervised Simultaneous Orthogonal Basis Clustering Feature Selection
Dongyoon Han and Junmo Kim
School of Electrical Engineering, KAIST, South Korea
In this paper, we propose a novel unsupervised feature selection method: Si-
multaneous Orthogonal basis Clustering Feature Selection (SOCFS). To per-
form feature selection on unlabeled data effectively, a regularized regression-
ased formulation with a new type of target matrix is designed. The target
matrix captures latent cluster centers of the projected data points by per-
forming the orthogonal basis clustering, and then guides the projection ma-
trix to select discriminative features. Unlike the recent unsupervised feature
selection methods, SOCFS does not explicitly use the pre-computed local
structure information for data points represented as additional terms of their
objective functions, but directly computes latent cluster information by the
target matrix conducting orthogonal basis clustering in a single unified term
of the proposed objective function.
Since the target matrix is put in a single unified term for regression of
the proposed objective function, feature selection and clustering are simul-
taneously performed. In this way, the projection matrix for feature selection
is more properly computed by the estimated latent cluster centers of the
projected data points. To the best of our knowledge, this is the first valid"
47541d04ec24662c0be438531527323d983e958e,British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library Library of Congress Control Number: 2008xxxxxx,Affective Information Processing
474b461cd12c6d1a2fbd67184362631681defa9e,Multi-resolution fusion of DTCWT and DCT for shift invariant face recognition,"014 IEEE International
Conference on Systems, Man
nd Cybernetics
(SMC 2014)
San Diego, California, USA
5-8 October 2014
Pages 1-789
IEEE Catalog Number:
ISBN:
CFP14SMC-POD
978-1-4799-3841-4"
47ca2df3d657d7938d7253bed673505a6a819661,"Fields of Study Major Field: Computer Vision Minor Field: Pattern Recognition, Image Procession, Statistical Learning Ix Abstract Facial Expression Analysis on Manifolds","UNIVERSITY OF CALIFORNIA
Santa Barbara
Facial Expression Analysis on Manifolds
A Dissertation submitted in partial satisfaction of the
requirements for the degree Doctor of Philosophy
in Computer Science
Ya Chang
Committee in charge:
Professor Matthew Turk, Chair
Professor Yuan-Fang Wang
Professor B.S. Manjunath
Professor Andy Beall
September 2006"
47d4838087a7ac2b995f3c5eba02ecdd2c28ba14,Automatic Recognition of Deceptive Facial Expressions of Emotion,"JOURNAL OF IEEE TRANSACTIONS ON AFFECTIVE COMPUTING, VOL. XX, NO. X, XXX 2017
Automatic Recognition of Facial Displays of
Unfelt Emotions
Kaustubh Kulkarni*, Ciprian Adrian Corneanu*, Ikechukwu Ofodile*, Student Member, IEEE, Sergio
Escalera, Xavier Bar´o, Sylwia Hyniewska, Member, IEEE, J¨uri Allik,
nd Gholamreza Anbarjafari, Senior Member, IEEE"
47a2727bd60e43f3253247b6d6f63faf2b67c54b,Semi-supervised Vocabulary-Informed Learning,"Semi-supervised Vocabulary-informed Learning
Yanwei Fu and Leonid Sigal
Disney Research"
47d3b923730746bfaabaab29a35634c5f72c3f04,Real-Time Facial Expression Recognition App Development on Smart Phones,"Humaid Alshamsi.et.al. Int. Journal of Engineering Research and Application              www.ijera.com
ISSN : 2248-9622, Vol. 7, Issue 7, ( Part -3) July 2017, pp.30-38
RESEARCH ARTICLE
OPEN ACCESS
Real-Time Facial Expression Recognition App Development on
Smart Phones
Humaid Alshamsi, Veton Kupuska
Electrical And Computer Engineering Department, Florida Institute Of Technology, Melbourne Fl,"
47e3029a3d4cf0a9b0e96252c3dc1f646e750b14,Facial expression recognition in still pictures and videos using active appearance models: a comparison approach,"International Conference on Computer Systems and Technologies - CompSysTech’07
Facial Expression Recognition in still pictures and videos using Active
Appearance Models. A comparison approach.
Drago(cid:1) Datcu
Léon Rothkrantz"
475e16577be1bfc0dd1f74f67bb651abd6d63524,DAiSEE: Towards User Engagement Recognition in the Wild,"DAiSEE: Towards User Engagement Recognition in the Wild
Abhay Gupta
Microsoft
Vineeth N Balasubramanian
Indian Institution of Technology Hyderabad"
471befc1b5167fcfbf5280aa7f908eff0489c72b,Class-Specific Kernel-Discriminant Analysis for Face Verification,"Class-Specific Kernel-Discriminant
Analysis for Face Verification
Georgios Goudelis, Stefanos Zafeiriou, Anastasios Tefas, Member, IEEE, and Ioannis Pitas, Fellow, IEEE
lass problems ("
47e8db3d9adb79a87c8c02b88f432f911eb45dc5,MAGMA: Multilevel Accelerated Gradient Mirror Descent Algorithm for Large-Scale Convex Composite Minimization,"MAGMA: Multi-level accelerated gradient mirror descent algorithm for
large-scale convex composite minimization
Vahan Hovhannisyan
Panos Parpas
Stefanos Zafeiriou
July 15, 2016"
47f5f740e225281c02c8a2ae809be201458a854f,Simultaneous Unsupervised Learning of Disparate Clusterings,"Simultaneous Unsupervised Learning of Disparate Clusterings
Prateek Jain*, Raghu Meka and Inderjit S. Dhillon
Department of Computer Sciences, University of Texas, Austin, TX 78712-1188, USA
Received 14 April 2008; accepted 05 May 2008
DOI:10.1002/sam.10007
Published online 3 November 2008 in Wiley InterScience (www.interscience.wiley.com)."
47bf7a8779c68009ea56a7c20e455ccdf0e3a8fa,Automatic Face Recognition System using Pattern Recognition Techniques: A Survey,"International Journal of Computer Applications (0975 – 8887)
Volume 83 – No 5, December 2013
Automatic Face Recognition System using Pattern
Recognition Techniques: A Survey
Ningthoujam Sunita Devi                                                                              Prof.K.Hemachandran
Department of Computer Science                                                                Department of Computer Science
Assam University, Silchar-788011                                                               Assam University, Silchar-788011"
47b508abdaa5661fe14c13e8eb21935b8940126b,An Efficient Method for Feature Extraction of Face Recognition Using PCA,"Volume 4, Issue 12, December 2014                                  ISSN: 2277 128X
International Journal of Advanced Research in
Computer Science and Software Engineering
Research Paper
Available online at: www.ijarcsse.com
An Efficient Method for Feature Extraction of Face
Recognition Using PCA
Tara Prasad Singh
(M.Tech. Student)
Computer Science & Engineering
Iftm University,Moradabad-244001 U.P."
