| 61084a25ebe736e8f6d7a6e53b2c20d9723c4608 | |
| 61f04606528ecf4a42b49e8ac2add2e9f92c0def | Deep Deformation Network for Object Landmark
Localization NEC Laboratories America, Department of Media Analytics |
| 614a7c42aae8946c7ad4c36b53290860f6256441 | 1
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks |
| 0d88ab0250748410a1bc990b67ab2efb370ade5d | Author(s) :
ERROR HANDLING IN MULTIMODAL BIOMETRIC SYSTEMS USING RELIABILITY MEASURES (ThuPmOR6) (EPFL, Switzerland) (EPFL, Switzerland) (EPFL, Switzerland) (EPFL, Switzerland) Plamen Prodanov |
| 0d467adaf936b112f570970c5210bdb3c626a717 | |
| 0d6b28691e1aa2a17ffaa98b9b38ac3140fb3306 | Review of Perceptual Resemblance of Local
Plastic Surgery Facial Images using Near Sets 1,2 Department of Computer Technology, YCCE Nagpur, India |
| 0db8e6eb861ed9a70305c1839eaef34f2c85bbaf | |
| 0dbf4232fcbd52eb4599dc0760b18fcc1e9546e9 | |
| 0d760e7d762fa449737ad51431f3ff938d6803fe | LCDet: Low-Complexity Fully-Convolutional Neural Networks for
Object Detection in Embedded Systems UC San Diego ∗ Gokce Dane Qualcomm Inc. UC San Diego Qualcomm Inc. UC San Diego |
| 0dd72887465046b0f8fc655793c6eaaac9c03a3d | Real-time Head Orientation from a Monocular
Camera using Deep Neural Network KAIST, Republic of Korea |
| 0d087aaa6e2753099789cd9943495fbbd08437c0 | |
| 0d8415a56660d3969449e77095be46ef0254a448 | |
| 0d735e7552af0d1dcd856a8740401916e54b7eee | |
| 0d06b3a4132d8a2effed115a89617e0a702c957a | |
| 0d2dd4fc016cb6a517d8fb43a7cc3ff62964832e | |
| 0d33b6c8b4d1a3cb6d669b4b8c11c2a54c203d1a | Detection and Tracking of Faces in Videos: A Review
© 2016 IJEDR | Volume 4, Issue 2 | ISSN: 2321-9939 of Related Work 1Student, 2Assistant Professor 1, 2Dept. of Electronics & Comm., S S I E T, Punjab, India ________________________________________________________________________________________________________ |
| 956317de62bd3024d4ea5a62effe8d6623a64e53 | Lighting Analysis and Texture Modification of 3D Human
Face Scans Author Zhang, Paul, Zhao, Sanqiang, Gao, Yongsheng Published 2007 Conference Title Digital Image Computing Techniques and Applications DOI 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 |
| 956c634343e49319a5e3cba4f2bd2360bdcbc075 | IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART B: CYBERNETICS, VOL. 36, NO. 4, AUGUST 2006
873 A Novel Incremental Principal Component Analysis and Its Application for Face Recognition |
| 958c599a6f01678513849637bec5dc5dba592394 | Noname manuscript No.
(will be inserted by the editor) Generalized Zero-Shot Learning for Action Recognition with Web-Scale Video Data Received: date / Accepted: date |
| 59fc69b3bc4759eef1347161e1248e886702f8f7 | Final Report of Final Year Project
HKU-Face: A Large Scale Dataset for Deep Face Recognition 3035141841 COMP4801 Final Year Project Project Code: 17007 |
| 59bfeac0635d3f1f4891106ae0262b81841b06e4 | Face Verification Using the LARK Face
Representation |
| 590628a9584e500f3e7f349ba7e2046c8c273fcf | |
| 59eefa01c067a33a0b9bad31c882e2710748ea24 | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
Fast Landmark Localization with 3D Component Reconstruction and CNN for Cross-Pose Recognition |
| 5945464d47549e8dcaec37ad41471aa70001907f | Noname manuscript No.
(will be inserted by the editor) Every Moment Counts: Dense Detailed Labeling of Actions in Complex Videos Received: date / Accepted: date |
| 59c9d416f7b3d33141cc94567925a447d0662d80 | Universität des Saarlandes
Max-Planck-Institut für Informatik AG5 Matrix factorization over max-times algebra for data mining Masterarbeit im Fach Informatik Master’s Thesis in Computer Science von / by angefertigt unter der Leitung von / supervised by begutachtet von / reviewers November 2013 UNIVERSITASSARAVIENSIS |
| 59a35b63cf845ebf0ba31c290423e24eb822d245 | The FaceSketchID System: Matching Facial
Composites to Mugshots tedious, and may not |
| 59f325e63f21b95d2b4e2700c461f0136aecc171 | 3070
978-1-4577-1302-6/11/$26.00 ©2011 IEEE FOR FACE RECOGNITION 1. INTRODUCTION |
| 5922e26c9eaaee92d1d70eae36275bb226ecdb2e | Boosting Classification Based Similarity
Learning by using Standard Distances Departament d’Informàtica, Universitat de València Av. de la Universitat s/n. 46100-Burjassot (Spain) |
| 59031a35b0727925f8c47c3b2194224323489d68 | Sparse Variation Dictionary Learning for Face Recognition with A Single
Training Sample Per Person ETH Zurich Switzerland |
| 926c67a611824bc5ba67db11db9c05626e79de96 | 1913
Enhancing Bilinear Subspace Learning by Element Rearrangement |
| 923ede53b0842619831e94c7150e0fc4104e62f7 | 978-1-4799-9988-0/16/$31.00 ©2016 IEEE
1293 ICASSP 2016 |
| 92b61b09d2eed4937058d0f9494d9efeddc39002 | Under review in IJCV manuscript No.
(will be inserted by the editor) BoxCars: Improving Vehicle Fine-Grained Recognition using 3D Bounding Boxes in Traffic Surveillance Received: date / Accepted: date |
| 920a92900fbff22fdaaef4b128ca3ca8e8d54c3e | LEARNING PATTERN TRANSFORMATION MANIFOLDS WITH PARAMETRIC ATOM
SELECTION 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 |
| 9282239846d79a29392aa71fc24880651826af72 | 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 |
| 92c2dd6b3ac9227fce0a960093ca30678bceb364 | 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 Date 2009-05-12 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 IEEE Link to publisher's version http://dx.doi.org/10.1109/TIP.2009.2017163 Item record http://hdl.handle.net/10379/1350 Some rights reserved. For more information, please see the item record link above. Downloaded 2018-11-06T00:40:53Z |
| 92fada7564d572b72fd3be09ea3c39373df3e27c | |
| 927ad0dceacce2bb482b96f42f2fe2ad1873f37a | Interest-Point based Face Recognition System
87 X Interest-Point based Face Recognition System Spain 1. Introduction Among all applications of face recognition systems, surveillance is one of the most challenging 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 all 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 algorithm (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 capabilities of current face recognition algorithms. Assuming that there will be false identifications, supervised surveillance systems seem to be the most realistic option: the automatic system issues an alarm whenever it detects a possible match with a criminal, and a human decides whether it is a false alarm or not. Figure 1 shows an example. However, even in a supervised scenario the requirements for the face recognition algorithm are extremely high: the false alarm rate must be low enough as to allow the human operator to cope with it; and the percentage of undetected criminals must be kept to a minimum in order to ensure security. Fulfilling both requirements at the same time is the main challenge, as a reduction in false alarm rate usually implies an increase of the percentage of undetected criminals. We propose a novel face recognition system based in the use of interest point detectors and local descriptors. In order to check the performances of our system, and particularly its performances in a surveillance application, we present experimental results in terms of Receiver Operating Characteristic curves or ROC curves. From the experimental results, it becomes clear that our system outperforms classical appearance based approaches. www.intechopen.com |
| 929bd1d11d4f9cbc638779fbaf958f0efb82e603 | 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/ c(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 copy-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 |
| 0c36c988acc9ec239953ff1b3931799af388ef70 | Face Detection Using Improved Faster RCNN
Huawei Cloud BU, China Figure1.Face detection results of FDNet1.0 |
| 0c5ddfa02982dcad47704888b271997c4de0674b | |
| 0cccf576050f493c8b8fec9ee0238277c0cfd69a | |
| 0c069a870367b54dd06d0da63b1e3a900a257298 | Author manuscript, published in "ICANN 2011 - International Conference on Artificial Neural Networks (2011)" |
| 0c75c7c54eec85e962b1720755381cdca3f57dfb | 2212
Face Landmark Fitting via Optimized Part Mixtures and Cascaded Deformable Model |
| 0ca36ecaf4015ca4095e07f0302d28a5d9424254 | Improving Bag-of-Visual-Words Towards Effective Facial Expressive
Image Classification 1Univ. 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. 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. |
| 0c54e9ac43d2d3bab1543c43ee137fc47b77276e | |
| 0c5afb209b647456e99ce42a6d9d177764f9a0dd | 97
Recognizing Action Units for Facial Expression Analysis |
| 0c377fcbc3bbd35386b6ed4768beda7b5111eec6 | 258
A Unified Probabilistic Framework for Spontaneous Facial Action Modeling and Understanding |
| 0cb2dd5f178e3a297a0c33068961018659d0f443 | |
| 0cf7da0df64557a4774100f6fde898bc4a3c4840 | Shape Matching and Object Recognition using Low Distortion Correspondences
Department of Electrical Engineering and Computer Science U.C. Berkeley |
| 0c4659b35ec2518914da924e692deb37e96d6206 | 1236
Registering a MultiSensor Ensemble of Images |
| 0c53ef79bb8e5ba4e6a8ebad6d453ecf3672926d | SUBMITTED TO JOURNAL
Weakly Supervised PatchNets: Describing and Aggregating Local Patches for Scene Recognition |
| 0c60eebe10b56dbffe66bb3812793dd514865935 | |
| 6601a0906e503a6221d2e0f2ca8c3f544a4adab7 | 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 | |
| 66d512342355fb77a4450decc89977efe7e55fa2 | 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
3D Face Recognition |
| 661ca4bbb49bb496f56311e9d4263dfac8eb96e9 | Datasheets for Datasets |
| 66d087f3dd2e19ffe340c26ef17efe0062a59290 | Dog Breed Identification
Brian Mittl Vijay Singh |
| 66a2c229ac82e38f1b7c77a786d8cf0d7e369598 | 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 Escuela Superior Politecnica del Litoral (ESPOL) Guayaquil-Ecuador |
| 66886997988358847615375ba7d6e9eb0f1bb27f | |
| 66837add89caffd9c91430820f49adb5d3f40930 | |
| 66a9935e958a779a3a2267c85ecb69fbbb75b8dc | FAST AND ROBUST FIXED-RANK MATRIX RECOVERY
Fast and Robust Fixed-Rank Matrix Recovery Antonio Lopez |
| 66533107f9abdc7d1cb8f8795025fc7e78eb1122 | Vi a Sevig f a Ue h wih E(cid:11)ecive ei Readig
i a Whee chai 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 ehabi iai b beca e he h a eae ehabi iai b i he ae evi e ad ha he bee(cid:12) f ehabi iai b ch a ai ay bi e f ci. ei eadig i e f he eeia f ci f h a fied y ehabi iai b i de ie he cf ad afey f a wh eed he. Fi f a he vea c e f a ew whee chai baed bic a ye ARES ad i h a b ieaci ech gie ae eeed. Ag he ech gie we cceae vi a evig ha a w hi bic a eae a y via vi a feedback. E(cid:11)ecive iei eadig ch a ecgizig he iive ad egaive eaig f he e i efed he bai f chage f he facia exei a d i ha i g y e aed he e iei whi e hi bic a vide he e wih a beveage. F he eÆcie vi a ifa i ceig g a aed iage ae ed c he ee caea head ha i caed i he ed e(cid:11)ec f he bic a. The vi a evig wih e(cid:11)ecive iei eadig i ccef y a ied eve a beveage f he e. d ci Whee chai baed bic ye ae ai y ed ai he e de y ad he diab ed wh have hadi ca i ey ad f ci i ib. S ch a ye ci f a weed whee chai ad a bic a ad ha y a bi e caabi iy h gh he whee chai b a a ai ay f ci via he bic a ad h ake ib e he c exiece f a e ad a b i he ae evi e. hi cae he e eed ieac wih he bic a i cfab e ad afe way. w Fig e 1: The whee chai baed bic a ad i h a b ieaci ech gie. eve i ha bee eed ha ay diÆc ie exi i h a bf ieaci i exiig ehabi iai b. F exa e a a c f he bic a ake a high cgiive ad he e a whi e hyica y diab ed e ay have diÆc ie i eaig jyick dexe y hig b f de icae vee [4]. addii AUS eva ai e eed ha he diÆc hig ig ehabi iai b i ay cad f a a adj e ad ay f ci kee i id a he begiig [4]. Theefe h a fied y h a b ieaci i e f eeia echi e i a whee chai baed bic a. hi ae we cide he whee chai baed bic ye ARES AST Rehabi iai E gieeig Sevice ye which we ae deve ig a a evice bic ye f he diab ed ad he e de y ad dic i h a b ieaci ech i e Fig. 1. Ag h a b ieaci ech i e vi a evig i dea wih a a aj ic. |
| 66810438bfb52367e3f6f62c24f5bc127cf92e56 | 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 | Noname manuscript No.
(will be inserted by the editor) Context Assisted Face Clustering Framework with Human-in-the-Loop Received: date / Accepted: date |
| 66e6f08873325d37e0ec20a4769ce881e04e964e | 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 Received: 27 February 2013 / Accepted: 28 December 2013 / Published online: 18 January 2014 © Springer Science+Business Media New York 2014 |
| 661da40b838806a7effcb42d63a9624fcd684976 | 53
An Illumination Invariant Accurate Face Recognition with Down Scaling of DCT Coefficients 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 coefficients are scaled down to compensate the illumi- nation variations. The value of scaling down factor and the number of low-frequency DCT coefficients, which are to be rescaled, are obtained experimentally. The classification is done using k−nearest neighbor classi- fication and nearest mean classification on the images obtained by inverse DCT on the processed coefficients. The correlation coefficient and Euclidean distance ob- tained using principal component analysis are used as distance metrics in classification. We have tested our face recognition method using Yale Face Database B. The results show that our method performs without any error (100% face recognition performance), even on the most extreme illumination variations. There are different schemes in the literature for illumination normalization under varying lighting conditions, but no one is claimed to give 100% recognition rate under all illumination variations for this database. The proposed technique is computationally efficient and can easily be implemented for real time face recognition system. Keywords: discrete cosine transform, correlation co- efficient, face recognition, illumination normalization, nearest neighbor classification 1. Introduction Two-dimensional pattern classification plays a crucial role in real-world applications. To build high-performance surveillance or information security systems, face recognition has been known as the key application attracting enor- mous researchers highlighting on related topics [1,2]. Even though current machine recognition systems have reached a certain level of matu- rity, their success is limited by the real appli- cations constraints, like pose, illumination and expression. The FERET evaluation shows that the performance of a face recognition system decline seriously with the change of pose and illumination conditions [31]. To solve the variable illumination problem a variety of approaches have been proposed [3, 7- 11, 26-29]. Early work in illumination invariant face recognition focused on image representa- tions that are mostly insensitive to changes in illumination. There were approaches in which the image representations and distance mea- sures were evaluated on a tightly controlled face database that varied the face pose, illumination, and expression. The image representations in- clude edge maps, 2D Gabor-like filters, first and second derivatives of the gray-level image, and the logarithmic transformations of the intensity image along with these representations [4]. The different approaches to solve the prob- lem of illumination invariant face recognition can be broadly classified into two main cate- gories. The first category is named as passive approach in which the visual spectrum images are analyzed to overcome this problem. The approaches belonging to other category named active, attempt to overcome this problem by employing active imaging techniques to obtain face images captured in consistent illumina- tion condition, or images of illumination invari- ant modalities. There is a hierarchical catego- rization of these two approaches. An exten- sive review of both approaches is given in [5]. |
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Space-Time Saliency |
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UFF UFF Asla S´a FGV IMPA |
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Age Group and Gender Estimation in the Wild with Deep RoR Architecture |
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3D Human Face Reconstruction Using a Hybrid of Photometric Stereo and Independent Component Analysis |
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Dept. of Computer Science K.U.Leuven, Belgium Dept. of Computer Science K.U.Leuven, Belgium Dept. of Computer Science K.U.Leuven, Belgium |
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Institut Eur´ecom Multimedia Communications Department BP 193, 06904 Sophia Antipolis Cedex, France |
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Zenith Angle of the Surface Normal Mario Castel´an1, Ana J. Almaz´an-Delf´ın2, Marco I. Ram´ırez-Sosa-Mor´an3, and Luz A. Torres-M´endez1 1 CINVESTAV Campus Saltillo, Ramos Arizpe 25900, Coahuila, M´exico 2 Universidad Veracruzana, Facultad de F´ısica e Inteligencia Artificial, Xalapa 91000, 3 ITESM, Campus Saltillo, Saltillo 25270, Coahuila, M´exico Veracruz, M´exico |
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Discriminative Block-Diagonal Representation Learning for Image Recognition |
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Convolutional Networks Tencent AI Lab, China |
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Microsoft Corporate One Microsoft Way, Redmond, WA 98052 |
| 688754568623f62032820546ae3b9ca458ed0870 | bioRxiv preprint first posted online Sep. 27, 2016;
doi: 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. 1 Univ. Grenoble Alpes, LPNC, F-38040, Grenoble, France 2 CNRS, LPNC UMR 5105, F-38040, Grenoble, France 3 IPSY, Université Catholique de Louvain, Louvain-la-Neuve, Belgium 4 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 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 assumption was differently supported by diverging results suggesting that it could be related to confounded factors. In the current study, we recorded the resting state vagal activity (reflected by High Frequency Heart Rate Variability, HF-HRV) of 77 (68 suitable for analysis) healthy human adults and measured their ability to identify dynamic emotional facial expressions. Results show that HF-HRV is not related to the recognition of emotional facial expressions in healthy human adults. We discuss this result in the frameworks of the polyvagal theory and the neurovisceral integration model. Keywords: HF-HRV; autonomic flexibility; emotion identification; dynamic EFEs; Polyvagal theory; Neurovisceral integration model Word count: 9810 10 11 12 13 14 15 16 17 Introduction The behavior of an animal is said social when involved in in- teractions with other animals (Ward & Webster, 2016). These interactions imply an exchange of information, signals, be- tween at least two animals. In humans, the face is an efficient communication channel, rapidly providing a high quantity of information. Facial expressions thus play an important role in the transmission of emotional information during social interactions. The result of the communication is the combina- tion of transmission from the sender and decoding from the receiver (Jack & Schyns, 2015). As a consequence, the quality of the interaction depends on the ability to both produce and identify facial expressions. Emotions are therefore a core feature of social bonding (Spoor & Kelly, 2004). Health of individuals and groups depend on the quality of social bonds in many animals (Boyer, Firat, & Leeuwen, 2015; S. L. Brown & Brown, 2015; Neuberg, Kenrick, & Schaller, 2011), 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 especially in highly social species such as humans (Singer & Klimecki, 2014). The recognition of emotional signals produced by others is not independent from its production by oneself (Niedenthal, 2007). The muscles of the face involved in the production of a facial expressions are also activated during the perception of the same facial expressions (Dimberg, Thunberg, & Elmehed, 2000). In other terms, the facial mimicry of the perceived emotional facial expression (EFE) triggers its sensorimotor simulation in the brain, which improves the recognition abili- ties (Wood, Rychlowska, Korb, & Niedenthal, 2016). Beyond that, the emotion can be seen as the body -including brain- dynamic itself (Gallese & Caruana, 2016) which helps to un- derstand why behavioral simulation is necessary to understand the emotion. The interplay between emotion production, emotion percep- tion, social communication and body dynamics has been sum- marized in the framework of the polyvagal theory (Porges, |
| 68f9cb5ee129e2b9477faf01181cd7e3099d1824 | ALDA Algorithms for Online Feature Extraction |
| 68bf34e383092eb827dd6a61e9b362fcba36a83a | |
| 6889d649c6bbd9c0042fadec6c813f8e894ac6cc | Analysis of Robust Soft Learning Vector
Quantization and an application to Facial Expression Recognition |
| 68c17aa1ecbff0787709be74d1d98d9efd78f410 | 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 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 classification 1. INTRODUCTION During the 90’s, one of the main issues addressed in the area of computer vision was face detection. Many methods and applications were developed including the face detection used in many digital cameras nowadays. Gender classification is important in many possible applications including electronic marketing. Displays at retail stores could show products and offers according to the person gender as the person passes in front of a camera at the store. This is not a simple task since faces are not rigid and depend on illumination, pose, gestures, facial expressions, occlusions (glasses), and other facial features (makeup, beard). The high variability in the appearance of the face directly affects their detection and classification. Auto- matic classification of gender from face images has a wide range of possible applica- tions, ranging from human-computer interaction to applications in real-time electronic marketing in retail stores (Shan 2012; Bekios-Calfa et al. 2011; Chu et al. 2010; Perez et al. 2010a). Automatic gender classification has a wide range of possible applications for improving human-machine interaction and face identification methods (Irick et al. ing.uchile.cl 92 |
| 6888f3402039a36028d0a7e2c3df6db94f5cb9bb | 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 |
| 574751dbb53777101502419127ba8209562c4758 | |
| 57b8b28f8748d998951b5a863ff1bfd7ca4ae6a5 | |
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| 57f8e1f461ab25614f5fe51a83601710142f8e88 | Region Selection for Robust Face Verification using UMACE Filters
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 characteristics 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. 1. Introduction Face recognition is a well established field of research, and 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 between any two vectors and then compare it with the preset threshold. Neural Networks are often used as classifiers due to their powerful generation ability [1]. Support Vector Machines (SVM) have been applied with encouraging results [2]. In biometric applications, one of the important tasks is the matching process between an individual biometrics against the database that has been prepared during the enrolment stage. For biometrics systems such as face authentication that use images as personal characteristics, biometrics sensor output and image pre-processing play an important role since the quality of a biometric input can change significantly due to illumination, noise and pose variations. Over the years, researchers have studied the role of illumination variation, pose variation, facial expression, and occlusions in affecting the performance of face verification systems [3]. The Minimum Average Correlation Energy (MACE) filters have been reported to be an alternative solution to these problems because of the advantages such as shift-invariance, close-form expressions and distortion-tolerance. MACE filters have been successfully applied in the field of automatic target recognition as well as in biometric verification [3][4]. Face and fingerprint verification using correlation filters have been investigated in [5] and [6], respectively. Savvides et.al performed face authentication and identification using correlation filters based on illumination variation [7]. In the process of implementing correlation filters, the number of training images used depends on the level of distortions applied to the images [5], [6]. In this study, we investigate which part of a face image is more tolerant to facial expression and retains its personal characteristics for the verification process. Four subdivided face images, i.e. bottom, top, left and right halves, with varying expressions are investigated. By identifying only the region of the face that gives the highest verification performance, that region can be used instead of the full-face to reduce storage requirements. 2. Unconstrained Minimum Average Correlation Energy (UMACE) Filter Correlation filter theory and the descriptions of the design of the correlation filter can be found in a tutorial survey paper [8]. According to [4][6], correlation filter evolves from matched filters which are optimal for detecting a known reference image in the presence of additive white Gaussian noise. However, the detection rate of matched filters decreases significantly due to even the small changes of scale, rotation and pose of the reference image. the pre-specified peak values In an effort to solve this problem, the Synthetic Discriminant Function (SDF) filter and the Equal Correlation Peak SDF (ECP SDF) filter ware introduced which allowed several training images to be represented by a single correlation filter. SDF filter produces pre-specified values called peak constraints. These peak values correspond to the authentic class or impostor class when an image is tested. However, to misclassifications when the sidelobes are larger than the controlled values at the origin. Savvides et.al developed the Minimum Average Correlation Energy (MACE) filters [5]. This filter reduces the large sidelobes and produces a sharp peak when the test image is from the same class as the images that have been used to design the filter. There are two kinds of variants that can be used in order to obtain a sharp peak when the test image belongs to the authentic class. The first MACE filter variant minimizes the average correlation energy of the training images while constraining the correlation output at the origin to a specific value for each of the training images. The second MACE filter variant is the Unconstrained Minimum Average Correlation Energy (UMACE) filter which also minimizes the average correlation output while maximizing the correlation output at the origin [4]. lead Proceedings of the International Conference onElectrical Engineering and InformaticsInstitut Teknologi Bandung, Indonesia June 17-19, 2007B-67ISBN 978-979-16338-0-2611 |
| 57a1466c5985fe7594a91d46588d969007210581 | A Taxonomy of Face-models for System Evaluation
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 composed of 3D structure models and skin textures, capturing many artifacts not easily parameterized. Can be combined with physics-based rendering to generate samples under different conditions. Semi–Synethetic – Use measured data such as 2D images or 3D facial scans. These are not truly synthetic as they are re-rendering’s of real measured data. Semi and Guided Synthetic data provide higher operational relevance while maintaining a high degree of control. Generating statistically significant size datasets for face matching system evaluation is both a laborious and expensive process. There is a gap in datasets that allow for evaluation of system issues including: Long distance recognition Blur caused by atmospherics Various weather conditions End to end systems evaluation Our contributions: Define a taxonomy of face-models for controlled experimentations Show how Synthetic addresses gaps in system evaluation Show a process for generating and validating synthetic models Use these models in long distance face recognition system evaluation Experimental Setup Results and Conclusions Example Models Original Pie Semi- Synthetic FaceGen Animetrics http://www.facegen.com http://www.animetrics.com/products/Forensica.php Guided- Synthetic Models Models generated using the well known CMU PIE [18] dataset. Each of the 68 subjects of PIE were modeled using a right profile and frontal image from the lights subset. Two modeling programs were used, Facegen and Animetrics. Both programs create OBJ files and textures Models are re-rendered using custom display software built with OpenGL, GLUT and DevIL libraries Custom Display Box housing a BENQ SP820 high powered projector rated at 4000 ANSI Lumens Canon EOS 7D withd a Sigma 800mm F5.6 EX APO DG HSM lens a 2x adapter imaging the display from 214 meters Normalized Example Captures Real PIE 1 Animetrics FaceGen 81M inside 214M outside Real PIE 2 Pre-cropped images were used for the commercial core Ground truth eye points + geometric/lighting normalization pre processing before running through the implementation of the V1 recognition algorithm found in [1]. Geo normalization highlights how the feature region of the models looks very similar to that of the real person. Each test consisted of using 3 approximately frontal gallery images NOT used to make the 3D model used as the probe, best score over 3 images determined score. Even though the PIE-3D-20100224A–D sets were imaged on the same day, the V1 core scored differently on each highlighting the synthetic data’s ability to help evaluate data capture methods and effects of varying atmospherics. The ISO setting varied which effects the shutter speed, with higher ISO generally yielding less blur. Dataset Range(m) Iso V1 Comm. Original PIE Images FaceGen ScreenShots Animetrics Screenshots PIE-3D-20100210B PIE-3D-20100224A PIE-3D-20100224B PIE-3D-20100224C PIE-3D-20100224D N/A N/A N/A 81m 214m 214m 214m 214m N/A N/A N/A 500 125 125 250 400 100 47.76 100 100 58.82 45.59 81.82 79.1 100 100 100 100 100 100 The same (100 percent) recognition rate on screenshots as original images validate the Anmetrics guided synthetic models and fails FaceGen Models. 100% recognition means dataset is too small/easy; exapanding pose and models underway. Expanded the photohead methodology into 3D Developed a robust modeling system allowing for multiple configurations of a single real life data set. Gabor+SVM based V1[15] significantly more impacted by atmospheric blur than the commercial algorithm Key References: [6 of 21] R. Bevridge, D. Bolme, M Teixeira, and B. Draper. The CSU Face Identification Evaluation System Users Guide: Version 5.0. Technical report, CSU 2003 [8 of 21] T. Boult and W. Scheirer. Long range facial image acquisition and quality. In M. Tisarelli, S. Li, and R. Chellappa. [15 of 21] N. Pinto, J. J. DiCarlo, and D. D. Cox. How far can you get with a modern face recognition test set using only simple features? In IEEE CVPR, 2009. [18 of 21] T. Sim, S. Baker, and M. Bsat. The CMU Pose, Illumination and Expression (PIE) Database. In Proceedings of the IEEE F&G, May 2002. |
| 5721216f2163d026e90d7cd9942aeb4bebc92334 | |
| 5753b2b5e442eaa3be066daa4a2ca8d8a0bb1725 | |
| 574ad7ef015995efb7338829a021776bf9daaa08 | AdaScan: Adaptive Scan Pooling in Deep Convolutional Neural Networks
for Human Action Recognition in Videos 1IIT Kanpur‡ 2SRI International 3UCSD |
| 57d37ad025b5796457eee7392d2038910988655a | GEERATVEEETATF
ERARC CAVETYDETECTR by DagaEha UdeheS eviif f.DahaWeiha ATheiS biediaia F (cid:28) efhe Re ieefheDegeef aefSciece a TheSch fC eScieceadEgieeig ebewUiveiyfe a eae 91904 Decebe2009 |
| 3b1260d78885e872cf2223f2c6f3d6f6ea254204 | |
| 3b1aaac41fc7847dd8a6a66d29d8881f75c91ad5 | Sparse Representation-based Open Set Recognition |
| 3bc776eb1f4e2776f98189e17f0d5a78bb755ef4 | |
| 3b4fd2aec3e721742f11d1ed4fa3f0a86d988a10 | Glimpse: Continuous, Real-Time Object Recognition on
Mobile Devices MIT CSAIL Microsoft Research MIT CSAIL Microsoft Research MIT CSAIL |
| 3b15a48ffe3c6b3f2518a7c395280a11a5f58ab0 | 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 Dipl.-Inform. born 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 D17 |
| 3ba8f8b6bfb36465018430ffaef10d2caf3cfa7e | Local Directional Number Pattern for Face
Analysis: Face and Expression Recognition |
| 3b80bf5a69a1b0089192d73fa3ace2fbb52a4ad5 | |
| 3b9d94752f8488106b2c007e11c193f35d941e92 | CVPR
#2052 000 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048 049 050 051 052 053 CVPR 2013 Submission #2052. CONFIDENTIAL REVIEW COPY. DO NOT DISTRIBUTE. CVPR #2052 Appearance, Visual and Social Ensembles for Face Recognition in Personal Photo Collections Anonymous CVPR submission Paper ID 2052 |
| 3be7b7eb11714e6191dd301a696c734e8d07435f | |
| 3b410ae97e4564bc19d6c37bc44ada2dcd608552 | Scalability Analysis of Audio-Visual Person
Identity Verification 1 Communications Laboratory, Universit´e catholique de Louvain, B-1348 Belgium, 2 IDIAP, CH-1920 Martigny, Switzerland |
| 6f5ce5570dc2960b8b0e4a0a50eab84b7f6af5cb | Low Resolution Face Recognition Using a
Two-Branch Deep Convolutional Neural Network Architecture |
| 6f288a12033fa895fb0e9ec3219f3115904f24de | Learning Expressionlets via Universal Manifold
Model for Dynamic Facial Expression Recognition |
| 6f2dc51d607f491dbe6338711c073620c85351ac | |
| 6f75697a86d23d12a14be5466a41e5a7ffb79fad | |
| 6f7d06ced04ead3b9a5da86b37e7c27bfcedbbdd | Pages 51.1-51.12
DOI: https://dx.doi.org/10.5244/C.30.51 |
| 6f7a8b3e8f212d80f0fb18860b2495be4c363eac | Creating Capsule Wardrobes from Fashion Images
UT-Austin UT-Austin |
| 6f6b4e2885ea1d9bea1bb2ed388b099a5a6d9b81 | Structured Output SVM Prediction of Apparent Age,
Gender and Smile From Deep Features Michal Uˇriˇc´aˇr CMP, Dept. of Cybernetics FEE, CTU in Prague Computer Vision Lab D-ITET, ETH Zurich Computer Vision Lab D-ITET, ETH Zurich PSI, ESAT, KU Leuven CVL, D-ITET, ETH Zurich Jiˇr´ı Matas CMP, Dept. of Cybernetics FEE, CTU in Prague |
| 6f35b6e2fa54a3e7aaff8eaf37019244a2d39ed3 | DOI 10.1007/s00530-005-0177-4
R E G U L A R PA P E R Learning probabilistic classifiers for human–computer interaction applications Published online: 10 May 2005 c(cid:1) Springer-Verlag 2005 intelligent interaction, |
| 6fa3857faba887ed048a9e355b3b8642c6aab1d8 | Face Recognition in Challenging Environments:
An Experimental and Reproducible Research Survey |
| 6f7ce89aa3e01045fcd7f1c1635af7a09811a1fe | 978-1-4673-0046-9/12/$26.00 ©2012 IEEE
937 ICASSP 2012 |
| 6fe2efbcb860767f6bb271edbb48640adbd806c3 | SOFT BIOMETRICS: HUMAN IDENTIFICATION USING COMPARATIVE DESCRIPTIONS
Soft Biometrics; Human Identification using Comparative Descriptions |
| 6fdc0bc13f2517061eaa1364dcf853f36e1ea5ae | DAISEE: Dataset for Affective States in
E-Learning Environments 1 Microsoft India R&D Pvt. Ltd. 2 Department of Computer Science, IIT Hyderabad |
| 6f5151c7446552fd6a611bf6263f14e729805ec7 | 5KHHAO /7 %:0 7
)>IJH=?J 9EJDE JDA ?JANJ B=?A ANFHAIIE ?=IIE?=JE KIEC JDA FH>=>EEJEAI JD=J A=?D A B IALAH= ?O ??KHHEC )7 CHKFI EI ?=IIIAF=H=>EEJO MAECDJEC /=>H M=LAAJI H FHE?EF= ?FAJI ==OIEI 2+) ! 1 JDEI F=FAH MA |
| 03c56c176ec6377dddb6a96c7b2e95408db65a7a | A Novel Geometric Framework on Gram Matrix
Trajectories for Human Behavior Understanding |
| 03d9ccce3e1b4d42d234dba1856a9e1b28977640 | |
| 0322e69172f54b95ae6a90eb3af91d3daa5e36ea | Face Classification using Adjusted Histogram in
Grayscale |
| 03f7041515d8a6dcb9170763d4f6debd50202c2b | Clustering Millions of Faces by Identity |
| 038ce930a02d38fb30d15aac654ec95640fe5cb0 | Approximate Structured Output Learning for Constrained Local
Models with Application to Real-time Facial Feature Detection and Tracking on Low-power Devices |
| 03c1fc9c3339813ed81ad0de540132f9f695a0f8 | Proceedings of Machine Learning Research 81:1–15, 2018
Conference on Fairness, Accountability, and Transparency Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification∗ MIT Media Lab 75 Amherst St. Cambridge, MA 02139 Microsoft Research 641 Avenue of the Americas, New York, NY 10011 Editors: Sorelle A. Friedler and Christo Wilson |
| 0339459a5b5439d38acd9c40a0c5fea178ba52fb | D|C|I&I 2009 Prague
Multimodal recognition of emotions in car environments |
| 03a8f53058127798bc2bc0245d21e78354f6c93b | Max-Margin Additive Classifiers for Detection
Sam Hare VGG Reading Group October 30, 2009 |
| 03fc466fdbc8a2efb6e3046fcc80e7cb7e86dc20 | A Real Time System for Model-based Interpretation of
the Dynamics of Facial Expressions Technische Universit¨at M¨unchen Boltzmannstr. 3, 85748 Garching 1. Motivation Recent progress in the field of Computer Vision allows intuitive interaction via speech, gesture or facial expressions between humans and technical systems.Model-based tech- niques facilitate accurately interpreting images with faces by exploiting a priori knowledge, such as shape and texture information. This renders them an inevitable component to realize the paradigm of intuitive human-machine interac- tion. Our demonstration shows model-based recognition of facial expressions in real-time via the state-of-the-art Candide-3 face model [1] as visible in Figure 1. This three- dimensional and deformable model is highly appropriate for real-world face interpretation applications. However, its complexity challenges the task of model fitting and we tackle this challenge with an algorithm that has been auto- matically learned from a large set of images. This solution provides both, high accuracy and runtime. Note, that our system is not limited to facial expression estimation. Gaze direction, gender and age are also estimated. 2. Face Model Fitting Models reduce the large amount of image data to a small number of model parameters to describe the im- age content, which facilitates and accelerates the subse- quent interpretation task. Cootes et al. [3] introduced mod- elling shapes with Active Contours. Further enhancements emerged the idea of expanding shape models with texture information [2]. Recent research considers modelling faces in 3D space [1, 10]. Fitting the face model is the computational challenge of finding the parameters that best describe the face within a given image. This task is often addressed by minimizing an objective function, such as the pixel error between the model’s rendered surface and the underlying image content. This section describes the four main components of model- based techniques, see [9]. The face model contains a parameter vector p that repre- sents its configurations. We integrate the complex and de- formable 3D wire frame Candide-3 face model [1]. The model consists of 116 anatomical landmarks and its param- eter vector p = (rx, ry, rz, s, tx, ty, σ, α)T describes the affine transformation (rx, ry, rz, s, tx, ty) and the deforma- tion (σ, α). The 79 deformation parameters indicate the shape of facial components such as the mouth, the eyes, or the eye brows, etc., see Figure 2. The localization algorithm computes an initial estimate of the model parameters that is further refined by the subse- quent fitting algorithm. Our system integrates the approach of [8], which detects the model’s affine transformation in case the image shows a frontal view face. The objective function yields a comparable value that specifies how accurately a parameterized model matches an image. Traditional approaches manually specify the objec- tive function in a laborious and erroneous task. In contrast, we automatically learn the objective function from a large set of training data based on objective information theoretic measures [9]. This approach does not require expert knowl- edge and it is domain-independently applicable. As a re- sult, this approach yields more robust and accurate objective functions, which greatly facilitate the task of the associated fitting algorithms. Accurately estimated model parameters in turn are required to infer correct high-level information, such as facial expression or gaze direction. Figure 1. Interpreting expressions with the Candide-3 face model. |
| 03b98b4a2c0b7cc7dae7724b5fe623a43eaf877b | Acume: A Novel Visualization Tool for Understanding Facial
Expression and Gesture Data |
| 03104f9e0586e43611f648af1132064cadc5cc07 | |
| 03f14159718cb495ca50786f278f8518c0d8c8c9 | 2015 IEEE International Conference on Control System, Computing and Engineering, Nov 27 – Nov 29, 2015 Penang, Malaysia
2015 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 3.00 pm – 3.15pm 3.15 pm – 3.30pm 3.30 pm – 3.45pm 3.45 pm – 4.00pm 4.00 pm – 4.15pm 4.15 pm – 4.30pm 4.30 pm – 4.45pm 1A 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 1A 02 ID35 Performance Evaluation of HOG and Gabor Features for Vision-based Vehicle Detection 1A 03 ID23 Experimental Method to Pre-Process Fuzzy Bit Planes before Low-Level Feature Extraction in Thermal Images Chan Wai Ti and Sim Kok Swee 1A 04 ID84 Fractal-based Texture and HSV Color Features for Fabric Image Retrieval Nanik Suciati, Darlis Herumurti and Arya Yudhi Wijaya 1A 05 ID168 Study of Automatic Melody Extraction Methods for Philippine Indigenous Music Jason Disuanco, Vanessa Tan, Franz de Leon 1A 06 ID211 Acoustical Comparison between Voiced and Voiceless Arabic Phonemes of Malay Speakers Ali Abd Almisreb, Ahmad Farid Abidin, Nooritawati Md Tahir *shaded cell is the proposed session chair viii ©Faculty of Electrical Engineering, Universiti Teknologi MARA |
| 0394040749195937e535af4dda134206aa830258 | Geodesic Entropic Graphs for Dimension and
Entropy Estimation in Manifold Learning December 16, 2003 |
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| 0394e684bd0a94fc2ff09d2baef8059c2652ffb0 | Median Robust Extended Local Binary Pattern
for Texture Classification Index Terms— Texture descriptors, rotation invariance, local binary 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 applications 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- cation primarily consists of two critical subproblems: feature extraction and classifier designation [1], [2]. It is generally agreed 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 focuses on the feature extraction part and numerous texture feature extraction methods have been developed, with excellent surveys given in [1]–[5]. Most existing methods have not, however, been capable of performing sufficiently well for real-world applications, which have demanding requirements including database size, nonideal environmental conditions, and running in real-time. |
| 03e88bf3c5ddd44ebf0e580d4bd63072566613ad | |
| 03f4c0fe190e5e451d51310bca61c704b39dcac8 | 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 Received: 30 March 2016 / Accepted: 22 August 2016 Ó Springer-Verlag Berlin Heidelberg 2016 |
| 031055c241b92d66b6984643eb9e05fd605f24e2 | Multi-fold MIL Training for Weakly Supervised Object Localization
Inria∗ |
| 0332ae32aeaf8fdd8cae59a608dc8ea14c6e3136 | Int J Comput Vis
DOI 10.1007/s11263-017-1009-7 Large Scale 3D Morphable Models 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
Virginia Tech |
| 03701e66eda54d5ab1dc36a3a6d165389be0ce79 | 179
Improved Principal Component Regression for Face Recognition Under Illumination Variations |
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Joint Patch and Multi-label Learning for Facial Action Unit and Holistic Expression Recognition classifiers without |
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| 9b474d6e81e3b94e0c7881210e249689139b3e04 | VG-RAM Weightless Neural Networks for
Face Recognition Departamento de Inform´atica Universidade Federal do Esp´ırito Santo Av. Fernando Ferrari, 514, 29075-910 - Vit´oria-ES Brazil 1. 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 active research areas in computer vision for many decades (Zhao et al.; 2003). Even though current 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 back 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 cases, the solution typically involves segmentation of faces from scenes (face detection), fea- ture extraction from the face regions, recognition, or verification. In this chapter, we examine the recognition of frontal face images required in the context of identification problems. Many approaches have been proposed to tackle the problem of face recognition. One can roughly divide these into (i) holistic approaches, (ii) feature-based approaches, and (iii) hybrid approaches (Zhao et al.; 2003). Holistic approaches use the whole face region as the raw input to a recognition system (a classifier). In feature-based approaches, local features, such as the eyes, nose, and mouth, are first extracted and their locations and local statistics (geometric and/or appearance based) are fed into a classifier. Hybrid approaches use both local features and the whole face region to recognize a face. Among fisher- faces (Belhumeur et al.; 1997; Etemad and Chellappa; 1997) have proved to be effective (Turk and Pentland; eigenfaces holistic approaches, 1991) and |
| 9bc01fa9400c231e41e6a72ec509d76ca797207c | |
| 9bcfadd22b2c84a717c56a2725971b6d49d3a804 | How to Detect a Loss of Attention in a Tutoring System
using Facial Expressions and Gaze Direction |
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Compression and Classification |
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Texture and Geometrical Features |
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MIT Lincoln Laboratory, Lexington MA 02420, USA |
| 9e9052256442f4e254663ea55c87303c85310df9 | International Journal of Advanced Research in Computer Engineering & Technology (IJARCET)
Volume 4 Issue 10, October 2015 Review On Attribute-assisted Reranking for Image Search |
| 9e0285debd4b0ba7769b389181bd3e0fd7a02af6 | From face images and attributes to attributes
Computer Vision Laboratory, ETH Zurich, Switzerland |
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| 040dc119d5ca9ea3d5fc39953a91ec507ed8cc5d | Noname manuscript No.
