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 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 Take down policy Explore Bristol Research is a digital archive and the intention is that deposited content should not be removed. However, if you believe that this version of the work breaches copyright law please contact nd include the following information in your message: • Your contact details • Bibliographic details for the item, including a URL • An outline of the nature of the complaint" 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 http://hdl.handle.net/10072/17889 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 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 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" 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 iy g S g iz ad Ze ga Biey y EECS AST 373 1  g Dg Y g G  Taej 305 701 REA z VR Cee ETR 161 ajg Dg Y g G  Taej 305 350 REA Abac Thee exi he c eaive aciviy bewee a h a beig ad ehabiiai b beca e he h a eae ehabiiai b i he ae evi e ad ha he bee(cid:12) f ehabiiai b  ch a ai ay  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  he vea  c e f a ew wheechai baed bic a ye ARES  ad i h a b ieaci echgie ae eeed. Ag 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" 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;" 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 It is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons). Disclaimer/Complaints regulations If you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask 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,"GEERATVEEETATF ERARCCAVETYDETECTR DagaEha UdeheS eviif f.DahaWeiha ATheiS biediaiaF (cid:28)efhe Re ieefheDegeef aefSciece TheSchfC eScieceadEgieeig ebewUiveiyfe aeae91904 Decebe2009" 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. 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 -++ +=IIEAHI 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 ?=IIEAH 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 ?=IIEAH =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 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 General rights 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>. 008, pp.1-8, 008, Anchorage, United . 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 7ELAHIEJO B JJ=M= J=HE )*564)+6 6DEI F=FAH = AM =FFH=?D J A= 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 J A=IKHA JDA =KJ B EBH=JE MA >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) >AJMAA JDA FFK=JE BA=JKHA q JDA FAHII BA=JKHA p 6DA >EAJHE? EBH=JE BH = IOI JA EI JDA A= D(p(cid:107)q) BH = FAHII E JDA FFK=JE 1 J FH=?JE?=O A=IKHA D(p(cid:107)q) MEJD I= 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" 69526cdf6abbfc4bcd39616acde544568326d856,Face Verification Using Template Matching,"[17] B. Moghaddam, T. Jebara, and A. Pentland, “Bayesian face recogni- tion,” Pattern Recognit., vol. 33, no. 11, pp. 1771–1782, Nov. 2000. [18] A. Nefian, “A hidden Markov model-based approach for face detection nd recognition,” Ph.D. dissertation, Dept. Elect. Comput. Eng. Elect. Eng., Georgia Inst. Technol., Atlanta, 1999. [19] P. J. Phillips et al., “Overview of the face recognition grand challenge,” presented at the IEEE CVPR, San Diego, CA, Jun. 2005. [20] H. T. Tanaka, M. Ikeda, and H. Chiaki, “Curvature-based face surface recognition using spherical correlation-principal direction for curved object recognition,” in Proc. Int. Conf. Automatic Face and Gesture Recognition, 1998, pp. 372–377. [21] M. Turk and A. Pentland, “Eigenfaces for recognition,” J. Cognit. Sci., pp. 71–86, 1991. [22] V. N. Vapnik, Statistical Learning Theory. New York: Wiley, 1998. [23] W. Zhao, R. Chellappa, A. Rosenfeld, and P. Phillips, “Face recogni- tion: A literature survey,” ACM Comput. Surveys, vol. 35, no. 44, pp. 99–458, 2003. [24] W. Zhao, R. Chellappa, and P. J. Phillips, “Subspace linear discrimi- nant analysis for face recognition,” UMD TR4009, 1999. Face Verification Using Template Matching" 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" 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" 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>. HAL Id: hal-01546192 https://hal.archives-ouvertes.fr/hal-01546192 Submitted on 23 Jun 2017 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" 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 © 2015 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 copyrighted component of this work in other works. For additional information about this publication click this link. http://qmro.qmul.ac.uk/xmlui/handle/123456789/10075 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" 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 General rights It is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons). Disclaimer/Complaints regulations If you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask 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. 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. . HAL Id: tel-00998798 https://tel.archives-ouvertes.fr/tel-00998798 Submitted on 2 Jun 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 recherche fran¸cais ou ´etrangers, des laboratoires publics ou priv´es." 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 Id: hal-00870059 https://hal.inria.fr/hal-00870059 Submitted on 4 Oct 2013 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" 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 Publisher rights (c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, 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 copyrighted components of this work in other works. General rights 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" 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. General rights It is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons). Disclaimer/Complaints regulations If you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask 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. Download date: 12 Sep 2017 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" 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. 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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. 