782188821963304fb78791e01665590f0cd869e8,Automatic Spatially-Aware Fashion Concept Discovery,"sleevelengthincreasing dress length+ mini =(b) Structured product browsing(c) Attribute-feedback product retrieval(a) Concept discoveryminimidimaxisleevelessshort-sleevelong-sleeveblueblackredyellowFigure1.(a)Weproposeaconceptdiscoveryapproachtoauto-maticallyclusterspatially-awareattributesintomeaningfulcon-cepts.Thediscoveredspatially-awareconceptsarefurtherutilizedfor(b)structuredproductbrowsing(visualizingimagesaccordingtoselectedconcepts)and(c)attribute-feedbackproductretrieval(refiningsearchresultsbyprovidingadesiredattribute).variousfeedback,includingtherelevanceofdisplayedim-ages[20,4],ortuningparameterslikecolorandtexture,andthenresultsareupdatedcorrespondingly.However,rel-evancefeedbackislimitedduetoitsslowconvergencetomeetthecustomerrequirements.Inadditiontocolorandtexture,customersoftenwishtoexploithigher-levelfea-tures,suchasneckline,sleevelength,dresslength,etc.Semanticattributes[13],whichhavebeenappliedef-fectivelytoobjectcategorization[15,27]andfine-grainedrecognition[12]couldpotentiallyaddresssuchchallenges.Theyaremid-levelrepresentationsthatdescribesemanticproperties.Recently,researchershaveannotatedclotheswithsemanticattributes[9,2,8,16,11](e.g.,material,pat-tern)asintermediaterepresentationsorsupervisorysignalstobridgethesemanticgap.However,annotatingsemanticattributesiscostly.Further,attributesconditionedonob-jectpartshaveachievedgoodperformanceinfine-grainedrecognition[3,33],confirmingthatspatialinformationiscriticalforattributes.Thisalsoholdsforclothingimages.Forexample,thenecklineattributeusuallycorrespondstothetoppartinimageswhilethesleeveattributeordinarily1"
783f3fccde99931bb900dce91357a6268afecc52,Adapted Active Appearance Models,"Hindawi Publishing Corporation
EURASIP Journal on Image and Video Processing
Volume 2009, Article ID 945717, 14 pages
doi:10.1155/2009/945717
Research Article
Adapted Active Appearance Models
Renaud S´eguier,1 Sylvain Le Gallou,2 Gaspard Breton,2 and Christophe Garcia2
SUP ´ELEC/IETR, Avenue de la Boulaie, 35511 Cesson-S´evign´e, France
Orange Labs—TECH/IRIS, 4 rue du clos courtel, 35 512 Cesson S´evign´e, France
Correspondence should be addressed to Renaud S´eguier,
Received 5 January 2009; Revised 2 September 2009; Accepted 20 October 2009
Recommended by Kenneth M. Lam
Active Appearance Models (AAMs) are able to align ef‌f‌iciently known faces under duress, when face pose and illumination are
ontrolled. We propose Adapted Active Appearance Models to align unknown faces in unknown poses and illuminations. Our
proposal is based on the one hand on a specific transformation of the active model texture in an oriented map, which changes the
AAM normalization process; on the other hand on the research made in a set of different precomputed models related to the most
dapted AAM for an unknown face. Tests on public and private databases show the interest of our approach. It becomes possible
to align unknown faces in real-time situations, in which light and pose are not controlled.
Copyright © 2009 Renaud S´eguier et al. This is an open access article distributed under the Creative Commons Attribution
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly"
78f438ed17f08bfe71dfb205ac447ce0561250c6,Bridging the Semantic Gap : Image and video Understanding by Exploiting Attributes,
781c2553c4ed2a3147bbf78ad57ef9d0aeb6c7ed,Tubelets: Unsupervised Action Proposals from Spatiotemporal Super-Voxels,"Int J Comput Vis
DOI 10.1007/s11263-017-1023-9
Tubelets: Unsupervised Action Proposals from Spatiotemporal
Super-Voxels
Mihir Jain1
Cees G. M. Snoek1
· Jan van Gemert2 · Hervé Jégou3 · Patrick Bouthemy3 ·
Received: 25 June 2016 / Accepted: 18 May 2017
© The Author(s) 2017. This article is an open access publication"
78df7d3fdd5c32f037fb5cc2a7c104ac1743d74e,Temporal Pyramid Pooling-Based Convolutional Neural Network for Action Recognition,"TEMPORAL PYRAMID POOLING CNN FOR ACTION RECOGNITION
Temporal Pyramid Pooling Based Convolutional
Neural Network for Action Recognition
Peng Wang, Yuanzhouhan Cao, Chunhua Shen, Lingqiao Liu, and Heng Tao Shen"
78fdf2b98cf6380623b0e20b0005a452e736181e,Dense Wide-Baseline Stereo with Varying Illumination and its Application to Face Recognition,
787c1bb6d1f2341c5909a0d6d7314bced96f4681,"Face Detection and Verification in Unconstrained Videos: Challenges, Detection, and Benchmark Evaluation","Face Detection and Verification in Unconstrained
Videos: Challenges, Detection, and Benchmark
Evaluation
Mahek Shah
IIIT-D-MTech-CS-GEN-13-106
July 16, 2015
Indraprastha Institute of Information Technology, Delhi
Thesis Advisors
Dr. Mayank Vatsa
Dr. Richa Singh
Submitted in partial fulfillment of the requirements
for the Degree of M.Tech. in Computer Science
(cid:13) Shah, 2015
Keywords: face recognition, face detection, face verification"
7808937b46acad36e43c30ae4e9f3fd57462853d,Describing people: A poselet-based approach to attribute classification,"Describing People: A Poselet-Based Approach to Attribute Classification ∗
Lubomir Bourdev1,2, Subhransu Maji1 and Jitendra Malik1
EECS, U.C. Berkeley, Berkeley, CA 94720
Adobe Systems, Inc., 345 Park Ave, San Jose, CA 95110"
8b2c090d9007e147b8c660f9282f357336358061,Emotion Classification based on Expressions and Body Language using Convolutional Neural Networks,"Lake Forest College
Lake Forest College Publications
Senior Theses
-23-2018
Student Publications
Emotion Classification based on Expressions and
Body Language using Convolutional Neural
Networks
Aasimah S. Tanveer
Lake Forest College,
Follow this and additional works at: https://publications.lakeforest.edu/seniortheses
Part of the Neuroscience and Neurobiology Commons
Recommended Citation
Tanveer, Aasimah S., ""Emotion Classification based on Expressions and Body Language using Convolutional Neural Networks""
(2018). Senior Theses.
This Thesis is brought to you for free and open access by the Student Publications at Lake Forest College Publications. It has been accepted for
inclusion in Senior Theses by an authorized administrator of Lake Forest College Publications. For more information, please contact"
8b547b87fd95c8ff6a74f89a2b072b60ec0a3351,Initial perceptions of a casual game to crowdsource facial expressions in the wild,"Initial Perceptions of a Casual Game to Crowdsource
Facial Expressions in the Wild
Chek Tien Tan
Hemanta Sapkota
Daniel Rosser
Yusuf Pisan
Games Studio, Faculty of Engineering and IT, University of Technology, Sydney"
8bf57dc0dd45ed969ad9690033d44af24fd18e05,Subject-Independent Emotion Recognition from Facial Expressions using a Gabor Feature RBF Neural Classifier Trained with Virtual Samples Generated by Concurrent Self-Organizing Maps,"Subject-Independent Emotion Recognition from Facial Expressions
using a Gabor Feature RBF Neural Classifier Trained with Virtual
Samples Generated by Concurrent Self-Organizing Maps
VICTOR-EMIL NEAGOE, ADRIAN-DUMITRU CIOTEC
Depart. Electronics, Telecommunications & Information Technology
Polytechnic University of Bucharest
Splaiul Independentei No. 313, Sector 6, Bucharest,
ROMANIA"
8b744786137cf6be766778344d9f13abf4ec0683,And Summarization by Sub-modular Inference,"978-1-4799-9988-0/16/$31.00 ©2016 IEEE
ICASSP 2016"
8bf647fed40bdc9e35560021636dfb892a46720e,Learning to hash-tag videos with Tag2Vec,"Learning to Hash-tag Videos with Tag2Vec
Aditya Singh
Saurabh Saini
Rajvi Shah
CVIT, KCIS, IIIT Hyderabad, India
P J Narayanan
http://cvit.iiit.ac.in/research/projects/tag2vec
Figure 1. Learning a direct mapping from videos to hash-tags : sample frames from short video clips with user-given hash-tags
(left); a sample frame from a query video and hash-tags suggested by our system for this query (right)."