(will be inserted by the editor) Large-scale Bisample Learning on ID vs. Spot Face Recognition Received: date / Accepted: date |
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| 0447bdb71490c24dd9c865e187824dee5813a676 | Manifold Estimation in View-based Feature
Space for Face Synthesis Across Pose Paper 27 |
| 044ba70e6744e80c6a09fa63ed6822ae241386f2 | TO APPEAR IN AUTONOMOUS ROBOTS, SPECIAL ISSUE IN LEARNING FOR HUMAN-ROBOT COLLABORATION
Early Prediction for Physical Human Robot Collaboration in the Operating Room |
| 04dcdb7cb0d3c462bdefdd05508edfcff5a6d315 | Assisting the training of deep neural networks
with applications to computer vision tesi doctoral està subjecta a la Aquesta CompartirIgual 4.0. Espanya de Creative Commons. Esta tesis doctoral está sujeta a la licencia Reconocimiento - NoComercial – CompartirIgual 4.0. 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 |
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| 0431e8a01bae556c0d8b2b431e334f7395dd803a | Learning Localized Perceptual Similarity Metrics for Interactive Categorization
Google Inc. google.com |
| 04b4c779b43b830220bf938223f685d1057368e9 | 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 | 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 |
| 6a3a07deadcaaab42a0689fbe5879b5dfc3ede52 | Learning to Estimate Pose by Watching Videos
Department of Computer Science and Engineering IIT Kanpur |
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Mahidol Wittayanusorn School Puttamonton, Nakornpatom 73170, Thailand |
| 6a806978ca5cd593d0ccd8b3711b6ef2a163d810 | Facial feature tracking for Emotional Dynamic
Analysis 1ISIR, CNRS UMR 7222 Univ. Pierre et Marie Curie, Paris 2LAMIA, EA 4540 Univ. of Fr. West Indies & Guyana |
| 6a8a3c604591e7dd4346611c14dbef0c8ce9ba54 | ENTERFACE’10, JULY 12TH - AUGUST 6TH, AMSTERDAM, THE NETHERLANDS.
58 An Affect-Responsive Interactive Photo Frame |
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| 6aefe7460e1540438ffa63f7757c4750c844764d | Non-rigid Segmentation using Sparse Low Dimensional Manifolds and
Deep Belief Networks ∗ Instituto de Sistemas e Rob´otica Instituto Superior T´ecnico, Portugal |
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| 322c063e97cd26f75191ae908f09a41c534eba90 | Noname manuscript No.
(will be inserted by the editor) Improving Image Classification using Semantic Attributes Received: date / Accepted: date |
| 325b048ecd5b4d14dce32f92bff093cd744aa7f8 | CVPR
#2670 000 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048 049 050 051 052 053 CVPR 2008 Submission #2670. CONFIDENTIAL REVIEW COPY. DO NOT DISTRIBUTE. CVPR #2670 Multi-Image Graph Cut Clothing Segmentation for Recognizing People Anonymous CVPR submission Paper ID 2670 |
| 321bd4d5d80abb1bae675a48583f872af3919172 | Wang et al. EURASIP Journal on Image and Video Processing (2016) 2016:44
DOI 10.1186/s13640-016-0152-3 EURASIP Journal on Image and Video Processing R EV I E W Entropy-weighted feature-fusion method for head-pose estimation Open Access |
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| 324b9369a1457213ec7a5a12fe77c0ee9aef1ad4 | Dynamic Facial Analysis: From Bayesian Filtering to Recurrent Neural Network
NVIDIA |
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1489 ICASSP 2011 |
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Classification: Structure for Robustness |
| 3538d2b5f7ab393387ce138611ffa325b6400774 | 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 |
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(will be inserted by the editor) Unconstrained Still/Video-Based Face Verification with Deep Convolutional Neural Networks Received: date / Accepted: date |
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Self-Supervised Video Representation Learning |
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Synchronization Disney Research |
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E. Ram´on-Balmaseda, J. Lorenzo-Navarro, and M. Castrill´on-Santana (cid:63) Universidad de Las Palmas de Gran Canaria SIANI Spain |
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| 35f084ddee49072fdb6e0e2e6344ce50c02457ef | A Bilinear Illumination Model
for Robust Face Recognition The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters Citation 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 repository, and is made available under the terms and conditions applicable to Other Posted Material, as set forth at http:// nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of- use#LAA |
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Matrix Completion for Weakly-supervised Multi-label Image Classification |
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MIT CSAIL |
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ETH Zurich, Switzerland |
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[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 and 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. 399–458, 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 MIT Media Lab 75 Amherst St, Cambridge, MA MERL 201 Broadway, Cambridge MA MIT Media Lab 75 Amherst St, Cambridge, MA |
| 69a9da55bd20ce4b83e1680fbc6be2c976067631 | |
| 6974449ce544dc208b8cc88b606b03d95c8fd368 | |
| 3cfbe1f100619a932ba7e2f068cd4c41505c9f58 | 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 |
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| 3c374cb8e730b64dacb9fbf6eb67f5987c7de3c8 | Measuring Gaze Orientation for Human-Robot
Interaction ∗ 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 between 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 detection of facial expression or video conferencing [8] but most of those appli- cations focus on front face images [9]. We developed an algorithm based on face features tracking using a parametric model. First we need face detection, then we detect face features in following order: eyes, mouth, nose. In order to achieve full profile detection we use sets of SURF to learn what eyes, mouth and nose look like once tracking is initialized. Once those sets of SURF are known they are used to detect and track face features. SURF have a descriptor which is often used to identify a key point and here we add some global geometry information by using the relative position between key points. Then we use a particle filter to track face features using those SURF based detectors, we compute the head pose angles from features position and pass the results through a median filter. This paper is organized as follows. Section 2 describes our modeling of visual features, section 3 presents our tracking implementation. Section 4 presents results we get with our implementation and future works in section 5. 2 Visual features We use some basic properties of facial features to initialize our algorithm : eyes are dark and circular, mouth is an horizontal dark line with a specific color,... |
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| 3c56acaa819f4e2263638b67cea1ec37a226691d | Body Joint guided 3D Deep Convolutional
Descriptors for Action Recognition |
| 3c8da376576938160cbed956ece838682fa50e9f | Chapter 4
Aiding Face Recognition with Social Context Association Rule based 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 association rules extracted from social context can be used to augment face recognition and improve the identification performance. 4.1 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 argued that encoding social context among individuals can be leveraged for improved automatic face recognition [175]. As shown in Figure 4.1, often times a person’s identity can be inferred based on the identity of other persons in the same photo, when some social context between them is known. A subject’s face in consumer photos generally co-occur along with their socially relevant people. With the advent of social networking services, the social context between individuals is readily available. Face recognition performance 105 |
| 56e885b9094391f7d55023a71a09822b38b26447 | FREQUENCY DECODED LOCAL BINARY PATTERN
Face Retrieval using Frequency Decoded Local Descriptor |
| 56a653fea5c2a7e45246613049fb16b1d204fc96 | 3287
Quaternion Collaborative and Sparse Representation With Application to Color Face Recognition representation-based |
| 5666ed763698295e41564efda627767ee55cc943 | Manuscript
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| 566038a3c2867894a08125efe41ef0a40824a090 | 978-1-4244-2354-5/09/$25.00 ©2009 IEEE
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Coding for Positive Definite Matrices |
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1 Fine-Grained Facial Expression Analysis Us- ing Dimensional Emotion Model |
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Marc T. Law Sorbonne Universit´es, UPMC Univ Paris 06, UMR 7606, LIP6, F-75005, Paris, France |
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FACE RECOGNITION WITH SINGLE SAMPLE PER PERSON School of Computing, KAIST, Republic of Korea |
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manifold learning for classification |
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Face Emotion Recognition DICGIM, Universit´a degli Studi di Palermo, V.le delle Scienze, Ed. 6, 90128 Palermo, Italy, DRAFT To appear in ICIAP 2015 |
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Random Multispace Quantization as an Analytic Mechanism for BioHashing of Biometric and Random Identity Inputs |
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Data-Dependent Label Distribution Learning for Age Estimation |
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A Subspace Model-Based Approach to Face Relighting Under Unknown Lighting and Poses |
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978-1-5090-4847-2/16/$31.00 ©2016 IEEE 4118 |
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| 58542eeef9317ffab9b155579256d11efb4610f2 | 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 Computer Science, BIT Noida, India |
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Scale Space and PDE Methods Spatio-Temporal Scale Selection in Video Data . . . . . . . . . . . . . . . . . . . . . Dynamic Texture Recognition Using Time-Causal Spatio-Temporal Scale-Space Filters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 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 16 29 41 Tubular Structure Segmentation Based on Heat Diffusion. . . . . . . . . . . . . . . 54 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, and Christoph Brune An Efficient and Stable Two-Pixel Scheme for 2D Forward-and-Backward Diffusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Martin Welk and Joachim Weickert 66 80 94 Restoration and Reconstruction Blind Space-Variant Single-Image Restoration of Defocus Blur. . . . . . . . . . . 109 Leah Bar, Nir Sochen, and Nahum Kiryati Denoising by Inpainting. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121 Robin Dirk Adam, Pascal Peter, and Joachim Weickert Stochastic Image Reconstruction from Local Histograms of Gradient Orientation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Agnès Desolneux and Arthur Leclaire 133 |
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| 0580edbd7865414c62a36da9504d1169dea78d6f | Baseline CNN structure analysis for facial expression recognition |
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1ETH Z¨urich 2Kooaba AG 3K.U. Leuven |
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Assistant Professor, ITM GOI M Tech, ITM GOI face video (Eigen passport-verification, |
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| 9ca7899338129f4ba6744f801e722d53a44e4622 | Deep Neural Networks Regularization for Structured
Output Prediction Soufiane Belharbi∗ INSA Rouen, LITIS 76000 Rouen, France INSA Rouen, LITIS 76000 Rouen, France INSA Rouen, LITIS 76000 Rouen, France INSA Rouen, LITIS 76000 Rouen, France Normandie Univ, UNIROUEN, UNIHAVRE, Normandie Univ, UNIROUEN, UNIHAVRE, Normandie Univ, UNIROUEN, UNIHAVRE, Normandie Univ, UNIROUEN, UNIHAVRE, |
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taxonomy and systematic classification Journal Title XX(X):1–32 c(cid:13)The Author(s) 2016 Reprints and permission: sagepub.co.uk/journalsPermissions.nav DOI: 10.1177/ToBeAssigned www.sagepub.com/ |
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Character Identification in Feature-Length Films Using Global Face-Name Matching and Yeh-Min Huang, Member, IEEE |
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| a44590528b18059b00d24ece4670668e86378a79 | Learning the Hierarchical Parts of Objects by Deep
Non-Smooth Nonnegative Matrix Factorization |
| a4c430b7d849a8f23713dc283794d8c1782198b2 | Video Concept Embedding
1. 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 amount of high dimensional data in videos. One strategy to address this is to summarize a video into concepts (eg. running, climbing, cooking). This allows us to represent a video in a very natural way to humans, such as a sequence of semantic events. However this has the same shortcom- ings that one-hot coding of words have. The goal of this project is to find a meaningful way to em- bed video concepts into a vector space. The hope would be to capture semantic relatedness of concepts in a vector representation, essentially doing for videos what word2vec did for text. Having a vector representation for video con- cepts would help in areas such as semantic video retrieval and video classification, as it would provide a statistically meaningful and robust way of representing videos as lower dimensional vectors. An interesting thing would be to ob- serve if such a vector representation would result in ana- logical reasoning using simple vector arithmetic. Figure 1 shows an example of concepts detected at differ- ent snapshots in the same video. For example, consider the scenario where the concepts Kicking a ball, Soccer and Running are detected in the three snapshots respectively (from left to right). Since, these snapshots belong in the same video, we expect that these concepts are semantically similar and that they should lie close in the resulting em- bedding space. The aim of this project is to find a vector space embedding for the space of concepts such that vector representations for semantically similar concepts (in this Figure 1. Example snapshots from the same video case, Running, Kicking and Soccer) lie in the vicinity of each other. 2. Related Work (Mikolov et al., 2013a) introduces the popular skip-gram model to learn distributed representations of words from very large linguistic datasets. Specifically, it uses each word as an input to a log-linear classifier and predict words within a certain range before and after the current word in the dataset. (Mikolov et al., 2013b) extends this model to learn representations for phrases, in addition to words, and also improve the quality of vectors and training speed. These works also show that the skip-gram model exhibits a linear structure that enables it to perform reasoning using basic vector arithmetic. The skip-gram model from these works is the basis of our model in learning representations for concepts. (Le & Mikolov, 2014) extends the concept of word vectors to sentences and paragraphs. Their approach is more in- volved than a simple bag of words approach, in that it tries to capture the nature of the words in the paragraph. They construct the paragraph vector in such a way that it can be used to predict the word vectors that are contained inside the paragraph. They do this by first learning word vectors, such that the probability of a word vector given its context is maximized. To learn paragraph vectors, the paragraph is essentially treated as a word, and the words it contains become the context. This provides a key insight in how a set of concept vectors can be used together to provide a more meaningful vector representation for videos, which can then be used for retrieval. (Hu et al.) utilizes structured knowledge in the data to learn distributed representations that improve semantic related- |
| a4cc626da29ac48f9b4ed6ceb63081f6a4b304a2 | |
| a4f37cfdde3af723336205b361aefc9eca688f5c | Recent Advances
in Face Recognition |
| a30869c5d4052ed1da8675128651e17f97b87918 | Fine-Grained Comparisons with Attributes |
| 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 |
| a3d8b5622c4b9af1f753aade57e4774730787a00 | Pose-Aware Person Recognition
Anoop Namboodiri (cid:63) (cid:63) CVIT, IIIT Hyderabad, India † Facebook AI Research |
| a3017bb14a507abcf8446b56243cfddd6cdb542b | Face Localization and Recognition in Varied
Expressions and Illumination Hui-Yu Huang, Shih-Hang Hsu |
| a378fc39128107815a9a68b0b07cffaa1ed32d1f | Determining a Suitable Metric When using Non-negative Matrix Factorization∗
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?
Face++, Megvii Inc. Face++, Megvii Inc. Face++, Megvii Inc. |
| a3f69a073dcfb6da8038607a9f14eb28b5dab2db | Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI-18)
1184 |
| a3f78cc944ac189632f25925ba807a0e0678c4d5 | Action Recognition in Realistic Sports Videos |
| a33f20773b46283ea72412f9b4473a8f8ad751ae | |
| a3a6a6a2eb1d32b4dead9e702824375ee76e3ce7 | Multiple Local Curvature Gabor Binary
Patterns for Facial Action Recognition Signal Processing Laboratory (LTS5), ´Ecole Polytechnique F´ed´erale de Lausanne, Switzerland |
| a32c5138c6a0b3d3aff69bcab1015d8b043c91fb | 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. |
| a3d78bc94d99fdec9f44a7aa40c175d5a106f0b9 | Recognizing Violence in Movies
CIS400/401 Project Final Report Univ. of Pennsylvania Philadelphia, PA Univ. of Pennsylvania Philadelphia, PA Ben Sapp Univ. of Pennsylvania Philadelphia, PA Univ. of Pennsylvania Philadelphia, PA |
| a3eab933e1b3db1a7377a119573ff38e780ea6a3 | 978-1-4244-4296-6/10/$25.00 ©2010 IEEE
838 ICASSP 2010 |
| a35d3ba191137224576f312353e1e0267e6699a1 | Increasing security in DRM systems
through biometric authentication. ecuring the exchange of intellectual property and providing protection to multimedia contents in distribution systems have enabled the advent 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- cially in the near future when universal multimedia access (UMA) becomes a reality, and any multimedia content 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 to supervise and restrict access to multimedia data, avoiding unauthorized or fraudulent operations. A key component of any DRM system, also known as intellectual property management and protection (IPMP) systems in the MPEG-21 framework, is user authentication to ensure that only those with specific rights are able to access the digital informa- tion. It is here that biometrics can play an essential role, reinforcing securi- ty at all stages where customer authentica- tion is needed. The ubiquity of users and devices, where the same user might want to access to multimedia contents from different environments (home, car, work, jogging, etc.) and also from different devices or media (CD, DVD, home computer, laptop, PDA, 2G/3G mobile phones, game consoles, etc.) strengthens the need for reliable and universal authentication of users. Classical user authentication systems have been based in something that you have (like a key, an identi- fication card, etc.) and/or something that you know (like a password, or a PIN). With biometrics, a new user authentication paradigm is added: something that you are (e.g., fingerprints or face) or something that you do or produce (e.g., handwritten signature or 50 IEEE SIGNAL PROCESSING MAGAZINE 1053-5888/04/$20.00©2004IEEE MARCH 2004 |
| b558be7e182809f5404ea0fcf8a1d1d9498dc01a | Bottom-up and top-down reasoning with convolutional latent-variable models
UC Irvine UC Irvine |
| b5fc4f9ad751c3784eaf740880a1db14843a85ba | SIViP (2007) 1:225–237
DOI 10.1007/s11760-007-0016-5 ORIGINAL PAPER Significance of image representation for face verification Received: 29 August 2006 / Revised: 28 March 2007 / Accepted: 28 March 2007 / Published online: 1 May 2007 © Springer-Verlag London Limited 2007 |
| b562def2624f59f7d3824e43ecffc990ad780898 | |
| b5160e95192340c848370f5092602cad8a4050cd | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, TO APPEAR
Video Classification With CNNs: Using The Codec As A Spatio-Temporal Activity Sensor |
| b52c0faba5e1dc578a3c32a7f5cfb6fb87be06ad | Journal of Applied Research and
Technology ISSN: 1665-6423 Centro de Ciencias Aplicadas y Desarrollo Tecnológico México Hussain Shah, Jamal; Sharif, Muhammad; Raza, Mudassar; Murtaza, Marryam; Ur-Rehman, Saeed Robust Face Recognition Technique under Varying Illumination Journal of Applied Research and Technology, vol. 13, núm. 1, febrero, 2015, pp. 97-105 Centro de Ciencias Aplicadas y Desarrollo Tecnológico Distrito Federal, México Available in: http://www.redalyc.org/articulo.oa?id=47436895009 How to cite Complete issue More information about this article Journal's homepage in redalyc.org Scientific Information System Network of Scientific Journals from Latin America, the Caribbean, Spain and Portugal Non-profit academic project, developed under the open access initiative |
| b52886610eda6265a2c1aaf04ce209c047432b6d | Microexpression Identification and Categorization
using a Facial Dynamics Map |
| b5857b5bd6cb72508a166304f909ddc94afe53e3 | SSIG and IRISA at Multimodal Person Discovery
1Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil 2IRISA & Inria Rennes , CNRS, Rennes, France |
| b59f441234d2d8f1765a20715e227376c7251cd7 | |
| b51e3d59d1bcbc023f39cec233f38510819a2cf9 | CBMM Memo No. 003
March 27, 2014 Can a biologically-plausible hierarchy effectively replace face detection, alignment, and recognition pipelines? by |
| b54c477885d53a27039c81f028e710ca54c83f11 | 1201
Semi-Supervised Kernel Mean Shift Clustering |
| b2a0e5873c1a8f9a53a199eecae4bdf505816ecb | Hybrid VAE: Improving Deep Generative Models
using Partial Observations Snap Research Microsoft Research |
| b2b535118c5c4dfcc96f547274cdc05dde629976 | JOURNAL OF IEEE TRANSACTIONS ON AFFECTIVE COMPUTING, VOL. XX, NO. X, XXX 2017
Automatic Recognition of Facial Displays of Unfelt Emotions Escalera, Xavier Bar´o, Sylwia Hyniewska, Member, IEEE, J¨uri Allik, |
| b235b4ccd01a204b95f7408bed7a10e080623d2e | Regularizing Flat Latent Variables with Hierarchical Structures |
| b2c25af8a8e191c000f6a55d5f85cf60794c2709 | 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 | A scalable 3D HOG model for fast object detection and viewpoint estimation
KU Leuven, ESAT/PSI - iMinds Kasteelpark Arenberg 10 B-3001 Leuven, Belgium |
| d94d7ff6f46ad5cab5c20e6ac14c1de333711a0c | 978-1-5090-4117-6/17/$31.00 ©2017 IEEE
3031 ICASSP 2017 |
| d9739d1b4478b0bf379fe755b3ce5abd8c668f89 | |
| d9318c7259e394b3060b424eb6feca0f71219179 | 406
Face Matching and Retrieval Using Soft Biometrics |
| d9a1dd762383213741de4c1c1fd9fccf44e6480d | |
| d9c4b1ca997583047a8721b7dfd9f0ea2efdc42c | Learning Inference Models for Computer Vision |
| aca232de87c4c61537c730ee59a8f7ebf5ecb14f | EBGM VS SUBSPACE PROJECTION FOR FACE RECOGNITION
19.5 Km Markopoulou Avenue, P.O. Box 68, Peania, Athens, Greece Athens Information Technology Keywords: Human-Machine Interfaces, Computer Vision, Face Recognition. |
| ac6a9f80d850b544a2cbfdde7002ad5e25c05ac6 | 779
Privacy-Protected Facial Biometric Verification Using Fuzzy Forest Learning |
| aca273a9350b10b6e2ef84f0e3a327255207d0f5 | |
| ac0d3f6ed5c42b7fc6d7c9e1a9bb80392742ad5e | |
| ac820d67b313c38b9add05abef8891426edd5afb | |
| ac26166857e55fd5c64ae7194a169ff4e473eb8b | Personalized Age Progression with Bi-level
Aging Dictionary Learning |
| ac8441e30833a8e2a96a57c5e6fede5df81794af | IEEE TRANSACTIONS ON IMAGE PROCESSING
Hierarchical Representation Learning for Kinship Verification |
| acb83d68345fe9a6eb9840c6e1ff0e41fa373229 | Kernel Methods in Computer Vision:
Object Localization, Clustering, and Taxonomy Discovery vorgelegt von Matthew Brian Blaschko, M.S. aus 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 D83 |
| adf7ccb81b8515a2d05fd3b4c7ce5adf5377d9be | Apprentissage de métrique appliqué à la
détection de changement de page Web et aux attributs relatifs 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 comment 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
October 19, 2016 |
| adaf2b138094981edd615dbfc4b7787693dbc396 | Statistical Methods For Facial
Shape-from-shading and Recognition Submitted for the degree of Doctor of Philosophy Department of Computer Science 20th February 2007 |
| ad6745dd793073f81abd1f3246ba4102046da022 | |
| adf62dfa00748381ac21634ae97710bb80fc2922 | ViFaI: A trained video face indexing scheme
1. 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 access 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 an extremely valuable potential training set. In this work, we explore how to leverage the afore- mentioned training set to solve the video indexing problem. 2. Problem Statement Use a labeled (tagged) training set of face images to extract relevant indexes from a collection of videos, and use these indexes to answer boolean queries of the form: “videos with ‘Person 1’ OP1 ‘Person 2’ OP2 ... OP(N-1) ‘Person N’ ”, where ‘Person N’ corresponds to a training label (tag) and OPN is a boolean operand such as AND, OR, NOT, XOR, and so on. 3. Proposed Scheme In this section, we outline our proposed scheme to address the problem we postulate in the previous sec- tion. We provide further details about the system im- plementation in Section 4. At a high level, we subdivide the problem into two key phases: the first ”off-line” executed once, and the second ”on-line” phase instantiated upon each query. For the purposes of this work, we define an index as follows: |
| bba281fe9c309afe4e5cc7d61d7cff1413b29558 | 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 1Laboratorio 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 |
| bb557f4af797cae9205d5c159f1e2fdfe2d8b096 | |
| bb06ef67a49849c169781657be0bb717587990e0 | Impact of Temporal Subsampling on Accuracy and
Performance in Practical Video Classification F. Scheidegger∗†, L. Cavigelli∗, M. Schaffner∗, A. C. I. Malossi†, C. Bekas†, L. Benini∗‡ ∗ETH Zürich, 8092 Zürich, Switzerland †IBM Research - Zürich, 8803 Rüschlikon, Switzerland ‡Università di Bologna, Italy |
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ABBA?JELAAII B KH =CHEJD ==OIEI 7IK=O = B=?E= ANFHAIIE ==OIEI IOIJA ?J=EI JDHAA IJ=CAI B=?A =?GKE 9DAJDAH KIEC *=OAIE= ?=IIEAH " & IKFFHJ LA?JH =?DEA 58 H AKH= HACEI E = IECA ?=IIEAH EI = ? IJH=JACO & 0MALAH J = ?= HACEI |
| bbe1332b4d83986542f5db359aee1fd9b9ba9967 | |
| bb7f2c5d84797742f1d819ea34d1f4b4f8d7c197 | TO APPEAR IN TPAMI
From Images to 3D Shape Attributes |
| bbf01aa347982592b3e4c9e4f433e05d30e71305 | |
| bbf1396eb826b3826c5a800975047beabde2f0de | |
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| d73d2c9a6cef79052f9236e825058d5d9cdc1321 | 2014-ENST-0040
EDITE - ED 130 Doctorat ParisTech T H È S E pour obtenir le grade de docteur délivré par TELECOM ParisTech Spécialité « Signal et Images » présentée et soutenue publiquement par le 08 juillet 2014 Cutting the Visual World into Bigger Slices for Improved Video Concept Detection Amélioration de la détection des concepts dans les vidéos par de plus grandes tranches du Monde Visuel Directeur de thèse : Bernard Mérialdo Jury M. Philippe-Henri Gosselin, Professeur, INRIA M. Georges Quénot, Directeur de recherche CNRS, LIG M. Georges Linares, Professeur, LIA M. François Brémond, Professeur, INRIA M. Bernard Mérialdo, Professeur, EURECOM Rapporteur Rapporteur Examinateur Examinateur Encadrant TELECOM ParisTech école de l’Institut Télécom - membre de ParisTech |
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| d708ce7103a992634b1b4e87612815f03ba3ab24 | FCVID: Fudan-Columbia Video Dataset
Available at: http://bigvid.fudan.edu.cn/FCVID/ 1 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 4,232 hours) annotated manually according to 239 categories. We believe that the release of FCVID can stimulate innovative research on this challenging and important problem. 2 COLLECTION AND ANNOTATION The categories in FCVID cover a wide range of topics like social events (e.g., “tailgate party”), procedural events (e.g., “making cake”), objects (e.g., “panda”), scenes (e.g., “beach”), etc. These categories were de- fined very carefully. Specifically, we conducted user surveys and used the organization structures on YouTube and Vimeo as references, and browsed nu- merous videos to identify categories that satisfy the following three criteria: (1) utility — high relevance in supporting practical application needs; (2) cover- age — a good coverage of the contents that people record; and (3) feasibility — likely to be automatically recognized in the next several years, and a high frequency of occurrence that is sufficient for training a recognition algorithm. This definition effort led to a set of over 250 candi- date categories. For each category, in addition to the official name used in the public release, we manually defined another alternative name. Videos were then downloaded from YouTube searches using the official and the alternative names as search terms. The pur- pose of using the alternative names was to expand the candidate video sets. For each search, we downloaded 1,000 videos, and after removing duplicate videos and some extremely long ones (longer than 30 minutes), there were around 1,000–1,500 candidate videos for each category. All the videos were annotated manually to ensure a high precision of the FCVID labels. In order to min- imize subjectivity, nearly 20 annotators were involved in the task, and a master annotator was assigned to monitor the entire process and double-check all the found positive videos. Some of the videos are multi- labeled, and thus filtering the 1,000–1,500 videos for each category with focus on just the single category label is not adequate. As checking the existence of all the 250+ classes for each video is extremely difficult, we use the following strategy to narrow down the “la- bel search space” for each video. We first grouped the categories according to subjective predictions of label co-occurrences, e.g., “wedding reception” & “wed- ding ceremony”, “waterfall” & “river”, “hiking” & “mountain”, and even “dog” & “birthday”. We then annotated the videos not only based on the target cat- egory label, but also according to the identified related labels. This helped produce a fairly complete label set for FCVID, but largely reduced the annotation workload. After removing the rare categories with less than 100 videos after annotation, the final FCVID dataset contains 91,223 videos and 239 categories, where 183 are events and 56 are objects, scenes, etc. Figure 1 shows the number of videos per category. “Dog” has the largest number of positive videos (1,136), while “making egg tarts” is the most infre- quent category containing only 108 samples. The total duration of FCVID is 4,232 hours with an average video duration of 167 seconds. Figure 2 further gives the average video duration of each category. The categories are organized using a hierarchy con- taining 11 high-level groups, as visualized in Figure 3. 3 COMPARISON WITH RELATED DATASETS We compare FCVID with the following datasets. Most of them have been widely adopted in the existing works on video categorization. KTH and Weizmann: The KTH [1] and the Weiz- mann [2] datasets are well-known benchmarks for human action recognition. The former contains 600 videos of 6 human actions performed by 25 people in four scenarios, and the latter consists of 81 videos associated with 9 actions performed by 9 actors. Hollywood Human Action: The Hollywood dataset [3] contains 8 action classes collected from 32 Hollywood movies with a total of 430 videos. |
| d7b6bbb94ac20f5e75893f140ef7e207db7cd483 | Griffith Research Online
https://research-repository.griffith.edu.au Face Recognition across Pose: A Review Author Zhang, Paul, Gao, Yongsheng Published 2009 Journal Title Pattern Recognition DOI 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 |
| d78373de773c2271a10b89466fe1858c3cab677f | |
| d03265ea9200a993af857b473c6bf12a095ca178 | Multiple deep convolutional neural
networks averaging for face alignment Zhouping Yin Downloaded From: http://electronicimaging.spiedigitallibrary.org/ on 05/28/2015 Terms of Use: http://spiedl.org/terms |
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| d0144d76b8b926d22411d388e7a26506519372eb | Improving Regression Performance with Distributional Losses |
| d02e27e724f9b9592901ac1f45830341d37140fe | DA-GAN: Instance-level Image Translation by Deep Attention Generative
Adversarial Networks The State Universtiy of New York at Buffalo The State Universtiy of New York at Buffalo Microsoft Research Microsoft Research |
| d0a21f94de312a0ff31657fd103d6b29db823caa | Facial Expression Analysis |
| d03e4e938bcbc25aa0feb83d8a0830f9cd3eb3ea | Face Recognition with Patterns of Oriented
Edge Magnitudes 1 Vesalis Sarl, Clermont Ferrand, France 2 Gipsa-lab, Grenoble INP, France |
| d00787e215bd74d32d80a6c115c4789214da5edb | Faster and Lighter Online
Sparse Dictionary Learning Project report |
| be8c517406528edc47c4ec0222e2a603950c2762 | Harrigan / The new handbook of methods in nonverbal behaviour research 02-harrigan-chap02 Page Proof page 7
17.6.2005 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 |
| be48b5dcd10ab834cd68d5b2a24187180e2b408f | FOR PERSONAL USE ONLY
Constrained Low-rank Learning Using Least Squares Based Regularization |
| be437b53a376085b01ebd0f4c7c6c9e40a4b1a75 | 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 BE, DSCE, Bangalore1 Assistant Professor, DSCE, Bangalore2 |
| bebea83479a8e1988a7da32584e37bfc463d32d4 | Discovery of Latent 3D Keypoints via
End-to-end Geometric Reasoning Google AI |
| bef503cdfe38e7940141f70524ee8df4afd4f954 | |
| beab10d1bdb0c95b2f880a81a747f6dd17caa9c2 | DeepDeblur: Fast one-step blurry face images restoration
Tsinghua Unversity |
| b331ca23aed90394c05f06701f90afd550131fe3 | 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 and Video Processing R ES EAR CH Double regularized matrix factorization for image classification and clustering Open Access |
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International Journal of Modern Engineering Research (IJMER) A novel approach for performance parameter estimation of face recognition based on clustering, shape and corner detection |
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3069 |
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| df90850f1c153bfab691b985bfe536a5544e438b | FACE TRACKING ALGORITHM ROBUST TO POSE,
ILLUMINATION AND FACE EXPRESSION CHANGES: A 3D PARAMETRIC MODEL APPROACH 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 | International Journal of Computer Applications (0975 – 8887)
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UC Berkeley |
| dfabe7ef245ca68185f4fcc96a08602ee1afb3f7 | |
| df51dfe55912d30fc2f792561e9e0c2b43179089 | Face Hallucination using Linear Models of Coupled
Sparse Support 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 between 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 category of super-resolution methods, since they can provide higher quality images and larger magnification factors. |
| df80fed59ffdf751a20af317f265848fe6bfb9c9 | 1666
Learning Deep Sharable and Structural Detectors for Face Alignment |
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(will be inserted by the editor) A Recursive Framework for Expression Recognition: From Web Images to Deep Models to Game Dataset Received: date / Accepted: date |
| df5fe0c195eea34ddc8d80efedb25f1b9034d07d | Robust Modified Active Shape Model for Automatic Facial Landmark
Annotation of Frontal Faces |
| df674dc0fc813c2a6d539e892bfc74f9a761fbc8 | 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 |
| da4170c862d8ae39861aa193667bfdbdf0ecb363 | Multi-task CNN Model for Attribute Prediction |
| da15344a4c10b91d6ee2e9356a48cb3a0eac6a97 | |
| da5bfddcfe703ca60c930e79d6df302920ab9465 | |
| dac2103843adc40191e48ee7f35b6d86a02ef019 | 854
Unsupervised Celebrity Face Naming in Web Videos |
| dae420b776957e6b8cf5fbbacd7bc0ec226b3e2e | RECOGNIZING EMOTIONS IN SPONTANEOUS FACIAL EXPRESSIONS
Institut f¨ur Nachrichtentechnik Universit¨at Karlsruhe (TH), Germany |
| daba8f0717f3f47c272f018d0a466a205eba6395 | |
| daefac0610fdeff415c2a3f49b47968d84692e87 | New Orleans, Louisiana, June 1 - 6, 2018. c(cid:13)2018 Association for Computational Linguistics
Proceedings of NAACL-HLT 2018, pages 1481–1491 1481 |
| b49affdff167f5d170da18de3efa6fd6a50262a2 | Author manuscript, published in "Workshop on Faces in 'Real-Life' Images: Detection, Alignment, and Recognition, Marseille : France
(2008)" |
| b41374f4f31906cf1a73c7adda6c50a78b4eb498 | 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 |
| b4d7ca26deb83cec1922a6964c1193e8dd7270e7 | |
| b4ee64022cc3ccd14c7f9d4935c59b16456067d3 | Unsupervised Cross-Domain Image Generation |
| b40290a694075868e0daef77303f2c4ca1c43269 | 第 40 卷 第 4 期
2014 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 AI Hai-Zhou1 |
| a2359c0f81a7eb032cff1fe45e3b80007facaa2a | Towards Structured Analysis of Broadcast Badminton Videos
C.V.Jawahar CVIT, KCIS, IIIT Hyderabad |
| a2d9c9ed29bbc2619d5e03320e48b45c15155195 | |
| a2b54f4d73bdb80854aa78f0c5aca3d8b56b571d | |
| a27735e4cbb108db4a52ef9033e3a19f4dc0e5fa | Intention from Motion |
| a50b4d404576695be7cd4194a064f0602806f3c4 | In Proceedings of BMVC, Edimburgh, UK, September 2006
Efficiently estimating facial expression and illumination in appearance-based tracking †ESCET, U. Rey Juan Carlos C/ Tulip´an, s/n 28933 M´ostoles, Spain ‡Facultad Inform´atica, UPM Campus de Montegancedo s/n 28660 Boadilla del Monte, Spain http://www.dia.fi.upm.es/~pcr |
| a56c1331750bf3ac33ee07004e083310a1e63ddc | Vol. xx, pp. x
c(cid:13) xxxx Society for Industrial and Applied Mathematics x–x Efficient Point-to-Subspace Query in (cid:96)1 with Application to Robust Object Instance Recognition |
| a54e0f2983e0b5af6eaafd4d3467b655a3de52f4 | Face Recognition Using Convolution Filters and
Neural Networks Head, Dept. of E&E,PEC Sec-12, Chandigarh – 160012 Department of CSE & IT, PEC Sec-12, Chandigarh – 160012 C.P. Singh Physics Department, CFSL, Sec-36, Chandigarh - 160036 a of to: (a) potential method |
| a55efc4a6f273c5895b5e4c5009eabf8e5ed0d6a | 818
Continuous Head Movement Estimator for Driver Assistance: Issues, Algorithms, and On-Road Evaluations Mohan Manubhai Trivedi, Fellow, IEEE |
| a5c04f2ad6a1f7c50b6aa5b1b71c36af76af06be | |
| a503eb91c0bce3a83bf6f524545888524b29b166 | |
| a5a44a32a91474f00a3cda671a802e87c899fbb4 | Moments in Time Dataset: one million
videos for event understanding |
| bd9eb65d9f0df3379ef96e5491533326e9dde315 | |
| bd07d1f68486052b7e4429dccecdb8deab1924db | |
| bd8e2d27987be9e13af2aef378754f89ab20ce10 | |
| bd2d7c7f0145028e85c102fe52655c2b6c26aeb5 | 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 IBM Watson |
| bdbba95e5abc543981fb557f21e3e6551a563b45 | 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 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- dimensional data. We compare this approach with the standard procedure of optimizing the hyperparameters only on the original input. We perform experiments with various state-of-the-art hyperparameter optimization algo- rithms such as random search, the tree of parzen estimators (TPEs), sequential model-based algorithm con¯guration (SMAC), and a genetic algorithm (GA). Our experiments indicate that it is possible to speed up the optimization process by using lower-dimensional data repre- sentations at the beginning, while increasing the dimensionality of the input later in the opti- mization process. This is independent of the underlying optimization procedure, making the approach promising for many existing hyperparameter optimization algorithms. Keywords: Hyperparameter optimization; hyperparameter importance; convolutional neural networks; genetic algorithm; Bayesian optimization. 1. Introduction The performance of many contemporary machine learning algorithms depends cru- cially on the speci¯c initialization of hyperparameters such as the general architec- ture, the learning rate, regularization parameters, and many others.1,2 Indeed, This is an Open Access article published by World Scienti¯c Publishing Company. It is distributed under the terms of the Creative Commons Attribution 4.0 (CC-BY) License. Further distribution of this work is permitted, provided the original work is properly cited. 1850008-1 Int. J. Comp. Intel. Appl. 2018.17. Downloaded from www.worldscientific.comby WSPC on 07/18/18. Re-use and distribution is strictly not permitted, except for Open Access articles. |
| d1dfdc107fa5f2c4820570e369cda10ab1661b87 | Super SloMo: High Quality Estimation of Multiple Intermediate Frames
for Video Interpolation Erik Learned-Miller1 1UMass Amherst 2NVIDIA 3UC Merced |
| d1a43737ca8be02d65684cf64ab2331f66947207 | IJB–S: IARPA Janus Surveillance Video Benchmark (cid:3)
Kevin O’Connor z |
| d1082eff91e8009bf2ce933ac87649c686205195 | (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 |
| d69df51cff3d6b9b0625acdcbea27cd2bbf4b9c0 | |
| d6102a7ddb19a185019fd2112d2f29d9258f6dec | Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17)
3721 |
| d6bfa9026a563ca109d088bdb0252ccf33b76bc6 | Unsupervised Temporal Segmentation of Facial Behaviour
Department of Computer Science and Engineering, IIT Kanpur |
| d6fb606e538763282e3942a5fb45c696ba38aee6 | |
| bc9003ad368cb79d8a8ac2ad025718da5ea36bc4 | Technische Universit¨at M¨unchen
Bildverstehen und Intelligente Autonome Systeme Facial Expression Recognition With A Three-Dimensional Face Model Vollst¨andiger Abdruck der von der Fakult¨at f¨ur Informatik der Technischen Uni- versit¨at M¨unchen zur Erlangung des akademischen Grades eines Doktors der Naturwissenschaften genehmigten Dissertation. Vorsitzender: Univ.-Prof. Dr. Johann Schlichter Pr¨ufer der Dissertation: 1. Univ.-Prof. Dr. Bernd Radig (i.R.) 2. Univ.-Prof. Gudrun J. Klinker, Ph.D. Die Dissertation wurde am 04.07.2011 bei der Technischen Universit¨at M¨unchen eingereicht und durch die Fakult¨at f¨ur Informatik am 02.12.2011 angenommen. |
| bcc346f4a287d96d124e1163e4447bfc47073cd8 | |
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2197 ICASSP 2017 |
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| bc2852fa0a002e683aad3fb0db5523d1190d0ca5 | |
| bcb99d5150d792001a7d33031a3bd1b77bea706b | |
| bc811a66855aae130ca78cd0016fd820db1603ec | Towards three-dimensional face recognition in the real
To cite this version: HAL Id: tel-00998798 https://tel.archives-ouvertes.fr/tel-00998798 Submitted on 2 Jun 2014 archive for the deposit and dissemination of sci- entific research documents, whether they are pub- teaching and research institutions in France or destin´ee au d´epˆot et `a la diffusion de documents recherche fran¸cais ou ´etrangers, des laboratoires |
| bc9af4c2c22a82d2c84ef7c7fcc69073c19b30ab | MoCoGAN: Decomposing Motion and Content for Video Generation
Snap Research NVIDIA |
| bcac3a870501c5510df80c2a5631f371f2f6f74a | CVPR
#1387 000 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048 049 050 051 052 053 CVPR 2013 Submission #1387. CONFIDENTIAL REVIEW COPY. DO NOT DISTRIBUTE. CVPR #1387 Structured Face Hallucination Anonymous CVPR submission Paper ID 1387 |
| aed321909bb87c81121c841b21d31509d6c78f69 | |
| ae936628e78db4edb8e66853f59433b8cc83594f | |
| ae2cf545565c157813798910401e1da5dc8a6199 | 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 and Video Processing RESEARCH Open Access Cascade of Boolean detector combinations |
| aebb9649bc38e878baef082b518fa68f5cda23a5 | |
| aeff403079022683b233decda556a6aee3225065 | DeepFace: Face Generation using Deep Learning |
| ae753fd46a744725424690d22d0d00fb05e53350 | 000
001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 Describing Clothing by Semantic Attributes Anonymous ECCV submission Paper ID 727 |
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| ae85c822c6aec8b0f67762c625a73a5d08f5060d | 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 |
| d861c658db2fd03558f44c265c328b53e492383a | Automated Face Extraction and Normalization of 3D Mesh Data |
| d83d2fb5403c823287f5889b44c1971f049a1c93 | 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 Ó Springer Science+Business Media New York 2013 |
| d8b568392970b68794a55c090c4dd2d7f90909d2 | PDA Face Recognition System
Using Advanced Correlation Filters Chee Kiat Ng 2005 Advisor: Prof. Khosla/Reviere |
| d83ae5926b05894fcda0bc89bdc621e4f21272da | version of the following thesis:
Frugal Forests: Learning a Dynamic and Cost Sensitive Feature Extraction Policy for Anytime Activity Classification |
| d89cfed36ce8ffdb2097c2ba2dac3e2b2501100d | Robust Face Recognition via Multimodal Deep
Face Representation |
| ab8f9a6bd8f582501c6b41c0e7179546e21c5e91 | Nonparametric Face Verification Using a Novel
Face Representation |
| ab58a7db32683aea9281c188c756ddf969b4cdbd | Efficient Solvers for Sparse Subspace Clustering |
| ab989225a55a2ddcd3b60a99672e78e4373c0df1 | Sample, Computation vs Storage Tradeoffs for
Classification Using Tensor Subspace Models |
| ab6776f500ed1ab23b7789599f3a6153cdac84f7 | International Journal of Scientific & Engineering Research, Volume 6, Issue 4, April-2015 1212
ISSN 2229-5518 A Survey on Various Facial Expression Techniques |
| ab2b09b65fdc91a711e424524e666fc75aae7a51 | Multi-modal Biomarkers to Discriminate Cognitive State*
1MIT Lincoln Laboratory, Lexington, Massachusetts, USA 2USARIEM, 3NSRDEC 1. 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 conditions 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 across different parts of the brain is essential in cognition (Figure 1). An example of timing and coordination within a modality is the set of finely timed and synchronized physiological components of speech production, while an example of coordination across modalities is the timing and synchrony that occurs across speech and facial expression while speaking. Third, we seek multimodal biomarkers that contribute in a complementary fashion under various channel and background conditions. In this chapter, as an illustration of this biomarker approach we focus on cognitive stress and the particular case of detecting different cognitive load levels. We also briefly show how similar feature-extraction principles can be applied to a neurological condition through the example of major depression disorder (MDD). MDD is one of several neurological disorders where multi-modal biomarkers based on principles of timing and coordination are important for detection [11]-[22]. In our cognitive load experiments, we use two easily obtained noninvasive modalities, voice and face, and show how these two modalities can be fused to produce results on par with more invasive, “gold-standard” EEG measurements. Vocal and facial biomarkers will also be used in our MDD case study. In both application areas we focus on timing and coordination relations within the components of each modality. * Distribution A: public release.This work is sponsored by the Assistant Secretary of Defense for Research & Engineering under Air Force contract #FA8721-05-C-0002. Opinions,interpretations, conclusions, and recommendations are those of the authors and are not necessarily endorsed by the United States Government. |
| ab87dfccb1818bdf0b41d732da1f9335b43b74ae | SUBMITTED TO IEEE TRANSACTIONS ON SIGNAL PROCESSING
Structured Dictionary Learning for Classification |
| ab1dfcd96654af0bf6e805ffa2de0f55a73c025d | |
| abeda55a7be0bbe25a25139fb9a3d823215d7536 | UNIVERSITATPOLITÈCNICADECATALUNYAProgramadeDoctorat:AUTOMÀTICA,ROBÒTICAIVISIÓTesiDoctoralUnderstandingHuman-CentricImages:FromGeometrytoFashionEdgarSimoSerraDirectors:FrancescMorenoNoguerCarmeTorrasMay2015 |
| ab1900b5d7cf3317d17193e9327d57b97e24d2fc | |
| ab8fb278db4405f7db08fa59404d9dd22d38bc83 | UNIVERSITÉ DE GENÈVE
Département d'Informatique FACULTÉ DES SCIENCES 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 par de Téhéran (IRAN) Thèse No 4368 GENÈVE Repro-Mail - Université de Genève 2011 |
| e5737ffc4e74374b0c799b65afdbf0304ff344cb | |
| e5823a9d3e5e33e119576a34cb8aed497af20eea | DocFace+: ID Document to Selfie* Matching |
| e5dfd17dbfc9647ccc7323a5d62f65721b318ba9 | |
| e56c4c41bfa5ec2d86c7c9dd631a9a69cdc05e69 | Human Activity Recognition Based on Wearable
Sensor Data: A Standardization of the State-of-the-Art Smart Surveillance Interest Group, Computer Science Department Universidade Federal de Minas Gerais, Brazil |
| e27c92255d7ccd1860b5fb71c5b1277c1648ed1e | |
| e200c3f2849d56e08056484f3b6183aa43c0f13a | |
| f437b3884a9e5fab66740ca2a6f1f3a5724385ea | Human Identification Technical Challenges
DARPA 3701 N. Fairfax Dr Arlington, VA 22203 |
| f442a2f2749f921849e22f37e0480ac04a3c3fec | |
| f4f6fc473effb063b7a29aa221c65f64a791d7f4 | 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. |
| f4c01fc79c7ead67899f6fe7b79dd1ad249f71b0 | |
| f4373f5631329f77d85182ec2df6730cbd4686a9 | Soft Computing manuscript No.