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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 ,350 08,000 .7 M Open access books available International authors and editors Downloads Our authors are among the Countries delivered to TOP 1% 2.2% most cited scientists Contributors from top 500 universities Selection of our books indexed in the Book Citation Index in Web of Science™ Core Collection (BKCI) Interested in publishing with us? Contact Numbers displayed above are based on latest data collected." 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. . 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! robot Stefan Sosnowski1 and Christoph Mayer2 and Kolja K¨uhnlenz3 and Bernd Radig4" c1d2d12ade031d57f8d6a0333cbe8a772d752e01,Convex optimization techniques for the efficient recovery of a sparsely corrupted low-rank matrix,"Journal of Math-for-Industry, Vol.2(2010B-5), pp.147–156 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" c10a15e52c85654db9c9343ae1dd892a2ac4a279,Learning the Relative Importance of Objects from Tagged Images for Retrieval and Cross-Modal Search,"Int J Comput Vis (2012) 100:134–153 DOI 10.1007/s11263-011-0494-3 Learning the Relative Importance of Objects from Tagged Images for Retrieval and Cross-Modal Search 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" c1ff88493721af1940df0d00bcfeefaa14f1711f,Subspace Regression: Predicting a Subspace from one Sample,"#1369 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" c11eb653746afa8148dc9153780a4584ea529d28,Global and Local Consistent Wavelet-domain Age Synthesis,"Global and Local Consistent Wavelet-domain Age Synthesis Peipei Li†, Yibo Hu†, Ran He Member, IEEE and Zhenan Sun Member, IEEE" c1ebbdb47cb6a0ed49c4d1cf39d7565060e6a7ee,Robust Facial Landmark Localization Based on Texture and Pose Correlated Initialization,"Robust Facial Landmark Localization Based on Yiyun Pan, Junwei Zhou, Member, IEEE, Yongsheng Gao, Senior Member, IEEE, Shengwu Xiong" c1dd69df9dfbd7b526cc89a5749f7f7fabc1e290,Unconstrained face identification with multi-scale block-based correlation,"Unconstrained face identification with multi-scale block-based orrelation Gaston, J., MIng, J., & Crookes, D. (2016). Unconstrained face identification with multi-scale block-based orrelation. In Proceedings of the 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (pp. 1477-1481). [978-1-5090-4117-6/17] Institute of Electrical and Electronics Engineers (IEEE). Published in: Proceedings of the 2017 IEEE International Conference on Acoustics, Speech and Signal Processing Document Version: Peer reviewed version Queen's University Belfast - Research Portal: Link to publication record in Queen's University Belfast Research Portal Publisher rights © 2017 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 copyrighted component of this work in other works. General rights 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" 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 Automated Face Recognition 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" c696c9bbe27434cb6279223a79b17535cd6e88c8,Facial Expression Recognition with Pyramid Gabor Features and Complete Kernel Fisher Linear Discriminant Analysis,"International Journal of Information Technology Vol.11 No.9 2005 Discriminant Analysis 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," c614450c9b1d89d5fda23a54dbf6a27a4b821ac0,Face Image Retrieval of Efficient Sparse Code words and Multiple Attribute in Binning Image,"Vol.60: e17160480, January-December 2017 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 Multiple Attribute in Binning Image Suchitra S1*. Srm Easwari Engineering College, Ramapuram, Bharathi Salai, Chennai, Tamil Nadu, India." c6f3399edb73cfba1248aec964630c8d54a9c534,A comparison of CNN-based face and head detectors for real-time video surveillance applications,"A Comparison of CNN-based Face and Head Detectors for 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 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/copyright" 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) ec90d333588421764dff55658a73bbd3ea3016d2,Protocol for Systematic Literature Review of Face Recognition in Uncontrolled Environment,"Research Article Protocol for Systematic Literature Review of Face Recognition in Uncontrolled Environment Faizan Ullah, Sabir Shah, Dilawar Shah, Abdusalam, Shujaat Ali Department of Computer Science, Bacha Khan University, Charsadda, KPK, Pakistan" ec1e03ec72186224b93b2611ff873656ed4d2f74,D Reconstruction of “ Inthe-Wild ” Faces in Images and Videos,"JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015 D Reconstruction of “In-the-Wild” Faces in Images and Videos James Booth, Anastasios Roussos, Evangelos Ververas, Epameinondas Anton- kos, Stylianos Ploumpis, Yannis Panagakis, and Stefanos Zafeiriou" 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 Review ARTICLE A Review of Scholastic Examination and Models for Face Recognition 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 fcastrouma.es M.J. Mar´ın-Jim´enez Univ. of Cordoba mjmarinuco.es N. Guil Univ. of Malaga nguiluma.es N. P´erez de la Blanca Univ. of Granada nicolasugr.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) Part II, LNCS 9887, pp. 80-87, Barcelona, Spain, September 2016 The final publication is available at Springer via http://dx.doi.org//10.1007/978-3-319-44781-0_10 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 http://www.informatik.uni-hamburg.de/WTM" 20a3ce81e7ddc1a121f4b13e439c4cbfb01adfba,Sparse-MVRVMs Tree for Fast and Accurate Head Pose Estimation in the Wild,"Sparse-MVRVMs Tree for Fast and Accurate Head Pose Estimation in the Wild 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 http://www.av.dfki.de" 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 in the Wild Fang Zhao, Jiashi Feng, Jian Zhao, Wenhan Yang, Shuicheng Yan" 209324c152fa8fab9f3553ccb62b693b5b10fb4d,Visual Genome Crowdsourced Visual Knowledge Representations 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,"CROWDSOURCED VISUAL KNOWLEDGE REPRESENTATIONS 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" 20532b1f80b509f2332b6cfc0126c0f80f438f10,A Deep Matrix Factorization Method for Learning Attribute Representations,"A