8bb21b1f8d6952d77cae95b4e0b8964c9e0201b0,Multimodal Interaction on a Social Robotic Platform,"Methoden
t 11/2013
(cid:2)(cid:2)(cid:2)
Multimodale Interaktion
uf einer sozialen Roboterplattform
Multimodal Interaction on a Social Robotic Platform
Jürgen Blume
Korrespondenzautor:
, Tobias Rehrl, Gerhard Rigoll, Technische Universität München
Zusammenfassung Dieser Beitrag beschreibt die multimo-
dalen Interaktionsmöglichkeiten mit der Forschungsroboter-
plattform ELIAS. Zunächst wird ein Überblick über die Ro-
oterplattform sowie die entwickelten Verarbeitungskompo-
nenten gegeben, die Einteilung dieser Komponenten erfolgt
nach dem Konzept von wahrnehmenden und agierenden Mo-
dalitäten. Anschließend wird das Zusammenspiel der Kom-
ponenten in einem multimodalen Spieleszenario näher be-
trachtet. (cid:2)(cid:2)(cid:2) Summary
This paper presents the mul-
timodal"
8b1db0894a23c4d6535b5adf28692f795559be90,How Reliable are Your Visual Attributes?,"Biometric and Surveillance Technology for Human and Activity Identification X, edited by Ioannis Kakadiaris,
Walter J. Scheirer, Laurence G. Hassebrook, Proc. of SPIE Vol. 8712, 87120Q · © 2013 SPIE
CCC code: 0277-786X/13/$18 · doi: 10.1117/12.2018974
Proc. of SPIE Vol. 8712  87120Q-1
From: http://proceedings.spiedigitallibrary.org/ on 06/07/2013 Terms of Use: http://spiedl.org/terms"
134db6ca13f808a848321d3998e4fe4cdc52fbc2,Dynamics of facial expression: recognition of facial actions and their temporal segments from face profile image sequences,"IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART B: CYBERNETICS, VOL. 36, NO. 2, APRIL 2006
Dynamics of Facial Expression: Recognition of
Facial Actions and Their Temporal Segments
From Face Profile Image Sequences
Maja Pantic, Member, IEEE, and Ioannis Patras, Member, IEEE"
133dd0f23e52c4e7bf254e8849ac6f8b17fcd22d,Active Clustering with Model-Based Uncertainty Reduction,"This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI
Active Clustering with Model-Based
Uncertainty Reduction
Caiming Xiong, David M. Johnson, and Jason J. Corso Senior Member, IEEE"
1369e9f174760ea592a94177dbcab9ed29be1649,Geometrical facial modeling for emotion recognition,"Geometrical Facial Modeling for Emotion Recognition
Giampaolo L. Libralon and Roseli A. F. Romero"
133900a0e7450979c9491951a5f1c2a403a180f0,Social Grouping for Multi-Target Tracking and Head Pose Estimation in Video,"JOURNAL OF LATEX CLASS FILES
Social Grouping for Multi-target Tracking and
Head Pose Estimation in Video
Zhen Qin and Christian R. Shelton"
13db9466d2ddf3c30b0fd66db8bfe6289e880802,Transfer Subspace Learning Model for Face Recognition at a Distance,"I.J. Image, Graphics and Signal Processing, 2017, 1, 27-32
Published Online January 2017 in MECS (http://www.mecs-press.org/)
DOI: 10.5815/ijigsp.2017.01.04
Transfer Subspace Learning Model for Face
Recognition at a Distance
Alwin Anuse
MIT, Pune ,India
Email:
Nilima Deshmukh
AISSM’S IOT,India
Email:
Vibha Vyas
College of Engineering Pune,India
Email:
learning  algorithms  work"
13141284f1a7e1fe255f5c2b22c09e32f0a4d465,Object Tracking by Oversampling Local Features,"Object Tracking by
Oversampling Local Features
Federico Pernici and Alberto Del Bimbo"
1394ca71fc52db972366602a6643dc3e65ee8726,EmoReact: a multimodal approach and dataset for recognizing emotional responses in children,"See	discussions,	stats,	and	author	profiles	for	this	publication	at:	https://www.researchgate.net/publication/308407783
EmoReact:	A	Multimodal	Approach	and	Dataset
for	Recognizing	Emotional	Responses	in	Children
Conference	Paper	·	November	2016
DOI:	10.1145/2993148.2993168
CITATIONS
READS
authors,	including:
Behnaz	Nojavanasghari
University	of	Central	Florida
PUBLICATIONS			20	CITATIONS
Tadas	Baltrusaitis
Carnegie	Mellon	University
0	PUBLICATIONS			247	CITATIONS
SEE	PROFILE
SEE	PROFILE
Charles	E.	Hughes
University	of	Central	Florida
85	PUBLICATIONS			1,248	CITATIONS
SEE	PROFILE"
133da0d8c7719a219537f4a11c915bf74c320da7,A Novel Method for 3D Image Segmentation with Fusion of Two Images using Color K-means Algorithm,"International Journal of Computer Applications (0975 – 8887)
Volume 123 – No.4, August 2015
A Novel Method for 3D Image Segmentation with Fusion
of Two Images using Color K-means Algorithm
Neelam Kushwah
Dept. of CSE
ITM Universe
Gwalior
Priusha Narwariya
Dept. of CSE
ITM Universe
Gwalior"
133f01aec1534604d184d56de866a4bd531dac87,Effective Unconstrained Face Recognition by Combining Multiple Descriptors and Learned Background Statistics,"Effective Unconstrained Face Recognition by
Combining Multiple Descriptors and Learned
Background Statistics
Lior Wolf, Member, IEEE, Tal Hassner, and Yaniv Taigman"
13841d54c55bd74964d877b4b517fa94650d9b65,Generalised ambient reflection models for Lambertian and Phong surfaces,"Generalised Ambient Reflection Models for Lambertian and
Phong Surfaces
Author
Zhang, Paul, Gao, Yongsheng
Published
Conference Title
Proceedings of the 2009 IEEE International Conference on Image Processing (ICIP 2009)
https://doi.org/10.1109/ICIP.2009.5413812
Copyright Statement
© 2009 IEEE. Personal use of this material is permitted. However, permission to reprint/
republish this material for advertising or promotional purposes or for creating new collective
works for resale or redistribution to servers or lists, or to reuse any copyrighted component of
this work in other works must be obtained from the IEEE.