(will be inserted by the editor) Recognizing Gender from Human Facial Regions using Genetic Algorithm Received: date / Accepted: date |
| f47404424270f6a20ba1ba8c2211adfba032f405 | 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 |
| f3fcaae2ea3e998395a1443c87544f203890ae15 | |
| f3d9e347eadcf0d21cb0e92710bc906b22f2b3e7 | NosePose: a competitive, landmark-free
methodology for head pose estimation in the wild IMAGO Research Group - Universidade Federal do Paran´a |
| f355e54ca94a2d8bbc598e06e414a876eb62ef99 | |
| f3ea181507db292b762aa798da30bc307be95344 | Covariance Pooling for Facial Expression Recognition
†Computer Vision Lab, ETH Zurich, Switzerland ‡VISICS, KU Leuven, Belgium |
| f3cf10c84c4665a0b28734f5233d423a65ef1f23 | 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 2008 URL 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 component of this work in other works must be obtained from the IEEE. |
| f3b7938de5f178e25a3cf477107c76286c0ad691 | JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, MARCH 2017
Object Detection with Deep Learning: A Review |
| ebedc841a2c1b3a9ab7357de833101648281ff0e | |
| eb526174fa071345ff7b1fad1fad240cd943a6d7 | Deeply Vulnerable – A Study of the Robustness of Face Recognition to
Presentation Attacks |
| eb566490cd1aa9338831de8161c6659984e923fd | From Lifestyle Vlogs to Everyday Interactions
EECS Department, UC Berkeley |
| eb9312458f84a366e98bd0a2265747aaed40b1a6 | 1-4244-1437-7/07/$20.00 ©2007 IEEE
IV - 473 ICIP 2007 |
| eb716dd3dbd0f04e6d89f1703b9975cad62ffb09 | Copyright
by 2012 |
| ebabd1f7bc0274fec88a3dabaf115d3e226f198f | Driver drowsiness detection system based on feature
representation learning using various deep networks School of Electrical Engineering, KAIST, Guseong-dong, Yuseong-gu, Dajeon, Rep. of Korea |
| ebb9d53668205c5797045ba130df18842e3eadef | |
| eb48a58b873295d719827e746d51b110f5716d6c | Face Alignment Using K-cluster Regression Forests
With Weighted Splitting |
| c7e4c7be0d37013de07b6d829a3bf73e1b95ad4e | The International Journal of Multimedia & Its Applications (IJMA) Vol.5, No.5, October 2013
DYNEMO: A VIDEO DATABASE OF NATURAL FACIAL EXPRESSIONS OF EMOTIONS 1LIP, Univ. Grenoble Alpes, BP 47 - 38040 Grenoble Cedex 9, France 2LIG, Univ. Grenoble Alpes, BP 53 - 38041 Grenoble Cedex 9, France |
| c7c5f0fe1fcaf3787c7f78f7dc62f3497dcfdf3c | THE IMPACT OF PRODUCT PHOTO ON ONLINE CONSUMER
PURCHASE INTENTION: AN IMAGE-PROCESSING ENABLED EMPIRICAL STUDY |
| c758b9c82b603904ba8806e6193c5fefa57e9613 | Heterogeneous Face Recognition with CNNs
INRIA Grenoble, Laboratoire Jean Kuntzmann |
| c7c8d150ece08b12e3abdb6224000c07a6ce7d47 | DeMeshNet: Blind Face Inpainting for Deep MeshFace Verification
National Laboratory of Pattern Recognition, CASIA Center for Research on Intelligent Perception and Computing, CASIA |
| c038beaa228aeec174e5bd52460f0de75e9cccbe | Temporal Segment Networks for Action
Recognition in Videos |
| c043f8924717a3023a869777d4c9bee33e607fb5 | Emotion Separation Is Completed Early and It Depends
on Visual Field Presentation Lab for Human Brain Dynamics, RIKEN Brain Science Institute, Wakoshi, Saitama, Japan, 2 Lab for Human Brain Dynamics, AAI Scientific Cultural Services Ltd., Nicosia Cyprus |
| c05a7c72e679745deab9c9d7d481f7b5b9b36bdd | NPS-CS-11-005
NAVAL POSTGRADUATE SCHOOL MONTEREY, CALIFORNIA by BIOMETRIC CHALLENGES FOR FUTURE DEPLOYMENTS: A STUDY OF THE IMPACT OF GEOGRAPHY, CLIMATE, CULTURE, AND SOCIAL CONDITIONS ON THE EFFECTIVE COLLECTION OF BIOMETRICS April 2011 Approved for public release; distribution is unlimited |
| c02847a04a99a5a6e784ab580907278ee3c12653 | Fine Grained Video Classification for
Endangered Bird Species Protection Non-Thesis MS Final Report 1. Introduction 1.1 Background This project is about detecting eagles in videos. Eagles are endangered species at the brim of extinction since 1980s. With the bans of harmful pesticides, the number of eagles keep increasing. However, recent studies on golden eagles’ activities in the vicinity of wind turbines have shown significant number of turbine blade collisions with eagles as the major cause of eagles’ mortality. [1] This project is a part of a larger research project to build an eagle detection and deterrent system on wind turbine toward reducing eagles’ mortality. [2] The critical component of this study is a computer vision system for eagle detection in videos. The key requirement are that the system should work in real time and detect eagles at a far distance from the camera (i.e. in low resolution). There are three different bird species in my dataset - falcon, eagle and seagull. The reason for involving only these three species is based on the real world situation. Wind turbines are always installed near coast and mountain hill where falcons and seagulls will be the majority. So my model will classify the minority eagles out of other bird species during the immigration season and protecting them by using the deterrent system. 1.2 Brief Approach Our approach represents a unified deep-learning architecture for eagle detection. Given videos, our goal is to detect eagle species at far distance from the camera, using both appearance and bird motion cues, so as to meet the recall-precision rates set by the user. Detecting eagle is a challenging task because of the following reasons. Frist, an eagle flies fast and high in the sky which means that we need a lens with wide angle such that captures their movement. However, a camera with wide angle produces a low resolution and low quality video and the detailed appearance of bird is compromised. Second, current neural network typically take as input low resolution images. This is because a higher resolution image will require larger filters and deeper networks which is turn hard to train [3]. So it is not clear whether the low resolution will cause challenge for fine-grained classification task. Last but not the least, there is not a large training database like PASCAL, MNIST |
| c0c8d720658374cc1ffd6116554a615e846c74b5 | JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015
Modeling Multimodal Clues in a Hybrid Deep Learning Framework for Video Classification |
| c0d5c3aab87d6e8dd3241db1d931470c15b9e39d | |
| eee8a37a12506ff5df72c402ccc3d59216321346 | Uredniki:
dr. Tomaž Erjavec Odsek za tehnologije znanja Institut »Jožef Stefan«, Ljubljana dr. Jerneja Žganec Gros Alpineon d.o.o, Ljubljana Založnik: Institut »Jožef Stefan«, Ljubljana Tisk: Birografika BORI d.o.o. Priprava zbornika: Mitja Lasič Oblikovanje naslovnice: dr. Damjan Demšar Tiskano iz predloga avtorjev Naklada: 50 Ljubljana, oktober 2008 Konferenco IS 2008 sofinancirata Ministrstvo za visoko šolstvo, znanost in tehnologijo Institut »Jožef Stefan« ISSN 1581-9973 CIP - Kataložni zapis o publikaciji Narodna in univerzitetna knjižnica, Ljubljana 004.934(082) 81'25:004.6(082) 004.8(063) oktober 2008, Ljubljana, Slovenia : zbornik 11. mednarodne Proceedings of the Sixth Language Technologies Conference, October 16th-17th, 2008 : proceedings of the 11th International Multiconference Information Society - IS 2008, volume C / uredila, edited by Tomaž Erjavec, Jerneja Žganec Gros. - Ljubljana : 1581-9973) ISBN 978-961-264-006-4 družba 4. Information society 5. Erjavec, Tomaž, 1960- 6. Ljubljana) 241520896 |
| ee18e29a2b998eddb7f6663bb07891bfc7262248 | 1119
Local Linear Discriminant Analysis Framework Using Sample Neighbors |
| ee461d060da58d6053d2f4988b54eff8655ecede | |
| eefb8768f60c17d76fe156b55b8a00555eb40f4d | Subspace Scores for Feature Selection in Computer Vision |
| eed1dd2a5959647896e73d129272cb7c3a2e145c | |
| ee92d36d72075048a7c8b2af5cc1720c7bace6dd | FACE RECOGNITION USING MIXTURES OF PRINCIPAL COMPONENTS
Video and Display Processing Philips Research USA Briarcliff Manor, NY 10510 |
| eedfb384a5e42511013b33104f4cd3149432bd9e | Multimodal Probabilistic Person
Tracking and Identification in Smart Spaces zur Erlangung des akademischen Grades eines Doktors der Ingenieurwissenschaften der Fakultät für Informatik der Universität Fridericiana zu Karlsruhe (TH) genehmigte Dissertation von aus Karlsruhe Tag der mündlichen Prüfung: 20.11.2009 Erster Gutachter: Zweiter Gutachter: Prof. Dr. A. Waibel Prof. Dr. R. Stiefelhagen |
| c9424d64b12a4abe0af201e7b641409e182babab | Article
Which, When, and How: Hierarchical Clustering with Human–Machine Cooperation Academic Editor: Tom Burr Received: 3 November 2016; Accepted: 14 December 2016; Published: 21 December 2016 |
| c903af0d69edacf8d1bff3bfd85b9470f6c4c243 | |
| fc1e37fb16006b62848def92a51434fc74a2431a | DRAFT
A Comprehensive Analysis of Deep Regression |
| fc516a492cf09aaf1d319c8ff112c77cfb55a0e5 | |
| fcd3d69b418d56ae6800a421c8b89ef363418665 | Effects of Aging over Facial Feature Analysis and Face
Recognition Bogaziçi Un. Electronics Eng. Dept. March 2010 |
| fcd77f3ca6b40aad6edbd1dab9681d201f85f365 | c(cid:13)Copyright 2014 |
| fcf8bb1bf2b7e3f71fb337ca3fcf3d9cf18daa46 | MANUSCRIPT SUBMITTED TO IEEE TRANS. PATTERN ANAL. MACH. INTELL., JULY 2010
Feature Selection via Sparse Approximation for Face Recognition |
| fcbf808bdf140442cddf0710defb2766c2d25c30 | IJCV manuscript No.
(will be inserted by the editor) Unsupervised Semantic Action Discovery from Video Collections Received: date / Accepted: date |
| fd4ac1da699885f71970588f84316589b7d8317b | JOURNAL OF LATEX CLASS FILES, VOL. 6, NO. 1, JANUARY 2007
Supervised Descent Method for Solving Nonlinear Least Squares Problems in Computer Vision |
| fdf533eeb1306ba418b09210387833bdf27bb756 | 951 |
| fdda5852f2cffc871fd40b0cb1aa14cea54cd7e3 | Im2Flow: Motion Hallucination from Static Images for Action Recognition
UT Austin UT Austin UT Austin |
| fdfaf46910012c7cdf72bba12e802a318b5bef5a | Computerized Face Recognition in Renaissance
Portrait Art |
| fd15e397629e0241642329fc8ee0b8cd6c6ac807 | Semi-Supervised Clustering with Neural Networks
IIIT-Delhi, India |
| fdca08416bdadda91ae977db7d503e8610dd744f |
ICT-2009.7.1 KSERA Project 2010-248085 Deliverable D3.1 Deliverable D3.1 Human Robot Interaction Human Robot Interaction 18 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 agreement n°2010-248085. |
| fdaf65b314faee97220162980e76dbc8f32db9d6 | Accepted Manuscript
Face recognition using both visible light image and near-infrared image and a deep network PII: DOI: Reference: S2468-2322(17)30014-8 10.1016/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: 10.1016/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 copyediting, 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 | |
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| f26097a1a479fb6f32b27a93f8f32609cfe30fdc | |
| f231046d5f5d87e2ca5fae88f41e8d74964e8f4f | We are IntechOpen,
the first native scientific publisher of Open Access books 3,350 108,000 1.7 M Open access books available International authors and editors Downloads Our authors are among the 151 Countries delivered to TOP 1% 12.2% 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? Numbers displayed above are based on latest data collected. For more information visit www.intechopen.com |
| f214bcc6ecc3309e2efefdc21062441328ff6081 | |
| f5770dd225501ff3764f9023f19a76fad28127d4 | Real Time Online Facial Expression Transfer
with Single Video Camera |
| f519723238701849f1160d5a9cedebd31017da89 | Impact of multi-focused images on recognition of soft biometric traits
aEURECOM, Campus SophiaTech, 450 Route des Chappes, CS 50193 - 06904 Biot Sophia Antipolis cedex, FRANCE |
| f558af209dd4c48e4b2f551b01065a6435c3ef33 | 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 #Student,Cse, CIET, Lam,Guntur, India * Assistant Professort,Cse, CIET, Lam,Guntur , India |
| e393a038d520a073b9835df7a3ff104ad610c552 | Automatic temporal segment
detection via bilateral long short- term memory recurrent neural networks 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 |
| e3657ab4129a7570230ff25ae7fbaccb4ba9950c | |
| e315959d6e806c8fbfc91f072c322fb26ce0862b | 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 |
| e3c011d08d04c934197b2a4804c90be55e21d572 | How to Train Triplet Networks with 100K Identities?
Orion Star Beijing, China Orion Star Beijing, China Orion Star Beijing, China |
| e39a0834122e08ba28e7b411db896d0fdbbad9ba | 1368
Maximum Likelihood Estimation of Depth Maps Using Photometric Stereo |
| e3917d6935586b90baae18d938295e5b089b5c62 | 152
Face Localization and Authentication Using Color and Depth Images |
| cfa572cd6ba8dfc2ee8ac3cc7be19b3abff1a8a2 | |
| cfffae38fe34e29d47e6deccfd259788176dc213 | TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. X, NO. X, DECEMBER 2012
Matrix Completion for Weakly-supervised Multi-label Image Classification |
| cfd4004054399f3a5f536df71f9b9987f060f434 | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. ??, NO. ??, ?? 20??
Person Recognition in Personal Photo Collections |
| cfb8bc66502fb5f941ecdb22aec1fdbfdb73adce | |
| cf875336d5a196ce0981e2e2ae9602580f3f6243 | 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 and talked of how a Bamey stuffed animal [the purple dinosaur for kids) "has emotions." He did not define what he meant by this, but after repeating it several times, it became apparent that children attributed 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 colleagues 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 computer 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 any philosophical sense. However, as an engineer I am interested in what capabilities I would require a machine to have before I would say that it "has emotions," if that is even possible. Theorists still grappl~ with the problem of defining emotion, after many decades of discussion, and no clean definition looks likely to emerge. Even without a precise definition, one can still begin to say concrete things about certain components of emotion, at least based on what is known about human and animal emo- tions. Of course, much is still u d a o w n about human emotions, so we are nowhere near being able to model them, much less dupli- cate all their functions in machines.'~lso, all scientific findings are subject to revision-history has certainly taught us humility, that what scientists believed to be true at one point has often been changed at a later date. I wish to begin by mentioning four motivations for giving machines certain emotional abilities (and there are more). One goal is to build robots and synthetic characters that can emulate living humans and animals-for example, to build a humanoid robot. A I |
| cf54a133c89f730adc5ea12c3ac646971120781c | |
| cfbb2d32586b58f5681e459afd236380acd86e28 | Improving Alignment of Faces for Recognition
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 | 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 Received: date / Accepted: date |
| cfdc632adcb799dba14af6a8339ca761725abf0a | Probabilistic Formulations of Regression with Mixed
Guidance |
| cfc30ce53bfc204b8764ebb764a029a8d0ad01f4 | Regularizing Deep Neural Networks by Noise:
Its Interpretation and Optimization Dept. of Computer Science and Engineering, POSTECH, Korea |
| cf86616b5a35d5ee777585196736dfafbb9853b5 | 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 |
| cad52d74c1a21043f851ae14c924ac689e197d1f | From Ego to Nos-vision:
Detecting Social Relationships in First-Person Views Universit`a degli Studi di Modena e Reggio Emilia Via Vignolese 905, 41125 Modena - Italy |
| cac8bb0e393474b9fb3b810c61efdbc2e2c25c29 | |
| cad24ba99c7b6834faf6f5be820dd65f1a755b29 | Understanding hand-object
manipulation by modeling the contextual relationship between actions, grasp types and object attributes Journal Title XX(X):1–14 c(cid:13)The Author(s) 2016 Reprints and permission: sagepub.co.uk/journalsPermissions.nav DOI: 10.1177/ToBeAssigned www.sagepub.com/ |
| cadba72aa3e95d6dcf0acac828401ddda7ed8924 | 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 par 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 2006 |
| ca606186715e84d270fc9052af8500fe23befbda | Using Subclass Discriminant Analysis, Fuzzy Integral and Symlet Decomposition for
Face Recognition Department of Electrical Engineering, Iran Univ. of Science and Technology, Narmak, Tehran, Iran Department of Electrical Engineering, Iran Univ. of Science and Technology, Department of Electrical Engineering, Iran Univ. of Science and Technology, Narmak, Tehran, Iran Narmak, Tehran, Iran |
| e465f596d73f3d2523dbf8334d29eb93a35f6da0 | |
| e4aeaf1af68a40907fda752559e45dc7afc2de67 | |
| e4c3d5d43cb62ac5b57d74d55925bdf76205e306 | |
| e4a1b46b5c639d433d21b34b788df8d81b518729 | JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015
Side Information for Face Completion: a Robust PCA Approach |
| e4c81c56966a763e021938be392718686ba9135e | |
| e4e95b8bca585a15f13ef1ab4f48a884cd6ecfcc | Face Recognition with Independent Component Based
Super-resolution aFaculty of Engineering and Natural Sciences, Sabanci Univ., Istanbul, Turkiye, 34956 bSchool 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 |
| e476cbcb7c1de73a7bcaeab5d0d59b8b3c4c1cbf | |
| e475e857b2f5574eb626e7e01be47b416deff268 | Facial Emotion Recognition Using Nonparametric
Weighted Feature Extraction and Fuzzy Classifier |
| e4391993f5270bdbc621b8d01702f626fba36fc2 | Author manuscript, published in "18th Scandinavian Conference on Image Analysis (2013)"
DOI : 10.1007/978-3-642-38886-6_31 |
| e4d8ba577cabcb67b4e9e1260573aea708574886 | 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 |
| e475deadd1e284428b5e6efd8fe0e6a5b83b9dcd | Accepted in Pattern Recognition Letters
Pattern Recognition Letters journal homepage: www.elsevier.com Are you eligible? Predicting adulthood from face images via class specific mean autoencoder IIIT-Delhi, New Delhi, 110020, India Article history: Received 15 March 2017 |
| e4d0e87d0bd6ead4ccd39fc5b6c62287560bac5b | Implicit Video Multi-Emotion Tagging by Exploiting Multi-Expression
Relations |
| fe9c460d5ca625402aa4d6dd308d15a40e1010fa | Neural Architecture for Temporal Emotion
Classification Universit¨at Ulm, Neuroinformatik, Germany |
| fe7c0bafbd9a28087e0169259816fca46db1a837 | |
| fe48f0e43dbdeeaf4a03b3837e27f6705783e576 | |
| fea83550a21f4b41057b031ac338170bacda8805 | Learning a Metric Embedding
for Face Recognition using the Multibatch Method Orcam Ltd., Jerusalem, Israel |
| feeb0fd0e254f38b38fe5c1022e84aa43d63f7cc | EURECOM
Multimedia Communications Department and Mobile Communications Department 2229, route des Crˆetes B.P. 193 06904 Sophia-Antipolis FRANCE Research Report RR-11-255 Search Pruning with Soft Biometric Systems: Efficiency-Reliability Tradeoff June 1st, 2011 Last update June 1st, 2011 1EURECOM’s research is partially supported by its industrial members: BMW Group, Cisco, Monaco Telecom, Orange, SAP, SFR, Sharp, STEricsson, Swisscom, Symantec, Thales. |
| fe108803ee97badfa2a4abb80f27fa86afd9aad9 | |
| fe0c51fd41cb2d5afa1bc1900bbbadb38a0de139 | 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 |
| c8db8764f9d8f5d44e739bbcb663fbfc0a40fb3d | 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 aus 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 conditional 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 applied a semantic scene match technique. These traditional algorithms can be applied to particular image generation tasks, such as texture synthesis and super-resolution. Their common characteristic 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, can we generate totally new images with the same distribution of the given ones? |
| c8a4b4fe5ff2ace9ab9171a9a24064b5a91207a3 | LOCATING FACIAL LANDMARKS WITH BINARY MAP CROSS-CORRELATIONS
J´er´emie Nicolle K´evin Bailly Univ. Pierre & Marie Curie, ISIR - CNRS UMR 7222, F-75005, Paris - France |
| c866a2afc871910e3282fd9498dce4ab20f6a332 | Noname manuscript No.
(will be inserted by the editor) Surveillance Face Recognition Challenge Received: date / Accepted: date |
| c82c147c4f13e79ad49ef7456473d86881428b89 | |
| c84233f854bbed17c22ba0df6048cbb1dd4d3248 | Exploring Locally Rigid Discriminative
Patches for Learning Relative Attributes http://researchweb.iiit.ac.in/~yashaswi.verma/ http://www.iiit.ac.in/~jawahar/ CVIT IIIT-Hyderabad, India http://cvit.iiit.ac.in |
| c8adbe00b5661ab9b3726d01c6842c0d72c8d997 | Deep Architectures for Face Attributes
Computer Vision and Machine Learning Group, Flickr, Yahoo, |
| fb4545782d9df65d484009558e1824538030bbb1 | |
| fb5280b80edcf088f9dd1da769463d48e7b08390 | |
| fba464cb8e3eff455fe80e8fb6d3547768efba2f |
International Journal of Engineering and Applied Sciences (IJEAS) ISSN: 2394-3661, Volume-3, Issue-2, February 2016 Survey Paper on Emotion Recognition |
| fbb2f81fc00ee0f257d4aa79bbef8cad5000ac59 | Reading Hidden Emotions: Spontaneous
Micro-expression Spotting and Recognition |
| fb9ad920809669c1b1455cc26dbd900d8e719e61 | 3D 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 PAR acceptée 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 2015 |
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Universit´e de Rennes 1/IRISA, CNRS/IRISA, INRIA Rennes-Bretagne Atlantique Campus de Beaulieu 35042 Rennes Cedex, France |
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(will be inserted by the editor) Transferring Object-Scene Convolutional Neural Networks for Event Recognition in Still Images Received: date / Accepted: date |
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(cid:2) Orange Labs, 4 rue Clos Courtel, 35512 Cesson-S´evign´e, France † Eurecom, 450 route des Chappes, 06410 Biot, France |
| 162403e189d1b8463952fa4f18a291241275c354 | Action Recognition with Spatio-Temporal
Visual Attention on Skeleton Image Sequences 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 based methods [9], [10]. Each skeleton frame is converted into a 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, a 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 learn the spatio-temporal relations with two RNN branches. the pre-defined body regions still |
| 160259f98a6ec4ec3e3557de5e6ac5fa7f2e7f2b | Discriminant Multi-Label Manifold Embedding for Facial Action Unit
Detection Signal Procesing Laboratory (LTS5), ´Ecole Polytechnique F´ed´erale de Lausanne, Switzerland |
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Detecting the Number of Clusters in n-Way Probabilistic Clustering |
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Anal´ı Alfaro P. Universidad Catolica de Chile P. Universidad Catolica de Chile P. Universidad Catolica de Chile Santiago, Chile Santiago, Chile Santiago, Chile |
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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 and Timing of Smiles Perceived as Amused, Polite, and Embarrassed/Nervous Ó Springer Science+Business Media, LLC 2008 |
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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, 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 |
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(Guest Editors) Volume 37 (2018), Number 2 GazeDirector: Fully Articulated Eye Gaze Redirection in Video ID: paper1004 |
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Automatic Facial Expression Recognition Using Features of Salient Facial Patches |
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BUPT BUPT BUPT |
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2EECS, UC Berkeley 1Facebook AI Research |
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| 42cc9ea3da1277b1f19dff3d8007c6cbc0bb9830 | Coordinated Local Metric Learning
Inria∗ |
| 42350e28d11e33641775bef4c7b41a2c3437e4fd | 212
Multilinear Discriminant Analysis for Face Recognition |
| 42e155ea109eae773dadf74d713485be83fca105 | |
| 4270460b8bc5299bd6eaf821d5685c6442ea179a | 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 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
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 to |
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| 42ecfc3221c2e1377e6ff849afb705ecd056b6ff | Pose Invariant Face Recognition under Arbitrary
Unknown Lighting using Spherical Harmonics Department of Computer Science, SUNY at Stony Brook, NY, 11790 |
| 421955c6d2f7a5ffafaf154a329a525e21bbd6d3 | 570
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 22, NO. 6, JUNE 2000 Evolutionary Pursuit and Its Application to Face Recognition |
| 42e0127a3fd6a96048e0bc7aab6d0ae88ba00fb0 | |
| 42df75080e14d32332b39ee5d91e83da8a914e34 | 4280
Illumination Compensation Using Oriented Local Histogram Equalization and Its Application to Face Recognition |
| 89945b7cd614310ebae05b8deed0533a9998d212 | Divide-and-Conquer Method for L1 Norm Matrix
Factorization in the Presence of Outliers and Missing Data |
| 89de30a75d3258816c2d4d5a733d2bef894b66b9 | |
| 8913a5b7ed91c5f6dec95349fbc6919deee4fc75 | BigBIRD: A Large-Scale 3D Database of Object Instances |
| 89d3a57f663976a9ac5e9cdad01267c1fc1a7e06 | Neural Class-Specific Regression for face
verification |
| 891b10c4b3b92ca30c9b93170ec9abd71f6099c4 | Facial landmark detection using structured output deep
neural networks Soufiane Belharbi ∗1, Cl´ement Chatelain∗1, Romain H´erault∗1, and S´ebastien 1LITIS EA 4108, INSA de Rouen, Saint ´Etienne du Rouvray 76800, France 2LITIS EA 4108, UFR des Sciences, Universit´e de Rouen, France. September 24, 2015 |
| 45c340c8e79077a5340387cfff8ed7615efa20fd | |
| 45e7ddd5248977ba8ec61be111db912a4387d62f | CHEN ET AL.: ADVERSARIAL POSENET
Adversarial Learning of Structure-Aware Fully Convolutional Networks for Landmark Localization |
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| 4560491820e0ee49736aea9b81d57c3939a69e12 | Investigating the Impact of Data Volume and
Domain Similarity on Transfer Learning Applications State Farm Insurance, Bloomington IL 61710, USA, |
| 4571626d4d71c0d11928eb99a3c8b10955a74afe | Geometry Guided Adversarial Facial Expression Synthesis
1National Laboratory of Pattern Recognition, CASIA 2Center for Research on Intelligent Perception and Computing, CASIA 3Center for Excellence in Brain Science and Intelligence Technology, CAS |
| 4534d78f8beb8aad409f7bfcd857ec7f19247715 | Under review as a conference paper at ICLR 2017
TRANSFORMATION-BASED MODELS OF VIDEO SEQUENCES Facebook AI Research |
| 459e840ec58ef5ffcee60f49a94424eb503e8982 | One-shot Face Recognition by Promoting Underrepresented Classes
Microsoft One Microsoft Way, Redmond, Washington, United States |
| 45fbeed124a8956477dbfc862c758a2ee2681278 | |
| 451c42da244edcb1088e3c09d0f14c064ed9077e | 1964
© EURASIP, 2011 - ISSN 2076-1465 19th European Signal Processing Conference (EUSIPCO 2011) INTRODUCTION |
| 4511e09ee26044cb46073a8c2f6e1e0fbabe33e8 | |
| 45a6333fc701d14aab19f9e2efd59fe7b0e89fec | 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 38271 Universidad de La Laguna, Spain Keywords: Image understanding, Gesture recognition, Hand dataset. |
| 1ffe20eb32dbc4fa85ac7844178937bba97f4bf0 | Face Clustering: Representation and Pairwise
Constraints |
| 1f8304f4b51033d2671147b33bb4e51b9a1e16fe | Noname manuscript No.
(will be inserted by the editor) Beyond Trees: MAP Inference in MRFs via Outer-Planar Decomposition Received: date / Accepted: date |
| 1f9ae272bb4151817866511bd970bffb22981a49 | An Iterative Regression Approach for Face Pose Estima-
tion from RGB Images 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 and 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 and 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
Computational Biomedicine Lab 4800 Calhoun Rd. Houston, TX, USA |
| 1fbde67e87890e5d45864e66edb86136fbdbe20e | The Action Similarity Labeling Challenge |
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| 1f2d12531a1421bafafe71b3ad53cb080917b1a7 | |
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Researchers IBM Research, Cambridge, MA, USA |
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UNC Charlotte Charlotte, NC 28223 NEC Lab America Cupertino, CA 95014 NEC Lab America Cupertino, CA 95014 UNC Charlotte Charlotte, NC 28223 |
| 1f745215cda3a9f00a65166bd744e4ec35644b02 | Facial Cosmetics Database and Impact Analysis on
Automatic Face Recognition # Computer Science Department, TU Muenchen Boltzmannstr. 3, 85748 Garching b. Muenchen, Germany ∗ Multimedia Communications Department, EURECOM 450 Route des Chappes, 06410 Biot, France |
| 1fff309330f85146134e49e0022ac61ac60506a9 | Data-Driven Sparse Sensor Placement for Reconstruction |
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| 73ed64803d6f2c49f01cffef8e6be8fc9b5273b8 | Noname manuscript No.
(will be inserted by the editor) Cooking in the kitchen: Recognizing and Segmenting Human Activities in Videos Received: date / Accepted: date |
| 7306d42ca158d40436cc5167e651d7ebfa6b89c1 | Noname manuscript No.