deep matrix factorization method for learning ttribute representations George Trigeorgis, Konstantinos Bousmalis, Student Member, IEEE, Stefanos Zafeiriou, Member, IEEE Bj¨orn W. Schuller, Senior member, IEEE" 205af28b4fcd6b569d0241bb6b255edb325965a4,Facial expression recognition and tracking for intelligent human-robot interaction,"Intel Serv Robotics (2008) 1:143–157 DOI 10.1007/s11370-007-0014-z 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 Inria∗ 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 Variable Lighting and Pose 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 To link to this article: https://doi.org/10.1080/02699931.2018.1472554 View supplementary material Published online: 11 May 2018. Submit your article to this journal View related articles View Crossmark data Full Terms & Conditions of access and use can be found at http://www.tandfonline.com/action/journalInformation?journalCode=pcem20" 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 DOI 10.1007/s11263-012-0564-1 Efficiently Scaling up Crowdsourced Video Annotation A Set of Best Practices for High Quality, Economical Video Labeling Carl Vondrick · Donald Patterson · Deva Ramanan Received: 31 October 2011 / Accepted: 20 August 2012 © 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, Department of Information Tech., Bharti Vidyapeeth Deemed University, Pune, India" 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. 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 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" 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 ,350 08,000 .7 M Open access books available International authors and editors Downloads Our authors are among the Countries delivered to TOP 1% 2.2% most cited scientists Contributors from top 500 universities Selection of our books indexed in the Book Citation Index in Web of Science™ Core Collection (BKCI) Interested in publishing with us? Contact Numbers displayed above are based on latest data collected." 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 Copyright & reuse Content in the Kent Academic Repository is made available for research purposes. Unless otherwise stated all ontent is protected by copyright and in the absence of an open licence (eg Creative Commons), permissions for further reuse of content should be sought from the publisher, author or other copyright holder. Versions of research The version in the Kent Academic Repository may differ from the final published version. Users are advised to check http://kar.kent.ac.uk for the status of the paper. Users should always cite the published version of record. Enquiries 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... Deutsch Deutsch Vorherige Beitrag Nächste Beitrag Beitrag markieren In den Warenkorb legen Zu gespeicherten Artikeln hinzufügen Permissions & Reprints Diesen Artikel empfehlen Ergebnisse finden Erweiterte Suche im gesamten Inhalt in dieser Zeitschrift in diesem Heft Diesen Beitrag exportieren Diesen Beitrag exportieren als RIS | Text" 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 It is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons). Disclaimer/Complaints regulations If you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask 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. 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 © 2017 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 copyrighted component of this work in other works. For additional information about this publication click this link. http://qmro.qmul.ac.uk/xmlui/handle/123456789/22607 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" 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 Rights © 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" 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 General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners nd it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. ? Users may download and print one copy of any publication from the public portal for the purpose of private study or research." 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]," beindependentandidenticallydis-tributed(i.i.d.)randomvectorswithvaluesinacompactsubsetof.The(-)nearestneighborof inisgivenby!""$%&(*,.%135 7 5where5 7 5istheusualEuclidean(<=)distanceinbe-tweenvector and .Forgeneralinteger?,the-nearestneighborofapointisdefinedinasimilarway.The-NNgraphputsanedgebetweeneachpointinandits-nearestneighbors.LetBCDBCDFHbethesetof-nearestneighborsof in.Thetotaledgelengthofthe-NNgraphisdefinedas:. 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, . <10.1117/12.2082817>. 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. . 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? — 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 Rah G ai ahew ad Si Bake The Rbic i e Caegie e Uiveiy 5000 Fbe Ave e ib gh A 15213 Abac.  ay face ecgii ak he e ad i iai dii f he be ad gaey iage ae di(cid:11)ee.  he cae  ie gaey  be iage 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 iage e  bjec ca ed a abiay e ad de abiay i iai d ay  be f be iage agai ca ed a abiay e ad de abiay i iai. The agih eae by eiaig he Fihe igh (cid:12)ed f he  bjec head f he i  gaey  be iage. achig bewee he be ad gaey i he efed ig he Fihe igh (cid:12)ed. d ci  ay face ecgii ceai he e f he be ad gaey iage ae di(cid:11)ee. The gaey cai he iage ed d ig aiig f he agih. The agih ae eed wih he iage i he be e. F exae he" 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 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. 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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. 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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. 