Downloaded from
http://hdl.handle.net/10072/30001
Griffith Research Online
https://research-repository.griffith.edu.au"
132f88626f6760d769c95984212ed0915790b625,Exploring Entity Resolution for Multimedia Person Identification,"UC Irvine
UC Irvine Electronic Theses and Dissertations
Title
Exploring Entity Resolution for Multimedia Person Identification
Permalink
https://escholarship.org/uc/item/9t59f756
Author
Zhang, Liyan
Publication Date
014-01-01
Peer reviewed|Thesis/dissertation
eScholarship.org
Powered by the California Digital Library
University of California"
13f6ab2f245b4a871720b95045c41a4204626814,Cortex commands the performance of skilled movement,"RESEARCH ARTICLE
Cortex commands the performance of
skilled movement
Jian-Zhong Guo, Austin R Graves, Wendy W Guo, Jihong Zheng, Allen Lee,
Juan Rodrı´guez-Gonza´ lez, Nuo Li, John J Macklin, James W Phillips,
Brett D Mensh, Kristin Branson, Adam W Hantman*
Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United
States"
13afc4f8d08f766479577db2083f9632544c7ea6,Multiple kernel learning for emotion recognition in the wild,"Multiple Kernel Learning for
Emotion Recognition in the Wild
Karan Sikka, Karmen Dykstra, Suchitra Sathyanarayana,
Gwen Littlewort and Marian S. Bartlett
Machine Perception Laboratory
EmotiW Challenge, ICMI, 2013"
13188a88bbf83a18dd4964e3f89d0bc0a4d3a0bd,Image Normalization Robust using Histogram Equalization and Logarithm Transform Frequency DCT Coefficients for Illumination in Facial Images,"Dr. V. S. Manjula
HOD, Department of Computer Science, St. Joseph College of Information Technology, Songea, Tanzania"
13d9da779138af990d761ef84556e3e5c1e0eb94,Learning to Locate Informative Features for Visual Identification,"Int J Comput Vis (2008) 77: 3–24
DOI 10.1007/s11263-007-0093-5
Learning to Locate Informative Features for Visual Identification
Andras Ferencz · Erik G. Learned-Miller ·
Jitendra Malik
Received: 18 August 2005 / Accepted: 11 September 2007 / Published online: 9 November 2007
© Springer Science+Business Media, LLC 2007"
7f511a6a2b38a26f077a5aec4baf5dffc981d881,Low-Latency Human Action Recognition with Weighted Multi-Region Convolutional Neural Network,"LOW-LATENCY HUMAN ACTION RECOGNITION WITH WEIGHTED MULTI-REGION
CONVOLUTIONAL NEURAL NETWORK
Yunfeng Wang(cid:63), Wengang Zhou(cid:63), Qilin Zhang†, Xiaotian Zhu(cid:63), Houqiang Li(cid:63)
(cid:63)University of Science and Technology of China, Hefei, Anhui, China
HERE Technologies, Chicago, Illinois, USA"
7ff42ee09c9b1a508080837a3dc2ea780a1a839b,Data Fusion for Real-time Multimodal Emotion Recognition through Webcams and Microphones in E-Learning,"Data Fusion for Real-time Multimodal Emotion Recognition through Webcams
nd Microphones in E-Learning
Kiavash Bahreini*, Rob Nadolski*, Wim Westera*
*Welten Institute, Research Centre for Learning, Teaching and Technology, Faculty of
Psychology and Educational Sciences, Open University of the Netherlands, Valkenburgerweg
77, 6419 AT Heerlen, The Netherlands
{kiavash.bahreini, rob.nadolski,"
7f533bd8f32525e2934a66a5b57d9143d7a89ee1,Audio-Visual Identity Grounding for Enabling Cross Media Search,"Audio-Visual Identity Grounding for Enabling Cross Media Search
Kevin Brady, MIT Lincoln Laboratory
Paper ID 22"
7f44f8a5fd48b2d70cc2f344b4d1e7095f4f1fe5,Sparse Output Coding for Scalable Visual Recognition,"Int J Comput Vis (2016) 119:60–75
DOI 10.1007/s11263-015-0839-4
Sparse Output Coding for Scalable Visual Recognition
Bin Zhao1 · Eric P. Xing1
Received: 15 May 2013 / Accepted: 16 June 2015 / Published online: 26 June 2015
© Springer Science+Business Media New York 2015"
7f4bc8883c3b9872408cc391bcd294017848d0cf,The Multimodal Focused Attribute Model : A Nonparametric Bayesian Approach to Simultaneous Object Classification and Attribute Discovery,"Computer
Sciences
Department
The Multimodal Focused Attribute Model:  A Nonparametric
Bayesian Approach to Simultaneous Object Classification and
Attribute Discovery
Jake Rosin
Charles R. Dyer
Xiaojin Zhu
Technical Report #1697
January 2012"
7f6061c83dc36633911e4d726a497cdc1f31e58a,YouTube-8M: A Large-Scale Video Classification Benchmark,"YouTube-8M: A Large-Scale Video Classification
Benchmark
Sami Abu-El-Haija
George Toderici
Nisarg Kothari
Joonseok Lee
Paul Natsev
Balakrishnan Varadarajan
Sudheendra Vijayanarasimhan
Google Research"
7f36dd9ead29649ed389306790faf3b390dc0aa2,Movement Differences between Deliberate and Spontaneous Facial Expressions: Zygomaticus Major Action in Smiling.,"MOVEMENT DIFFERENCES BETWEEN DELIBERATE
AND SPONTANEOUS FACIAL EXPRESSIONS:
ZYGOMATICUS MAJOR ACTION IN SMILING
Karen L. Schmidt, Zara Ambadar, Jeffrey F. Cohn, and L. Ian Reed"
7f6cd03e3b7b63fca7170e317b3bb072ec9889e0,A Face Recognition Signature Combining Patch-based Features with Soft Facial Attributes,"A Face Recognition Signature Combining Patch-based
Features with Soft Facial Attributes
L. Zhang, P. Dou, I.A. Kakadiaris
Computational Biomedicine Lab, 4849 Calhoun Rd, Rm 373, Houston, TX 77204"
7f97a36a5a634c30de5a8e8b2d1c812ca9f971ae,Incremental Classifier Learning with Generative Adversarial Networks,"Incremental Classifier Learning with Generative Adversarial Networks
Yue Wu1 Yinpeng Chen2 Lijuan Wang2 Yuancheng Ye3
Zicheng Liu2 Yandong Guo2 Zhengyou Zhang2 Yun Fu1
Northeastern University 2Microsoft Research 3City University of New York"
7f268f29d2c8f58cea4946536f5e2325777fa8fa,Facial Emotion Recognition in Curvelet Domain,"Facial Emotion Recognition in Curvelet Domain
Gyanendra K Verma and Bhupesh Kumar Singh
Indian Institute of Informaiton Technology, Allahabad, India
Allahabad, India - 211012"
7f3a73babe733520112c0199ff8d26ddfc7038a0,Robust Face Identification with Small Sample Sizes using Bag of Words and Histogram of Oriented Gradients,
7af38f6dcfbe1cd89f2307776bcaa09c54c30a8b,Learning in Computer Vision and Beyond: Development,"eaig i C
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de i
ia  he
way ai
a ad h	
a ea. The 
achie 
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7a81967598c2c0b3b3771c1af943efb1defd4482,Do We Need More Training Data?,"Do We Need More Training Data?