(will be inserted by the editor) Transductive Zero-Shot Action Recognition by Word-Vector Embedding Received: date / Accepted: date |
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http://jivp.eurasipjournals.com/content/2013/1/8 R ES EAR CH Open Access Automatic landmark point detection and tracking for human facial expressions |
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1 Graduate School of Psychology RESEARCH MASTER’S PSYCHOLOGY THESıS REPORT Teachers’ differing classroom behaviors: The role of emotional sensitivity and cultural tolerance Research Master’s, Social Psychology Ethics Committee Reference Code: 2016-SP-7084 |
| 73fbdd57270b9f91f2e24989178e264f2d2eb7ae | 978-1-4673-0046-9/12/$26.00 ©2012 IEEE
1945 ICASSP 2012 |
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| 871f5f1114949e3ddb1bca0982086cc806ce84a8 | Discriminative Learning of Apparel Features
1 Computer Vision Laboratory, D-ITET, ETH Z¨urich, Switzerland 2 ESAT - PSI / IBBT, K.U. Leuven, Belgium |
| 878169be6e2c87df2d8a1266e9e37de63b524ae7 | CBMM Memo No. 089
May 10, 2018 Image interpretation above and below the object level |
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Deep Facial Part Responses |
| 87e592ee1a7e2d34e6b115da08700a1ae02e9355 | Deep Pictorial Gaze Estimation
AIT Lab, Department of Computer Science, ETH Zurich |
| 87bb183d8be0c2b4cfceb9ee158fee4bbf3e19fd | Craniofacial Image Analysis |
| 8006219efb6ab76754616b0e8b7778dcfb46603d | CONTRIBUTIONSTOLARGE-SCALELEARNINGFORIMAGECLASSIFICATIONZeynepAkataPhDThesisl’´EcoleDoctoraleMath´ematiques,SciencesetTechnologiesdel’Information,InformatiquedeGrenoble |
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1831 ICASSP 2016 |
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EMOTIONS |
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| 80345fbb6bb6bcc5ab1a7adcc7979a0262b8a923 | Research Article
Soft Biometrics for a Socially Assistive Robotic Platform Open Access |
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Ottawa, Ontario, Canada, May 11 – 12, 2015 Paper No. 126 Subspace LDA Methods for Solving the Small Sample Size Problem in Face Recognition 101 KwanFu Rd., Sec. 2, Hsinchu, Taiwan |
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Progressive Face Identification |
| 74408cfd748ad5553cba8ab64e5f83da14875ae8 | Facial Expressions Tracking and Recognition: Database Protocols for Systems Validation
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| 747d5fe667519acea1bee3df5cf94d9d6f874f20 | |
| 74dbe6e0486e417a108923295c80551b6d759dbe | 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 Department of Management and Information Department of Management and Information Systems Science 1603-1 Kamitomioka, Nagaoka Niigata, Japan Systems Science 1603-1 Kamitomioka, Nagaoka Niigata, Japan |
| 747c25bff37b96def96dc039cc13f8a7f42dbbc7 | EmoNets: Multimodal deep learning approaches for emotion
recognition in video |
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978-1-5090-4846-5/16/$31.00 ©2016 IEEE 2784 |
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| 749d605dd12a4af58de1fae6f5ef5e65eb06540e | Multi-Task Video Captioning with Video and Entailment Generation
UNC Chapel Hill |
| 74c19438c78a136677a7cb9004c53684a4ae56ff | RESOUND: Towards Action Recognition
without Representation Bias UC San Diego |
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MLBoost Revisited: A Faster Metric Learning Algorithm for Identity-Based Face Retrieval Frederic Jurie Normandie Univ, UNICAEN, ENSICAEN, CNRS France |
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Matrix Approximation and Clustering |
| 744db9bd550bf5e109d44c2edabffec28c867b91 | FX e-Makeup for Muscle Based Interaction
1 Department of Informatics, PUC-Rio, Rio de Janeiro, Brazil 2 Department of Mechanical Engineering, PUC-Rio, Rio de Janeiro, Brazil 3 Department of Administration, PUC-Rio, Rio de Janeiro, Brazil |
| 744d23991a2c48d146781405e299e9b3cc14b731 | This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TIP.2016.2535284, IEEE
Transactions on Image Processing Aging Face Recognition: A Hierarchical Learning Model Based on Local Patterns Selection |
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White Rose Research Online URL for this paper: http://eprints.whiterose.ac.uk/131760/ Version: Accepted Version Proceedings Paper: Dai, Hang, Pears, Nicholas Edwin orcid.org/0000-0001-9513-5634, Smith, William Alfred Peter orcid.org/0000-0002-6047-0413 et al. (1 more author) (2018) Symmetric Shape Morphing for 3D Face and Head Modelling. In: The 13th IEEE Conference on Automatic Face and Gesture Recognition. IEEE . Reuse Items deposited in White Rose Research Online are protected by copyright, with all rights reserved unless indicated otherwise. They may be downloaded and/or printed for private study, or other acts as permitted by national copyright laws. The publisher or other rights holders may allow further reproduction and re-use of the full text version. This is indicated by the licence information on the White Rose Research Online record for the item. Takedown If you consider content in White Rose Research Online to be in breach of UK law, please notify us by https://eprints.whiterose.ac.uk/ |
| 1a3eee980a2252bb092666cf15dd1301fa84860e | PCA GAUSSIANIZATION FOR IMAGE PROCESSING
Image Processing Laboratory (IPL), Universitat de Val`encia Catedr´atico A. Escardino - 46980 Paterna, Val`encia, Spain |
| 1a031378cf1d2b9088a200d9715d87db8a1bf041 | Workshop track - ICLR 2018
DEEP DICTIONARY LEARNING: SYNERGIZING RE- CONSTRUCTION AND CLASSIFICATION |
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Discriminant Subspace Analysis for Face Recognition with Small Number of Training Samples |
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DIKU and MPIs T¨ubingen∗ Denmark and Germany DTU Compute∗ Lyngby, Denmark |
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| 1a7a2221fed183b6431e29a014539e45d95f0804 | Person Identification Using Text and Image Data
David S. Bolme, J. Ross Beveridge and Adele E. Howe Computer Science Department Colorado State Univeristy Fort Collins, Colorado 80523 |
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| 28d7029cfb73bcb4ad1997f3779c183972a406b4 | Discriminative Nonlinear Analysis Operator
Learning: When Cosparse Model Meets Image Classification |
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2662 ICASSP 2016 |
| 28cd46a078e8fad370b1aba34762a874374513a5 | CVPAPER.CHALLENGE IN 2016, JULY 2017
cvpaper.challenge in 2016: Futuristic Computer Vision through 1,600 Papers Survey |
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Ross Girshick5 David Weiss7 |
| 287900f41dd880802aa57f602e4094a8a9e5ae56 | |
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| 2866cbeb25551257683cf28f33d829932be651fe | In Proceedings of the 2018 IEEE International Conference on Image Processing (ICIP)
The final publication is available at: http://dx.doi.org/10.1109/ICIP.2018.8451026 A TWO-STEP LEARNING METHOD FOR DETECTING LANDMARKS ON FACES FROM DIFFERENT DOMAINS Erickson R. Nascimento Universidade Federal de Minas Gerais (UFMG), Brazil |
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| 28b061b5c7f88f48ca5839bc8f1c1bdb1e6adc68 | Predicting User Annoyance Using Visual Attributes
Virginia Tech Goibibo Virginia Tech Virginia Tech |
| 17a85799c59c13f07d4b4d7cf9d7c7986475d01c | ADVERTIMENT. La consulta d’aquesta tesi queda condicionada a l’acceptació de les següents
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Lehrstuhl f¨ur Mensch-Maschine-Kommunikation Immersive Interactive Data Mining and Machine Learning Algorithms for Big Data Visualization 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. sc.techn. Andreas Herkersdorf Pr¨ufer der Dissertation: 1. Univ.-Prof. Dr.-Ing. habil. Gerhard Rigoll 2. Univ.-Prof. Dr.-Ing. habil. Dirk Wollherr 3. Prof. Dr. Mihai Datcu Die Dissertation wurde am 13.08.2015 bei der Technischen Universit¨at M¨unchen eingerei- cht und durch die Fakult¨at f¨ur Elektrotechnik und Informationstechnik am 16.02.2016 angenommen. |
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Morphological and Bilateral Filters 1Signal Processing Laboratory (LTS5) ´Ecole Polytechnique F´ed´erale de Lausanne, Switzerland 2nViso SA Lausanne, Switzerland |
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| 10e7dd3bbbfbc25661213155e0de1a9f043461a2 | Cross Euclidean-to-Riemannian Metric Learning
with Application to Face Recognition from Video |
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| 10ce3a4724557d47df8f768670bfdd5cd5738f95 | Fihe igh Fie d f Face Recgii
Ac e ad iai Ra h 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 cdii f he be ad ga ey iage ae di(cid:11)ee. he cae i e ga ey be iage ay be avai ab e each ca ed f a di(cid:11)ee e ad de a di(cid:11)ee i iai. We e a face ecgii a gih which ca e ay be f ga ey iage e bjec ca ed a abiay e ad de abiay i iai ad ay be f be iage agai ca ed a abiay e ad de abiay i iai. The a gih eae by eiaig he Fihe igh (cid:12)e d f he bjec head f he i ga ey be iage. achig bewee he be ad ga ey i he efed ig he Fihe igh (cid:12)e d. d ci ay face ecgii ceai he e f he be ad ga ey iage ae di(cid:11)ee. The ga ey cai he iage ed d ig aiig f he a gih. The a gih ae eed wih he iage i he be e. F exa e he ga ey iage igh be a fa \ g h" ad he be iage igh be a 3/4 view ca ed f a caea i he ce f he . The be f ga ey ad be iage ca a vay. F exa e he ga ey ay ci f a ai f iage f each bjec a fa g h ad f (cid:12) e view ike he iage yica y ca ed by ice deae. The be ay be a ii a ai f iage a ig e 3/4 view eve a c eci f view f ad e. Face ecgii ac e i.e. face ecgii whee he ga ey ad be iage d have he ae e ha eceived vey i e aei. A gih have bee ed which ca ecgize face [1] e geea bjec [2] a a vaiey f e. weve f hee a gih e ie ga ey iage a evey e. A gih have bee ed which d geea ize ac e f exa e [3] b hi a gih c e 3D head de ig a ga ey caiig a age be f iage e bjec ca ed ig c ed i iai vaiai. ca be ed wih abiay ga ey ad be e. Afe e vaiai he ex igi(cid:12)ca fac a(cid:11)ecig he aea ace f face i i iai. A be f a gih have bee deve ed f face ecgii ac i iai b hey yica y y dea wih fa face [4 5]. y a few aache have bee ed had e bh e ad i iai vaiai a he ae ie. F exa e [3] c e a 3D head |
| 102e374347698fe5404e1d83f441630b1abf62d9 | Facial Image Analysis for Fully-Automatic
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| 19fb5e5207b4a964e5ab50d421e2549ce472baa8 | International Conference on Computer Systems and Technologies - CompSysTech’14
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Natural Image Statistics and Low-Complexity Feature Selection |
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Google Inc. Google Inc. Google Inc. |
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aDepartment of Computer Systems, Universidad Polit´ecnica de Madrid bDepartment of Applied Mathematics, Universidad Polit´ecnica de Madrid |
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Intel Labs China |
| 19eb486dcfa1963c6404a9f146c378fc7ae3a1df | |
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| 4c6e1840451e1f86af3ef1cb551259cb259493ba | HAND POSTURE DATASET CREATION FOR GESTURE
RECOGNITION Instituto de Sistemas Inteligentes y Aplicaciones Numericas en Ingenieria Campus Universitario de Tafira, 35017 Gran Canaria, Spain Departamento de E.I.O. y Computacion 38271 Universidad de La Laguna, Spain Keywords: Image understanding, Gesture recognition, Hand dataset. |
| 4c29e1f31660ba33e46d7e4ffdebb9b8c6bd5adc | |
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2470 ICASSP 2010 |
| 4c4236b62302957052f1bbfbd34dbf71ac1650ec | SEMI-SUPERVISED FACE RECOGNITION WITH LDA SELF-TRAINING
Multimedia Communications Department, EURECOM 2229 Route des Crêtes , BP 193, F-06560 Sophia-Antipolis Cedex, France |
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IIIT-Delhi, India |
| 26a72e9dd444d2861298d9df9df9f7d147186bcd | DOI 10.1007/s00138-016-0768-4
ORIGINAL PAPER Collecting and annotating the large continuous action dataset 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 |
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A Mixture of Transformed Hidden Markov Models for Elastic Motion Estimation |
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| 26ad6ceb07a1dc265d405e47a36570cb69b2ace6 | 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 Prepared for: Office of Naval Research |
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DOI 10.1007/s11265-017-1276-0 Hybrid Facial Regions Extraction for Micro-expression Recognition System Received: 2 February 2016 / Revised: 20 October 2016 / Accepted: 10 August 2017 © Springer Science+Business Media, LLC 2017 |
| 21ef129c063bad970b309a24a6a18cbcdfb3aff5 | 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 |
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face encoding and recognition 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 |
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Facial Expression Classification Method Based on Pseudo Zernike Moment and Radial Basis Function Network |
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Are 1,000 Features Worth A Picture? Department of Computer Science and Center for Human-Computer Interaction Virginia Tech, Arlington, VA, USA |
| 4dd6d511a8bbc4d9965d22d79ae6714ba48c8e41 | |
| 4d7e1eb5d1afecb4e238ba05d4f7f487dff96c11 | 978-1-5090-4117-6/17/$31.00 ©2017 IEEE
2352 ICASSP 2017 |
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| 4d0ef449de476631a8d107c8ec225628a67c87f9 | © 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, creating 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 |
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This paper summarises and analyses the cross-dataset recognition transfer learning techniques with the emphasis on what kinds of methods can be used when the available source and target data are presented in different forms for boosting the target task. This paper for the first time summarises several transferring criteria in details from the concept level, which are the key bases to guide what kind of knowledge to transfer between datasets. In addition, a taxonomy of cross-dataset scenarios and problems is proposed according the properties of data that define how different datasets are diverged, thereby review the recent advances on each specific problem under different scenarios. Moreover, some real world applications and corresponding commonly used benchmarks of cross-dataset recognition are reviewed. Lastly, several future directions are identified. Additional Key Words and Phrases: Cross-dataset, transfer learning, domain adaptation 1. INTRODUCTION It has been explored how human would transfer learning in one context to another similar context [Woodworth and Thorndike 1901; Perkins et al. 1992] in the field of Psychology and Education. For example, learning to drive a car helps a person later to learn more quickly to drive a truck, and learning mathematics prepares students to study physics. The machine learning algorithms are mostly inspired by human brains. However, most of them require a huge amount of training examples to learn a new model from scratch and fail to apply knowledge learned from previous domains or tasks. This may be due to that a basic assumption of statistical learning theory is that the training and test data are drawn from the same distribution and belong to the same task. Intuitively, learning from scratch is not realistic and practical, because it violates how human learn things. In addition, manually labelling a large amount of data for new domain or task is labour extensive, especially for the modern “data- hungry” and “data-driven” learning techniques (i.e. deep learning). However, the big data era provides a huge amount available data collected for other domains and tasks. Hence, how to use the previously available data smartly for the current task with scarce data will be beneficial for real world applications. To reuse the previous knowledge for current tasks, the differences between old data and new data need to be taken into account. Take the object recognition as an ex- ample. As claimed by Torralba and Efros [2011], despite the great efforts of object datasets creators, the datasets appear to have strong build-in bias caused by various factors, such as selection bias, capture bias, category or label bias, and negative set bias. This suggests that no matter how big the dataset is, it is impossible to cover the complexity of the real visual world. Hence, the dataset bias needs to be consid- ered before reusing data from previous datasets. Pan and Yang [2010] summarise that the differences between different datasets can be caused by domain divergence (i.e. distribution shift or feature space difference) or task divergence (i.e. conditional dis- tribution shift or label space difference), or both. For example, in visual recognition, the distributions between the previous and current data can be discrepant due to the different environments, lighting, background, sensor types, resolutions, view angles, and post-processing. Those external factors may cause the distribution divergence or even feature space divergence between different domains. On the other hand, the task divergence between current and previous data is also ubiquitous. For example, it is highly possible that an animal species that we want to recognize have not been seen ACM Journal Name, Vol. V, No. N, Article A, Publication date: January YYYY. |
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S.W. Chew∗, S. Lucey†, P. Lucey‡, S. Sridharan∗, and J.F. Cohn‡ |
| 75cd81d2513b7e41ac971be08bbb25c63c37029a | |
| 75e5ba7621935b57b2be7bf4a10cad66a9c445b9 | |
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Level Fusion |
| 758d7e1be64cc668c59ef33ba8882c8597406e53 | IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild |
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Efficient Landmark Localization |
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Smart Surveillance Interest Group, Department of Computer Science Universidade Federal de Minas Gerais, Belo Horizonte, Brazil |
| 81a142c751bf0b23315fb6717bc467aa4fdfbc92 | 978-1-5090-4117-6/17/$31.00 ©2017 IEEE
1767 ICASSP 2017 |
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A Robust Quadratic Criterion Stella X. Yu, Member, IEEE |
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1Caltech 2Google 3Cornell Tech 4iNaturalist |
| 81831ed8e5b304e9d28d2d8524d952b12b4cbf55 | |
| 81b2a541d6c42679e946a5281b4b9dc603bc171c | 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 aus Kairo, ¨Agypten Ulm, Deutschland 2010 |
| 8160b3b5f07deaa104769a2abb7017e9c031f1c1 | 683
Exploiting Discriminant Information in Nonnegative Matrix Factorization With Application to Frontal Face Verification |
| 816eff5e92a6326a8ab50c4c50450a6d02047b5e | fLRR: Fast Low-Rank Representation Using
Frobenius Norm 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 clustering 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 as those in X. Both X and A can be considered as matrices. A linear representation of X with respect to A is a matrix Z that satisfies the equation X = AZ. The data set A is called a dictionary. In general, this linear matrix equation will have infinite solutions, and any solution can be considered to be a representation of X associated with the dictionary A. To obtain an unique Z and explore the latent structure of the given data set, various assumptions could be enforced over Z. Liu et al. recently proposed Low Rank Representation (LRR) [1] by assuming that data are approximately sampled from an union of low-rank subspaces. Mathematically, LRR aims at solving min rank(Z) s.t. X = AZ, (1) where rank(Z) could be defined as the number of nonzero eigenvalues of the matrix Z. Clearly, (1) is non-convex and discontinuous, whose convex relaxation is as follows, min kZk∗ s.t. X = AZ, (2) where kZk∗ is the nuclear norm, which is a convex and continuous optimization problem. Considering the possible corruptions, the objective function of LRR is min kZk∗ + λkEkp s.t. X = AZ + E, (3) where k · kp could be ℓ1-norm for describing sparse corruption or ℓ2,1- norm for characterizing sample-specified corruption. The above nuclear-norm-based optimization problems are generally solved using Augmented Lagrange Multiplier algorithm (ALM) [2] which requires repeatedly performing Single Value Decomposition (SVD) over Z. Hence, this optimization program is inefficient. Beyond the nuclear-norm, do other norms exist that can be used as a surrogates for rank-minimization problem in LRR? Can we develop a fast algorithm to calculate LRR? This letter addresses these problems by theoretically showing the equivalence between the solutions of a Frobenius-norm-based problem and the original LRR problem. And we further develop fast Low Rank Representation (fLRR) based on the theoretical results. Theoretical Analysis: In the following analyses, Theorem 1 and Theorem 3 prove that Frobenius-norm-based problem is a surrogate of the rank-minimization problem of LRR in the case of clean data and corrupted ones, respectively. Theorem 2 shows that our Frobenius-norm- based method could produce a block-diagonal Z under some conditions. This property is helpful to subspace clustering. Let A ∈ Rm×n be a matrix with rank r. The full SVD and skinny SVD of A are A = U ΣV T and A = UrΣrV T r , where U and V are two orthogonal matrices with the size of m × m and n × n, respectively. In addition, Σ is an m × n rectangular diagonal matrix, its diagonal elements are nonnegative real numbers. Σr is a r × r diagonal matrix with singular values located on the diagonal in decreasing order, Ur and Vr consist of the first r columns of U and V , respectively. Clearly, Ur and Vr are column orthogonal matrices, i.e., U T r Vr = Ir, where Ir denotes the r Ur = Ir, V T identity matrix with the size of r × r. The pseudoinverse of A is defined by A† = VrΣ−1 r U T r . Given a matrix M ∈ Rm×n, the Frobenius norm of M is defined by kM kF =ptrace (M T M ) =qPmin{m,n} value of M . Clearly, kM kF = 0 if and only if M = 0. i=1 σ2 i , where σi is a singular Lemma 1: Suppose P is a column orthogonal matrix, i.e., P T P = I. Then, kP M kF = kM kF . Lemma 2: For the matrices M and N with same number of columns, it holds that = kM k2 F + kN k2 F . (4) N (cid:21)(cid:13)(cid:13)(cid:13)(cid:13) (cid:13)(cid:13)(cid:13)(cid:13) (cid:20) M The proofs of the above two lemmas are trivial. Theorem 1: minimization problem Suppose that X ∈ span{A}, the Frobenius norm min kZkF s.t. X = AZ, (5) has an unique solution Z ∗ = A†X which is also the lowest-rank solution of LRR in terms of (1). Proof: Let the full and skinny SVDs of A be A = U ΣV T and A = r U T UrΣrV T r . r , respectively. Then, the pseudoinverse of A is A† = VrΣ−1 Defining Vc by V T =(cid:20) V T V T (cid:21) and V T c Vr = 0. Moreover, it can be easily checked that Z ∗ satisfies X = AZ ∗ owing to X ∈ span{A}. To prove that Z ∗ is the unique solution of the optimization problem (5), two steps are required. First, we will prove that, for any solution Z of X = AZ, it must hold that kZkF ≥ kZ ∗kF . Using Lemma 1, we have kZkF = (cid:13)(cid:13)(cid:13)(cid:13) = (cid:13)(cid:13)(cid:13)(cid:13) V T (cid:20) V T (cid:20) V T (cid:21) [Z ∗ + (Z − Z ∗)](cid:13)(cid:13)(cid:13)(cid:13)F c (Z − Z ∗) (cid:21)(cid:13)(cid:13)(cid:13)(cid:13)F r (Z − Z ∗) r Z ∗ + V T c Z ∗ + V T V T As A (Z − Z ∗) = 0, r (Z − Z ∗) = 0. Denote B = Σ−1 V T V T c Vr = 0, we have V T i.e., UrΣrV T r U T c VrB = 0. Then, r (Z − Z ∗) = 0, r X, follows that then Z ∗ = VrB. Because it c Z ∗ = V T (cid:20) kZkF =(cid:13)(cid:13)(cid:13)(cid:13) V T c (Z − Z ∗) (cid:21)(cid:13)(cid:13)(cid:13)(cid:13)F By Lemma 2, kZk2 F = kBk2 F + kV T c (Z − Z ∗)k2 F , then, kZkF ≥ kBkF . By Lemma 1, kBkF = kVrBkF = kZ ∗kF , (6) (7) (8) thus, kZkF ≥ kZ ∗kF for any solution Z of X = AZ. In the second step, we will prove that if there exists another solution Z of (5), Z = Z ∗ must hold. Clearly, Z is a solution of (5) which implies that X = AZ and kZkF = kZ ∗kF . From (7) and (8), kZk2 F + kV T F = kZ ∗k2 Since kZkF = kZ ∗kF , c (Z − Z ∗) k2 F . c (Z − Z ∗) kF = 0, r (Z − Z ∗) = 0, this gives and so V T V T (Z − Z ∗) = 0. Because V is an orthogonal matrix, it must hold that Z = Z ∗. The above proves that Z ∗ is the unique solution of the optimization problem (5). c (Z − Z ∗) = 0. Together with V T it must hold that kV T (9) Next, we prove that Z ∗ is also a solution of the LRR optimization problem (1). Clearly, for any solution Z of X = AZ, it holds that rank(Z) ≥ rank(AZ) = rank(X). On the other hand, rank(Z ∗) = rank(A†X) ≤ rank(X). Thus, rank(Z ∗) = rank(X). This shows that Z ∗ is the lowest-rank solution of the LRR optimization problem (1). The proof is complete. (cid:4) In the following, Theorem 2 will show that the optimal Z of (5) will be block-diagonal if the data are sampled from a set of independent subspaces {S1, S2, · · · , Sk}, where the dimensionality of Si is ri and i = {1, 2, · · · , k}. Note that, {S1, S2, · · · , Sk} are independent if and only if SiTPj6=i Sj = {0}. Suppose that X = [X1, X2, · · · , Xk] and A = [A1, A2, · · · , Ak], where Ai and Xi contain mi and ni data points ELECTRONICS LETTERS 12th December 2011 Vol. 00 No. 00 |
| 8149c30a86e1a7db4b11965fe209fe0b75446a8c | Semi-Supervised Multiple Instance Learning based
Domain Adaptation for Object Detection Siemens Corporate Research Siemens Corporate Research Siemens Corporate Research Amit Kale Bangalore Bangalore {chhaya.methani, Bangalore rahul.thota, |
| 86b69b3718b9350c9d2008880ce88cd035828432 | Improving Face Image Extraction by Using Deep Learning Technique
National Library of Medicine, NIH, Bethesda, MD |
| 86904aee566716d9bef508aa9f0255dc18be3960 | Learning Anonymized Representations with
Adversarial Neural Networks |
| 867e709a298024a3c9777145e037e239385c0129 | 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 (M.Tech)1, Assistant Professor2, Assistant Professor3, HOD of CSE Department4 |
| 86c053c162c08bc3fe093cc10398b9e64367a100 | Cascade of Forests for Face Alignment |
| 86b985b285c0982046650e8d9cf09565a939e4f9 | |
| 861802ac19653a7831b314cd751fd8e89494ab12 | Time-of-Flight and Depth Imaging. Sensors, Algorithms
and 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 a lot of attention recently and have been improved considerably over the last few years. The present techniques... |
| 861b12f405c464b3ffa2af7408bff0698c6c9bf0 | 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 Computer Department, GHRCEM, Pune, India Computer Department, GHRCEM, Pune, India |
| 86e1bdbfd13b9ed137e4c4b8b459a3980eb257f6 | The Kinetics Human Action Video Dataset
Jo˜ao Carreira Paul Natsev |
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1013 ICME 2006 |
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| 8699268ee81a7472a0807c1d3b1db0d0ab05f40d | |
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DOI 10.1007/s11222-006-9004-9 Understanding the role of facial asymmetry in human face identification Received: May 2005 / Accepted: September 2006 / Published online: 30 January 2007 C(cid:1) Springer Science + Business Media, LLC 2007 |
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| 721b109970bf5f1862767a1bec3f9a79e815f79a | |
| 72ecaff8b57023f9fbf8b5b2588f3c7019010ca7 | Facial Keypoints Detection |
| 72591a75469321074b072daff80477d8911c3af3 | Group Component Analysis for Multi-block Data:
Common and Individual Feature Extraction |
| 729dbe38538fbf2664bc79847601f00593474b05 | |
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| 4414a328466db1e8ab9651bf4e0f9f1fe1a163e4 | 1164
© EURASIP, 2010 ISSN 2076-1465 18th European Signal Processing Conference (EUSIPCO-2010) INTRODUCTION |
| 442f09ddb5bb7ba4e824c0795e37cad754967208 | |
| 446a99fdedd5bb32d4970842b3ce0fc4f5e5fa03 | A Pose-Adaptive Constrained Local Model For
Accurate Head Pose Tracking Eikeo 11 rue Leon Jouhaux, F-75010, Paris, France Sorbonne Universit´es UPMC Univ Paris 06 CNRS UMR 7222, ISIR F-75005, Paris, France Eikeo 11 rue Leon Jouhaux, F-75010, Paris, France |
| 44b1399e8569a29eed0d22d88767b1891dbcf987 | 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 |
| 446dc1413e1cfaee0030dc74a3cee49a47386355 | Recent Advances in Zero-shot Recognition |
| 44a3ec27f92c344a15deb8e5dc3a5b3797505c06 | A Taxonomy of Part and Attribute Discovery
Techniques |
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Models to Initialization when Applied to Automatic Facial Landmarking |
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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 Received: 17 November 2008 / Revised: 20 February 2009 / Accepted: 7 July 2009 © Springer-Verlag London Limited 2009 |
| 44eb4d128b60485377e74ffb5facc0bf4ddeb022 | |
| 448ed201f6fceaa6533d88b0b29da3f36235e131 | |
| 447a5e1caf847952d2bb526ab2fb75898466d1bc | 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
Anonymous Author 1 Unknown Institution 1 Anonymous Author 2 Unknown Institution 2 Anonymous Author 3 Unknown Institution 3 |
| 2aaa6969c03f435b3ea8431574a91a0843bd320b | |
| 2ad7cef781f98fd66101fa4a78e012369d064830 | |
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Facial Scans |
| 2ae139b247057c02cda352f6661f46f7feb38e45 | Combining Modality Specific Deep Neural Networks for
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| 2a5903bdb3fdfb4d51f70b77f16852df3b8e5f83 | 121
The Effect of Computer-Generated Descriptions on Photo-Sharing Experiences of People With Visual Impairments Like sighted people, visually impaired people want to share photographs on social networking services, but find it difficult to identify and select photos from their albums. We aimed to address this problem by incorporating state-of-the-art computer-generated descriptions into Facebook’s photo-sharing feature. We interviewed 12 visually impaired participants to understand their photo-sharing experiences and designed a photo description feature for the Facebook mobile application. We evaluated this feature with six participants in a seven-day diary study. We found that participants used the descriptions to recall and organize their photos, but they hesitated to upload photos without a sighted person’s input. In addition to basic information about photo content, participants wanted to know more details about salient objects and people, and whether the photos reflected their personal aesthetic. We discuss these findings from the lens of self-disclosure and self-presentation theories and propose new computer vision research directions that will better support visual content sharing by visually impaired people. CCS Concepts: • Information interfaces and presentations → Multimedia and information systems; • Social and professional topics → People with disabilities KEYWORDS Visual impairments; computer-generated descriptions; SNSs; photo sharing; self-disclosure; self-presentation ACM Reference format: The Effect of Computer-Generated Descriptions On Photo-Sharing Experiences of People With Visual Impairments. Proc. ACM Hum.-Comput. Interact. 1, CSCW. 121 (November 2017), 22 pages. DOI: 10.1145/3134756 1 INTRODUCTION Sharing memories and experiences via photos is a common way to engage with others on social networking services (SNSs) [39,46,51]. For instance, Facebook users uploaded more than 350 million photos a day [24] and Twitter, which initially supported only text in tweets, now has more than 28.4% of tweets containing images [39]. Visually impaired people (both blind and low vision) have a strong presence on SNS and are interested in sharing photos [50]. They take photos for the same reasons that sighted people do: sharing daily moments with their sighted friends and family [30,32]. A prior study showed that visually impaired people shared a relatively large number of photos on Facebook—only slightly less than their sighted counterparts [50]. PACM on Human-Computer Interaction, Vol. 1, No. 2, Article 121. Publication date: November 2017 |
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Computer Science Division U.C. Berkeley Berkeley, CA 94720 |
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and management Universit`a di Palermo - Dipartimento di Ingegneria Informatica Viale delle Scienze, 90128, Palermo, Italy |
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Institut für Informatik Technische Universität München D-85748 Garching, Germany |
| 439ec47725ae4a3660e509d32828599a495559bf | Facial Expressions Tracking and Recognition: Database Protocols for Systems Validation
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Intel Labs China |
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Computer Science, U.C. Berkeley Computer Science, UMass Amherst Computer Science, U.C. Berkeley |
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Using Support Vector Machines to Enhance the Performance of Bayesian Face Recognition |
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Analysis and Interpretation Department of Informatics, Technische Universit¨at M¨unchen 85748 Garching, Germany |
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Action Classification in Short Social Video Clips Center for Visual Information Technology, IIIT Hyderabad, India |
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| 88f2952535df5859c8f60026f08b71976f8e19ec | A neural network framework for face
recognition by elastic bunch graph matching |
| 8818b12aa0ff3bf0b20f9caa250395cbea0e8769 | Fashion Conversation Data on Instagram
∗Graduate School of Culture Technology, KAIST, South Korea †Department of Communication Studies, UCLA, USA |
| 8878871ec2763f912102eeaff4b5a2febfc22fbe | 3781
Human Action Recognition in Unconstrained Videos by Explicit Motion Modeling |
| 8855d6161d7e5b35f6c59e15b94db9fa5bbf2912 | COGNITION IN PREGNANCY AND THE POSTPARTUM PERIOD |
| 88bee9733e96958444dc9e6bef191baba4fa6efa | Extending Face Identification to
Open-Set Face Recognition Department of Computer Science Universidade Federal de Minas Gerais Belo Horizonte, Brazil |
| 88fd4d1d0f4014f2b2e343c83d8c7e46d198cc79 | 978-1-4799-9988-0/16/$31.00 ©2016 IEEE
2697 ICASSP 2016 |
| 9fa1be81d31fba07a1bde0275b9d35c528f4d0b8 | Identifying Persons by Pictorial and
Contextual Cues Nicholas Leonard Pi¨el Thesis submitted for the degree of Master of Science Supervisor: April 2009 |
| 9f094341bea610a10346f072bf865cb550a1f1c1 | Recognition and Volume Estimation of Food Intake using a Mobile Device
Sarnoff Corporation 201 Washington Rd, Princeton, NJ, 08540 |
| 6b333b2c6311e36c2bde920ab5813f8cfcf2b67b | |
| 6b9aa288ce7740ec5ce9826c66d059ddcfd8dba9 | |
| 6b089627a4ea24bff193611e68390d1a4c3b3644 | CROSS-POLLINATION OF NORMALISATION
TECHNIQUES FROM SPEAKER TO FACE AUTHENTICATION USING GAUSSIAN MIXTURE MODELS Idiap-RR-03-2012 JANUARY 2012 Centre du Parc, Rue Marconi 19, P.O. Box 592, CH - 1920 Martigny |
| 6be0ab66c31023762e26d309a4a9d0096f72a7f0 | Enhance Visual Recognition under Adverse
Conditions via Deep Networks |
| 6b18628cc8829c3bf851ea3ee3bcff8543391819 | Face recognition based on subset selection via metric learning on manifold.
1058. [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 ORCID: http://orcid.org/0000-0001-7441-4749 Front Inform Technol & Electron Eng |
| 6b1b43d58faed7b457b1d4e8c16f5f7e7d819239 | |
| 6b35b15ceba2f26cf949f23347ec95bbbf7bed64 | |
| 6b6493551017819a3d1f12bbf922a8a8c8cc2a03 | Pose Normalization for Local Appearance-Based
Face Recognition Computer Science Department, Universit¨at Karlsruhe (TH) Am Fasanengarten 5, Karlsruhe 76131, Germany http://isl.ira.uka.de/cvhci |
| 6bb630dfa797168e6627d972560c3d438f71ea99 | |
| 0728f788107122d76dfafa4fb0c45c20dcf523ca | The Best of Both Worlds: Combining Data-independent and Data-driven
Approaches for Action Recognition |
| 071099a4c3eed464388c8d1bff7b0538c7322422 | FACIAL EXPRESSION RECOGNITION IN THE WILD USING RICH DEEP FEATURES
Microsoft Advanced Technology labs, Microsoft Technology and Research, Cairo, Egypt |
| 071af21377cc76d5c05100a745fb13cb2e40500f | |
| 0754e769eb613fd3968b6e267a301728f52358be | Towards a Watson That Sees: Language-Guided Action Recognition for
Robots |
| 0717b47ab84b848de37dbefd81cf8bf512b544ac | 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 System |
| 0750a816858b601c0dbf4cfb68066ae7e788f05d | CosFace: Large Margin Cosine Loss for Deep Face Recognition
Tencent AI Lab |
| 0716e1ad868f5f446b1c367721418ffadfcf0519 | Interactively Guiding Semi-Supervised
Clustering via Attribute-Based Explanations Virginia Tech, Blacksburg, VA, USA |
| 073eaa49ccde15b62425cda1d9feab0fea03a842 | |
| 0726a45eb129eed88915aa5a86df2af16a09bcc1 | Introspective Perception: Learning to Predict Failures in Vision Systems |
| 38d56ddcea01ce99902dd75ad162213cbe4eaab7 | Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17)
2648 |
| 389334e9a0d84bc54bcd5b94b4ce4c5d9d6a2f26 | FACIAL PARAMETER EXTRACTION SYSTEM BASED ON ACTIVE CONTOURS
Universitat Politècnica de Catalunya, Barcelona, Spain |
| 380dd0ddd5d69adc52defc095570d1c22952f5cc | |
| 38679355d4cfea3a791005f211aa16e76b2eaa8d | Title
Evolutionary cross-domain discriminative Hessian Eigenmaps Author(s) Si, S; Tao, D; Chan, KP Citation 1086 Issued Date 2010 URL 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 copyrighted component of this work in other works must be obtained from the IEEE. |
| 38682c7b19831e5d4f58e9bce9716f9c2c29c4e7 | International Journal of Computer Trends and Technology (IJCTT) – Volume 18 Number 5 – Dec 2014
Movie Character Identification Using Graph Matching Algorithm M.Tech Scholar, Dept of CSE, QISCET, ONGOLE, Dist: Prakasam, AP, India. Associate Professor, Department of CSE, QISCET, ONGOLE, Dist: Prakasam, AP, India |
| 3803b91e784922a2dacd6a18f61b3100629df932 | Temporal Multimodal Fusion
for Video Emotion Classification in the Wild Orange Labs Cesson-Sévigné, France Orange Labs Cesson-Sévigné, France Normandie Univ., UNICAEN, ENSICAEN, CNRS Caen, France |
| 38eea307445a39ee7902c1ecf8cea7e3dcb7c0e7 | Noname manuscript No.