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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" 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: Peer reviewed version Queen's University Belfast - Research Portal: Link to publication record in Queen's University Belfast Research Portal General rights 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 ensure that content in the Research Portal does not infringe any person's rights, or applicable UK laws. If you discover content in the Research Portal that you believe breaches copyright or violates any law, please contact Download date:05. Nov. 2018" 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," 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 including for reviewing information, this collection of information is estimated to average 1 hour per response, the data needed, and completing and reviewing this collection of instructions, The public reporting burden Send comments searching existing data sources, gathering and maintaining to Washington regarding this burden estimate or any other aspect of Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington VA, 22202-4302. Headquarters Services, Directorate" 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: Please note: Changes made as a result of publishing processes such as copy-editing, formatting and page numbers may not be reflected in this version. For the definitive version of this publication, please refer to the published source. You are advised to consult the publisher’s version if you wish to cite this paper. This version is being made available in accordance with publisher policies. See http://orca.cf.ac.uk/policies.html for usage policies. Copyright and moral rights for publications 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" 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) 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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 Reuse This article is distributed under the terms of the Creative Commons Attribution (CC BY) licence. This licence llows you to distribute, remix, tweak, and build upon the work, even commercially, as long as you credit the uthors for the original work. More information and the full terms of the licence here: https://creativecommons.org/licenses/ Takedown 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/" 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 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. 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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. 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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 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. . 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. . 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. . 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 This article is distributed under the terms of the Creative Commons Attribution (CC BY) licence. This licence llows you to distribute, remix, tweak, and build upon the work, even commercially, as long as you credit the uthors for the original work. More information and the full terms of the licence here: https://creativecommons.org/licenses/ Takedown 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 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" 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" 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 General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners nd it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. ? 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. . 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 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 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" 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). General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners nd it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. ? Users may download and print one copy of any publication from the public portal for the purpose of private study or research. ? You may not further distribute the material or use it for any profit-making activity or commercial gain ? You may freely distribute the URL identifying the publication in the public portal ?" 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—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 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 Adler A., Youmaran R. and Loyka S., “Towards a Measure of Biometric Information”, Canadian Conference on Electrical and Computer Engineering, pp. 210-213, 2006. Ahmed A.A.E. and Traore I., “Anomaly Intrusion Detection Based on Biometrics”, IEEE Workshop on Information Assurance, United States Military Academy, West Point, New York, pp. 452-458, 2005. Ahmed A.A.E. and Traore I., “Detecting Computer Intrusions using Behavioural Biometrics”, Third Annual Conference on Privacy, Security and Trust, St. Andrews, New Brunswick, Canada, pp. 1-8, 005. Al-Zubi S., Bromme A. and Tonnies K., “Using an Active Shape Structural Model for Biometric Sketch Recognition”, Proceedings of DAGM, Magdeburg, Germany, Vol. 2781, pp. 187-195, 2003. Angle S., Bhagtani R. and Chheda H., “Biometrics: a Further Echelon of Security”, The First UAE International Conference on Biological nd Medical Physics, pp. 1-4, 2005. Avraam Kasapis., “MLPs and Pose, Expression Classification”, Proceedings of UNiS Report, pp. 1-87, 2003. Banikazemi M., Poff D. and Abali B., “Storage-based Intrusion" 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" 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. 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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 e Vii ad Beyd: Devee h . Weg Deae f C e Sciece ichiga Sae Uiveiy Ea aig  48824 Abac Thi chae id ce wha i caed he deveea aach  c e vii i aic a ad ai(cid:12)cia ieigece i geea.  dic e he c e baic aadig f de veig a ye ad i f daea iiai. The deveea aach i ivaed y h a cgiive devee f ifacy  ad hd. A deveea eaig ag ih i deeied befe he \bih"" f he ye. Afe he \bih"" i eabe he ye  ea ew ak wih  a eed f egaig. The aj ga f he deveea ach i  eaize a ai f geea  e eaig ha eabe achie  ef deveea eaig ve a g eid. S ch eaig i cd ced i a de iia  he way aia ad h a ea. The achie   ea diecy f ci   ey i   ea whie ieacig wih he evie ic dig h a eache.  hi eaig de deveig ieige ga f vai  ak i eaized h gh ea ie ieac" 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" 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 © 2005 - 2016 JATIT & LLS. All rights reserved. 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: ,"