Xiangxin Zhu · Carl Vondrick · Charless C. Fowlkes · Deva Ramanan"
7ae0212d6bf8a067b468f2a78054c64ea6a577ce,Human Face Processing Techniques With Application To Large Scale Video Indexing,"Human Face Processing Techniques
With Application To
Large Scale Video Indexing
LE DINH DUY
DOCTOR OF
PHILOSOPHY
Department of Informatics,
School of Multidisciplinary Sciences,
The Graduate University for Advanced Studies (SOKENDAI)
006 (School Ｙear)
September 2006"
7a0fb972e524cb9115cae655e24f2ae0cfe448e0,Facial Expression Classification Using RBF AND Back-Propagation Neural Networks,"Facial Expression Classification Using RBF AND Back-Propagation Neural Networks
R.Q.Feitosa1,2,
M.M.B.Vellasco1,2,
D.T.Oliveira1,
D.V.Andrade1,
S.A.R.S.Maffra1
– Catholic University of Rio de Janeiro, Brazil
Department of Electric Engineering
– State University of Rio de Janeiro, Brazil
Department of Computer Engineering
e-mail: [raul, -rio.br, [diogo,"
7ad77b6e727795a12fdacd1f328f4f904471233f,Supervised Local Descriptor Learning for Human Action Recognition,"Supervised Local Descriptor Learning
for Human Action Recognition
Xiantong Zhen, Feng Zheng, Ling Shao, Senior Member, IEEE, Xianbin Cao, Senior Member, IEEE, and Dan Xu"
7a97de9460d679efa5a5b4c6f0b0a5ef68b56b3b,Constrained Joint Cascade Regression Framework for Simultaneous Facial Action Unit Recognition and Facial Landmark Detection,"nd Face shape relationship2)AU relationship3)Face shape patternUpdate facial landmark locationsUpdate AU activation probabilitiesAU activation probabilitiesCurrent landmark locationsFigure1.Constrainedjointcascaderegressionframeworkforsi-multaneousfacialactionunitrecognitionandlandmarkdetection.wouldenablethemachineunderstandingofhumanfacialbehavior,intent,emotionetc.Facialactionunitrecognitionandfaciallandmarkdetec-tionarerelatedtasks,buttheyareseldomlyexploitedjointlyintheliteratures.Forexample,thefaceshapedefinedbythelandmarklocationsareconsideredaseffectivefeaturesforAUrecognition.But,thelandmarklocationinforma-tionisusuallyextractedbeforehandwithfaciallandmarkdetectionalgorithms.Ontheotherhand,theActionUnitinformationisrarelyutilizedintheliteraturetohelpfaciallandmarkdetection,eventhoughthefacialmusclemove-mentsandtheactivationofspecificfacialactionunitcancausetheappearanceandshapechangesofthefacewhichsignificantlyaffectfaciallandmarkdetection.Themutualinformationandintertwinedrelationshipamongfacialac-tionunitrecognitionandfaciallandmarkdetectionshouldbeutilizedtoboosttheperformancesofbothtasks.Cascaderegressionframeworkhasbeenshowntobeaneffectivemethodforfacealignmentrecently[19][13].Itstartsfromaninitialfaceshape(e.g.meanface)anditit-erativelyupdatesthefaciallandmarklocationsbasedonthelocalappearancefeaturesuntilconvergence.Severalregres-sionmodelshavebeenappliedtolearnthemappingfromthelocalappearancefeaturestothefaceshapeupdate.Toleveragethesuccessofthecascaderegressionframe-workandtoachievethegoalofjointfacialactionunit13400"
7aa4c16a8e1481629f16167dea313fe9256abb42,Multi-task learning for face identification and attribute estimation,"978-1-5090-4117-6/17/$31.00 ©2017 IEEE
ICASSP 2017"
7ad7897740e701eae455457ea74ac10f8b307bed,Random Subspace Two-dimensional LDA for Face Recognition,"Random Subspace Two-dimensional LDA for Face Recognition*
Garrett Bingham1"
7a7b1352d97913ba7b5d9318d4c3d0d53d6fb697,Attend and Rectify: a Gated Attention Mechanism for Fine-Grained Recovery,"Attend and Rectify: a Gated Attention
Mechanism for Fine-Grained Recovery
Pau Rodr´ıguez†, Josep M. Gonfaus‡, Guillem Cucurull†,
F. Xavier Roca†, Jordi Gonz`alez†
Computer Vision Center and Universitat Aut`onoma de Barcelona (UAB),
Campus UAB, 08193 Bellaterra, Catalonia Spain
Visual Tagging Services, Parc de Recerca, Campus UAB"
7aa062c6c90dba866273f5edd413075b90077b51,Minimizing Separability : A Comparative Analysis of Illumination Compensation Techniques in Face Recognition,"I.J. Information Technology and Computer Science, 2017, 5, 40-51
Published Online May 2017 in MECS (http://www.mecs-press.org/)
DOI: 10.5815/ijitcs.2017.05.06
Minimizing Separability: A Comparative Analysis
of Illumination Compensation Techniques in Face
Recognition
Chollette C. Olisah
Department of Computer Science and IT, Baze University, Abuja, Nigeria
E-mail:"
1451e7b11e66c86104f9391b80d9fb422fb11c01,Image privacy protection with secure JPEG transmorphing,"IET Signal Processing
Research Article
Image privacy protection with secure JPEG
transmorphing
ISSN 1751-9675
Received on 30th December 2016
Revised 13th July 2017
Accepted on 11th August 2017
doi: 10.1049/iet-spr.2016.0756
www.ietdl.org
Lin Yuan1  , Touradj Ebrahimi1
Multimedia Signal Processing Group, Electrical Engineering Department, EPFL, Station 11, Lausanne, Switzerland
E-mail:"
14761b89152aa1fc280a33ea4d77b723df4e3864,Zero-Shot Learning via Visual Abstraction,
14fdec563788af3202ce71c021dd8b300ae33051,Social Influence Analysis based on Facial Emotions,"Social Influence Analysis based on Facial Emotions
Pankaj Mishra, Rafik Hadfi, and Takayuki Ito
Department of Computer Science and Engineering
Nagoya Institute of Technology, Gokiso, Showa-ku, Nagoya, 466-8555 Japan
{pankaj.mishra,"
1459d4d16088379c3748322ab0835f50300d9a38,Cross-Domain Visual Matching via Generalized Similarity Measure and Feature Learning,"Cross-Domain Visual Matching via Generalized
Similarity Measure and Feature Learning
Liang Lin, Guangrun Wang, Wangmeng Zuo, Xiangchu Feng, and Lei Zhang"
1450296fb936d666f2f11454cc8f0108e2306741,Learning to Discover Cross-Domain Relations with Generative Adversarial Networks,"Learning to Discover Cross-Domain Relations
with Generative Adversarial Networks
Taeksoo Kim 1 Moonsu Cha 1 Hyunsoo Kim 1 Jung Kwon Lee 1 Jiwon Kim 1"
1442319de86d171ce9595b20866ec865003e66fc,Vision-Based Fall Detection with Convolutional Neural Networks,"Vision-Based Fall Detection with Convolutional
Neural Networks
Adri´an Nu˜nez-Marcos1, Gorka Azkune1, Ignacio Arganda-Carreras234
DeustoTech - University of Deusto
Avenida de las Universidades, 24 - 48007, Bilbao, Spain
Dept. of Computer Science and Artificial Intelligence, Basque
Country University, San Sebastian, Spain
P. Manuel Lardizabal, 1 - 20018, San Sebastian, Spain
Ikerbasque, Basque Foundation for Science, Bilbao, Spain
Maria Diaz de Haro, 3 - 48013 Bilbao, Spain
Donostia International Physics Center (DIPC), San Sebastian, Spain
P. Manuel Lardizabal, 4 - 20018, San Sebastian, Spain"
1462bc73834e070201acd6e3eaddd23ce3c1a114,Face Authentication /recognition System for Forensic Application Using Sketch Based on the Sift Features Approach,"International Journal of Science, Engineering and Technology Research (IJSETR), Volume 3, Issue 4, April 2014
FACE AUTHENTICATION /RECOGNITION
SYSTEM FOR FORENSIC APPLICATION
USING SKETCH BASED ON THE SIFT
FEATURES APPROACH
Poonam A. Katre
Department of Electronics Engineering KITS,
RTMNU Nagpur University, India"
140c95e53c619eac594d70f6369f518adfea12ef,Pushing the frontiers of unconstrained face detection and recognition: IARPA Janus Benchmark A,"Pushing the Frontiers of Unconstrained Face Detection and Recognition: IARPA Janus Benchmark A
Brendan F. Klare, Emma Taborsky , Austin Blanton , Jordan Cheney , Kristen Allen , Patrick Grother , Alan Mah , Anil K. Jain
The development of accurate and scalable unconstrained face recogni-
tion algorithms is a long term goal of the biometrics and computer vision
ommunities. The term “unconstrained” implies a system can perform suc-
essful identifications regardless of face image capture presentation (illumi-
nation, sensor, compression) or subject conditions (facial pose, expression,
occlusion). While automatic, as well as human, face identification in certain
scenarios may forever be elusive, such as when a face is heavily occluded or
aptured at very low resolutions, there still remains a large gap between au-
tomated systems and human performance on familiar faces. In order to close
this gap, large annotated sets of imagery are needed that are representative
of the end goals of unconstrained face recognition. This will help continue
to push the frontiers of unconstrained face detection and recognition, which
re the primary goals of the IARPA Janus program.