(will be inserted by the editor) Multi-distance Support Matrix Machine Received: date / Accepted: date |
| 385750bcf95036c808d63db0e0b14768463ff4c6 | |
| 384f972c81c52fe36849600728865ea50a0c4670 | 1
Multi-Fold Gabor, PCA and ICA Filter Convolution Descriptor for Face Recognition |
| 380d5138cadccc9b5b91c707ba0a9220b0f39271 | Deep Imbalanced Learning for Face Recognition
and Attribute Prediction |
| 38861d0d3a0292c1f54153b303b0d791cbba1d50 | |
| 38192a0f9261d9727b119e294a65f2e25f72d7e6 | |
| 00fb2836068042c19b5197d0999e8e93b920eb9c | |
| 0077cd8f97cafd2b389783858a6e4ab7887b0b6b | MAI et al.: ON THE RECONSTRUCTION OF DEEP FACE TEMPLATES
On the Reconstruction of Deep Face Templates |
| 00214fe1319113e6649435cae386019235474789 | 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: Matrikelnummer 252400 Gutachter: Prof. Dr.-Ing. H. Ney Prof. Dr. B. Leibe Betreuer: September 2009 |
| 0004f72a00096fa410b179ad12aa3a0d10fc853c | |
| 00f0ed04defec19b4843b5b16557d8d0ccc5bb42 | |
| 0037bff7be6d463785d4e5b2671da664cd7ef746 | 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 |
| 00a967cb2d18e1394226ad37930524a31351f6cf | Fully-adaptive Feature Sharing in Multi-Task Networks with Applications in
Person Attribute Classification UC San Diego IBM Research IBM Research Binghamton Univeristy, SUNY UC San Diego Rogerio Feris IBM Research |
| 00a3cfe3ce35a7ffb8214f6db15366f4e79761e3 | 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 classification, Fusion algorithm, Support vector machine (SVM) ORCID: http://orcid.org/0000-0002-5021-9057 Front Inform Technol & Electron Eng |
| 004a1bb1a2c93b4f379468cca6b6cfc6d8746cc4 | Balanced k-Means and Min-Cut Clustering |
| 00d94b35ffd6cabfb70b9a1d220b6823ae9154ee | Discriminative Bayesian Dictionary Learning
for Classification |
| 006f283a50d325840433f4cf6d15876d475bba77 | 756
Preserving Structure in Model-Free Tracking |
| 0059b3dfc7056f26de1eabaafd1ad542e34c2c2e | |
| 6e198f6cc4199e1c4173944e3df6f39a302cf787 | MORPH-II: Inconsistencies and Cleaning Whitepaper
NSF-REU Site at UNC Wilmington, Summer 2017 |
| 6eaf446dec00536858548fe7cc66025b70ce20eb | |
| 6e91be2ad74cf7c5969314b2327b513532b1be09 | Dimensionality Reduction with Subspace Structure
Preservation Department of Computer Science SUNY Buffalo Buffalo, NY 14260 |
| 6eba25166fe461dc388805cc2452d49f5d1cdadd | 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 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 |
| 6ecd4025b7b5f4894c990614a9a65e3a1ac347b2 | 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, |
| 6eaeac9ae2a1697fa0aa8e394edc64f32762f578 | |
| 6ee2ea416382d659a0dddc7a88fc093accc2f8ee | |
| 6e3a181bf388dd503c83dc324561701b19d37df1 | Finding a low-rank basis in a matrix subspace
Andr´e Uschmajew |
| 6e8c3b7d25e6530a631ea01fbbb93ac1e8b69d2f | Deep Episodic Memory: Encoding, Recalling, and Predicting
Episodic Experiences for Robot Action Execution |
| 6e911227e893d0eecb363015754824bf4366bdb7 | Wasserstein Divergence for GANs
1 Computer Vision Lab, ETH Zurich, Switzerland 2 VISICS, KU Leuven, Belgium |
| 6ee8a94ccba10062172e5b31ee097c846821a822 | 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 Institut f¨ur Neuroinformatik Ruhr-Universit¨at Bochum Bochum D-44801, Germany Editor: David Dunson |
| 6e379f2d34e14efd85ae51875a4fa7d7ae63a662 | A NEW MULTI-MODAL BIOMETRIC SYSTEM
BASED ON FINGERPRINT AND FINGER VEIN RECOGNITION Master's Thesis Department of Software Engineering JULY-2014 I |
| 6e1802874ead801a7e1072aa870681aa2f555f35 | 1424407281/07/$20.00 ©2007 IEEE
I 629 ICASSP 2007 |
| 6ed22b934e382c6f72402747d51aa50994cfd97b | Customized Expression Recognition for Performance-Driven
Cutout Character Animation †NEC Laboratories America ‡Snapchat |
| 6e93fd7400585f5df57b5343699cb7cda20cfcc2 | http://journalofvision.org/9/2/22/
Comparing a novel model based on the transferable belief model with humans during the recognition of partially occluded facial expressions 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 ability 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 Transferable Belief Model (TBM). Three simulations demonstrated the suitability of the TBM-based model to deal with partially occluded facial parts and revealed the differences between the facial information used by humans and by the model. This opens promising perspectives for the future development of the model. Keywords: facial features behavior, facial expressions classification, Transferable Belief Model, Bubbles Citation: Hammal, Z., Arguin, M., & Gosselin, F. (2009). Comparing a novel model based on the transferable belief http://journalofvision.org/9/2/22/, doi:10.1167/9.2.22. Introduction Facial expressions communicate information from which we can quickly infer the state of mind of our peers and adjust our behavior accordingly (Darwin, 1872). To illustrate, take a person like patient SM with complete bilateral damage to the amygdala nuclei that prevents her from recognizing facial expressions of fear. SM would be incapable of interpreting the fearful expression on the face of a bystander, who has encountered a furious Grizzly bear, as a sign of potential threat (Adolphs, Tranel, Damasio, & Damasio, 1994). Facial expressions are typically arranged into six universally recognized basic categories Happiness, Sur- prise, Disgust, Anger, Sadness, and Fear that are similarly expressed across different backgrounds and cultures (Cohn, 2006; Ekman, 1999; Izard, 1971, 1994). Facial expressions result from the precisely choreographed deformation of facial features, which are often described using the 46 Action Units (AUs; Ekman & Friesen, 1978). Facial expression recognition and computer vision The study of human facial expressions has an impact in several areas of life such as art, social interaction, cognitive science, medicine, security, affective computing, and human-computer interaction (HCI). An automatic facial expressions classification system may contribute signifi- cantly to the development of all these disciplines. However, the development of such a system constitutes a significant challenge because of the many constraints that are imposed by its application in a real-world context (Pantic & Bartlett, 2007; Pantic & Patras, 2006). In particular, such systems need to provide great accuracy and robustness without demanding too many interventions from the user. There have been major advances in computer vision over the past 15 years for the recognition of the six basic facial expressions (for reviews, see Fasel & Luettin, 2003; Pantic & Rothkrantz, 2000b). The main approaches can be divided in two classes: Model-based and fiducial points approaches. The model-based approach requires the design of a deterministic physical model that can represent doi: 10.1167/9.2.22 Received January 28, 2008; published February 26, 2009 ISSN 1534-7362 * ARVO |
| 6e12ba518816cbc2d987200c461dc907fd19f533 | |
| 9ab463d117219ed51f602ff0ddbd3414217e3166 | Weighted Transmedia
Relevance Feedback for Image Retrieval and Auto-annotation TECHNICAL REPORT N° 0415 December 2011 Project-Teams LEAR - INRIA and TVPA - XRCE |
| 9ac82909d76b4c902e5dde5838130de6ce838c16 | Recognizing Facial Expressions Automatically
from Video 1 Introduction Facial expressions, resulting from movements of the facial muscles, are the face changes in response to a person’s internal emotional states, intentions, or social communications. There is a considerable history associated with the study on fa- cial expressions. Darwin (1872) was the first to describe in details the specific fa- cial expressions associated with emotions in animals and humans, who argued that all mammals show emotions reliably in their faces. Since that, facial expression analysis 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 communication, 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- curity, medical diagnosis, law enforcement, and awareness systems (Shan, 2007). Therefore, it has been an active research topic in multiple disciplines such as psy- chology, cognitive science, human-computer interaction, and pattern recognition. Meanwhile, as a promising unobtrusive solution, automatic facial expression analy- sis from video or images has received much attention in last two decades (Pantic and Rothkrantz, 2000a; Fasel and Luettin, 2003; Tian, Kanade, and Cohn, 2005; Pantic and Bartlett, 2007). This chapter introduces recent advances in computer recognition of facial expres- sions. Firstly, we describe the problem space, which includes multiple dimensions: level of description, static versus dynamic expression, facial feature extraction and |
| 9ac15845defcd0d6b611ecd609c740d41f0c341d | Copyright
by 2011 |
| 9af1cf562377b307580ca214ecd2c556e20df000 | 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 Electrical Engineering Dept., AmirKabir Univarsity of Technology Tehran, Iran |
| 9a23a0402ae68cc6ea2fe0092b6ec2d40f667adb | High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
1NVIDIA Corporation 2UC 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 change labels in the original label map to create new scenes, like replacing trees with buildings. (c) Our framework also allows 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
(cid:63) UC Berkeley / †ICSI |
| 9a276c72acdb83660557489114a494b86a39f6ff | Emotion Classification through Lower Facial Expressions using Adaptive
Support Vector Machines Department of Information Technology, Faculty of Industrial Technology and Management, |
| 9a42c519f0aaa68debbe9df00b090ca446d25bc4 | Face Recognition via Centralized Coordinate
Learning |
| 9aad8e52aff12bd822f0011e6ef85dfc22fe8466 | Temporal-Spatial Mapping for Action Recognition |
| 36b40c75a3e53c633c4afb5a9309d10e12c292c7 | |
| 3646b42511a6a0df5470408bc9a7a69bb3c5d742 | 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 Technical Campus, Bhilai Vikas Singh Technical Campus, Bhilai Abha Choubey Technical Campus, Bhilai |
| 365f67fe670bf55dc9ccdcd6888115264b2a2c56 | |
| 36fe39ed69a5c7ff9650fd5f4fe950b5880760b0 | Tracking von Gesichtsmimik
mit Hilfe von Gitterstrukturen zur Klassifikation von schmerzrelevanten Action Units 1Fraunhofer-Institut f¨ur Integrierte Schaltungen IIS, Erlangen, 2Otto-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 automatische (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. 1 Einleitung Menschliche Empfindungen wie Emotionen oder Schmerz l¨osen spezifische Mu- ster von Kontraktionen der Gesichtsmuskulatur aus, die Grundlage dessen sind, was wir Mimik nennen. Aus der Beobachtung der Mimik kann wiederum auf menschliche Empfindungen r¨uckgeschlossen werden. Im Rahmen der Schmerz- forschung werden Videoaufnahmen von Probanden hinsichtlich des mimischen Schmerzausdrucks analysiert. Zur Beschreibung des mimischen Ausdrucks und dessen Ver¨anderungen wird das Facial Action Coding System (FACS) [1] verwen- det, das anatomisch begr¨undet, kleinste sichtbare Muskelbewegungen im Gesicht beschreibt und als einzelne Action Units (AUs) kategorisiert. Eine Vielzahl von Untersuchungen hat gezeigt, dass spezifische Muster von Action Units auftre- ten, wenn Probanden Schmerzen angeben [2]. Die manuelle Klassifikation und Markierung der Action Units von Probanden in Videosequenzen bedarf einer langwierigen Beobachtung durch ausgebildete FACS-Coder. Eine automatische (Vor-)klassifikation kann hierbei den klinischen Workflow unterst¨utzen und dieses Verfahren zum brauchbaren diagnostischen Instrument machen. Bisher realisier- te Ans¨atze zum Erkennen von Gesichtsausdr¨ucken basieren auf der Klassifikation |
| 36ce0b68a01b4c96af6ad8c26e55e5a30446f360 | Multimed Tools Appl
DOI 10.1007/s11042-014-2322-6 Facial expression recognition based on a mlp neural network using constructive training algorithm Received: 5 February 2014 / Revised: 22 August 2014 / Accepted: 13 October 2014 © Springer Science+Business Media New York 2014 |
| 3674f3597bbca3ce05e4423611d871d09882043b | 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 277 279 289 295 303 309 314 |
| 362a70b6e7d55a777feb7b9fc8bc4d40a57cde8c | 978-1-4799-9988-0/16/$31.00 ©2016 IEEE
2792 ICASSP 2016 |
| 366d20f8fd25b4fe4f7dc95068abc6c6cabe1194 | |
| 3630324c2af04fd90f8668f9ee9709604fe980fd | This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TCSVT.2016.2607345, IEEE
Transactions on Circuits and Systems for Video Technology Image Classification with Tailored Fine-Grained Dictionaries |
| 362ba8317aba71c78dafca023be60fb71320381d | |
| 36cf96fe11a2c1ea4d999a7f86ffef6eea7b5958 | RGB-D Face Recognition with Texture and
Attribute Features Member, IEEE |
| 36018404263b9bb44d1fddaddd9ee9af9d46e560 | OCCLUDED FACE RECOGNITION BY USING GABOR
FEATURES 1 Department of Electrical And Electronics Engineering, METU, Ankara, Turkey 2 7h%ł7$.(cid:3)%ł/7(1(cid:15)(cid:3)$QNDUD(cid:15)(cid:3)7XUNH\ |
| 5c4ce36063dd3496a5926afd301e562899ff53ea | |
| 5c2a7518fb26a37139cebff76753d83e4da25159 | |
| 5c2e264d6ac253693469bd190f323622c457ca05 | 978-1-4799-2341-0/13/$31.00 ©2013 IEEE
4367 ICIP 2013 |
| 5c473cfda1d7c384724fbb139dfe8cb39f79f626 | |
| 5c5e1f367e8768a9fb0f1b2f9dbfa060a22e75c0 | 2132
Reference Face Graph for Face Recognition |
| 5c35ac04260e281141b3aaa7bbb147032c887f0c | Face Detection and Tracking Control with Omni Car
CS 231A Final Report June 31, 2016 |
| 5c717afc5a9a8ccb1767d87b79851de8d3016294 | 978-1-4673-0046-9/12/$26.00 ©2012 IEEE
1845 ICASSP 2012 |
| 0952ac6ce94c98049d518d29c18d136b1f04b0c0 | |
| 09137e3c267a3414314d1e7e4b0e3a4cae801f45 | Noname manuscript No.
(will be inserted by the editor) Two Birds with One Stone: Transforming and Generating Facial Images with Iterative GAN Received: date / Accepted: date |
| 09926ed62511c340f4540b5bc53cf2480e8063f8 | Action Tubelet Detector for Spatio-Temporal Action Localization |
| 09718bf335b926907ded5cb4c94784fd20e5ccd8 | 875
Recognizing Partially Occluded, Expression Variant Faces From Single Training Image per Person With SOM and Soft k-NN Ensemble |
| 0903bb001c263e3c9a40f430116d1e629eaa616f | CVPR
#987 000 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048 049 050 051 052 053 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 | 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 |
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| 09f853ce12f7361c4b50c494df7ce3b9fad1d221 | myjournal manuscript No.
(will be inserted by the editor) Random forests for real time 3D face analysis Received: date / Accepted: date |
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| 09750c9bbb074bbc4eb66586b20822d1812cdb20 | 978-1-4673-0046-9/12/$26.00 ©2012 IEEE
1385 ICASSP 2012 |
| 097f674aa9e91135151c480734dda54af5bc4240 | 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 CSIRO Telecommunications & Industrial Physics Australia Tel: 612 9372 4104, Fax: 612 9372 4411, Email: |
| 5d485501f9c2030ab33f97972aa7585d3a0d59a7 | |
| 5de5848dc3fc35e40420ffec70a407e4770e3a8d | WebVision Database: Visual Learning and Understanding from Web Data
1 Computer Vision Laboratory, ETH Zurich 2 Google Switzerland |
| 5da139fc43216c86d779938d1c219b950dd82a4c | 1-4244-1437-7/07/$20.00 ©2007 IEEE
II - 205 ICIP 2007 |
| 5dc056fe911a3e34a932513abe637076250d96da | |
| 5d233e6f23b1c306cf62af49ce66faac2078f967 | RESEARCH ARTICLE
Optimal Geometrical Set for Automated Marker Placement to Virtualized Real-Time Facial Emotions School of Mechatronic Engineering, Universiti Malaysia Perlis, 02600, Ulu Pauh, Arau, Perlis, West Malaysia |
| 5d7f8eb73b6a84eb1d27d1138965eb7aef7ba5cf | Robust Registration of Dynamic Facial Sequences |
| 5dcf78de4d3d867d0fd4a3105f0defae2234b9cb | |
| 5db4fe0ce9e9227042144758cf6c4c2de2042435 | INTERNATIONAL JOURNAL OF ELECTRICAL AND ELECTRONIC SYSTEMS RESEARCH, VOL.3, JUNE 2010
Recognition of Facial Expression Using Haar Wavelet Transform for paper features investigates |
| 5d5cd6fa5c41eb9d3d2bab3359b3e5eb60ae194e | Face Recognition Algorithms
June 16, 2010 Ion Marqu´es Supervisor: Manuel Gra˜na |
| 5d09d5257139b563bd3149cfd5e6f9eae3c34776 | 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 a Instituto Politécnico Nacional – CITEDI, Ave. del Parque 1310, Mesade Otay, Tijuana B.C. 22510, México b Department of Computer Science, CICESE, Carretera Ensenada-Tijuana 3918, Ensenada B.C. 22860, México c 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 a r t i c l e i n f o a 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 Composite correlation filters Multi-objective evolutionary algorithm Combinatorial optimization Composite correlation filters are used for solving a wide variety of pattern recognition problems. These filters are given by a combination of several training templates chosen by a designer in an ad hoc manner. In this work, we present a new approach for the design of composite filters based on multi-objective combinatorial optimization. Given a vast search space of training templates, an iterative algorithm is used to synthesize a filter with an optimized performance in terms of several competing criteria. Moreover, by employing a suggested binary-search procedure a filter bank with a minimum number of filters can be constructed, for a prespecified trade-off of performance metrics. Computer simulation results obtained with the proposed method in recognizing geometrically distorted versions of a target in cluttered and noisy scenes are discussed and compared in terms of recognition performance and complexity with existing state-of-the-art filters. & Elsevier B.V. All rights reserved. 1. Introduction Nowadays, object recognition receives much research interest due to its high impact in real-life activities, such as robotics, bio- metrics, and target tracking [1,2]. Object recognition consists in solving two essential tasks: detection of a target within an ob- served scene and determination of the exact position of the de- tected object. Different approaches can be utilized to address these tasks, that is feature-based methods [3–6] and template matching algorithms [7,8]. In feature-based methods the observed scene is processed to extract relevant features of potential targets within the scene. Next, the extracted features are processed and analyzed to make decisions. Feature-based methods yield good results in many applications. However, they depend on several subjective decisions which often require optimization [9,10]. On the other hand, correlation filtering is a template matching processing. In this approach, the coordinates of the maximum of the filter output are taken as estimates of the target coordinates in the observed scene. Correlation filters possess a good mathematical basis and they can be implemented by exploiting massive parallelism either in hybrid opto-digital correlators [11,12] or in high-performance n Corresponding author. Tel.: þ52 664 623 1344x82856. http://dx.doi.org/10.1016/j.optcom.2014.10.038 0030-4018/& Elsevier B.V. All rights reserved. hardware such as graphics processing units (GPUs) [13] or field programmable gate arrays (FPGAs) [14] at high rate. Additionally, these filters are capable to reliably recognize a target in highly cluttered and noisy environments [8,15,16]. Moreover, they are able to estimate very accurately the position of the target within the scene [17]. Correlation filters are usually designed by a opti- mization of various criteria [18,19]. The filters can be broadly classified in to two main categories: analytical and composite fil- ters. Analytical filters optimize a performance criterion using mathematical models of signals and noise [20,21]. Composite fil- ters are constructed by combination of several training templates, each of them representing an expected target view in the observed scene [22,21]. In practice, composite filters are effective for real- life degradations of targets such as rotations and scaling. Compo- site filters are synthesized by means of a supervised training process. Thus, the performance of the filters highly depends on a proper selection of image templates used for training [20,23]. Normally, the training templates are chosen by a designer in an ad hoc manner. Such a subjective procedure is not optimal. In addi- tion, Kumar and Pochavsky [24] showed that the signal to noise ratio of a composite filter gradually reduces when the number of training templates increases. In order to synthesize composite filters with improved performance in terms of several competing metrics, a search and optimization strategy is required to auto- matically choose the set of training templates. |
| 5d01283474b73a46d80745ad0cc0c4da14aae194 | |
| 5d197c8cd34473eb6cde6b65ced1be82a3a1ed14 | AFaceImageDatabaseforEvaluatingOut-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 |
| 31aa20911cc7a2b556e7d273f0bdd5a2f0671e0a | |
| 31b05f65405534a696a847dd19c621b7b8588263 | |
| 318e7e6daa0a799c83a9fdf7dd6bc0b3e89ab24a | Sparsity in Dynamics of Spontaneous
Subtle Emotions: Analysis & Application |
| 31c0968fb5f587918f1c49bf7fa51453b3e89cf7 | Deep Transfer Learning for Person Re-identification |
| 31e57fa83ac60c03d884774d2b515813493977b9 | |
| 316e67550fbf0ba54f103b5924e6537712f06bee | Multimodal semi-supervised learning
for image classification LEAR team, INRIA Grenoble, France |
| 31ef5419e026ef57ff20de537d82fe3cfa9ee741 | Facial Expression Analysis Based on
High Dimensional Binary Features ´Ecole Polytechique de Montr´eal, Universit´e de Montr´eal, Montr´eal, Canada |
| 31b58ced31f22eab10bd3ee2d9174e7c14c27c01 | |
| 31ace8c9d0e4550a233b904a0e2aabefcc90b0e3 | Learning Deep Face Representation
Megvii Inc. Megvii Inc. Megvii Inc. Megvii Inc. Megvii Inc. |
| 312afff739d1e0fcd3410adf78be1c66b3480396 | |
| 31bb49ba7df94b88add9e3c2db72a4a98927bb05 | |
| 91811203c2511e919b047ebc86edad87d985a4fa | Expression Subspace Projection for Face
Recognition from Single Sample per Person |
| 91e57667b6fad7a996b24367119f4b22b6892eca | 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 |
| 91883dabc11245e393786d85941fb99a6248c1fb | |
| 917bea27af1846b649e2bced624e8df1d9b79d6f | Ultra Power-Efficient CNN Domain Specific Accelerator with 9.3TOPS/Watt for
Mobile and Embedded Applications Gyrfalcon Technology Inc. 1900 McCarthy Blvd. Milpitas, CA 95035 |
| 91b1a59b9e0e7f4db0828bf36654b84ba53b0557 | 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 (SVD) for performing both |
| 911bef7465665d8b194b6b0370b2b2389dfda1a1 | RANJAN, ROMERO, BLACK: LEARNING HUMAN OPTICAL FLOW
Learning Human Optical Flow 1 MPI for Intelligent Systems Tübingen, Germany 2 Amazon Inc. |
| 91ead35d1d2ff2ea7cf35d15b14996471404f68d | Combining and Steganography of 3D Face Textures |
| 919d0e681c4ef687bf0b89fe7c0615221e9a1d30 | |
| 912a6a97af390d009773452814a401e258b77640 | |
| 91d513af1f667f64c9afc55ea1f45b0be7ba08d4 | Automatic Face Image Quality Prediction |
| 918b72a47b7f378bde0ba29c908babf6dab6f833 | |
| 91e58c39608c6eb97b314b0c581ddaf7daac075e | Pixel-wise Ear Detection with Convolutional
Encoder-Decoder Networks |
| 91d2fe6fdf180e8427c65ffb3d895bf9f0ec4fa0 | |
| 9131c990fad219726eb38384976868b968ee9d9c | Deep Facial Expression Recognition: A Survey |
| 915d4a0fb523249ecbc88eb62cb150a60cf60fa0 | 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 http://www.atvs.diac.upm.es |
| 65b737e5cc4a565011a895c460ed8fd07b333600 | Transfer Learning For Cross-Dataset Recognition: A Survey
This paper summarises and analyses the cross-dataset recognition transfer learning techniques with the emphasis on what kinds of methods can be used when the available source and target data are presented in different forms for boosting the target task. This paper for the first time summarises several transferring criteria in details from the concept level, which are the key bases to guide what kind of knowledge to transfer between datasets. In addition, a taxonomy of cross-dataset scenarios and problems is proposed according the properties of data that define how different datasets are diverged, thereby review the recent advances on each specific problem under different scenarios. Moreover, some real world applications and corresponding commonly used benchmarks of cross-dataset recognition are reviewed. Lastly, several future directions are identified. Additional Key Words and Phrases: Cross-dataset, transfer learning, domain adaptation 1. INTRODUCTION It has been explored how human would transfer learning in one context to another similar context [Woodworth and Thorndike 1901; Perkins et al. 1992] in the field of Psychology and Education. For example, learning to drive a car helps a person later to learn more quickly to drive a truck, and learning mathematics prepares students to study physics. The machine learning algorithms are mostly inspired by human brains. However, most of them require a huge amount of training examples to learn a new model from scratch and fail to apply knowledge learned from previous domains or tasks. This may be due to that a basic assumption of statistical learning theory is that the training and test data are drawn from the same distribution and belong to the same task. Intuitively, learning from scratch is not realistic and practical, because it violates how human learn things. In addition, manually labelling a large amount of data for new domain or task is labour extensive, especially for the modern “data- hungry” and “data-driven” learning techniques (i.e. deep learning). However, the big data era provides a huge amount available data collected for other domains and tasks. Hence, how to use the previously available data smartly for the current task with scarce data will be beneficial for real world applications. To reuse the previous knowledge for current tasks, the differences between old data and new data need to be taken into account. Take the object recognition as an ex- ample. As claimed by Torralba and Efros [2011], despite the great efforts of object datasets creators, the datasets appear to have strong build-in bias caused by various factors, such as selection bias, capture bias, category or label bias, and negative set bias. This suggests that no matter how big the dataset is, it is impossible to cover the complexity of the real visual world. Hence, the dataset bias needs to be consid- ered before reusing data from previous datasets. Pan and Yang [2010] summarise that the differences between different datasets can be caused by domain divergence (i.e. distribution shift or feature space difference) or task divergence (i.e. conditional dis- tribution shift or label space difference), or both. For example, in visual recognition, the distributions between the previous and current data can be discrepant due to the different environments, lighting, background, sensor types, resolutions, view angles, and post-processing. Those external factors may cause the distribution divergence or even feature space divergence between different domains. On the other hand, the task divergence between current and previous data is also ubiquitous. For example, it is highly possible that an animal species that we want to recognize have not been seen ACM Journal Name, Vol. V, No. N, Article A, Publication date: January YYYY. |
| 6582f4ec2815d2106957215ca2fa298396dde274 | JUNE 2007
1005 Discriminative Learning and Recognition of Image Set Classes Using Canonical Correlations |
| 655d9ba828eeff47c600240e0327c3102b9aba7c | IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART B: CYBERNETICS, VOL. 35, NO. 3, JUNE 2005
489 Kernel Pooled Local Subspaces for Classification |
| 656a59954de3c9fcf82ffcef926af6ade2f3fdb5 | Convolutional Network Representation
for Visual Recognition Doctoral Thesis Stockholm, Sweden, 2017 |
| 656f05741c402ba43bb1b9a58bcc5f7ce2403d9a | |
| 65817963194702f059bae07eadbf6486f18f4a0a | http://dx.doi.org/10.1007/s11263-015-0814-0
WhittleSearch: Interactive Image Search with Relative Attribute Feedback Received: date / Accepted: date |
| 6581c5b17db7006f4cc3575d04bfc6546854a785 | 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 von aus Erlangen Tag der mündlichen Prüfung: 18. 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 |
| 65babb10e727382b31ca5479b452ee725917c739 | Label Distribution Learning |
| 62dccab9ab715f33761a5315746ed02e48eed2a0 | A Short Note about Kinetics-600
Jo˜ao Carreira |
| 62d1a31b8acd2141d3a994f2d2ec7a3baf0e6dc4 | Ding et al. EURASIP Journal on Image and Video Processing (2017) 2017:43
DOI 10.1186/s13640-017-0188-z EURASIP Journal on Image and Video Processing R ES EAR CH Noise-resistant network: a deep-learning method for face recognition under noise Open Access |
| 62694828c716af44c300f9ec0c3236e98770d7cf | 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 1Facultad de Estadística e Informática, Universidad Veracruzana, Mexico // 2Universidad Juárez Autónoma de for Mathematics Huerta-Pacheco1 *Corresponding author |
| 620339aef06aed07a78f9ed1a057a25433faa58b | |
| 62b3598b401c807288a113796f424612cc5833ca | |
| 628a3f027b7646f398c68a680add48c7969ab1d9 | Plan for Final Year Project:
HKU-Face: A Large Scale Dataset for Deep Face Recognition 3035140108 3035141841 Introduction Face recognition has been one of the most successful techniques in the field of artificial intelligence because of its surpassing human-level performance in academic experiments and broad application in the industrial world. Gaussian-face[1] and Facenet[2] hold state-of-the-art record using statistical method and deep-learning method respectively. What’s more, face recognition has been applied in various areas like authority checking and recording, fostering a large number of start-ups like Face++. Our final year project will deal with the face recognition task by building a large-scaled and carefully- filtered dataset. Our project plan specifies our roadmap and current research process. This plan first illustrates the significance and potential enhancement in constructing large-scale face dataset for both academics and companies. Then objectives to accomplish and related literature review will be expressed in detail. Next, methodologies used, scope of our project and challenges faced by us are described. The detailed timeline for this project follows as well as a small summary. 2 Motivation Nowadays most of the face recognition tasks are supervised learning tasks which use dataset annotated by human beings. This contains mainly two drawbacks: (1) limited size of dataset due to limited human effort; (2) accuracy problem resulted from human perceptual bias. Parkhi et al.[3] discuss the first problem, showing that giant companies hold private face databases with larger size of data (See the comparison in Table 1). Other research institution could only get access to public but smaller databases like LFW[4, 5], which acts like a barricade to even higher performance. Dataset IJB-A [6] LFW [4, 5] YFD [7] CelebFaces [8] CASIA-WebFace [9] MS-Celeb-1M [10] Availability public public public public public public private private identities 500 5K 1595 10K 10K 100K 4K 8M images 5712 13K 3425 videos 202K 500K about 10M 4400K 100-200M Table 1: Face recognition datasets |
| 6257a622ed6bd1b8759ae837b50580657e676192 | |
| 626859fe8cafd25da13b19d44d8d9eb6f0918647 | Activity Recognition based on a
Magnitude-Orientation Stream Network Smart Surveillance Interest Group, Department of Computer Science Universidade Federal de Minas Gerais, Belo Horizonte, Brazil |
| 620e1dbf88069408b008347cd563e16aeeebeb83 | |
| 62007c30f148334fb4d8975f80afe76e5aef8c7f | Eye In-Painting with Exemplar Generative Adversarial Networks
Facebook Inc. 1 Hacker Way, Menlo Park (CA), USA |
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Fast Algorithm for Updating the Discriminant Vectors of Dual-Space LDA |
| 62e0380a86e92709fe2c64e6a71ed94d152c6643 | Facial Emotion Recognition With Expression Energy
Albert Cruz Center for Research in Intelligent Systems 216 Winston Chung Hall Center for Research in Intelligent Systems 216 Winston Chung Hall Center for Research in Intelligent Systems 216 Winston Chung Hall Riverside, CA, 92521-0425, Riverside, CA, 92521-0425, Riverside, CA, 92521-0425, USA USA USA |
| 961a5d5750f18e91e28a767b3cb234a77aac8305 | Face Detection without Bells and Whistles
1 ESAT-PSI/VISICS, iMinds, KU Leuven, Belgium 2 MPI Informatics, Saarbrücken, Germany 3 D-ITET/CVL, ETH Zürich, Switzerland |
| 9626bcb3fc7c7df2c5a423ae8d0a046b2f69180c | UPTEC STS 17033
Examensarbete 30 hp November 2017 A deep learning approach for action classification in American football video sequences |
| 9696b172d66e402a2e9d0a8d2b3f204ad8b98cc4 | J Inf Process Syst, Vol.9, No.1, March 2013
pISSN 1976-913X eISSN 2092-805X Region-Based Facial Expression Recognition in Still Images |
| 964a3196d44f0fefa7de3403849d22bbafa73886 | |
| 9606b1c88b891d433927b1f841dce44b8d3af066 | Principal Component Analysis with Tensor Train
Subspace |
| 96b1000031c53cd4c1c154013bb722ffd87fa7da | ContextVP: Fully Context-Aware Video
Prediction 1 NVIDIA, Santa Clara, CA, USA 2 ETH Zurich, Zurich, Switzerland 3 The Swiss AI Lab IDSIA, Manno, Switzerland 4 NNAISENSE, Lugano, Switzerland |
| 968f472477a8afbadb5d92ff1b9c7fdc89f0c009 | Firefly-based Facial Expression Recognition |
| 9636c7d3643fc598dacb83d71f199f1d2cc34415 | |
| 3a2fc58222870d8bed62442c00341e8c0a39ec87 | Probabilistic Local Variation
Segmentation Technion - Computer Science Department - M.Sc. Thesis MSC-2014-02 - 2014 |
| 3abc833f4d689f37cc8a28f47fb42e32deaa4b17 | Noname manuscript No.
(will be inserted by the editor) Large Scale Retrieval and Generation of Image Descriptions Received: date / Accepted: date |
| 3a60678ad2b862fa7c27b11f04c93c010cc6c430 | JANUARY-MARCH 2012
A Multimodal Database for Affect Recognition and Implicit Tagging |
| 3a0a839012575ba455f2b84c2d043a35133285f9 | 444
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 | Biometrics & Biostatistics International Journal
Comparing treatment means: overlapping standard errors, overlapping confidence intervals, and tests of hypothesis |
| 3a846704ef4792dd329a5c7a2cb8b330ab6b8b4e | 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 copyrighted component of this work in other works. Pre-print of article that appeared at the IEEE Computer Society Workshop on Biometrics 2010. 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 |
| 3a4f522fa9d2c37aeaed232b39fcbe1b64495134 | 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 |
| 54969bcd728b0f2d3285866c86ef0b4797c2a74d | IEEE TRANSACTION SUBMISSION
Learning for Video Compression |
| 5456166e3bfe78a353df988897ec0bd66cee937f | Improved Boosting Performance by Exclusion
of Ambiguous Positive Examples Computer Vision and Active Perception, KTH, Stockholm 10800, Sweden Keywords: Boosting, Image Classification, Algorithm Evaluation, Dataset Pruning, VOC2007. |
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Dhruv Mahajan Yahoo! Labs, Bangalore, India |
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Human-Annotated Data Set for Object Detection in Video Google Brain Google Brain Google Research Google Brain Google Brain |
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(will be inserted by the editor) Efficient Human Action Recognition using Histograms of Motion Gradients and VLAD with Descriptor Shape Information Received: date / Accepted: date |
| 982fed5c11e76dfef766ad9ff081bfa25e62415a | |
| 98519f3f615e7900578bc064a8fb4e5f429f3689 | Dictionary-based Domain Adaptation Methods
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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 M.Tech (CSE) GNDEC Bidar- 585401 BIDAR, INDIA M.Tech (CSE) VKIT, Bangalore- 560040 BANGALORE, INDIA |
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IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 10, NO. 4, APRIL 2001 A Shape- and Texture-Based Enhanced Fisher Classifier for Face Recognition |
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autobiographical memory system 1Sheffield Centre for Robotics (SCentRo), Univ. of Sheffield, Sheffield, S10 2TN, UK 2Dept. of Computer Science, Univ. of Sheffield, Sheffield, S1 4DP, UK 3 CVAP Lab, KTH, Stockholm, Sweden |
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H. Russello |
| 3f5cf3771446da44d48f1d5ca2121c52975bb3d3 | |
| 3f14b504c2b37a0e8119fbda0eff52efb2eb2461 | 5727
Joint Facial Action Unit Detection and Feature Fusion: A Multi-Conditional Learning Approach |
| 3f9a7d690db82cf5c3940fbb06b827ced59ec01e | VIP: Finding Important People in Images
Virginia Tech Google Inc. Virginia Tech Project: https://computing.ece.vt.edu/~mclint/vip/ Demo: http://cloudcv.org/vip/ |
| 3fd90098551bf88c7509521adf1c0ba9b5dfeb57 | Page 1 of 21
*****For Peer Review Only***** 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Attribute-Based Classification for Zero-Shot Visual Object Categorization |
| 3f63f9aaec8ba1fa801d131e3680900680f14139 | Facial Expression Recognition using Local Binary
Patterns and Kullback Leibler Divergence AnushaVupputuri, SukadevMeher divergence. role |
| 3f0e0739677eb53a9d16feafc2d9a881b9677b63 | Efficient Two-Stream Motion and Appearance 3D CNNs for
Video Classification ESAT-KU Leuven Ali Pazandeh Sharif UTech ESAT-KU Leuven, ETH Zurich |
| 30870ef75aa57e41f54310283c0057451c8c822b | Overcoming Catastrophic Forgetting with Hard Attention to the Task |
| 303065c44cf847849d04da16b8b1d9a120cef73a | |
| 3046baea53360a8c5653f09f0a31581da384202e | Deformable Face Alignment via Local
Measurements and Global Constraints |
| 3028690d00bd95f20842d4aec84dc96de1db6e59 | Leveraging Union of Subspace Structure to Improve Constrained Clustering |
| 30c96cc041bafa4f480b7b1eb5c45999701fe066 | 1090
Discrete Cosine Transform Locality-Sensitive Hashes for Face Retrieval |
| 306957285fea4ce11a14641c3497d01b46095989 | FACE RECOGNITION UNDER VARYING LIGHTING BASED ON
DERIVATES OF LOG IMAGE 2ICT-ISVISION Joint R&D Laboratory for Face Recognition, CAS, Beijing 100080, China 1Graduate School, CAS, Beijing, 100039, China |
| 302c9c105d49c1348b8f1d8cc47bead70e2acf08 | 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 |
| 304a306d2a55ea41c2355bd9310e332fa76b3cb0 | |
| 5e7e055ef9ba6e8566a400a8b1c6d8f827099553 | |
| 5e28673a930131b1ee50d11f69573c17db8fff3e | Author manuscript, published in "Workshop on Faces in 'Real-Life' Images: Detection, Alignment, and Recognition, Marseille : France
(2008)" |
| 5e6ba16cddd1797853d8898de52c1f1f44a73279 | Face Identification with Second-Order Pooling |
| 5e821cb036010bef259046a96fe26e681f20266e | |
| 5bfc32d9457f43d2488583167af4f3175fdcdc03 | 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 |
| 5ba7882700718e996d576b58528f1838e5559225 | 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 |
| 5bb684dfe64171b77df06ba68997fd1e8daffbe1 | |
| 5bae9822d703c585a61575dced83fa2f4dea1c6d | MOTChallenge 2015:
Towards a Benchmark for Multi-Target Tracking |
| 5babbad3daac5c26503088782fd5b62067b94fa5 | Are You Sure You Want To Do That?