The current state of the art in unconstrained face recognition is high
ccuracy (roughly 99% true accept rate at a false accept rate of 1.0%) on
faces that can be detected with a commodity face detectors, but unknown
ccuracy on other faces. Despite the fact that face detection and recognition
research generally has advanced somewhat independently, the frontal face"
1467c4ab821c3b340abe05a1b13a19318ebbce98,Multitask and transfer learning for multi-aspect data,"Multitask and Transfer Learning for
Multi-Aspect Data
Bernardino Romera Paredes
A dissertation submitted in partial fulfillment
of the requirements for the degree of
Doctor of Philosophy of University College London."
142dcfc3c62b1f30a13f1f49c608be3e62033042,Adaptive region pooling for object detection,"Adaptive Region Pooling for Object Detection
Yi-Hsuan Tsai
UC Merced
Onur C. Hamsici
Qualcomm Research, San Diego
Ming-Hsuan Yang
UC Merced"
14e428f2ff3dc5cf96e5742eedb156c1ea12ece1,Facial Expression Recognition Using Neural Network Trained with Zernike Moments,"Facial Expression Recognition Using Neural Network Trained with Zernike
Moments
Mohammed Saaidia
Dept. Génie-Electrique
Université M.C.M Souk-Ahras
Souk-Ahras, Algeria"
14a5feadd4209d21fa308e7a942967ea7c13b7b6,Content-based vehicle retrieval using 3D model and part information,"978-1-4673-0046-9/12/$26.00 ©2012 IEEE
ICASSP 2012"
14fee990a372bcc4cb6dc024ab7fc4ecf09dba2b,Modeling Spatio-Temporal Human Track Structure for Action Localization,"Modeling Spatio-Temporal Human Track Structure for Action
Localization
Guilhem Ch´eron · Anton Osokin · Ivan Laptev · Cordelia Schmid"
14ee4948be56caeb30aa3b94968ce663e7496ce4,SmileNet: Registration-Free Smiling Face Detection,"SmileNet: Registration-Free Smiling Face Detection In The Wild.
Jang, Y; Gunes, H; Patras, I
© Copyright 2018 IEEE
For additional information about this publication click this link.
http://qmro.qmul.ac.uk/xmlui/handle/123456789/36405
Information about this research object was correct at the time of download; we occasionally
make corrections to records, please therefore check the published record when citing. For
more information contact"
8ee62f7d59aa949b4a943453824e03f4ce19e500,Robust Head-Pose Estimation Based on Partially-Latent Mixture of Linear Regressions,"Robust Head-Pose Estimation Based on
Partially-Latent Mixture of Linear Regression
Vincent Drouard∗, Radu Horaud∗, Antoine Deleforge†, Sil`eye Ba∗ and Georgios Evangelidis∗
INRIA Grenoble Rhˆone-Alpes, Montbonnot Saint-Martin, France
INRIA Rennes Bretagne Atlantique, Rennes, France"
8e33183a0ed7141aa4fa9d87ef3be334727c76c0,Robustness of Face Recognition to Image Manipulations,"– COS429 Written Report, Fall 2017 –
Robustness of Face Recognition to Image Manipulations
Cathy Chen (cc27), Zachary Liu (zsliu), and Lindy Zeng (lindy)
. Motivation
We can often recognize pictures of people we know even if the image has low resolution or obscures
part of the face, if the camera angle resulted in a distorted image of the subject’s face, or if the
subject has aged or put on makeup since we last saw them. Although this is a simple recognition task
for a human, when we think about how we accomplish this task, it seems non-trivial for computer
lgorithms to recognize faces despite visual changes.
Computer facial recognition is relied upon for many application where accuracy is important.
Facial recognition systems have applications ranging from airport security and suspect identification
to personal device authentication and face tagging [7]. In these real-world applications, the system
must continue to recognize images of a person who looks slightly different due to the passage of
time, a change in environment, or a difference in clothing.
Therefore, we are interested in investigating face recognition algorithms and their robustness to
image changes resulting from realistically plausible manipulations. Furthermore, we are curious
bout whether the impact of image manipulations on computer algorithms’ face recognition ability
mirrors related insights from neuroscience about humans’ face recognition abilities.
. Goal
In this project, we implement both face recognition algorithms and image manipulations. We then"
8e3d0b401dec8818cd0245c540c6bc032f169a1d,McGan: Mean and Covariance Feature Matching GAN,"McGan: Mean and Covariance Feature Matching GAN
Youssef Mroueh * 1 2 Tom Sercu * 1 2 Vaibhava Goel 2"
8e94ed0d7606408a0833e69c3185d6dcbe22bbbe,For your eyes only,"© 2012 IEEE. Personal use of this material is permitted. Permission from IEEE
must  be  obtained  for  all  other  uses,  in  any  current  or  future  media,  including
reprinting/republishing  this  material  for  advertising  or  promotional  purposes,
reating  new  collective  works,  for  resale  or  redistribution  to  servers  or  lists,  or
reuse of any copyrighted component of this work in other works.
Pre-print of article that will appear at WACV 2012."
8e461978359b056d1b4770508e7a567dbed49776,LOMo: Latent Ordinal Model for Facial Analysis in Videos,"LOMo: Latent Ordinal Model for Facial Analysis in Videos
Karan Sikka1,∗
Gaurav Sharma2,3,†
Marian Bartlett1,∗,‡
UCSD, USA
MPI for Informatics, Germany
IIT Kanpur, India"
8ea30ade85880b94b74b56a9bac013585cb4c34b,From turbo hidden Markov models to turbo state-space models [face recognition applications],"FROM TURBO HIDDEN MARKOV MODELS TO TURBO STATE-SPACE MODELS
Florent Perronnin and Jean-Luc Dugelay
Institut Eur´ecom
Multimedia Communications Department
BP 193, 06904 Sophia Antipolis Cedex, France
fflorent.perronnin,"
8e8e3f2e66494b9b6782fb9e3f52aeb8e1b0d125,"Detecting and classifying scars, marks, and tattoos found in the wild","in  any  current  or
future  media,
for  all  other  uses,
 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be
obtained
including
reprinting/republishing  this  material  for  advertising  or  promotional  purposes,  creating
new  collective  works,  for  resale  or  redistribution  to  servers  or  lists,  or  reuse  of  any
opyrighted component of this work in other works.