Classification with Verification |
| 5b9d9f5a59c48bc8dd409a1bd5abf1d642463d65 | 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 Received: date / Accepted: date |
| 5bf70c1afdf4c16fd88687b4cf15580fd2f26102 | Accepted in Pattern Recognition Letters
Pattern Recognition Letters journal homepage: www.elsevier.com Residual Codean Autoencoder for Facial Attribute Analysis IIIT-Delhi, New Delhi, India Article history: Received 29 March 2017 |
| 5b2cfee6e81ef36507ebf3c305e84e9e0473575a | |
| 5be3cc1650c918da1c38690812f74573e66b1d32 | Relative Parts: Distinctive Parts for Learning Relative Attributes
Center for Visual Information Technology, IIIT Hyderabad, India - 500032 |
| 5b0ebb8430a04d9259b321fc3c1cc1090b8e600e | |
| 3765c26362ad1095dfe6744c6d52494ea106a42c | |
| 3727ac3d50e31a394b200029b2c350073c1b69e3 | |
| 37f2e03c7cbec9ffc35eac51578e7e8fdfee3d4e | WACV
#394 000 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048 049 050 051 052 053 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 |
| 377a1be5113f38297716c4bb951ebef7a93f949a | 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 |
| 377c6563f97e76a4dc836a0bd23d7673492b1aae | |
| 370e0d9b89518a6b317a9f54f18d5398895a7046 | 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 and S´ebastien Marcel, Member, IEEE |
| 37eb666b7eb225ffdafc6f318639bea7f0ba9a24 | MSU Technical Report (2014): MSU-CSE-14-5
Age, Gender and Race Estimation from Unconstrained Face Images |
| 375435fb0da220a65ac9e82275a880e1b9f0a557 | 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 |
| 37b6d6577541ed991435eaf899a2f82fdd72c790 | Vision-based Human Gender Recognition: A Survey
Universiti Tunku Abdul Rahman, Kuala Lumpur, Malaysia. |
| 370b5757a5379b15e30d619e4d3fb9e8e13f3256 | Labeled Faces in the Wild: A Database for Studying
Face Recognition in Unconstrained Environments |
| 08d2f655361335bdd6c1c901642981e650dff5ec | 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: |
| 08ae100805d7406bf56226e9c3c218d3f9774d19 | Gavrilescu and Vizireanu EURASIP Journal on Image and Video Processing (2017) 2017:59
DOI 10.1186/s13640-017-0211-4 EURASIP Journal on Image and Video Processing R ES EAR CH Predicting the Sixteen Personality Factors (16PF) of an individual by analyzing facial features Open Access |
| 08c18b2f57c8e6a3bfe462e599a6e1ce03005876 | A Least-Squares Framework
for Component Analysis |
| 081a431107eb38812b74a8cd036ca5e97235b499 | |
| 0831a511435fd7d21e0cceddb4a532c35700a622 | |
| 080c204edff49bf85b335d3d416c5e734a861151 | CLAD: A Complex and Long Activities
Dataset with Rich Crowdsourced Annotations Journal Title XX(X):1–6 c(cid:13)The Author(s) 2016 Reprints and permission: sagepub.co.uk/journalsPermissions.nav DOI: 10.1177/ToBeAssigned www.sagepub.com/ |
| 08f4832507259ded9700de81f5fd462caf0d5be8 | 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 J. S. Rangole Assistant Professor V. U. Deshmukh Assistant Professor |
| 08d40ee6e1c0060d3b706b6b627e03d4b123377a | Human Action Localization
with Sparse Spatial Supervision |
| 08c1f8f0e69c0e2692a2d51040ef6364fb263a40 | |
| 088aabe3da627432fdccf5077969e3f6402f0a80 | 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 |
| 08e24f9df3d55364290d626b23f3d42b4772efb6 | ENHANCING FACIAL EXPRESSION CLASSIFICATION BY INFORMATION
FUSION I. Buciu1, Z. Hammal 2, A. Caplier2, N. Nikolaidis 1, and I. Pitas 1 GR-54124, Thessaloniki, Box 451, Greece 2 Laboratoire des Images et des Signaux / Institut National Polytechnique de Grenoble web: http://www.aiia.csd.auth.gr 38031 Grenoble, France web: http://www.lis.inpg.fr |
| 0830c9b9f207007d5e07f5269ffba003235e4eff | |
| 081fb4e97d6bb357506d1b125153111b673cc128 | |
| 0857281a3b6a5faba1405e2c11f4e17191d3824d | 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 Open Access |
| 08f6745bc6c1b0fb68953ea61054bdcdde6d2fc7 | Understanding Kin Relationships in a Photo |
| 082ad50ac59fc694ba4369d0f9b87430553b11db | |
| 6dd052df6b0e89d394192f7f2af4a3e3b8f89875 | 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 |
| 6dd5dbb6735846b214be72983e323726ef77c7a9 | Josai Mathematical Monographs
vol. 7 (2014), pp. 25-40 A Survey on Newer Prospective Biometric Authentication Modalities |
| 6d10beb027fd7213dd4bccf2427e223662e20b7d | |
| 6dddf1440617bf7acda40d4d75c7fb4bf9517dbb | 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 |
| 6de18708218988b0558f6c2f27050bb4659155e4 | |
| 6d91da37627c05150cb40cac323ca12a91965759 | |
| 6d8c9a1759e7204eacb4eeb06567ad0ef4229f93 | Face Alignment Robust to Pose, Expressions and
Occlusions |
| 6d66c98009018ac1512047e6bdfb525c35683b16 | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 25, NO. 9, SEPTEMBER 2003
1063 Face Recognition Based on Fitting a 3D Morphable Model |
| 016cbf0878db5c40566c1fbc237686fbad666a33 | |
| 01bef320b83ac4405b3fc5b1cff788c124109fb9 | 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 CHILI Laboratory Łukasz Kidzi´nski CHILI Laboratory CHILI Laboratory École polytechnique fédérale École polytechnique fédérale École polytechnique fédérale |
| 01c8d7a3460422412fba04e7ee14c4f6cdff9ad7 | (IJACSA) International Journal of Advanced Computer Science and Applications,
Vol. 4, No. 7, 2013 Rule Based System for Recognizing Emotions Using Multimodal Approach Information System SBM, SVKM’s NMIMS Mumbai, India |
| 01e12be4097fa8c94cabeef0ad61498c8e7762f2 | |
| 0163d847307fae508d8f40ad193ee542c1e051b4 | JOURNAL OF LATEX CLASS FILES, VOL. 6, NO. 1, JANUARY 2007
Classemes and Other Classifier-based Features for Efficient Object Categorization - Supplementary material - 1 LOW-LEVEL FEATURES We extract the SIFT [1] features for our descriptor according 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. 2 CLASSEMES The LSCOM categories were developed specifically for multimedia annotation and retrieval, and have been used in the TRECVID video retrieval series. We took the LSCOM CYC ontology dated 2006-06-30, which contains 2832 unique categories. We removed |
| 01c4cf9c7c08f0ad3f386d88725da564f3c54679 | Interpretability Beyond Feature Attribution:
Quantitative Testing with Concept Activation Vectors (TCAV) |
| 017ce398e1eb9f2eed82d0b22fb1c21d3bcf9637 | FACE RECOGNITION WITH HARMONIC DE-LIGHTING
2ICT-ISVISION Joint R&D Laboratory for Face Recognition, CAS, Beijing, China, 100080 1Graduate School, CAS, Beijing, China, 100080 Emails: {lyqing, sgshan, wgao}jdl.ac.cn |
| 014e3d0fa5248e6f4634dc237e2398160294edce | Int J Comput Vis manuscript No.
(will be inserted by the editor) What does 2D geometric information really tell us about 3D face shape? Received: date / Accepted: date |
| 01beab8f8293a30cf48f52caea6ca0fb721c8489 | |
| 0178929595f505ef7655272cc2c339d7ed0b9507 | |
| 01b4b32c5ef945426b0396d32d2a12c69c282e29 | |
| 0113b302a49de15a1d41ca4750191979ad756d2f | 1424403677/06/$20.00 ©2006 IEEE
537 ICME 2006 |
| 064b797aa1da2000640e437cacb97256444dee82 | Coarse-to-fine Face Alignment with Multi-Scale Local Patch Regression
Megvii Inc. Megvii Inc. Megvii Inc. |
| 06f146dfcde10915d6284981b6b84b85da75acd4 | Scalable Face Image Retrieval using
Attribute-Enhanced Sparse Codewords |
| 0697bd81844d54064d992d3229162fe8afcd82cb | User-driven mobile robot storyboarding: Learning image interest and
saliency from pairwise image comparisons |
| 06e7e99c1fdb1da60bc3ec0e2a5563d05b63fe32 | WhittleSearch: Image Search with Relative Attribute Feedback
(Supplementary Material) 1 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” as the target image. This is because using the proposed approach, the user was able to comment explicitly on the desired openness property. |
| 066d71fcd997033dce4ca58df924397dfe0b5fd1 | (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)
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01(cid:21)(cid:1)2(cid:4)(cid:1)(cid:12)(cid:8)(cid:17)(cid:4)(cid:18)(cid:1)(cid:24)(cid:16)(cid:6)(cid:11)(cid:1)(cid:18)(cid:8)(cid:24)(cid:8)(cid:10)(cid:8)(cid:11)(cid:4)(cid:1)(cid:24)(cid:16)(cid:4)(cid:1)(cid:26)(cid:20)(cid:8)(cid:12)(cid:6)(cid:8)(cid:12)(cid:1)3(cid:8)(cid:27)(cid:4)(cid:1)(cid:19)(cid:8)(cid:24)(cid:8)(cid:10)(cid:8)(cid:11)(cid:4)(cid:1)4(cid:26)3(cid:19)(cid:23)5(cid:21)(cid:1)’(cid:15)(cid:1)(cid:4)(cid:29)(cid:8)(cid:5))(cid:8)(cid:24)(cid:4)(cid:1)(cid:24)(cid:16)(cid:4)(cid:1)(cid:18)(cid:8)(cid:24)(cid:8)(cid:10)(cid:8)(cid:11)(cid:4)(cid:1)(cid:24)(cid:16)(cid:4)(cid:1)(cid:4)6((cid:4)(cid:20)(cid:6)(cid:17)(cid:4)(cid:12)(cid:24)(cid:8)(cid:5)(cid:1)(cid:20)(cid:4)(cid:11))(cid:5)(cid:24)(cid:1)(cid:15)(cid:25)(cid:1)(cid:8)(cid:1)(cid:12)(cid:4).(cid:1)(cid:25)(cid:8)(cid:27)(cid:6)(cid:8)(cid:5)(cid:1) 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8)(cid:17)(cid:8)(cid:12)(cid:1) (cid:25)(cid:8)(cid:27)(cid:4)(cid:1) (cid:6)(cid:11)(cid:1) (cid:24)(cid:16)(cid:4)(cid:1) (cid:17)(cid:15)(cid:11)(cid:24)(cid:1) (cid:27)(cid:15)(cid:17)(cid:17)(cid:15)(cid:12)(cid:1) (cid:8)(cid:12)(cid:18)(cid:1) )(cid:11)(cid:4)(cid:25))(cid:5)(cid:1) (cid:7)(cid:4)(cid:30)(cid:1) (cid:24)(cid:15)(cid:1) (cid:8)(cid:1) ((cid:4)(cid:20)(cid:11)(cid:15)(cid:12)9(cid:11)(cid:1) (cid:6)(cid:18)(cid:4)(cid:12)(cid:24)(cid:6)(cid:24)(cid:30)(cid:21)(cid:1) (cid:9)(cid:11)(cid:1) (cid:16))(cid:17)(cid:8)(cid:12)(cid:11)(cid:14)(cid:1) .(cid:4)(cid:1) (cid:8)(cid:20)(cid:4)(cid:1) (cid:8)(cid:10)(cid:5)(cid:4)(cid:1) (cid:24)(cid:15)(cid:1) (cid:27)(cid:8)(cid:24)(cid:4)-(cid:15)(cid:20)(cid:6)(cid:22)(cid:4)(cid:1) (cid:8)(cid:1) 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(cid:18)(cid:4)(cid:24)(cid:4)(cid:27)(cid:24)(cid:6)(cid:15)(cid:12)(cid:1) 4(cid:10)(cid:4)(cid:11)(cid:6)(cid:18)(cid:4)(cid:1) (cid:15)(cid:24)(cid:16)(cid:4)(cid:20)(cid:1) (cid:18)(cid:8)(cid:24)(cid:8)(cid:10)(cid:8)(cid:11)(cid:4)(cid:11)5(cid:14)(cid:1) (cid:11)(cid:24))(cid:18)(cid:30)(cid:6)(cid:12)-(cid:1) (cid:6)(cid:12)(cid:25)(cid:5))(cid:4)(cid:12)(cid:27)(cid:4)(cid:1) (cid:15)(cid:25)(cid:1) (cid:27)(cid:8)(cid:20)(cid:4)(cid:4)(cid:20)(cid:1) (cid:8)(cid:12)(cid:18)(cid:1) (cid:7)(cid:6)(cid:12)(cid:18)(cid:1) (cid:15)(cid:25)(cid:1) (cid:11)(cid:7)(cid:6)(cid:12)(cid:1) (cid:15)(cid:12)(cid:1) (cid:8)-(cid:6)(cid:12)-(cid:14)(cid:1)(cid:8)(cid:12)(cid:18)(cid:1)(cid:15)(cid:24)(cid:16)(cid:4)(cid:20)(cid:1)(cid:11)(cid:6)(cid:17)(cid:6)(cid:5)(cid:8)(cid:20)(cid:1)(cid:20)(cid:4)(cid:11)(cid:4)(cid:8)(cid:20)(cid:27)(cid:16)(cid:4)(cid:11)(cid:21) (cid:26)(cid:12)(cid:1) (cid:24)(cid:16)(cid:4)(cid:1) 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(cid:8)(((cid:5)(cid:30)(cid:6)(cid:12)- (cid:8)(cid:1) (cid:12)(cid:4).(cid:1) (cid:25)(cid:8)(cid:27)(cid:6)(cid:8)(cid:5)(cid:1) (cid:25)(cid:4)(cid:8)(cid:24))(cid:20)(cid:4)(cid:1) (cid:18)(cid:4)(cid:24)(cid:4)(cid:27)(cid:24)(cid:6)(cid:15)(cid:12)(cid:1)(cid:8)(cid:5)-(cid:15)(cid:20)(cid:6)(cid:24)(cid:16)(cid:17)(cid:21)(cid:1) (cid:30) (cid:15)(cid:31)(cid:5)(cid:13)(cid:11)(cid:5)(cid:4)(cid:24)(cid:6)(cid:7)(cid:3)(cid:8)(cid:9)(cid:6)(cid:1)(cid:25)(cid:3)(cid:24)(cid:9)(cid:6)(cid:10)(cid:3)(cid:11)(cid:3)(cid:12)(cid:3)(cid:13)(cid:9)(cid:13) (cid:3)(cid:8)(cid:12)(cid:30)(cid:1) (cid:25)(cid:8)(cid:27)(cid:4)(cid:1) (cid:18)(cid:8)(cid:24)(cid:8)(cid:10)(cid:8)(cid:11)(cid:4)(cid:11)(cid:1) (cid:8)(cid:20)(cid:4)(cid:1) (cid:20)(cid:4)(cid:27)(cid:15)(cid:20)(cid:18)(cid:4)(cid:18)(cid:1) )(cid:12)(cid:18)(cid:4)(cid:20)(cid:1) (cid:8)(cid:1) (cid:29)(cid:8)(cid:20)(cid:6)(cid:4)(cid:24)(cid:30)(cid:1) (cid:15)(cid:25)(cid:1) (cid:27)(cid:15)(cid:12)(cid:18)(cid:6)(cid:24)(cid:6)(cid:15)(cid:12)(cid:11)(cid:1)(cid:8)(cid:12)(cid:18)(cid:1).(cid:6)(cid:24)(cid:16)(cid:1)(cid:29)(cid:8)(cid:20)(cid:6)(cid:15))(cid:11)(cid:1)(cid:8)(((cid:5)(cid:6)(cid:27)(cid:8)(cid:24)(cid:6)(cid:15)(cid:12)(cid:11)(cid:1)(cid:6)(cid:12)(cid:1)(cid:17)(cid:6)(cid:12)(cid:18)(cid:21)(cid:1)(cid:9)(cid:5)(cid:15)(cid:12)-(cid:1).(cid:6)(cid:24)(cid:16)(cid:1) (cid:24)(cid:16)(cid:4)(cid:1) (cid:18)(cid:4)(cid:29)(cid:4)(cid:5)(cid:15)((cid:17)(cid:4)(cid:12)(cid:24)(cid:1) (cid:15)(cid:25)(cid:1) (cid:25)(cid:8)(cid:27)(cid:4)(cid:1) (cid:20)(cid:4)(cid:27)(cid:15)-(cid:12)(cid:6)(cid:24)(cid:6)(cid:15)(cid:12)(cid:1) (cid:8)(cid:12)(cid:18)(cid:1) (cid:25)(cid:8)(cid:27)(cid:6)(cid:8)(cid:5)(cid:1) (cid:4)6((cid:20)(cid:4)(cid:11)(cid:11)(cid:6)(cid:15)(cid:12)(cid:1) (cid:8)(cid:12)(cid:8)(cid:5)(cid:30)(cid:11)(cid:6)(cid:11)(cid:1) (cid:8)(cid:5)-(cid:15)(cid:20)(cid:6)(cid:24)(cid:16)(cid:17)(cid:11)(cid:14)(cid:1) (cid:8)(cid:1) (cid:27)(cid:15)(cid:17)((cid:8)(cid:20)(cid:8)(cid:24)(cid:6)(cid:29)(cid:4)(cid:5)(cid:30)(cid:1) (cid:5)(cid:8)(cid:20)-(cid:4)(cid:1) (cid:12))(cid:17)(cid:10)(cid:4)(cid:20)(cid:1) (cid:15)(cid:25)(cid:1) (cid:25)(cid:8)(cid:27)(cid:4)(cid:1) A(cid:8)(cid:5)(cid:4)(cid:1)<0=(cid:14)(cid:1)(cid:3)(cid:26)’(cid:1) (cid:30) (cid:29) (cid:7)(cid:15)!(cid:15)"(cid:6)(cid:10)(cid:3)(cid:11)(cid:3)(cid:12)(cid:3)(cid:13)(cid:9) ’(cid:16)(cid:4)(cid:1) 3(cid:8)(cid:27)(cid:6)(cid:8)(cid:5)(cid:1) !(cid:4)(cid:27)(cid:15)-(cid:12)(cid:6)(cid:24)(cid:6)(cid:15)(cid:12)(cid:1) ’(cid:4)(cid:27)(cid:16)(cid:12)(cid:15)(cid:5)(cid:15)-(cid:30)(cid:1) 43#!#’5(cid:1) (cid:18)(cid:8)(cid:24)(cid:8)(cid:10)(cid:8)(cid:11)(cid:4)(cid:1) .(cid:8)(cid:11)(cid:1)(cid:27)(cid:15)(cid:5)(cid:5)(cid:4)(cid:27)(cid:24)(cid:4)(cid:18)(cid:1) (cid:8)(cid:24)(cid:1) D(cid:4)(cid:15)(cid:20)-(cid:4)(cid:1)(cid:3)(cid:8)(cid:11)(cid:15)(cid:12)(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:8)(cid:12)(cid:18)(cid:1)(cid:24)(cid:16)(cid:4)(cid:1) (cid:28)"(cid:1) (cid:9)(cid:20)(cid:17)(cid:30)(cid:1) !(cid:4)(cid:11)(cid:4)(cid:8)(cid:20)(cid:27)(cid:16)(cid:1)F(cid:8)(cid:10)(cid:15)(cid:20)(cid:8)(cid:24)(cid:15)(cid:20)(cid:30)(cid:1)(cid:25)(cid:8)(cid:27)(cid:6)(cid:5)(cid:6)(cid:24)(cid:6)(cid:4)(cid:11)(cid:1) (cid:8)(cid:11)(cid:1)((cid:8)(cid:20)(cid:24)(cid:1)(cid:15)(cid:25)(cid:1) (cid:24)(cid:16)(cid:4)(cid:1)3#!#’(cid:1) ((cid:20)(cid:15)-(cid:20)(cid:8)(cid:17)(cid:1) <(cid:2)*=(cid:21)(cid:1)(cid:26)(cid:12)(cid:1)3#!#’(cid:1)(cid:18)(cid:8)(cid:24)(cid:8)(cid:10)(cid:8)(cid:11)(cid:4)(cid:1)(cid:6)(cid:17)(cid:8)-(cid:4)(cid:11)(cid:1)(cid:15)(cid:25)(cid:1)(cid:2)(cid:2)BB(cid:1)(cid:11))(cid:10) (cid:4)(cid:27)(cid:24)(cid:1)(cid:4)6(cid:6)(cid:11)(cid:24)(cid:1)(cid:6)(cid:12)(cid:1)BE/, (cid:18)(cid:6)(cid:25)(cid:25)(cid:4)(cid:20)(cid:4)(cid:12)(cid:24)(cid:1) ((cid:15)(cid:11)(cid:4)(cid:11)(cid:14)(cid:1) /(cid:1) (cid:25)(cid:8)(cid:27)(cid:6)(cid:8)(cid:5)(cid:1) (cid:4)6((cid:20)(cid:4)(cid:11)(cid:11)(cid:6)(cid:15)(cid:12)(cid:11)(cid:1) (cid:8)(cid:12)(cid:18)(cid:1) /(cid:1) (cid:18)(cid:6)(cid:25)(cid:25)(cid:4)(cid:20)(cid:4)(cid:12)(cid:24)(cid:1) (cid:6)(cid:5)(cid:5))(cid:17)(cid:6)(cid:12)(cid:8)(cid:24)(cid:6)(cid:15)(cid:12)(cid:11)(cid:1)(cid:6)(cid:12)(cid:1)/(cid:1)(cid:18)(cid:6)(cid:25)(cid:25)(cid:4)(cid:20)(cid:4)(cid:12)(cid:24)(cid:1)(cid:24)(cid:6)(cid:17)(cid:4)(cid:11)(cid:21)(cid:1)(cid:1)’(cid:16)(cid:4)(cid:20)(cid:4)(cid:1)(cid:8)(cid:20)(cid:4) (cid:2)>(cid:14),1(cid:2)(cid:1)(cid:6)(cid:17)(cid:8)-(cid:4)(cid:11)(cid:1)(cid:6)(cid:12)(cid:1) /1+G*0>(cid:1)((cid:6)6(cid:4)(cid:5)(cid:11)(cid:1)(cid:6)(cid:12)(cid:1)(cid:11)(cid:6)(cid:22)(cid:4)(cid:21)(cid:1)(cid:26)(cid:17)(cid:8)-(cid:4)(cid:11)(cid:1).(cid:4)(cid:20)(cid:4)(cid:1)(cid:27)(cid:15)(cid:5)(cid:5)(cid:4)(cid:27)(cid:24)(cid:4)(cid:18)(cid:1)(cid:8)(cid:24)(cid:1)(cid:24)(cid:16)(cid:4)(cid:1)(cid:25)(cid:15)(cid:5)(cid:5)(cid:15).(cid:6)(cid:12)-(cid:1) ((cid:15)(cid:11)(cid:4)(cid:11)$(cid:1)(cid:20)(cid:6)-(cid:16)(cid:24)(cid:1)(cid:8)(cid:12)(cid:18)(cid:1)(cid:5)(cid:4)(cid:25)(cid:24)(cid:1)((cid:20)(cid:15)(cid:25)(cid:6)(cid:5)(cid:4)(cid:14)(cid:1)(cid:20)(cid:6)-(cid:16)(cid:24)(cid:1)(cid:8)(cid:12)(cid:18)(cid:1)(cid:5)(cid:4)(cid:25)(cid:24)(cid:1);)(cid:8)(cid:20)(cid:24)(cid:4)(cid:20)(cid:1)((cid:20)(cid:15)(cid:25)(cid:6)(cid:5)(cid:4)(cid:1)(cid:8)(cid:12)(cid:18)(cid:1) (cid:20)(cid:6)-(cid:16)(cid:24)(cid:1) (cid:8)(cid:12)(cid:18)(cid:1) (cid:5)(cid:4)(cid:25)(cid:24)(cid:1) (cid:16)(cid:8)(cid:5)(cid:25)(cid:1) ((cid:20)(cid:15)(cid:25)(cid:6)(cid:5)(cid:4)(cid:21)(cid:1) (cid:26)(cid:12)(cid:1) (cid:24)(cid:16)(cid:4)(cid:11)(cid:4)(cid:1) (cid:27)(cid:8)(cid:24)(cid:4)-(cid:15)(cid:20)(cid:6)(cid:4)(cid:11)(cid:1) (cid:6)(cid:17)(cid:8)-(cid:4)(cid:11)(cid:1) .(cid:4)(cid:20)(cid:4)(cid:1) (cid:20)(cid:4)(cid:27)(cid:15)(cid:20)(cid:18)(cid:4)(cid:18)(cid:1)(cid:25)(cid:15)(cid:20)(cid:1)1,0(cid:1)(cid:24)(cid:15)(cid:1)B0,(cid:1)(cid:11))(cid:10) (cid:4)(cid:27)(cid:24)(cid:11)(cid:21) (cid:30) (cid:30)(cid:6)(cid:7)#$(cid:22)(cid:15)"(cid:6)(cid:23)(cid:24)(cid:5)(cid:4)(cid:24)(cid:6)(cid:10)(cid:3)(cid:11)(cid:3)(cid:12)(cid:3)(cid:13)(cid:9) (cid:2) ’(cid:16)(cid:4)(cid:1)3DE(cid:13)#’(cid:1)(cid:9)-(cid:6)(cid:12)-(cid:1)(cid:19)(cid:8)(cid:24)(cid:8)(cid:10)(cid:8)(cid:11)(cid:4)(cid:1).(cid:8)(cid:11)(cid:1)-(cid:4)(cid:12)(cid:4)(cid:20)(cid:8)(cid:24)(cid:4)(cid:18)(cid:1)(cid:8)(cid:11)(cid:1)((cid:8)(cid:20)(cid:24)(cid:1)(cid:15)(cid:25)(cid:1)(cid:24)(cid:16)(cid:4)(cid:1) #)(cid:20)(cid:15)((cid:4)(cid:8)(cid:12)(cid:1) (cid:28)(cid:12)(cid:6)(cid:15)(cid:12)(cid:1) ((cid:20)(cid:15) (cid:4)(cid:27)(cid:24)(cid:1) 3DE(cid:13)#’(cid:1) 43(cid:8)(cid:27)(cid:4)(cid:1) (cid:8)(cid:12)(cid:18)(cid:1) D(cid:4)(cid:11)(cid:24))(cid:20)(cid:4)(cid:1) !(cid:4)(cid:27)(cid:15)-(cid:12)(cid:6)(cid:24)(cid:6)(cid:15)(cid:12)(cid:1) !(cid:4)(cid:11)(cid:4)(cid:8)(cid:20)(cid:27)(cid:16)(cid:1) (cid:13)(cid:4)(cid:24).(cid:15)(cid:20)(cid:7)5(cid:21)’(cid:16)(cid:6)(cid:11)(cid:1) (cid:18)(cid:8)(cid:24)(cid:8)(cid:10)(cid:8)(cid:11)(cid:4)(cid:1) (cid:6)(cid:11)(cid:1) (cid:27)(cid:15)(cid:12)(cid:24)(cid:8)(cid:6)(cid:12)(cid:6)(cid:12)-(cid:1) (cid:2),,/(cid:1) (cid:11)(cid:27)(cid:8)(cid:12)(cid:12)(cid:4)(cid:18)(cid:1) (cid:25)(cid:8)(cid:27)(cid:4)(cid:1) (cid:6)(cid:17)(cid:8)-(cid:4)(cid:11)(cid:1) (cid:11)(cid:16)(cid:15).(cid:6)(cid:12)-(cid:1) 0/(cid:1) (cid:11))(cid:10) (cid:4)(cid:27)(cid:24)(cid:11)(cid:1) (cid:8)(cid:24)(cid:1) (cid:18)(cid:6)(cid:25)(cid:25)(cid:4)(cid:20)(cid:4)(cid:12)(cid:24)(cid:1) (cid:8)-(cid:4)(cid:11)(cid:21)(cid:1)(cid:26)(cid:17)(cid:8)-(cid:4)(cid:11)(cid:1)(cid:16)(cid:8)(cid:29)(cid:4)(cid:1)(cid:29)(cid:8)(cid:20)(cid:30)(cid:6)(cid:12)-(cid:1)(cid:20)(cid:4)(cid:11)(cid:15)(cid:5))(cid:24)(cid:6)(cid:15)(cid:12)?(cid:1)(cid:8)(((cid:20)(cid:15)6(cid:6)(cid:17)(cid:8)(cid:24)(cid:4)(cid:5)(cid:30)(cid:1)>,,G1,, ((cid:6)6(cid:4)(cid:5)(cid:11)(cid:21)(cid:1) ’(cid:16)(cid:4)(cid:1) (cid:18)(cid:8)(cid:24)(cid:8)(cid:10)(cid:8)(cid:11)(cid:4)(cid:1) .(cid:8)(cid:11)(cid:1) (cid:18)(cid:4)(cid:29)(cid:4)(cid:5)(cid:15)((cid:4)(cid:18)(cid:1) (cid:6)(cid:12)(cid:1) (cid:8)(cid:12)(cid:1) (cid:8)(cid:24)(cid:24)(cid:4)(cid:17)((cid:24)(cid:1) (cid:24)(cid:15)(cid:1) (cid:8)(cid:11)(cid:11)(cid:6)(cid:11)(cid:24)(cid:1) (cid:20)(cid:4)(cid:11)(cid:4)(cid:8)(cid:20)(cid:27)(cid:16)(cid:4)(cid:20)(cid:11)(cid:1) .(cid:16)(cid:15)(cid:1) (cid:6)(cid:12)(cid:29)(cid:4)(cid:11)(cid:24)(cid:6)-(cid:8)(cid:24)(cid:4)(cid:1) (cid:24)(cid:16)(cid:4)(cid:1) (cid:4)(cid:25)(cid:25)(cid:4)(cid:27)(cid:24)(cid:11)(cid:1) (cid:15)(cid:25)(cid:1) (cid:8)-(cid:6)(cid:12)-(cid:1) (cid:15)(cid:12)(cid:1) (cid:25)(cid:8)(cid:27)(cid:6)(cid:8)(cid:5)(cid:1) (cid:8)(((cid:4)(cid:8)(cid:20)(cid:8)(cid:12)(cid:27)(cid:4)(cid:1)<(cid:2)> =(cid:21) (cid:30) % (cid:22)(cid:9)(cid:9)(cid:14)(cid:6)(cid:7)(cid:19)(cid:2)(cid:6)(cid:23)(cid:6)(cid:22)(cid:9)(cid:20)(cid:6)(cid:10)(cid:3)(cid:11)(cid:3)(cid:12)(cid:3)(cid:13)(cid:9) (cid:26)(cid:12)(cid:1)(cid:15)(cid:20)(cid:18)(cid:4)(cid:20)(cid:1)(cid:24)(cid:15)(cid:1) (cid:10))(cid:6)(cid:5)(cid:18)(cid:14)(cid:1) (cid:24)(cid:20)(cid:8)(cid:6)(cid:12)(cid:1) (cid:8)(cid:12)(cid:18)(cid:1) (cid:20)(cid:4)(cid:5)(cid:6)(cid:8)(cid:10)(cid:5)(cid:30)(cid:1) (cid:24)(cid:4)(cid:11)(cid:24)(cid:1) (cid:8)-(cid:4)(cid:1) (cid:27)(cid:5)(cid:8)(cid:11)(cid:11)(cid:6)(cid:25)(cid:6)(cid:27)(cid:8)(cid:24)(cid:6)(cid:15)(cid:12)(cid:1) (cid:8)(cid:5)-(cid:15)(cid:20)(cid:6)(cid:24)(cid:16)(cid:17)(cid:11)(cid:14)(cid:1)(cid:18)(cid:8)(cid:24)(cid:8)(cid:10)(cid:8)(cid:11)(cid:4)(cid:11)(cid:1).(cid:6)(cid:24)(cid:16)(cid:1)(cid:27)(cid:15)(cid:12)(cid:24)(cid:20)(cid:15)(cid:5)(cid:5)(cid:4)(cid:18)(cid:1)(cid:29)(cid:8)(cid:20)(cid:6)(cid:8)(cid:24)(cid:6)(cid:15)(cid:12)(cid:11)(cid:1)(cid:15)(cid:25)(cid:1)(cid:25)(cid:8)(cid:27)(cid:24)(cid:15)(cid:20)(cid:11)(cid:1)(cid:11))(cid:27)(cid:16)(cid:1) (cid:8)(cid:11)(cid:1)(cid:8)-(cid:4)(cid:14)(cid:1)(cid:25)(cid:8)(cid:27)(cid:4)(cid:1)((cid:15)(cid:11)(cid:4)(cid:14)(cid:1)(cid:25)(cid:8)(cid:27)(cid:6)(cid:8)(cid:5)(cid:1)(cid:4)6((cid:20)(cid:4)(cid:11)(cid:11)(cid:6)(cid:15)(cid:12)(cid:14)(cid:1)(cid:15)(cid:27)(cid:27)(cid:5))(cid:11)(cid:6)(cid:15)(cid:12)(cid:1)(cid:25)(cid:8)(cid:27)(cid:6)(cid:8)(cid:5)(cid:1)(cid:16)(cid:8)(cid:6)(cid:20)(cid:14)(cid:1)(cid:8)(cid:12)(cid:18)(cid:1) (cid:6)(cid:5)(cid:5))(cid:17)(cid:6)(cid:12)(cid:8)(cid:24)(cid:6)(cid:15)(cid:12)(cid:1) (cid:6)(cid:11)(cid:1) (cid:12)(cid:4)(cid:4)(cid:18)(cid:4)(cid:18)(cid:21)(cid:1) (cid:26)(cid:12)(cid:1) (cid:11)((cid:6)(cid:24)(cid:4)(cid:1) (cid:15)(cid:25)(cid:1) (cid:29)(cid:8)(cid:20)(cid:6)(cid:15))(cid:11)(cid:1) (cid:18)(cid:8)(cid:24)(cid:8)(cid:10)(cid:8)(cid:11)(cid:4)(cid:11)(cid:14)(cid:1) (cid:24)(cid:16)(cid:4)(cid:20)(cid:4)(cid:1) (cid:6)(cid:11)(cid:1) (cid:12)(cid:15)(cid:24)(cid:1)(cid:8)(cid:12)(cid:1)(cid:8)(((cid:20)(cid:15)((cid:20)(cid:6)(cid:8)(cid:24)(cid:4)(cid:1)(cid:15)(cid:12)(cid:4)(cid:1)(cid:25)(cid:15)(cid:20)(cid:1)(cid:8)-(cid:4)(cid:1)(cid:27)(cid:5)(cid:8)(cid:11)(cid:11)(cid:6)(cid:25)(cid:6)(cid:27)(cid:8)(cid:24)(cid:6)(cid:15)(cid:12)(cid:21)(cid:1)(cid:3)(cid:15)(cid:11)(cid:24)(cid:1)(cid:27))(cid:20)(cid:20)(cid:4)(cid:12)(cid:24)(cid:1)(cid:18)(cid:8)(cid:24)(cid:8)(cid:1) (cid:10)(cid:8)(cid:11)(cid:4)(cid:11)(cid:1)(cid:18)(cid:15)(cid:12):(cid:24)(cid:1)(cid:16)(cid:8)(cid:29)(cid:4)(cid:1)(cid:6)(cid:17)(cid:8)-(cid:4)(cid:11)(cid:1)(cid:15)(cid:25)(cid:1)((cid:4)(cid:15)((cid:5)(cid:4)(cid:1)(cid:6)(cid:12)(cid:1)(cid:18)(cid:6)(cid:25)(cid:25)(cid:4)(cid:20)(cid:4)(cid:12)(cid:24)(cid:1)(cid:8)-(cid:4)(cid:11)(cid:14)(cid:1)(cid:8)(cid:12)(cid:18)(cid:1)(cid:6)(cid:25)(cid:1)(cid:24)(cid:16)(cid:4)(cid:30)(cid:1) (cid:16)(cid:8)(cid:29)(cid:4)(cid:14)(cid:1) (cid:24)(cid:16)(cid:4)(cid:30)(cid:1) (cid:18)(cid:15)(cid:1) (cid:12)(cid:15)(cid:24)(cid:1) (cid:17)(cid:4)(cid:12)(cid:24)(cid:6)(cid:15)(cid:12)(cid:1) (cid:24)(cid:16)(cid:4)(cid:6)(cid:20)(cid:1) (cid:8)-(cid:4)(cid:11)(cid:21)(cid:1) 3DE(cid:13)#’(cid:1) (cid:18)(cid:8)(cid:24)(cid:8)(cid:10)(cid:8)(cid:11)(cid:4)(cid:1) (cid:27)(cid:15)(cid:12)(cid:24)(cid:8)(cid:6)(cid:12)(cid:11)(cid:1) (cid:11)(cid:27)(cid:8)(cid:12)(cid:12)(cid:4)(cid:18)(cid:1) (cid:6)(cid:17)(cid:8)-(cid:4)(cid:11)(cid:1) (cid:15)(cid:25)(cid:1) ((cid:4)(cid:20)(cid:11)(cid:15)(cid:12)(cid:11)(cid:1) .(cid:6)(cid:24)(cid:16)(cid:1) (cid:17)(cid:4)(cid:12)(cid:24)(cid:6)(cid:15)(cid:12)(cid:6)(cid:12)-(cid:1) (cid:24)(cid:16)(cid:4)(cid:6)(cid:20)(cid:1) (cid:8)-(cid:4)(cid:11)?(cid:1)(cid:10))(cid:24)(cid:1)(cid:18)(cid:6)(cid:25)(cid:25)(cid:4)(cid:20)(cid:4)(cid:12)(cid:24)(cid:1)(cid:5)(cid:6)-(cid:16)(cid:24)(cid:6)(cid:12)-(cid:1)(cid:27)(cid:15)(cid:12)(cid:18)(cid:6)(cid:24)(cid:6)(cid:15)(cid:12)(cid:11)(cid:14)(cid:1)(cid:10)(cid:8)(cid:27)(cid:7)-(cid:20)(cid:15))(cid:12)(cid:18)(cid:14)(cid:1)((cid:15)(cid:11)(cid:4)(cid:11)(cid:1)(cid:8)(cid:12)(cid:18)(cid:1) (cid:4)6((cid:20)(cid:4)(cid:11)(cid:11)(cid:6)(cid:15)(cid:12)(cid:11)(cid:21)(cid:1)(cid:23)(cid:30)(cid:1)(cid:11)(cid:24))(cid:18)(cid:30)(cid:6)(cid:12)-(cid:1)(cid:15)(cid:24)(cid:16)(cid:4)(cid:20)(cid:1)(cid:18)(cid:8)(cid:24)(cid:8)(cid:10)(cid:8)(cid:11)(cid:4)(cid:11)(cid:1)(cid:6)(cid:24)(cid:1) .(cid:8)(cid:11)(cid:1) (cid:27)(cid:15)(cid:12)(cid:27)(cid:5))(cid:18)(cid:4)(cid:18)(cid:1)(cid:24)(cid:15)(cid:1) ((cid:20)(cid:15)(cid:29)(cid:6)(cid:18)(cid:4)(cid:1) (cid:8)(cid:1) (cid:18)(cid:8)(cid:24)(cid:8)(cid:10)(cid:8)(cid:11)(cid:4)(cid:1) .(cid:6)(cid:24)(cid:16)(cid:1) (cid:27)(cid:15)(cid:12)(cid:18)(cid:6)(cid:24)(cid:6)(cid:15)(cid:12)(cid:11)(cid:1) (cid:15)(cid:25)(cid:1) (cid:8)(cid:12)(cid:1) (cid:8)-(cid:4)(cid:1) (cid:27)(cid:5)(cid:8)(cid:11)(cid:11)(cid:6)(cid:25)(cid:6)(cid:27)(cid:8)(cid:24)(cid:6)(cid:15)(cid:12)(cid:11)(cid:1) ((cid:20)(cid:15) (cid:4)(cid:27)(cid:24)(cid:21)(cid:1) (cid:9)-(cid:4)(cid:14)(cid:1) (cid:4)(cid:12)(cid:15))-(cid:16)(cid:1) (cid:20)(cid:4)(cid:11)(cid:15)(cid:5))(cid:24)(cid:6)(cid:15)(cid:12)(cid:1) (cid:25)(cid:15)(cid:20)(cid:1) .(cid:20)(cid:6)(cid:12)(cid:7)(cid:5)(cid:4)(cid:1) (cid:8)(cid:12)(cid:8)(cid:5)(cid:30)(cid:11)(cid:6)(cid:11)(cid:1) (cid:8)(cid:12)(cid:18)(cid:1) (cid:25)(cid:20)(cid:15)(cid:12)(cid:24)(cid:8)(cid:5)(cid:1)((cid:15)(cid:11)(cid:4)(cid:11)(cid:1)(cid:8)(cid:20)(cid:4)(cid:1)(cid:10)(cid:8)(cid:11)(cid:6)(cid:27)(cid:1)(cid:12)(cid:4)(cid:4)(cid:18)(cid:11)(cid:1)(cid:25)(cid:15)(cid:20)(cid:1)(cid:24)(cid:16)(cid:6)(cid:11)(cid:1)(cid:25)(cid:6)(cid:4)(cid:5)(cid:18)(cid:21)(cid:1) % (cid:10)(cid:9)(cid:13)(cid:8)(cid:2)(cid:5)&(cid:11)(cid:5)(cid:19)(cid:4)(cid:6) ’((cid:6) (cid:10)(cid:3)(cid:11)(cid:3)(cid:12)(cid:3)(cid:13)(cid:9) (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:16)(cid:4)(cid:1) (cid:26)(cid:20)(cid:8)(cid:12)(cid:6)(cid:8)(cid:12)(cid:1) 3(cid:8)(cid:27)(cid:4)(cid:1) (cid:19)(cid:8)(cid:24)(cid:8)(cid:10)(cid:8)(cid:11)(cid:4)(cid:14)(cid:1) (cid:24)(cid:16)(cid:4)(cid:1) (cid:25)(cid:6)(cid:20)(cid:11)(cid:24)(cid:1) (cid:6)(cid:17)(cid:8)-(cid:4)(cid:1) (cid:18)(cid:8)(cid:24)(cid:8)(cid:10)(cid:8)(cid:11)(cid:4)(cid:1) (cid:6)(cid:12)(cid:1) (cid:17)(cid:6)(cid:18)(cid:18)(cid:5)(cid:4)E(cid:4)(cid:8)(cid:11)(cid:24)(cid:14)(cid:1)(cid:27)(cid:15)(cid:12)(cid:24)(cid:8)(cid:6)(cid:12)(cid:11)(cid:1)(cid:27)(cid:15)(cid:5)(cid:15)(cid:20)(cid:1)(cid:25)(cid:8)(cid:27)(cid:6)(cid:8)(cid:5)(cid:1)(cid:6)(cid:17)(cid:8)-(cid:4)(cid:20)(cid:30)(cid:1)(cid:15)(cid:25)(cid:1)(cid:8)(cid:1)(cid:5)(cid:8)(cid:20)-(cid:4)(cid:1)(cid:12))(cid:17)(cid:10)(cid:4)(cid:20)(cid:1)(cid:15)(cid:25)(cid:1) (cid:26)(cid:20)(cid:8)(cid:12)(cid:6)(cid:8)(cid:12)(cid:1)(cid:11))(cid:10) (cid:4)(cid:27)(cid:24)(cid:11) (cid:10)(cid:4)(cid:24).(cid:4)(cid:4)(cid:12)(cid:1)/(cid:1)(cid:8)(cid:12)(cid:18)(cid:1)01(cid:1)(cid:30)(cid:4)(cid:8)(cid:20)(cid:11)(cid:1)(cid:15)(cid:5)(cid:18)(cid:21) (cid:26)3(cid:19)(cid:23)(cid:1)(cid:6)(cid:11)(cid:1)(cid:8)(cid:1)(cid:5)(cid:8)(cid:20)-(cid:4)(cid:1)(cid:18)(cid:8)(cid:24)(cid:8)(cid:10)(cid:8)(cid:11)(cid:4)(cid:1)(cid:24)(cid:16)(cid:8)(cid:24)(cid:1)(cid:27)(cid:8)(cid:12)(cid:1)(cid:11))(((cid:15)(cid:20)(cid:24)(cid:1)(cid:11)(cid:24))(cid:18)(cid:6)(cid:4)(cid:11)(cid:1)(cid:15)(cid:25)(cid:1)(cid:24)(cid:16)(cid:4)(cid:1)(cid:8)-(cid:4)(cid:1) (cid:27)(cid:5)(cid:8)(cid:11)(cid:11)(cid:6)(cid:25)(cid:6)(cid:27)(cid:8)(cid:24)(cid:6)(cid:15)(cid:12)(cid:1) (cid:11)(cid:30)(cid:11)(cid:24)(cid:4)(cid:17)(cid:11)(cid:21)(cid:1) (cid:26)(cid:24)(cid:1) (cid:27)(cid:15)(cid:12)(cid:24)(cid:8)(cid:6)(cid:12)(cid:11)(cid:1) (cid:15)(cid:29)(cid:4)(cid:20)(cid:1) *(cid:14)+,,(cid:1) (cid:27)(cid:15)(cid:5)(cid:15)(cid:20)(cid:1) (cid:6)(cid:17)(cid:8)-(cid:4)(cid:11)(cid:1) (cid:13)(cid:15)(cid:1)(cid:20)(cid:4)(cid:11)(cid:24)(cid:20)(cid:6)(cid:27)(cid:24)(cid:6)(cid:15)(cid:12)(cid:11)(cid:1)(cid:15)(cid:12)(cid:1).(cid:4)(cid:8)(cid:20)(cid:1)4(cid:27)(cid:5)(cid:15)(cid:24)(cid:16)(cid:4)(cid:11)(cid:14)(cid:1)-(cid:5)(cid:8)(cid:11)(cid:11)(cid:4)(cid:11)(cid:14)(cid:1)(cid:4)(cid:24)(cid:27)(cid:21)5(cid:14)(cid:1) (cid:17)(cid:8)(cid:7)(cid:4)E)((cid:14)(cid:1)(cid:16)(cid:8)(cid:6)(cid:20)(cid:1) (cid:11)(cid:24)(cid:30)(cid:5)(cid:4)(cid:14)(cid:1) (cid:25)(cid:8)(cid:27)(cid:6)(cid:8)(cid:5)(cid:1) (cid:16)(cid:8)(cid:6)(cid:20)(cid:1) .(cid:4)(cid:20)(cid:4)(cid:1) (cid:6)(cid:17)((cid:15)(cid:11)(cid:4)(cid:18)(cid:1) (cid:24)(cid:15)(cid:1) ((cid:8)(cid:20)(cid:24)(cid:6)(cid:27)(cid:6)((cid:8)(cid:12)(cid:24)(cid:11)(cid:21)(cid:1) D(cid:20)(cid:15))(cid:12)(cid:18)E(cid:24)(cid:20))(cid:24)(cid:16)(cid:1) (cid:6)(cid:12)(cid:25)(cid:15)(cid:20)(cid:17)(cid:8)(cid:24)(cid:6)(cid:15)(cid:12)(cid:14)(cid:1)(cid:6)(cid:12)(cid:27)(cid:5))(cid:18)(cid:6)(cid:12)-(cid:1)(cid:26)(cid:19)(cid:14)(cid:1)(cid:8)-(cid:4)(cid:14)(cid:1)(cid:7)(cid:6)(cid:12)(cid:18)(cid:1)(cid:15)(cid:25) ((cid:15)(cid:11)(cid:4)(cid:1)(cid:15)(cid:20)(cid:1)(cid:4)6((cid:20)(cid:4)(cid:11)(cid:11)(cid:6)(cid:15)(cid:12)(cid:1)(cid:8)(cid:12)(cid:18)(cid:1) (cid:6)(cid:25)(cid:1) (cid:24)(cid:16)(cid:4)(cid:1) (cid:11))(cid:10) (cid:4)(cid:27)(cid:24)(cid:1) (cid:16)(cid:8)(cid:11)(cid:1) -(cid:5)(cid:8)(cid:11)(cid:11)(cid:4)(cid:11)(cid:1) (cid:6)(cid:11)(cid:1) ((cid:20)(cid:15)(cid:29)(cid:6)(cid:18)(cid:4)(cid:18)(cid:21)(cid:1) #6((cid:4)(cid:20)(cid:6)(cid:17)(cid:4)(cid:12)(cid:24)(cid:8)(cid:5)(cid:1) (cid:11))(cid:10) (cid:4)(cid:27)(cid:24)(cid:11)(cid:1) .(cid:4)(cid:20)(cid:4)(cid:1)((cid:16)(cid:15)(cid:24)(cid:15)-(cid:20)(cid:8)((cid:16)(cid:4)(cid:18)(cid:1).(cid:6)(cid:24)(cid:16)(cid:1)(cid:8)(cid:1)(cid:25)(cid:6)(cid:12)(cid:4)E(cid:20)(cid:4)(cid:11)(cid:15)(cid:5))(cid:24)(cid:6)(cid:15)(cid:12)(cid:1)(cid:27)(cid:15)(cid:5)(cid:15)(cid:20)(cid:1)(cid:18)(cid:6)-(cid:6)(cid:24)(cid:8)(cid:5)(cid:1)(cid:27)(cid:8)(cid:17)(cid:4)(cid:20)(cid:8)(cid:1) (cid:6)(cid:12)(cid:1)(cid:18)(cid:8)(cid:30)(cid:5)(cid:6)-(cid:16)(cid:24)(cid:21)(cid:1)’(cid:16)(cid:4)(cid:1)(cid:11))(cid:10) (cid:4)(cid:27)(cid:24)(cid:11)(cid:1).(cid:4)(cid:20)(cid:4)(cid:1)(cid:11)(cid:4)(cid:8)(cid:24)(cid:4)(cid:18)(cid:1)(cid:15)(cid:12)(cid:1)(cid:8)(cid:1)(cid:11)(cid:24)(cid:15)(cid:15)(cid:5)(cid:1)(cid:8)(cid:12)(cid:18)(cid:1)(cid:6)(cid:12)(cid:11)(cid:24)(cid:20))(cid:27)(cid:24)(cid:4)(cid:18)(cid:1) (cid:24)(cid:15)(cid:1) (cid:17)(cid:8)(cid:6)(cid:12)(cid:24)(cid:8)(cid:6)(cid:12)(cid:1) (cid:8)(cid:1) (cid:27)(cid:15)(cid:12)(cid:11)(cid:24)(cid:8)(cid:12)(cid:24)(cid:1) (cid:16)(cid:4)(cid:8)(cid:18)(cid:1) ((cid:15)(cid:11)(cid:6)(cid:24)(cid:6)(cid:15)(cid:12)(cid:1) 4(cid:8)(cid:5)(cid:24)(cid:16)(cid:15))-(cid:16)(cid:1) (cid:11)(cid:5)(cid:6)-(cid:16)(cid:24)(cid:1) (cid:17)(cid:15)(cid:29)(cid:4)(cid:17)(cid:4)(cid:12)(cid:24)(cid:11)(cid:1).(cid:4)(cid:20)(cid:4)(cid:1))(cid:12)(cid:8)(cid:29)(cid:15)(cid:6)(cid:18)(cid:8)(cid:10)(cid:5)(cid:4)5(cid:21) ’(cid:16)(cid:4)(cid:1)(cid:6)(cid:17)(cid:8)-(cid:4)(cid:11)(cid:1)(cid:8)(cid:20)(cid:4)(cid:1)(cid:6)(cid:12)(cid:1)>0,G+>,(cid:1)((cid:6)6(cid:4)(cid:5)(cid:11)(cid:1)(cid:20)(cid:4)(cid:11)(cid:15)(cid:5))(cid:24)(cid:6)(cid:15)(cid:12)(cid:14)(cid:1)/>(cid:1)(cid:10)(cid:6)(cid:24)(cid:1)(cid:18)(cid:4)((cid:24)(cid:16) (cid:14)(cid:1) (cid:8)(cid:10)(cid:15))(cid:24)(cid:1)>,(cid:1)(cid:31)(cid:10)(cid:30)(cid:24)(cid:4)(cid:11)(cid:1)(cid:11)(cid:6)(cid:22)(cid:4)(cid:1)(cid:8)(cid:12)(cid:18)(cid:1)CHD(cid:1)(cid:25)(cid:15)(cid:20)(cid:17)(cid:8)(cid:24) (cid:21)(cid:1) #(cid:12)(cid:15))-(cid:16)(cid:1) (cid:5))(cid:17)(cid:6)(cid:12)(cid:15)(cid:11)(cid:6)(cid:24)(cid:30)(cid:1) (cid:25)(cid:15)(cid:20)(cid:1) .(cid:20)(cid:6)(cid:12)(cid:7)(cid:5)(cid:4)(cid:1) ((cid:20)(cid:15)(cid:27)(cid:4)(cid:11)(cid:11)(cid:6)(cid:12)-(cid:1) (cid:8)(cid:12)(cid:18)(cid:1) (cid:25)(cid:8)(cid:27)(cid:6)(cid:8)(cid:5)(cid:1) (cid:25)(cid:4)(cid:8)(cid:24))(cid:20)(cid:4)(cid:11)(cid:1) .(cid:6)(cid:24)(cid:16)(cid:15))(cid:24)(cid:1) (cid:11)(cid:16)(cid:8)(cid:18)(cid:15).(cid:11)(cid:1) (cid:6)(cid:11)(cid:1) (cid:12)(cid:4)(cid:4)(cid:18)(cid:4)(cid:18)(cid:1) 4(cid:6)(cid:12)(cid:1) (cid:8)-(cid:4)(cid:1) (cid:27)(cid:5)(cid:8)(cid:11)(cid:11)(cid:6)(cid:25)(cid:6)(cid:27)(cid:8)(cid:24)(cid:6)(cid:15)(cid:12)(cid:1) .(cid:20)(cid:6)(cid:12)(cid:7)(cid:5)(cid:4)(cid:1) (cid:18)(cid:4)(cid:24)(cid:4)(cid:27)(cid:24)(cid:6)(cid:15)(cid:12)(cid:1) (cid:8)(cid:12)(cid:18)(cid:1) (cid:8)(cid:12)(cid:8)(cid:5)(cid:30)(cid:11)(cid:6)(cid:11)(cid:1) (cid:24)(cid:16)(cid:4)(cid:1) (cid:18)(cid:6)(cid:11)(cid:24)(cid:6)(cid:12)-)(cid:6)(cid:11)(cid:16)(cid:6)(cid:12)-(cid:1)(cid:15)(cid:25)(cid:1)(cid:11)(cid:4)(cid:12)(cid:6)(cid:15)(cid:20)(cid:11)(cid:1)(cid:25)(cid:20)(cid:15)(cid:17)(cid:1)(cid:24)(cid:16)(cid:15)(cid:11)(cid:4)(cid:1)(cid:6)(cid:12)(cid:1)(cid:24)(cid:16)(cid:4)(cid:1)(cid:30)(cid:15))(cid:12)-(cid:4)(cid:20)(cid:1)(cid:27)(cid:8)(cid:24)(cid:4)-(cid:15)(cid:20)(cid:6)(cid:4)(cid:11)(cid:1) <(cid:2)=5(cid:21)(cid:1) ")(cid:10) (cid:4)(cid:27)(cid:24)(cid:11)(cid:1) .(cid:4)(cid:20)(cid:4)(cid:1) ((cid:16)(cid:15)(cid:24)(cid:15)-(cid:20)(cid:8)((cid:16)(cid:4)(cid:18)(cid:1) .(cid:6)(cid:24)(cid:16)(cid:15))(cid:24)(cid:1) (cid:8)(cid:12)(cid:30)(cid:1) ((cid:20)(cid:15) (cid:4)(cid:27)(cid:24)(cid:15)(cid:20)(cid:11)(cid:1) (cid:15)(cid:20)(cid:1) (cid:6)(cid:17)((cid:15)(cid:20)(cid:24)(cid:8)(cid:12)(cid:24)(cid:1) (cid:25)(cid:15)(cid:20)(cid:1) (cid:6)(cid:11)(cid:1) |
| 06526c52a999fdb0a9fd76e84f9795a69480cecf | |
| 06fe63b34fcc8ff68b72b5835c4245d3f9b8a016 | Mach Learn
DOI 10.1007/s10994-013-5336-9 Learning semantic representations of objects and their parts Received: 24 May 2012 / Accepted: 26 February 2013 © The Author(s) 2013 |
| 06aab105d55c88bd2baa058dc51fa54580746424 | Image Set based Collaborative Representation for
Face Recognition |
| 06262d14323f9e499b7c6e2a3dec76ad9877ba04 | Real-Time Pose Estimation Piggybacked on Object Detection
Brno, Czech Republic |
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Preferred Networks inc., Japan |
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1Computer Vision Lab ETH Z¨urich, Switzerland 2ESAT, PSI-VISICS K.U. Leuven, Belgium |
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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 allows you to distribute, remix, tweak, and build upon the work, even commercially, as long as you credit the authors 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 https://eprints.whiterose.ac.uk/ |
| 06a9ed612c8da85cb0ebb17fbe87f5a137541603 | Deep Learning of Player Trajectory Representations for Team
Activity Analysis |
| 06ad99f19cf9cb4a40741a789e4acbf4433c19ae | SenTion: A framework for Sensing Facial
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IIT Hyderabad IIT Hyderabad |
| 6c2b392b32b2fd0fe364b20c496fcf869eac0a98 | DOI 10.1007/s00138-012-0423-7
ORIGINAL PAPER Fully automatic face recognition framework based on local and global features Received: 30 May 2011 / Revised: 21 February 2012 / Accepted: 29 February 2012 / Published online: 22 March 2012 © Springer-Verlag 2012 |
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| 6c8c7065d1041146a3604cbe15c6207f486021ba | Attention Modeling for Face Recognition via Deep Learning
Department of Computing, Hung Hom, Kowloon Hong Kong, 999077 CHINA Department of Computing, Hung Hom, Kowloon Hong Kong, 99907 CHINA Department of Computing, Hung Hom, Kowloon Hong Kong, 99907 CHINA Department of Computing, Hung Hom, Kowloon Hong Kong, 99907 CHINA |
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by David Lieh-Chiang Chen 2012 |
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ISSN: 2231-2307, Volume-4 Issue-4, September 2014 An Efficient Method for Face Recognition based on Fusion of Global and Local Feature Extraction |
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| 392c3cabe516c0108b478152902a9eee94f4c81e | Computer Science and Artificial Intelligence Laboratory
Technical Report MIT-CSAIL-TR-2007-024 April 23, 2007 Tiny images 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 |
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Computational Biomedicine Lab 4800 Calhoun Rd. Houston, TX, USA |
| 39b452453bea9ce398613d8dd627984fd3a0d53c | |
| 3958db5769c927cfc2a9e4d1ee33ecfba86fe054 | Describable Visual Attributes for
Face Verification and Image Search |
| 39b5f6d6f8d8127b2b97ea1a4987732c0db6f9df | |
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Model Based Face Recognition Across Facial Expressions 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 building block for social interaction. Consequently, these capabilities are an important step towards the acceptance of many technical systems. trees as a classifier lies not only |
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> TIP-05732-2009< 1 Normalization of Face Illumination Based on Large- and Small- Scale Features |
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108 GraphiCon’2012 |
| 5506a1a1e1255353fde05d9188cb2adc20553af5 | |
| 55c81f15c89dc8f6eedab124ba4ccab18cf38327 | |
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{tag} {/tag} International Journal of Computer Applications Foundation of Computer Science (FCS), NY, USA Volume 126 - Number 5 Year of Publication: 2015 Authors: 10.5120/ijca2015906055 {bibtex}2015906055.bib{/bibtex} |
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1Dept. of Electronics and Information Engineering, Huazhong Univ. of Science and Technology, China 2Microsoft Research, Beijing, China |
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REPORT OF THE RESEARCH PROJECT OPTION: COMPUTER VISION Pose and Face Recovery via Spatio-temporal GrabCut Human Segmentation Date: 13/07/2010 |
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UT Austin USC UT Austin |
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LTCI, CNRS, T´el´ecom ParisTech, Universit´e Paris-Saclay, 75013, Paris, France J´erˆome Gauthier LADIS, CEA, LIST, 91191, Gif-sur-Yvette, France Normandie Universit´e, UR, LITIS EA 4108, Avenue de l’universit´e, 76801, Saint-Etienne-du-Rouvray, France |
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Toward Wide-Angle Microvision Sensors Todd Zickler, Member, IEEE |
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The final version of record is available at http://dx.doi.org/10.1109/TIP.2016.2539502 Discriminant Incoherent Component Analysis |
| 0a6a25ee84fc0bf7284f41eaa6fefaa58b5b329a | |
| 0ae9cc6a06cfd03d95eee4eca9ed77b818b59cb7 | Noname manuscript No.
(will be inserted by the editor) Multi-task, multi-label and multi-domain learning with residual convolutional networks for emotion recognition Received: date / Accepted: date |
| 0acf23485ded5cb9cd249d1e4972119239227ddb | Dual coordinate solvers for large-scale structural SVMs
UC Irvine This manuscript describes a method for training linear SVMs (including binary SVMs, SVM regression, and 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 algorithms 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 as 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) We show how to generalize the solver to other types of SVM problems such as (latent) structural SVMs (2) We show how to modify it to behave as a partially-online algorithm, which only requires access to small amounts of data at a time. Overview: Sec. 1 describes a general formulation of an SVM problem that encompasses many standard tasks such as multi-class classification and (latent) structural prediction. Sec. 2 derives its dual QP, and Sec. 3 describes a dual coordinate descent optimization algorithm. Sec. 4 describes modifications for optimizing in an online fashion, allowing one to learn near-optimal models with a single pass over large, out-of-core datasets. Sec. 5 briefly touches on some theoretical issues that are necessary to ensure convergence. Finally, Sec. 6 and Sec. 7 describe modifications to our basic formulation to accommodate non-negativity constraints and flexible regularization schemes during learning. 1 Generalized SVMs We first describe a general formulation of a SVM which encompasses various common problems such as binary classification, regression, and structured prediction. Assume we are given training data where the ith example is described by a set of Ni vectors {xij} and a set of Ni scalars {lij}, where j varies from 1 to Ni. We wish to solve the following optimization problem: (0, lij − wT xij) max j∈Ni (1) (cid:88) argmin L(w) = ||w||2 + |
| 0ad4a814b30e096ad0e027e458981f812c835aa0 | |
| 6448d23f317babb8d5a327f92e199aaa45f0efdc | |
| 6412d8bbcc01f595a2982d6141e4b93e7e982d0f | Deep Convolutional Neural Network using Triplets of Faces, Deep Ensemble, and
Score-level Fusion for Face Recognition 1Department of Creative IT Engineering, POSTECH, Korea 2Department of Computer Science and Engineering, POSTECH, Korea |
| 649eb674fc963ce25e4e8ce53ac7ee20500fb0e3 | |
| 642c66df8d0085d97dc5179f735eed82abf110d0 | |
| 641f34deb3bdd123c6b6e7b917519c3e56010cb7 | |
| 645de797f936cb19c1b8dba3b862543645510544 | Deep Temporal Linear Encoding Networks
1ESAT-PSI, KU Leuven, 2CVL, ETH Z¨urich |
| 6462ef39ca88f538405616239471a8ea17d76259 | |
| 90ac0f32c0c29aa4545ed3d5070af17f195d015f | |
| 90cb074a19c5e7d92a1c0d328a1ade1295f4f311 | 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 MIT Media Laboratory Cambridge, MA 02139 |
| 90b11e095c807a23f517d94523a4da6ae6b12c76 | |
| 9028fbbd1727215010a5e09bc5758492211dec19 | Solving the Uncalibrated Photometric Stereo
Problem using Total Variation 1 IRIT, UMR CNRS 5505, Toulouse, France 2 Dept. of Computer Science, Univ. of Copenhagen, Denmark |
| bf1e0279a13903e1d43f8562aaf41444afca4fdc | 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 DWT information to get desired information Introduction ---------------------------------------------------------------------***--------------------------------------------------------------------- |
| bf5940d57f97ed20c50278a81e901ae4656f0f2c | Query-free Clothing Retrieval via Implicit
Relevance Feedback |
| bfb98423941e51e3cd067cb085ebfa3087f3bfbe | Sparseness helps: Sparsity Augmented
Collaborative Representation for Classification |
| d3b73e06d19da6b457924269bb208878160059da | Proceedings of the 5th International Conference on Computing and Informatics, ICOCI 2015
11-13 August, 2015 Istanbul, Turkey. Universiti Utara Malaysia (http://www.uum.edu.my ) Paper No. 065 IMPLEMENTATION OF AN AUTOMATED SMART HOME CONTROL FOR DETECTING HUMAN EMOTIONS VIA FACIAL DETECTION Osman4 |
| d3d71a110f26872c69cf25df70043f7615edcf92 | 2736
Learning Compact Feature Descriptor and Adaptive Matching Framework for Face Recognition improvements |
| d309e414f0d6e56e7ba45736d28ee58ae2bad478 | 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 | |
| d33fcdaf2c0bd0100ec94b2c437dccdacec66476 | Neurons with Paraboloid Decision Boundaries for
Improved Neural Network Classification Performance |
| d444368421f456baf8c3cb089244e017f8d32c41 | CNN for IMU Assisted Odometry Estimation using Velodyne LiDAR |
| d4c7d1a7a03adb2338704d2be7467495f2eb6c7b | |
| d4ebf0a4f48275ecd8dbc2840b2a31cc07bd676d | |
| d44a93027208816b9e871101693b05adab576d89 | |
| d4b88be6ce77164f5eea1ed2b16b985c0670463a | TECHNICAL REPORT JAN.15.2016
A Survey of Different 3D Face Reconstruction Methods Department of Computer Science and Engineering |
| d44ca9e7690b88e813021e67b855d871cdb5022f | 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 2009, Hotel Windsor Suites Bangkok, Bangkok. © Copyright 2009 Springer-Verlag GmbH Berlin Heidelberg |
| bafb8812817db7445fe0e1362410a372578ec1fc | 805
Image-Quality-Based Adaptive Face Recognition |
| ba816806adad2030e1939450226c8647105e101c | MindLAB at the THUMOS Challenge
Fabi´an P´aez 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 ,1,2), Fr´ed´eric Jurie(1) (∗) equal contribution (1)Normandie Univ, UNICAEN, ENSICAEN, CNRS (2)Safran Electronics and Defense September 11, 2018 |
| ba788365d70fa6c907b71a01d846532ba3110e31 | |
| ba8a99d35aee2c4e5e8a40abfdd37813bfdd0906 | 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 1Univerza v Ljubljani, Fakulteta za elektrotehniko, Trˇzaˇska 25, 1000 Ljubljana, Slovenija 2Univerza v Ljubljani, Fakulteta za raˇcunalniˇstvo in informatiko, Trˇzaˇska 25, 1000 Ljubljana, Slovenija 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 our broad research on the usage of affect and personality in recommender systems. We improved the accuracy of content- based recommender system with the inclusion of affective parameters of user and item modeling. We improved the accuracy of a content filtering recommender system under the cold start conditions with the introduction of a personality based user similarity measure. Furthermore we developed a system for implicit tagging of content with affective metadata. 1 UVOD Uporabniki (porabniki) multimedijskih (MM) vsebin so v ˇcedalje teˇzjem poloˇzaju, saj v veliki koliˇcini vse- bin teˇzko najdejo zanje primerne. Pomagajo si s pri- poroˇcilnimi sistemi, ki na podlagi osebnih preferenc uporabnikov izberejo manjˇso koliˇcino relevantnih MM vsebin, med katerimi uporabnik laˇze izbira. Noben danes znan priporoˇcilni sistem ne zadoˇsˇca v celoti potrebam uporabnikov, saj je izbor priporoˇcenih vsebin obiˇcajno nezadovoljive kakovosti [10]. Cilj tega ˇclanka je pred- staviti metode emotivno pogojenega raˇcunalniˇstva (ang. affective computing - glej [12]) za izboljˇsanje kakovosti priporoˇcilnih sistemov in utrditi za slovenski prostor novo terminologijo. 1.1 Opis problema Za izboljˇsanje kakovosti priporoˇcilnih sistemov sta na voljo dve poti: (i) optimizacija algoritmov ali (ii) uporaba boljˇsih znaˇcilk, ki bolje razloˇzijo neznano Prejet 13. oktober, 2010 Odobren 1. februar, 2011 varianco [8]. V tem ˇclanku predstavljamo izboljˇsanje priporoˇcilnih sistemov z uporabo novih znaˇcilk, ki te- meljijo na emotivnih odzivih uporabnikov in na njiho- vih osebnostnih lastnostih. Te znaˇcilke razloˇzijo velik del uporabnikovih preferenc, ki se izraˇzajo v obliki ocen posameznih vsebin (npr. Likertova lestvica, binarne ocene itd.). Ocene vsebin se pri priporoˇcilnih sistemih zajemajo eksplicitno (ocena) ali implicitno, pri ˇcemer o oceni sklepamo na podlagi opazovanj (npr. ˇcas gledanja kot indikator vˇseˇcnosti [7]. Izboljˇsanja uˇcinkovitosti priporoˇcilnih sistemov smo se lotili na treh podroˇcjih: (i) uporaba emotivnega modeliranja uporabnikov v vsebinskem priporoˇcilnem sistemu, (ii) neinvazivna (implicitna) detekcija emocij za emotivno modeliranje in (iii) uporaba osebnostne mere podobnosti v skupinskem priporoˇcilnem sistemu. Slika 1 prikazuje arhitekturo emotivnega priporoˇcilnega sistema in mesta, kjer smo vnesli opisane izboljˇsave. Preostanek ˇclanka je strukturiran tako: v razdelku 2 je predstavljen zajem podatkov. V razdelku 3 je predstavljen vsebinski priporoˇcilni sistem z emotivnimi metapodatki. V razdelku 4 je predstavljen skupinski priporoˇcilni sistem, ki uporablja mero podobnosti na podlagi osebnosti, v razdelku 5 pa algoritem za razpo- znavo emocij. Vsak od teh razdelov je sestavljen iz opisa eksperimenta in predstavitve rezultatov. V razdelku 6 so predstavljeni sklepi. 1.2 Sorodno delo Najbolj groba delitev priporoˇcilnih sistemov je na vse- binske, skupinske ter hibridne sisteme [1]. Z izjemo vse- binskih priporoˇcilnih sistemov, ki sta ga razvila Arapakis [2] in Tkalˇciˇc [14], sorodnega dela na podroˇcju emotivno pogojenih priporoˇcilnih sistemov takorekoˇc ni. Panti´c in |
| ba29ba8ec180690fca702ad5d516c3e43a7f0bb8 | |
| bab88235a30e179a6804f506004468aa8c28ce4f | |
| badd371a49d2c4126df95120902a34f4bee01b00 | GONDA, WEI, PARAG, PFISTER: PARALLEL SEPARABLE 3D CONVOLUTION
Parallel Separable 3D Convolution for Video and Volumetric Data Understanding Harvard John A. Paulson School of Engineering and Applied Sciences Camabridge MA, USA Toufiq Parag Hanspeter Pfister |
| a0f94e9400938cbd05c4b60b06d9ed58c3458303 | 1118
Value-Directed Human Behavior Analysis from Video Using Partially Observable Markov Decision Processes |
| a022eff5470c3446aca683eae9c18319fd2406d5 | 2017-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 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 Jury 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 M. Sid-Ahmed BERRANI, Directeur de l’Innovation/HDR, Algérie Télécom Présidente Rapporteur Rapporteur Examinateur Encadrant Directeur de Thèse Invité TÉLÉCOM ParisTech école de l’Institut Télécom - membre de ParisTech N°: 2009 ENAM XXXX T H È S E |
| a0c37f07710184597befaa7e6cf2f0893ff440e9 | |
| a0fb5b079dd1ee5ac6ac575fe29f4418fdb0e670 | |
| a0fd85b3400c7b3e11122f44dc5870ae2de9009a | Learning Deep Representation for Face
Alignment with Auxiliary Attributes |
| a0dfb8aae58bd757b801e2dcb717a094013bc178 | Reconocimiento de expresiones faciales con base
en la din´amica de puntos de referencia faciales 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 con 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- cados 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? 1 Comp. Science & Eng. Univ. of CA, San Diego 2 Electrical Engineering California Inst. of Tech. 3 Lab of Neuro Imaging Univ. of CA, Los Angeles |
| a000149e83b09d17e18ed9184155be140ae1266e | Chapter 9
Action Recognition in Realistic Sports Videos |
| a784a0d1cea26f18626682ab108ce2c9221d1e53 | Anchored Regression Networks applied to Age Estimation and Super Resolution
D-ITET, ETH Zurich Switzerland D-ITET, ETH Zurich Merantix GmbH D-ITET, ETH Zurich ESAT, KU Leuven |
| a74251efa970b92925b89eeef50a5e37d9281ad0 | |
| a7664247a37a89c74d0e1a1606a99119cffc41d4 | Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17)
3287 |
| a7a6eb53bee5e2224f2ecd56a14e3a5a717e55b9 | 11th International Symposium of Robotics Research (ISRR2003), pp.192-201, 2003
Face Recognition Using Multi-viewpoint Patterns for Robot Vision Corporate Research and Development Center, TOSHIBA Corporation 1, KomukaiToshiba-cho, Saiwai-ku, Kawasaki 212-8582 Japan |
| a75ee7f4c4130ef36d21582d5758f953dba03a01 | DD2427 Final Project Report
DD2427 Final Project Report Human face attributes prediction with Deep Learning |
| a775da3e6e6ea64bffab7f9baf665528644c7ed3 | International Journal of Computer Applications (0975 – 8887)
Volume 142 – No.9, May 2016 Human Face Pose Estimation based on Feature Extraction Points Research scholar, Department of ECE SBSSTC, Moga Road, Ferozepur, Punjab, India |
| b8dba0504d6b4b557d51a6cf4de5507141db60cf | Comparing Performances of Big Data Stream
Processing Platforms with RAM3S |
| b8378ab83bc165bc0e3692f2ce593dcc713df34a | |
| b8f3f6d8f188f65ca8ea2725b248397c7d1e662d | Selfie Detection by Synergy-Constriant Based
Convolutional Neural Network Electrical and Electronics Engineering, NITK-Surathkal, India. |
| b81cae2927598253da37954fb36a2549c5405cdb | Experiments on Visual Information Extraction with the Faces of Wikipedia
D´epartement de g´enie informatique et g´enie logiciel, Polytechnique Montr´eal 2500, Chemin de Polytechnique, Universit´e de Montr´eal, Montr`eal, Qu´ebec, Canada |
| b8a829b30381106b806066d40dd372045d49178d | 1872
A Probabilistic Framework for Joint Pedestrian Head and Body Orientation Estimation |
| b1d89015f9b16515735d4140c84b0bacbbef19ac | Too Far to See? Not Really!