Pre-print of article that will appear at BTAS 2012.!!"
8e378ef01171b33c59c17ff5798f30293fe30686,A system for automatic face analysis based on statistical shape and texture models,"Lehrstuhl f¨ur Mensch-Maschine-Kommunikation
der Technischen Universit¨at M¨unchen
A System for Automatic Face Analysis
Based on
Statistical Shape and Texture Models
Ronald M¨uller
Vollst¨andiger Abdruck der von der Fakult¨at
f¨ur Elektrotechnik und Informationstechnik
der Technischen Universit¨at M¨unchen
zur Erlangung des akademischen Grades eines
Doktor-Ingenieurs
genehmigten Dissertation
Vorsitzender: Prof. Dr. rer. nat. Bernhard Wolf
Pr¨ufer der Dissertation:
. Prof. Dr.-Ing. habil. Gerhard Rigoll
. Prof. Dr.-Ing. habil. Alexander W. Koch
Die Dissertation wurde am 28.02.2008 bei der Technischen Universit¨at M¨unchen
eingereicht und durch die Fakult¨at f¨ur Elektrotechnik und Informationstechnik
m 18.09.2008 angenommen."
8ed051be31309a71b75e584bc812b71a0344a019,Class-Based Feature Matching Across Unrestricted Transformations,"Class-based feature matching across unrestricted
transformations
Evgeniy Bart and Shimon Ullman"
8e36100cb144685c26e46ad034c524b830b8b2f2,Modeling Facial Geometry using Compositional VAEs,"Modeling Facial Geometry using Compositional VAEs
Timur Bagautdinov∗1, Chenglei Wu2, Jason Saragih2, Pascal Fua1, Yaser Sheikh2
´Ecole Polytechnique F´ed´erale de Lausanne
Facebook Reality Labs, Pittsburgh"
8e0becfc5fe3ecdd2ac93fabe34634827b21ef2b,Learning from Longitudinal Face Demonstration - Where Tractable Deep Modeling Meets Inverse Reinforcement Learning,"International Journal of Computer Vision manuscript No.
(will be inserted by the editor)
Learning from Longitudinal Face Demonstration -
Where Tractable Deep Modeling Meets Inverse Reinforcement Learning
Chi Nhan Duong · Kha Gia Quach · Khoa Luu · T. Hoang Ngan Le · Marios
Savvides · Tien D. Bui
Received: date / Accepted: date"
22043cbd2b70cb8195d8d0500460ddc00ddb1a62,Separability-Oriented Subclass Discriminant Analysis,"Separability-Oriented Subclass Discriminant
Analysis
Huan Wan, Hui Wang, Gongde Guo, Xin Wei"
22137ce9c01a8fdebf92ef35407a5a5d18730dde,Recognition of Faces from single and Multi-View Videos,
22264e60f1dfbc7d0b52549d1de560993dd96e46,UnitBox: An Advanced Object Detection Network,"UnitBox: An Advanced Object Detection Network
Jiahui Yu1,2
Yuning Jiang2
Zhangyang Wang1
Zhimin Cao2
Thomas Huang1
University of Illinois at Urbana−Champaign
Megvii Inc
{jyu79, zwang119, {jyn,"
223ec77652c268b98c298327d42aacea8f3ce23f,Acted Facial Expressions In The Wild Database,"TR-CS-11-02
Acted Facial Expressions In The Wild
Database
Abhinav Dhall, Roland Goecke, Simon
Lucey, Tom Gedeon
September 2011
ANU Computer Science Technical Report Series"
228558a2a38a6937e3c7b1775144fea290d65d6c,Nonparametric Context Modeling of Local Appearance for Pose- and Expression-Robust Facial Landmark Localization,"Nonparametric Context Modeling of Local Appearance
for Pose- and Expression-Robust Facial Landmark Localization
Brandon M. Smith1
Jonathan Brandt2
University of Wisconsin–Madison
Zhe Lin2
Adobe Research
Li Zhang1
http://www.cs.wisc.edu/~lizhang/projects/face-landmark-localization/"
22fdd8d65463f520f054bf4f6d2d216b54fc5677,Efficient Small and Capital Handwritten Character Recognition with Noise Reduction,"International Journal of Emerging Technology and Advanced Engineering
Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 3, Issue 8, August 2013)
Efficient Small and Capital Handwritten Character
Recognition with Noise Reduction
Beerendra Kumar Pal, Prof. Shailendra Tiwari, Prof. Sandeep Kumar
Department of Computer Science Engg., IES College of Technology, Bhopal"
2251a88fbccb0228d6d846b60ac3eeabe468e0f1,Matrix-Based Kernel Subspace Methods,"Matrix-Based Kernel Subspace Methods
S. Kevin Zhou
Integrated Data Systems Department
Siemens Corporate Research
755 College Road East, Princeton, NJ 08540
Email:"
227b18fab568472bf14f9665cedfb95ed33e5fce,Compositional Dictionaries for Domain Adaptive Face Recognition,"Compositional Dictionaries for Domain Adaptive
Face Recognition
Qiang Qiu, and Rama Chellappa, Fellow, IEEE."
227b1a09b942eaf130d1d84cdcabf98921780a22,Multi-feature shape regression for face alignment,"Yang et al. EURASIP Journal on Advances in Signal Processing  (2018) 2018:51
https://doi.org/10.1186/s13634-018-0572-6
EURASIP Journal on Advances
in Signal Processing
R ES EAR CH
Multi-feature shape regression for face
lignment
Wei-Jong Yang, Yi-Chen Chen, Pau-Choo Chung and Jar-Ferr Yang*
Open Access"
22dabd4f092e7f3bdaf352edd925ecc59821e168,Exploiting side information in locality preserving projection,"Deakin Research Online
This is the published version:
An, Senjian, Liu, Wanquan and Venkatesh, Svetha 2008, Exploiting side information in
locality preserving projection, in CVPR 2008 : Proceedings of the 26th IEEE Conference on
Computer Vision and Pattern Recognition, IEEE, Washington, D. C., pp. 1-8.
Available from Deakin Research Online:
http://hdl.handle.net/10536/DRO/DU:30044576
Reproduced with the kind permissions of the copyright owner.
Personal use of this material is permitted. However, permission to reprint/republish this
material for advertising or promotional purposes or for creating new collective works for
resale or redistribution to servers or lists, or to reuse any copyrighted component of this work
in other works must be obtained from the IEEE.
Copyright : 2008, IEEE"
22e189a813529a8f43ad76b318207d9a4b6de71a,What will Happen Next? Forecasting Player Moves in Sports Videos,"What will Happen Next?
Forecasting Player Moves in Sports Videos
Panna Felsen
UC Berkeley, STATS
Pulkit Agrawal
UC Berkeley
Jitendra Malik
UC Berkeley"
25c19d8c85462b3b0926820ee5a92fc55b81c35a,Pose-Invariant Facial Expression Recognition Using Variable-Intensity Templates,"Noname manuscript No.
(will be inserted by the editor)
Pose-Invariant Facial Expression Recognition
Using Variable-Intensity Templates
Shiro Kumano · Kazuhiro Otsuka · Junji Yamato ·
Eisaku Maeda · Yoichi Sato
Received: date / Accepted: date"
258a8c6710a9b0c2dc3818333ec035730062b1a5,Benelearn 2005 Annual Machine Learning Conference of Belgium and the Netherlands CTIT P ROCEEDINGS OF THE FOURTEENTH,"Benelearn 2005
Annual Machine Learning Conference of
Belgium and the Netherlands
CTIT PROCEEDINGS OF THE FOURTEENTH
ANNUAL MACHINE LEARNING CONFERENCE
OF BELGIUM AND THE NETHERLANDS
Martijn van Otterlo, Mannes Poel and Anton Nijholt (eds.)"
25695abfe51209798f3b68fb42cfad7a96356f1f,An Investigation into Combining Both Facial Detection and Landmark Localisation into a Unified Procedure Using Gpu Computing,"AN INVESTIGATION INTO COMBINING
BOTH FACIAL DETECTION AND
LANDMARK LOCALISATION INTO A
UNIFIED PROCEDURE USING GPU
COMPUTING
J M McDonagh
MSc by Research"
250ebcd1a8da31f0071d07954eea4426bb80644c,DenseBox: Unifying Landmark Localization with End to End Object Detection,"DenseBox: Unifying Landmark Localization with
End to End Object Detection
Lichao Huang1
Yi Yang2
Yafeng Deng2
Institute of Deep Learning
Baidu Research
Yinan Yu3"
25337690fed69033ef1ce6944e5b78c4f06ffb81,Strategic Engagement Regulation: an Integration of Self-enhancement and Engagement,"STRATEGIC ENGAGEMENT REGULATION:
AN INTEGRATION OF SELF-ENHANCEMENT AND ENGAGEMENT
Jordan B. Leitner
A dissertation submitted to the Faculty of the University of Delaware in partial
fulfillment of the requirements for the degree of Doctor of Philosophy in Psychology
Spring 2014
© 2014 Jordan B. Leitner
All Rights Reserved"
25d3e122fec578a14226dc7c007fb1f05ddf97f7,The first facial expression recognition and analysis challenge,"The First Facial Expression Recognition and Analysis Challenge
Michel F. Valstar, Bihan Jiang, Marc Mehu, Maja Pantic, and Klaus Scherer"
2597b0dccdf3d89eaffd32e202570b1fbbedd1d6,Towards Predicting the Likeability of Fashion Images,"Towards predicting the likeability of fashion images
Jinghua Wang, Abrar Abdul Nabi, Gang Wang, Member, IEEE, Chengde Wan, Tian-Tsong Ng, Member, IEEE,"
25982e2bef817ebde7be5bb80b22a9864b979fb0,Facial Feature Tracking Under Varying Facial Expressions and Face Poses Based on Restricted Boltzmann Machines,"(a)26facialfeaturepointsthatwetrack(b)oneexamplesequenceFigure1.Facialfeaturepointtrackingunderexpressionvariationandocclusion.Inrecentyears,thesemodelshavebeenusedexplicitlytohandletheshapevariations[17][5].Thenonlinearityem-beddedinRBManditsvariantsmakesthemmoreeffectiveandefficienttorepresentthenonrigiddeformationsofob-jectscomparedtothelinearmethods.Theirlargenumberofhiddennodesanddeeparchitecturesalsocanimposesuffi-cientconstraintsaswellasenoughdegreesoffreedomsintotherepresentationsofthetargetobjects.Inthispaper,wepresentaworkthatcaneffectivelytrackfacialfeaturepointsusingfaceshapepriormodelsthatareconstructedbasedonRBM.Thefacialfeaturetrackercantrack26facialfeaturepoints(Fig.1(a))eveniffaceshavedifferentfacialexpressions,varyingposes,orocclu-sion(Fig.1(b)).Unlikethepreviousworksthattrackfacialfeaturepointsindependentlyorbuildashapemodeltocap-turethevariationsoffaceshapeorappearanceregardlessofthefacialexpressionsandfaceposes,theproposedmodelcouldcapturethedistinctionsaswellasthevariationsoffaceshapesduetofacialexpressionandposechangeinaunifiedframework.Specifically,wefirstconstructamodel1"
25c108a56e4cb757b62911639a40e9caf07f1b4f,Recurrent Scale Approximation for Object Detection in CNN,"Recurrent Scale Approximation for Object Detection in CNN
Yu Liu1,2, Hongyang Li2, Junjie Yan1, Fangyin Wei1, Xiaogang Wang2, Xiaoou Tang2
Multimedia Laboratory at The Chinese University of Hong Kong
SenseTime Group Limited"
25e05a1ea19d5baf5e642c2a43cca19c5cbb60f8,Label Distribution Learning,"Label Distribution Learning
Xin Geng*, Member, IEEE"
2559b15f8d4a57694a0a33bdc4ac95c479a3c79a,Contextual Object Localization With Multiple Kernel Nearest Neighbor,"Contextual Object Localization With Multiple
Kernel Nearest Neighbor
Brian McFee, Student Member, IEEE, Carolina Galleguillos, Student Member, IEEE, and
Gert Lanckriet, Member, IEEE"
25f1f195c0efd84c221b62d1256a8625cb4b450c,Experiments with Facial Expression Recognition using Spatiotemporal Local Binary Patterns,"-4244-1017-7/07/$25.00 ©2007 IEEE
ICME 2007"
25885e9292957feb89dcb4a30e77218ffe7b9868,Analyzing the Affect of a Group of People Using Multi-modal Framework,"JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2016
Analyzing the Affect of a Group of People Using
Multi-modal Framework
Xiaohua Huang, Abhinav Dhall, Xin Liu, Guoying Zhao, Jingang Shi, Roland Goecke and Matti Pietik¨ainen"
259706f1fd85e2e900e757d2656ca289363e74aa,Improving People Search Using Query Expansions: How Friends Help To Find People,"Improving People Search Using Query Expansions
How Friends Help To Find People
Thomas Mensink and Jakob Verbeek
LEAR - INRIA Rhˆone Alpes - Grenoble, France"
258a2dad71cb47c71f408fa0611a4864532f5eba,Discriminative Optimization of Local Features for Face Recognition,"Discriminative Optimization
of Local Features for Face Recognition
H O S S E I N   A Z I Z P O U R
Master of Science Thesis
Stockholm, Sweden 2011"
25127c2d9f14d36f03d200a65de8446f6a0e3bd6,Evaluating the Performance of Deep Supervised Auto Encoder in Single Sample Face Recognition Problem Using Kullback-leibler Divergence Sparsity Regularizer,"Journal of Theoretical and Applied Information Technology
20th May 2016. Vol.87. No.2
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ISSN: 1992-8645                                                       www.jatit.org                                                          E-ISSN: 1817-3195
EVALUATING THE PERFORMANCE OF DEEP SUPERVISED
AUTO ENCODER IN SINGLE SAMPLE FACE RECOGNITION
PROBLEM USING KULLBACK-LEIBLER DIVERGENCE
SPARSITY REGULARIZER
OTNIEL Y. VIKTORISA, 2ITO WASITO, 2ARIDA F. SYAFIANDINI
Faculty of Computer  of Computer Science, Universitas Indonesia, Kampus UI Depok, Indonesia
E-mail:  ,"