— Pedestrian Detection with Scale-aware Localization Policy |
| b14b672e09b5b2d984295dfafb05604492bfaec5 | LearningImageClassificationandRetrievalModelsThomasMensink |
| b171f9e4245b52ff96790cf4f8d23e822c260780 | |
| b1a3b19700b8738b4510eecf78a35ff38406df22 | 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 and Maja Pantic, Fellow, IEEE |
| b1301c722886b6028d11e4c2084ee96466218be4 | |
| b1c5581f631dba78927aae4f86a839f43646220c | |
| b1444b3bf15eec84f6d9a2ade7989bb980ea7bd1 | LOCAL DIRECTIONAL RELATION PATTERN
Local Directional Relation Pattern for Unconstrained and Robust Face Retrieval |
| b19e83eda4a602abc5a8ef57467c5f47f493848d | JOURNAL OF LATEX CLASS FILES
Heat Kernel Based Local Binary Pattern for Face Representation |
| dd8084b2878ca95d8f14bae73e1072922f0cc5da | Model Distillation with Knowledge Transfer from
Face Classification to Alignment and Verification Beijing Orion Star Technology Co., Ltd. Beijing, China |
| dd0760bda44d4e222c0a54d41681f97b3270122b | |
| ddea3c352f5041fb34433b635399711a90fde0e8 | Facial Expression Classification using Visual Cues and Language
Department of Computer Science and Engineering, IIT Kanpur |
| ddbd24a73ba3d74028596f393bb07a6b87a469c0 | Multi-region two-stream R-CNN
for action detection Inria(cid:63) |
| ddf099f0e0631da4a6396a17829160301796151c | IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
Learning Face Image Quality from Human Assessments |
| dd0a334b767e0065c730873a95312a89ef7d1c03 | Eigenexpressions: Emotion Recognition using Multiple
Eigenspaces Luis Marco-Gim´enez1, Miguel Arevalillo-Herr´aez1, and Cristina Cuhna-P´erez2 Burjassot. Valencia 46100, Spain, 2 Universidad Cat´olica San Vicente M´artir de Valencia (UCV), Burjassot. Valencia. Spain |
| dd2f6a1ba3650075245a422319d86002e1e87808 | |
| dd8d53e67668067fd290eb500d7dfab5b6f730dd | 69
A Parameter-Free Framework for General Supervised Subspace Learning |
| ddbb6e0913ac127004be73e2d4097513a8f02d37 | 264
IEEE TRANSACTIONS ON MULTIMEDIA, VOL. 1, NO. 3, SEPTEMBER 1999 Face Detection Using Quantized Skin Color Regions Merging and Wavelet Packet Analysis |
| dd600e7d6e4443ebe87ab864d62e2f4316431293 | |
| dcb44fc19c1949b1eda9abe998935d567498467d | Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17)
1916 |
| dc77287bb1fcf64358767dc5b5a8a79ed9abaa53 | Fashion Conversation Data on Instagram
∗Graduate School of Culture Technology, KAIST, South Korea †Department of Communication Studies, UCLA, USA |
| dc2e805d0038f9d1b3d1bc79192f1d90f6091ecb | |
| dc974c31201b6da32f48ef81ae5a9042512705fe | Am I done? Predicting Action Progress in Video
1 Media Integration and Communication Center, Univ. of Florence, Italy 2 Department of Mathematics “Tullio Levi-Civita”, Univ. of Padova, Italy |
| b6c047ab10dd86b1443b088029ffe05d79bbe257 | |
| b6c53891dff24caa1f2e690552a1a5921554f994 | |
| b613b30a7cbe76700855479a8d25164fa7b6b9f1 | 1
Identifying User-Specific Facial Affects from Spontaneous Expressions with Minimal Annotation |
| b6f682648418422e992e3ef78a6965773550d36b | February 8, 2017 |
| b656abc4d1e9c8dc699906b70d6fcd609fae8182 | |
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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 |
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| a98316980b126f90514f33214dde51813693fe0d | Collaborations on YouTube: From Unsupervised Detection to the
Impact on Video and Channel Popularity Multimedia Communications Lab (KOM), Technische Universität Darmstadt, Germany |
| a93781e6db8c03668f277676d901905ef44ae49f | Recent Datasets on Object Manipulation: A Survey |
| a9adb6dcccab2d45828e11a6f152530ba8066de6 | Aydınlanma Alt-uzaylarına dayalı Gürbüz Yüz Tanıma
Illumination Subspaces based Robust Face Recognition Interactive Systems Labs, Universität Karlsruhe (TH) 76131 Karlsruhe, Almanya web: http://isl.ira.uka.de/face_recognition Özetçe yönlerine aydınlanma kaynaklanan sonra, yüz uzayı Bu çalışmada aydınlanma alt-uzaylarına dayalı bir yüz tanıma sistemi sunulmuştur. Bu sistemde, ilk olarak, baskın aydınlanma 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 kılındıktan aydınlanmadan kişi kimliklerinden kaynaklanan değişimlerden ayırmak için- bu üç aydınlanma uzayına bölünmüştür. Daha sonra, ek aydınlanma yönü bilgisinden faydalanmak için aydınlanma alt-uzaylarına dayalı yüz tanıma algoritması kullanılmıştır. Önerilen yaklaşım, CMU PIE veritabanında, “illumination” ve “lighting” kümelerinde yer alan yüz imgeleri üzerinde sınanmıştır. Elde edilen deneysel sonuçlar, aydınlanma yönünden yararlanmanın ve aydınlanma alt-uzaylarına dayalı yüz tanıma algoritmasının yüz tanıma başarımını önemli ölçüde arttırdığını göstermiştir. değişimleri, farklı |
| a95dc0c4a9d882a903ce8c70e80399f38d2dcc89 | TR-IIS-14-003
Review and Implementation of High-Dimensional Local Binary Patterns and Its Application to Face Recognition 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 |
| a92b5234b8b73e06709dd48ec5f0ec357c1aabed | |
| d50c6d22449cc9170ab868b42f8c72f8d31f9b6c | Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17)
1668 |
| d522c162bd03e935b1417f2e564d1357e98826d2 | 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 |
| d59f18fcb07648381aa5232842eabba1db52383e | 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. IIT Kanpur Kanpur 208016, India Kanpur 208016, India IIT Kanpur Dept. of Computer Sc. and Engg. IIT Kanpur Kanpur 208016, India While approaches based on 3D deformable facial model have achieved expression recognition rates of as high as 98% [2], they are computationally inefficient and require considerable apriori training based on 3D information, which is often unavailable. Recognition from 2D images remains a difficult yet important problem for areas such as image database querying and classification. The accuracy rates achieved for 2D images are around 90% [3,4,5,11]. In a recent review of expression recognition, Fasel [1] considers the problem along several dimensions: whether features such as lips or eyebrows are first identified in the face (local [4] vs holistic [11]), or whether the image model used is 2D or 3D. Methods proposed for expression recognition from 2D images include the Gabor-Wavelet [5] or Holistic Optical flow [11] approach. This paper describes a more robust system for facial expression recognition from image sequences using 2D appearance-based local approach for the extraction of intransient facial features, i.e. features such as eyebrows, lips, or mouth, which are always present in the image, but may be deformed [1] (in contrast, transient features are wrinkles or bulges that disappear at other times). The main advantages of such an approach is low computational requirements, ability to work with both colored and grayscale images and robustness in handling partial occlusions [3]. Edge projection analysis which is used here for feature extraction (eyebrows and lips) is well known [6]. Unlike [6] which describes a template based matching as an essential starting point, we use contours analysis. Our system computes a feature vector based on geometrical model of the face and then classifies it into four expression classes using a feed-forward basis function net. The system detects open and closed state of the mouth as well. The algorithm presented here works on both color and grayscale image sequences. An important aspect of our work is the use of color information for robust and more accurate segmentation of lip region in case of color images. The novel lip-enhancement transform is based on Gaussian modeling of skin and lip color. To place the work in a larger context of face analysis and recognition, the overall task requires that the part of the image involving the face be detected and segmented. We assume that a near-frontal view of the face is available. Tests on a grayscale and two color face image databases ([8] and [9,10]) demonstrate a superior recognition rate for four facial expressions (smile, surprise, disgust and sad against neutral). image sequences |
| d588dd4f305cdea37add2e9bb3d769df98efe880 |
Audio-Visual Authentication System over the Internet Protocol abandoned. in illumination based is developed with the objective to |
| d5444f9475253bbcfef85c351ea9dab56793b9ea | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
BoxCars: Improving Fine-Grained Recognition of Vehicles using 3D Bounding Boxes in Traffic Surveillance in contrast |
| d5ab6aa15dad26a6ace5ab83ce62b7467a18a88e | 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 Intelligent Biometric Group, School of Electrical and Electronic Engineering, Engineering Campus, Universiti Sains Malaysia, Pulau Pinang, Malaysia Copyright © 2014 Horizon Research Publishing All rights reserved. |
| d56fe69cbfd08525f20679ffc50707b738b88031 | Training of multiple classifier systems utilizing
partially labelled sequences 89069 Ulm - Germany |
| d50751da2997e7ebc89244c88a4d0d18405e8507 | |
| d511e903a882658c9f6f930d6dd183007f508eda | |
| d59404354f84ad98fa809fd1295608bf3d658bdc | 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 Received: date / Accepted: date |
| d5e1173dcb2a51b483f86694889b015d55094634 | |
| d2eb1079552fb736e3ba5e494543e67620832c52 | ANNUNZIATA, SAGONAS, CALÌ: DENSELY FUSED SPATIAL TRANSFORMER NETWORKS1
DeSTNet: Densely Fused Spatial Transformer Networks1 Onfido Research 3 Finsbury Avenue London, UK |
| d24dafe10ec43ac8fb98715b0e0bd8e479985260 | 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 and Contagion: Feeling Negative, but Smiling Politely • Gerben A. van Kleef2 • Agneta H. Fischer2 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 IIT Bombay Mumbai, Maharashtra, India AITOE Labs Mumbai, Maharashtra, India AITOE Labs Mumbai, Maharashtra, India Rishabh Iyer AITOE Labs Seattle, Washington, USA AITOE Labs Seattle, Washington, USA Narsimha Raju IIT Bombay Mumbai, Maharashtra, India IIT Bombay Mumbai, Maharashtra, India IIT Bombay Mumbai, Maharashtra, India May 30, 2018 |
| aafb271684a52a0b23debb3a5793eb618940c5dd | |
| aa52910c8f95e91e9fc96a1aefd406ffa66d797d | FACE RECOGNITION SYSTEM BASED
ON 2DFLD AND PCA E&TC Department Sinhgad Academy of Engineering Pune, India Mr. Hulle Rohit Rajiv ME E&TC [Digital System] Sinhgad Academy of Engineering Pune, India |
| aadfcaf601630bdc2af11c00eb34220da59b7559 | Multi-view Hybrid Embedding:
A Divide-and-Conquer Approach |
| aaa4c625f5f9b65c7f3df5c7bfe8a6595d0195a5 | Biometrics in Ambient Intelligence |
| aa331fe378056b6d6031bb8fe6676e035ed60d6d | |
| aae0e417bbfba701a1183d3d92cc7ad550ee59c3 | 844
A Statistical Method for 2-D Facial Landmarking |
| aa577652ce4dad3ca3dde44f881972ae6e1acce7 | Deep Attribute Networks
Department of EE, KAIST Daejeon, South Korea Department of EE, KAIST Daejeon, South Korea Department of EE, KAIST Daejeon, South Korea Department of EE, KAIST Daejeon, South Korea |
| aa94f214bb3e14842e4056fdef834a51aecef39c | Reconhecimento de padrões faciais: Um estudo
Universidade Federal Rural do Semi-Árido Departamento de Ciências Naturais Mossoró, RN - 59625-900 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, conveniência, 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. biométrica é o uso da biometria para reconhecimento, identi- ficação ou verificação, de um ou mais traços biométricos de um indivíduo com o objetivo de autenticar sua identidade. Os traços biométricos são os atributos analisados pelas técnicas de reconhecimento biométrico. A tarefa de reconhecimento facial é composta por três processos distintos: Registro, verificação e identificação bio- métrica. Os processos se diferenciam pela forma de determinar a identidade de um indivíduo. Na Figura 1 são descritos os processos de registro, verificação e identificação biométrica. I. INTRODUÇÃO Biometria é a ciência que estabelece a identidade de um indivíduo baseada em seus atributos físicos, químicos ou comportamentais [1]. Possui inúmeras aplicações em diver- sas áreas, se destacando mais na área de segurança, como por exemplo sistemas de gerenciamento de identidade, cuja funcionalidade é autenticar a identidade de um indivíduo no contexto de uma aplicação. O reconhecimento facial é uma técnica biométrica que consiste em identificar padrões em características faciais como formato da boca, do rosto, distância dos olhos, entre outros. Um humano é capaz de reconhecer uma pessoa familiar mesmo com muitos obstáculos com distância, sombras ou apenas a visão parcial do rosto. Uma máquina, no entanto, precisa realizar inúmeros processos para detectar e reconhecer um conjunto de padrões específicos para rotular uma face como conhecida ou desconhecida. Para isso, exitem métodos capazes de detectar, extrair e classificar as características faciais, fornecendo um reconhecimento automático de pessoas. II. RECONHECIMENTO FACIAL A tecnologia biométrica oferece vantagens em relação a outros métodos tradicionais de identificação como senhas, documentos e tokens. Entre elas estão o fato de que os traços biométricos não podem ser perdidos ou esquecidos, são difíceis de serem copiados, compartilhados ou distribuídos. Os métodos requerem que a pessoa autenticada esteja presente na hora e lugar da autenticação, evitando que pessoas má intencionadas tenham acesso sem autorização. A autenticação é o ato de estabelecer ou confirmar alguém, ou alguma coisa, como autêntico, isto é, que as alegações feitas por ou sobre a coisa é verdadeira [2]. Autenticação (a) (b) (c) Figura 1: Registro biométrico (a), identificação biométrica (b) e verificação biométrica (c) A Figura 1a descreve o processo de registro de dados |
| af8fe1b602452cf7fc9ecea0fd4508ed4149834e | |
| af6e351d58dba0962d6eb1baf4c9a776eb73533f | How to Train Your Deep Neural Network with
Dictionary Learning *IIIT Delhi Okhla Phase 3 Delhi, 110020, India +IIIT Delhi Okhla Phase 3 #IIIT Delhi Okhla Phase 3 Delhi, 110020, India Delhi, 110020, India |
| af6cae71f24ea8f457e581bfe1240d5fa63faaf7 | |
| af54dd5da722e104740f9b6f261df9d4688a9712 | |
| afc7092987f0d05f5685e9332d83c4b27612f964 | Person-Independent Facial Expression Detection using Constrained
Local Models |
| b730908bc1f80b711c031f3ea459e4de09a3d324 | 2024
Active Orientation Models for Face Alignment In-the-Wild |
| b7cf7bb574b2369f4d7ebc3866b461634147041a | 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 Received: 2 August 2010 / Accepted: 3 August 2011 / Published online: 19 August 2011 Ó Springer-Verlag London Limited 2011 |
| b7eead8586ffe069edd190956bd338d82c69f880 | 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 |
| b75cee96293c11fe77ab733fc1147950abbe16f9 | |
| b7f05d0771da64192f73bdb2535925b0e238d233 | MVA2005 IAPR Conference on Machine VIsion Applications, May 16-18, 2005 Tsukuba Science City, Japan
4-3 Robust Active Shape Model using AdaBoosted Histogram Classifiers W ataru Ito Imaging Software Technology Center Imaging Software Technology Center FUJI PHOTO FILM CO., LTD. FUJI PHOTO FILM CO., LTD. |
| b755505bdd5af078e06427d34b6ac2530ba69b12 | To appear in the International Joint Conf. Biometrics, Washington D.C., October, 2011
NFRAD: Near-Infrared Face Recognition at a Distance aDept. of Brain and Cognitive Eng. Korea Univ., Seoul, Korea bDept. of Comp. Sci. & Eng. Michigan State Univ., E. Lansing, MI, USA 48824 |
| b73fdae232270404f96754329a1a18768974d3f6 | |
| b76af8fcf9a3ebc421b075b689defb6dc4282670 | Face Mask Extraction in Video Sequence |
| b747fcad32484dfbe29530a15776d0df5688a7db | |
| b7f7a4df251ff26aca83d66d6b479f1dc6cd1085 | 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 cascade: application to multiview and occluded face detection |
| db227f72bb13a5acca549fab0dc76bce1fb3b948 | 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 1Chitti Babu, 2Yasmeen Jaweed, 3G.Vijay Kumar |
| dbaf89ca98dda2c99157c46abd136ace5bdc33b3 | Nonlinear Cross-View Sample Enrichment for
Action Recognition Institut Mines-T´el´ecom; T´el´ecom ParisTech; CNRS LTCI |
| dbab6ac1a9516c360cdbfd5f3239a351a64adde7 | |
| dbe255d3d2a5d960daaaba71cb0da292e0af36a7 | Evolutionary Cost-sensitive Extreme Learning
Machine 1 |
| dbb0a527612c828d43bcb9a9c41f1bf7110b1dc8 | Chapter 7
Machine Learning Techniques for Face Analysis |
| dbb7f37fb9b41d1aa862aaf2d2e721a470fd2c57 | Face Image Analysis With
Convolutional Neural Networks Dissertation Zur Erlangung des Doktorgrades der Fakult¨at f¨ur Angewandte Wissenschaften an der Albert-Ludwigs-Universit¨at Freiburg im Breisgau von Stefan Duffner 2007 |
| a83fc450c124b7e640adc762e95e3bb6b423b310 | Deep Face Feature for Face Alignment |
| a85e9e11db5665c89b057a124547377d3e1c27ef | Dynamics of Driver’s Gaze: Explorations in
Behavior Modeling & Maneuver Prediction |
| a8117a4733cce9148c35fb6888962f665ae65b1e | IEEE TRANSACTIONS ON XXXX, VOL. XX, NO. XX, XX 201X
A Good Practice Towards Top Performance of Face Recognition: Transferred Deep Feature Fusion |
| a8035ca71af8cc68b3e0ac9190a89fed50c92332 | 000
001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 IIIT-CFW: A Benchmark Database of Cartoon Faces in the Wild 1 IIIT Chittoor, Sri City, India 2 CVIT, KCIS, IIIT Hyderabad, India |
| a88640045d13fc0207ac816b0bb532e42bcccf36 | ARXIV VERSION
Simultaneously Learning Neighborship and Projection Matrix for Supervised Dimensionality Reduction |
| a8a30a8c50d9c4bb8e6d2dd84bc5b8b7f2c84dd8 | 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 allows you to distribute, remix, tweak, and build upon the work, even commercially, as long as you credit the authors 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 https://eprints.whiterose.ac.uk/ |
| a8e75978a5335fd3deb04572bb6ca43dbfad4738 | Sparse Graphical Representation based Discriminant
Analysis for Heterogeneous Face Recognition |
| ded968b97bd59465d5ccda4f1e441f24bac7ede5 | Noname manuscript No.
(will be inserted by the editor) Large scale 3D Morphable Models Zafeiriou Received: date / Accepted: date |
| de0eb358b890d92e8f67592c6e23f0e3b2ba3f66 | ACCEPTED BY IEEE TRANS. PATTERN ANAL. AND MACH. INTELL.
Inference-Based Similarity Search in Randomized Montgomery Domains for Privacy-Preserving Biometric Identification |
| dee406a7aaa0f4c9d64b7550e633d81bc66ff451 | Content-Adaptive Sketch Portrait Generation by
Decompositional Representation Learning |
| dedabf9afe2ae4a1ace1279150e5f1d495e565da | 3294
Robust Face Recognition With Structurally Incoherent Low-Rank Matrix Decomposition |
| de398bd8b7b57a3362c0c677ba8bf9f1d8ade583 | Hierarchical Bayesian Theme Models for
Multi-pose Facial Expression Recognition |
| ded41c9b027c8a7f4800e61b7cfb793edaeb2817 | |
| defa8774d3c6ad46d4db4959d8510b44751361d8 | FEBEI - Face Expression Based Emoticon Identification
CS - B657 Computer Vision Robert J Henderson - rojahend |
| b0c512fcfb7bd6c500429cbda963e28850f2e948 | |
| b09b693708f412823053508578df289b8403100a | WANG et al.: TWO-STREAM SR-CNNS FOR ACTION RECOGNITION IN VIDEOS
Two-Stream SR-CNNs for Action Recognition in Videos 1 Advanced Interactive Technologies Lab ETH Zurich Zurich, Switzerland 2 Computer Vision Lab ETH Zurich Zurich, Switzerland |
| b07582d1a59a9c6f029d0d8328414c7bef64dca0 | Employing Fusion of Learned and Handcrafted
Features for Unconstrained Ear Recognition Maur´ıcio Pamplona Segundo∗† October 24, 2017 |
| b03d6e268cde7380e090ddaea889c75f64560891 | |
| b0c1615ebcad516b5a26d45be58068673e2ff217 | 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 | (will be inserted by the editor)
A robust and efficient video representation for action recognition Received: date / Accepted: date |
| a66d89357ada66d98d242c124e1e8d96ac9b37a0 | Failure Detection for Facial Landmark Detectors
Computer Vision Lab, D-ITET, ETH Zurich, Switzerland |
| a608c5f8fd42af6e9bd332ab516c8c2af7063c61 | 2408
Age Estimation via Grouping and Decision Fusion |
| a6eb6ad9142130406fb4ffd4d60e8348c2442c29 | Video Description: A Survey of Methods,
Datasets and Evaluation Metrics |
| a6583c8daa7927eedb3e892a60fc88bdfe89a486 | |
| a6590c49e44aa4975b2b0152ee21ac8af3097d80 | https://doi.org/10.1007/s11263-018-1074-6
3D Interpreter Networks for Viewer-Centered Wireframe Modeling Received: date / Accepted: date |
| a694180a683f7f4361042c61648aa97d222602db | Face Recognition using Scattering Wavelet under Illicit Drug Abuse Variations
IIIT-Delhi India |
| a6db73f10084ce6a4186363ea9d7475a9a658a11 | |
| a6634ff2f9c480e94ed8c01d64c9eb70e0d98487 | |
| b9d0774b0321a5cfc75471b62c8c5ef6c15527f5 | Fishy Faces: Crafting Adversarial Images to Poison Face Authentication
imec-DistriNet, KU Leuven imec-DistriNet, KU Leuven imec-DistriNet, KU Leuven imec-DistriNet, KU Leuven imec-DistriNet, KU Leuven |
| b908edadad58c604a1e4b431f69ac8ded350589a | Deep Face Feature for Face Alignment |
| b9f2a755940353549e55690437eb7e13ea226bbf | Unsupervised Feature Learning from Videos for Discovering and Recognizing Actions |
| b9cedd1960d5c025be55ade0a0aa81b75a6efa61 | INEXACT KRYLOV SUBSPACE ALGORITHMS FOR LARGE
MATRIX EXPONENTIAL EIGENPROBLEM FROM DIMENSIONALITY REDUCTION |
| b971266b29fcecf1d5efe1c4dcdc2355cb188ab0 | MAI et al.: ON THE RECONSTRUCTION OF FACE IMAGES FROM DEEP FACE TEMPLATES
On the Reconstruction of Face Images from Deep Face Templates |
| a158c1e2993ac90a90326881dd5cb0996c20d4f3 | OPEN ACCESS
ISSN 2073-8994 Article 1 DMA, Università degli Studi di Palermo, via Archirafi 34, 90123 Palermo, Italy 2 CITC, Università degli Studi di Palermo, via Archirafi 34, 90123 Palermo, Itlay 3 Istituto Nazionale di Ricerche Demopolis, via Col. Romey 7, 91100 Trapani, Italy † Deceased on 15 March 2009. 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 1Computer Vision and Active Perception Laboratory (CVAP), KTH, Sweden 2INRIA, WILLOW, Laboratoire d’Informatique de l’Ecole Normale Superieure |
| a1b1442198f29072e907ed8cb02a064493737158 | 456
Crowdsourcing Facial Responses to Online Videos |
| a15c728d008801f5ffc7898568097bbeac8270a4 | Concise Preservation by Combining Managed Forgetting
and 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- WP4: centration D4.4: Information analysis, consolidation and concentration techniques, and evalua- tion - Final release. Vasileios Mezaris (CERTH) Walter Allasia (EURIX) PU 31-01-2016 (M36) 31-01-2016 Final multidocument summarization, semantic en- richment, feature extraction, concept de- tection, event detection, image/video qual- ity, image/video aesthetic quality, face de- tection/clustering, im- age/video summarization, image/video near duplicate detection, data deduplication, con- densation, consolidation image clustering, |
| a1132e2638a8abd08bdf7fc4884804dd6654fa63 | 6
Real-Time Video Face Recognition for Embedded Devices Tessera, Galway, Ireland 1. 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 challenges of video face recognition in real life scenarios, describing a general architecture of a 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. 2. Video face recognition Face recognition remains a very active topic in computer vision and receives attention from a 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 these algorithms and turn them into commercially viable product. Biometric based security systems are becoming more popular due to their non-invasive nature and their increasing reliability. Surveillance applications based on face recognition are gaining increasing attention after the United States’ 9/11 events and with the ongoing security threats. The Face Recognition Vendor Test (FRVT) (Phillips et al., 2003) includes video face recognition testing starting with the 2002 series of tests. Recently, face recognition technology was deployed in consumer applications such as organizing a collection of images using the faces present in the images (Picassa; Corcoran & Costache, 2005), prioritizing family members for best capturing conditions when taking pictures, or directly annotating the images as they are captured (Costache et al., 2006). Video face recognition, compared with more traditional still face recognition, has the main advantage of using multiple instances of the same individual in sequential frames for recognition to occur. In still recognition case, the system has only one input image to make the decision if the person is or is not in the database. If the image is not suitable for recognition (due to face orientation, expression, quality or facial occlusions) the recognition result will most likely be incorrect. In the video image there are multiple frames which can www.intechopen.com |
| a14ae81609d09fed217aa12a4df9466553db4859 | REVISED VERSION, JUNE 2011
Face Identification Using Large Feature Sets |
| a1e97c4043d5cc9896dc60ae7ca135782d89e5fc | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
Re-identification of Humans in Crowds using Personal, Social and Environmental Constraints |
| efd308393b573e5410455960fe551160e1525f49 | Tracking Persons-of-Interest via
Unsupervised Representation Adaptation |
| ef4ecb76413a05c96eac4c743d2c2a3886f2ae07 | 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 aEPFL, Lausanne, Switzerland; bOc´e, Paris, France |
| ef032afa4bdb18b328ffcc60e2dc5229cc1939bc | 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 and Video Processing RESEARCH Open Access Attribute-enhanced metric learning for face retrieval |
| ef5531711a69ed687637c48930261769465457f0 | Studio2Shop: from studio photo shoots to fashion articles
Zalando Research, Muehlenstr. 25, 10243 Berlin, Germany Keywords: computer vision, deep learning, fashion, item recognition, street-to-shop |
| efa08283656714911acff2d5022f26904e451113 | Active Object Localization in Visual Situations |
| ef999ab2f7b37f46445a3457bf6c0f5fd7b5689d | Calhoun: The NPS Institutional Archive
DSpace Repository Theses and Dissertations 1. Thesis and Dissertation Collection, all items 2017-12 Improving face verification in photo albums by combining facial recognition and metadata with cross-matching Monterey, California: Naval Postgraduate School http://hdl.handle.net/10945/56868 Downloaded from NPS Archive: Calhoun |
| c3beae515f38daf4bd8053a7d72f6d2ed3b05d88 | |
| c3dc4f414f5233df96a9661609557e341b71670d | 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 Open Access |
| c398684270543e97e3194674d9cce20acaef3db3 | Chapter 2
Comparative Face Soft Biometrics for Human Identification |
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IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART B: CYBERNETICS, VOL. 42, NO. 2, APRIL 2012 Intrinsic Illumination Subspace for Lighting Insensitive Face Recognition |
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| c37a971f7a57f7345fdc479fa329d9b425ee02be | A Novice Guide towards Human Motion Analysis and Understanding |
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2014 IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP) 978-1-4799-2893-4/14/$31.00 ©2014 IEEE 1. INTRODUCTION |
| c37de914c6e9b743d90e2566723d0062bedc9e6a | ©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 |
| c4f1fcd0a5cdaad8b920ee8188a8557b6086c1a4 | 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 Received: 7 March 2013 / Accepted: 8 January 2014 / Published online: 20 February 2014 © Springer Science+Business Media New York 2014 |
| c4dcf41506c23aa45c33a0a5e51b5b9f8990e8ad | Understanding Activity: Learning the Language of Action
Univ. of Rochester and Maryland 1.1 Overview Understanding observed activity is an important problem, both from the standpoint of practical applications, and 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 understanding is central to understanding intelligence. Analyzing what is happening in the environment, and acting on the results of that analysis is, to a large extent, what natural intelligent systems do, whether they are human or animal. Artificial intelligences, if we want them to work with people in the natural world, will need commensurate abilities. The importance of the problem has not gone unrecognized. There is a substantial body of work on various components of the problem, most especially on change detection, motion analysis, and tracking. More recently, in the context of surveillance applications, there have been some preliminary efforts to come up with a general ontology of human activity. These efforts have largely been top-down in the classic AI tradition, and, as with earlier analogous effort in areas such as object recognition and scene understanding, have seen limited practical application because of the difficulty in robustly extracting the putative primitives on which the top- down formalism is based. We propose a novel alternative approach, where understanding activity is centered on |
| c49aed65fcf9ded15c44f9cbb4b161f851c6fa88 | Multiscale Facial Expression Recognition using Convolutional Neural Networks
IDIAP, Martigny, Switzerland |
| eac6aee477446a67d491ef7c95abb21867cf71fc | JOURNAL
A survey of sparse representation: algorithms and applications |
| ea482bf1e2b5b44c520fc77eab288caf8b3f367a | Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17)
2592 |
| eafda8a94e410f1ad53b3e193ec124e80d57d095 | Jeffrey F. Cohn
13 Observer-Based Measurement of Facial Expression With the Facial Action Coding System Facial expression has been a focus of emotion research for over a 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, 1990), 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- cesses (Hatfield, Cacioppo, & Rapson, 1992; Hess & Kirouac, 2000), and emotion disorder (Kaiser, 2002; Sloan, Straussa, Quirka, & Sajatovic, 1997), to name a few. Because of its importance to the study of emotion, a num- ber of observer-based systems of facial expression measure- ment have been developed (Ekman & Friesen, 1978, 1982; Ekman, Friesen, & Tomkins, 1971; Izard, 1979, 1983; Izard & Dougherty, 1981; Kring & Sloan, 1991; Tronick, Als, & Brazelton, 1980). Of these various systems for describing facial expression, the Facial Action Coding System (FACS; Ekman & Friesen, 1978; Ekman, Friesen, & Hager, 2002) is the most comprehensive, psychometrically rigorous, and widely used (Cohn & Ekman, 2005; Ekman & Rosenberg, 2005). Using FACS and viewing video-recorded facial behav- ior at frame rate and slow motion, coders can manually code nearly all possible facial expressions, which are decomposed into action units (AUs). Action units, with some qualifica- tions, are the smallest visually discriminable facial move- ments. By comparison, other systems are less thorough (Malatesta et al., 1989), fail to differentiate between some anatomically distinct movements (Oster, Hegley, & Nagel, 1992), consider movements that are not anatomically dis- tinct as separable (Oster et al., 1992), and often assume a one- to-one mapping between facial expression and emotion (for a review of these systems, see Cohn & Ekman, in press). Unlike systems that use emotion labels to describe ex- pression, FACS explicitly distinguishes between facial actions and inferences about what they mean. FACS itself is descrip- tive and includes no emotion-specified descriptors. Hypoth- eses and inferences about the emotional meaning of facial actions are extrinsic to FACS. If one wishes to make emo- tion-based inferences from FACS codes, a variety of related resources exist. These include the FACS Investigators’ Guide (Ekman et al., 2002), the FACS interpretive database (Ekman, Rosenberg, & Hager, 1998), and a large body of empirical research.(Ekman & Rosenberg, 2005). These resources sug- gest combination rules for defining emotion-specified expres- sions from FACS action units, but this inferential step remains extrinsic to FACS. Because of its descriptive power, FACS is regarded by many as the standard measure for facial be- havior and is used widely in diverse fields. Beyond emo- tion science, these include facial neuromuscular disorders (Van Swearingen & Cohn, 2005), neuroscience (Bruce & Young, 1998; Rinn, 1984, 1991), computer vision (Bartlett, 203 UNPROOFED PAGES |
| ea85378a6549bb9eb9bcc13e31aa6a61b655a9af | Diplomarbeit
Template Protection for PCA-LDA-based 3D Face Recognition System von Technische Universität Darmstadt Fachbereich Informatik Fachgebiet Graphisch-Interaktive Systeme Fraunhoferstraße 5 64283 Darmstadt |
| ea2ee5c53747878f30f6d9c576fd09d388ab0e2b | Viola-Jones based Detectors: How much affects
the Training Set? SIANI Edif. Central del Parque Cient´ıfico Tecnol´ogico Universidad de Las Palmas de Gran Canaria 35017 - Spain |
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| e10a257f1daf279e55f17f273a1b557141953ce2 | |
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| e1ab3b9dee2da20078464f4ad8deb523b5b1792e | Pre-Training CNNs Using Convolutional
Autoencoders TU Berlin TU Berlin Sabbir Ahmmed TU Berlin TU Berlin |
| e16efd2ae73a325b7571a456618bfa682b51aef8 | |
| e19ebad4739d59f999d192bac7d596b20b887f78 | Learning Gating ConvNet for Two-Stream based Methods in Action
Recognition |
| e13360cda1ebd6fa5c3f3386c0862f292e4dbee4 | |
| e1d726d812554f2b2b92cac3a4d2bec678969368 | J Electr Eng Technol.2015; 10(?): 30-40
http://dx.doi.org/10.5370/JEET.2015.10.2.030 ISSN(Print) 1975-0102 ISSN(Online) 2093-7423 Human Action Recognition Bases on Local Action Attributes and Mohan S Kankanhalli** |
| e1e6e6792e92f7110e26e27e80e0c30ec36ac9c2 | TSINGHUA SCIENCE AND TECHNOLOGY
ISSNll1007-0214 0?/?? pp???–??? DOI: 10.26599/TST.2018.9010000 Volume 1, Number 1, Septembelr 2018 Ranking with Adaptive Neighbors |
| cd9666858f6c211e13aa80589d75373fd06f6246 | A Novel Time Series Kernel for
Sequences Generated by LTI Systems V.le delle Scienze Ed.6, DIID, Universit´a degli studi di Palermo, Italy |
| cd4c047f4d4df7937aff8fc76f4bae7718004f40 | |
| cd596a2682d74bdfa7b7160dd070b598975e89d9 | Mood Detection: Implementing a facial
expression recognition system 1. Introduction Facial expressions play a significant role in human dialogue. As a result, there has been considerable 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 across multiple databases. In this project we develop our own mood detection system that addresses this challenge. The system involves pre-processing image data by normalizing and applying 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 2. Image pre-processing We performed pre-processing on the images used to train and test our algorithms as follows: 1. The location of the eyes is first selected manually 2. Images are scaled and cropped to a fixed size (170 x 130) keeping the eyes in all images aligned 3. The image is histogram equalized using the mean histogram of all the training images to make it invariant to lighting, skin color etc. 4. A fixed oval mask is applied to the image to extract face region. This serves to eliminate the background, hair, ears and other extraneous features in the image which provide no information about facial expression. This approach works reasonably well in capturing expression-relevant facial information across all databases. Examples of pre-processed images from the various datasets are shown in Figure- 2a below. |
| cda4fb9df653b5721ad4fe8b4a88468a410e55ec | Gabor wavelet transform and its application |
| cd3005753012409361aba17f3f766e33e3a7320d | Multilinear Biased Discriminant Analysis: A Novel Method for Facial
Action Unit Representation |
| cd7a7be3804fd217e9f10682e0c0bfd9583a08db | Women also Snowboard:
Overcoming Bias in Captioning Models |
| ccfcbf0eda6df876f0170bdb4d7b4ab4e7676f18 | JOURNAL OF LATEX CLASS FILES, VOL. 6, NO. 1, JUNE 2011
A Dynamic Appearance Descriptor Approach to Facial Actions Temporal Modelling |
| ccbfc004e29b3aceea091056b0ec536e8ea7c47e | |
| cc3c273bb213240515147e8be68c50f7ea22777c | Gaining Insight Into Films
Via Topic Modeling & Visualization KEYWORDS Collaboration, computer vision, cultural analytics, economy of abundance, interactive data visualization We moved beyond misuse when the software actually became useful for film analysis with the addition of audio analysis, subtitle analysis, facial recognition, and topic modeling. Using multiple types of visualizations and a back-and-fourth workflow between people and AI we arrived at an approach for cultural analytics that can 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 | 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 Department of Electrical Engineering, IIT Delhi, New Delhi, India Available online at: www.ijcseonline.org Received: 07/Nov/2017, Revised: 22/Nov/2017, Accepted: 14/Dec/2017, Published: 31/Dec/2017 |
| cc31db984282bb70946f6881bab741aa841d3a7c | ALBANIE, VEDALDI: LEARNING GRIMACES BY WATCHING TV
Learning Grimaces by Watching TV http://www.robots.ox.ac.uk/~albanie http://www.robots.ox.ac.uk/~vedaldi Engineering Science Department Univeristy of Oxford Oxford, UK |
| cc8bf03b3f5800ac23e1a833447c421440d92197 | |
| cc96eab1e55e771e417b758119ce5d7ef1722b43 | An Empirical Study of Recent
Face Alignment Methods |
| e64b683e32525643a9ddb6b6af8b0472ef5b6a37 | Face Recognition and Retrieval in Video |
| e6b45d5a86092bbfdcd6c3c54cda3d6c3ac6b227 | Pairwise Relational Networks for Face
Recognition 1 Department of Creative IT Engineering, POSTECH, Korea 2 Department of Computer Science and Engineering, POSTECH, Korea |
| e6865b000cf4d4e84c3fe895b7ddfc65a9c4aaec | Chapter 15. The critical role of the
cold-start problem and incentive systems in emotional Web 2.0 services |
| e6dc1200a31defda100b2e5ddb27fb7ecbbd4acd | 1921
Flexible Manifold Embedding: A Framework for Semi-Supervised and Unsupervised Dimension Reduction 0 = , the linear regression function ( |
| e6e5a6090016810fb902b51d5baa2469ae28b8a1 | 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 DOI Published paper URL Authors (contact) 10.1109/ISCAS.2018.8351458 |
| e6540d70e5ffeed9f447602ea3455c7f0b38113e | |
| e6ee36444038de5885473693fb206f49c1369138 | |
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| f0cee87e9ecedeb927664b8da44b8649050e1c86 | |
| f0f4f16d5b5f9efe304369120651fa688a03d495 | Temporal Generative Adversarial Nets
Preferred Networks inc., Japan |
| f06b015bb19bd3c39ac5b1e4320566f8d83a0c84 | |
| f0a3f12469fa55ad0d40c21212d18c02be0d1264 | Sparsity Sharing Embedding for Face
Verification Department of Electrical Engineering, KAIST, Daejeon, Korea |
| f7dea4454c2de0b96ab5cf95008ce7144292e52a | |
| f7b422df567ce9813926461251517761e3e6cda0 | FACE AGING WITH CONDITIONAL GENERATIVE ADVERSARIAL NETWORKS
(cid:63) Orange Labs, 4 rue Clos Courtel, 35512 Cesson-S´evign´e, France † Eurecom, 450 route des Chappes, 06410 Biot, France |
| f79c97e7c3f9a98cf6f4a5d2431f149ffacae48f | 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 Date 2009-05-12 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 IEEE Link to publisher's version http://dx.doi.org/10.1109/TIP.2009.2017163 Item record http://hdl.handle.net/10379/1350 Some rights reserved. For more information, please see the item record link above. Downloaded 2017-06-17T22:38:27Z |
| f7452a12f9bd927398e036ea6ede02da79097e6e | |
| f7dcadc5288653ec6764600c7c1e2b49c305dfaa | Copyright
by Adriana Ivanova Kovashka 2014 |
| f7de943aa75406fe5568fdbb08133ce0f9a765d4 | Project 1.5: Human Identification at a Distance - Hornak, Adjeroh, Cukic, Gautum, & Ross
Project 1.5 Biometric Identification and Surveillance1 Year 5 Deliverable Technical Report: and Research Challenges in Biometrics Indexed biography of relevant biometric research literature Donald Adjeroh, Bojan Cukic, Arun Ross April, 2014 1 "This research was supported by the United States Department of Homeland Security through the National Center for Border Security and 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
ENEE 631: Digital Image and Video Processing Instructor: Dr. K. J. Ray Liu Term Project - Spring 2006 1. 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 changes 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 ambient 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 algorithms on these new features is analyzed. |
| f78863f4e7c4c57744715abe524ae4256be884a9 | |
| f77c9bf5beec7c975584e8087aae8d679664a1eb | Local Deep Neural Networks for Age and Gender Classification
March 27, 2017 |
| e8410c4cd1689829c15bd1f34995eb3bd4321069 | |
| e8fdacbd708feb60fd6e7843b048bf3c4387c6db | Deep Learning
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 1960s 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 as 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 an accuracy comparable to that of a human operator [5]. In March this year Facebook announced their DeepFace algorithm that is able to match faces in photos with Facebook users almost as accurately as a human can do [9]. Deep learning and other AI are here to stay and will become more and more present in our daily lives, so we had better make ourselves acquainted with the technology. Let’s dive into the deep water and try not to drown! Data Representations Before presenting data to an AI algorithm, we would normally prepare the data to make it feasible to work with. For instance, if the data consists of images, we would take each |
| e8b2a98f87b7b2593b4a046464c1ec63bfd13b51 | CMS-RCNN: Contextual Multi-Scale
Region-based CNN for Unconstrained Face Detection |
| e8c6c3fc9b52dffb15fe115702c6f159d955d308 | 13
Linear Subspace Learning for Facial Expression Analysis Philips Research The Netherlands 1. 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 and 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 animals 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 communication. 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- computer interaction, computer animation, surveillance and security, medical diagnosis, law enforcement, and awareness system (Shan, 2007). Driven by its potential applications and theoretical interests of cognitive and psychological scientists, automatic facial expression analysis has attracted much attention in last two decades (Pantic & Rothkrantz, 2000a; Fasel & Luettin, 2003; Tian et al, 2005; Pantic & Bartlett, 2007). It has been studied in multiple disciplines such as psychology, cognitive science, computer vision, pattern Source: Machine Learning, Book edited by: Abdelhamid Mellouk and Abdennacer Chebira, ISBN 978-3-902613-56-1, pp. 450, February 2009, I-Tech, Vienna, Austria www.intechopen.com |
| fab83bf8d7cab8fe069796b33d2a6bd70c8cefc6 | Draft: Evaluation Guidelines for Gender
Classification and Age Estimation 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 classification 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- cols 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 is approximately balanced in terms of age, gender and ethnicity. 2 Gender Classification In this task the goal is to determine the gender of the persons depicted in the individual images. 2.1 Data In previous works one of the most commonly used databases is the Feret database [1, 2]. We decided here not to take this database, because of its low number of im- ages. |
| fa08a4da5f2fa39632d90ce3a2e1688d147ece61 | Supplementary material for
“Unsupervised Creation of Parameterized Avatars” 1 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. 2 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. 3 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))))||. argmin x∈Xp (1) 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. 4 Detailed Architecture of the Various Networks In this section we describe the architectures of the networks used in for the emoji and avatar experiments. 4.1 TOS Network g maps DeepFace’s 256-dimensional representation [5] into 64 × 64 RGB emoji images. Follow- ing [4], this is done through a network with 9 blocks, each consisting of a convolution, batch-normalization and ReLU, except the last layer which employs Tanh activation. The odd blocks 1,3,5,7,9 perform upscaling convolutions with 512-256-128-64-3 filters respectively of spatial size 4 × 4. The even ones perform 1 × 1 convolutions [2]. The odd blocks use a stride of 2 and padding of 1, excluding the first one which does not use stride or padding. Network e maps emoji parameterization into the matching 64× 64 RGB emoji. The parameterization is given as binary vectors in R813 for emojis; Avatar parameterization is in R354. While there are dependencies among the various dimensions (an emoji cannot have two hairstyles at once), the binary representation is chosen for its simplicity and generality. e is trained in a fully supervised way, using pairs of matching parameterization vectors and images in a supervised manner. The architecture of e employs five upscaling convolutions with 512-256-128-64-3 filters respectively, each of spatial size 4×4. All layers except the last one are batch normalized followed by a ReLU activation. The last layer is followed by Tanh activation, generating an RGB image with values in range [−1, 1]. All the layers use a stride of 2 and padding of 1, excluding the first one which does not use stride or padding. |
| faead8f2eb54c7bc33bc7d0569adc7a4c2ec4c3b | |
| faf5583063682e70dedc4466ac0f74eeb63169e7 | |
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Attention LSTM Networks |
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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 1 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, 2 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 |
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Semiconductor sidewall shape estimation Oak Ridge National Laboratory Oak Ridge, Tennessee 37831-6010 |
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HOGs and Geometric Prior Models 1 Computer Vision Center , Universitat Aut`onoma de Barcelona 2 Universitat Oberta de Catalunya 3 Dept. de Matem`atica Aplicada i An`alisi Universitat de Barcelona |
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ISSN: 2249 – 8958, Volume-2, Issue-4, April 2013 Algorithm for Face Matching Using Normalized Cross-Correlation |
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Computer Vision Lab D-ITET ETH Zurich Computer Vision Lab D-ITET ETH Zurich ESAT, KU Leuven D-ITET, ETH Zurich |
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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 1Department of Automation & Robotics, 2Department of Computer Science & Engg. |
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Kokkinos1,2 1 Ecole Centrale Paris,2 INRIA, 3TTI-Chicago (cid:63) |
| e9fcd15bcb0f65565138dda292e0c71ef25ea8bb | 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 1ICT CAS,2Cambricon,3Alibaba Infrastructure Service, Alibaba Group 4IFLYTEK,5JD,6RDA Microelectronics,7AMD |
| f16a605abb5857c39a10709bd9f9d14cdaa7918f | Fast greyscale road sign model matching
and recognition Centre de Visió per Computador Edifici O – Campus UAB, 08193 Bellaterra, Barcelona, Catalonia, Spain |
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| f19ab817dd1ef64ee94e94689b0daae0f686e849 | 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: 1. Univ.-Prof. Dr.-Ing. habil. Gerhard Rigoll 2. 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 angenommen. |
| e76798bddd0f12ae03de26b7c7743c008d505215 | |
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Analysis Joint Sparse Representation |
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Action Understanding with Multiple Classes of Actors |
| cb84229e005645e8623a866d3d7956c197f85e11 | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. X, NO. X, MONTH 201X
Disambiguating Visual Verbs |
| cbe859d151466315a050a6925d54a8d3dbad591f | GAZE SHIFTS AS DYNAMICAL RANDOM SAMPLING
Dipartimento di Scienze dell’Informazione Universit´a di Milano Via Comelico 39/41 20135 Milano, Italy |
| f8c94afd478821681a1565d463fc305337b02779 |
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 2 |
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ISSN 1976-913X (Print) ISSN 2092-805X (Electronic) Extreme Learning Machine Ensemble Using Bagging for Facial Expression Recognition |
| f869601ae682e6116daebefb77d92e7c5dd2cb15 | |
| f8ed5f2c71e1a647a82677df24e70cc46d2f12a8 | 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 |
| cef841f27535c0865278ee9a4bc8ee113b4fb9f3 | |
| ce85d953086294d989c09ae5c41af795d098d5b2 | 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 |
| ce691a37060944c136d2795e10ed7ba751cd8394 | |
| ce3f3088d0c0bf236638014a299a28e492069753 | |
| ce9a61bcba6decba72f91497085807bface02daf | Eigen-Harmonics Faces: Face Recognition under Generic Lighting
1Graduate School, CAS, Beijing, China, 100080 2ICT-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 through consistent |
| cebfafea92ed51b74a8d27c730efdacd65572c40 | JANUARY 2006
31 Matching 2.5D Face Scans to 3D Models |
| ce54e891e956d5b502a834ad131616786897dc91 | 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 1ECE & KUK 2Assistant 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 appears 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. |
| e0dedb6fc4d370f4399bf7d67e234dc44deb4333 | Supplementary Material: Multi-Task Video Captioning with Video and
Entailment Generation UNC Chapel Hill 1 Experimental Setup 1.1 Datasets 1.1.1 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 collected using Amazon Mechanical Turk (AMT). All our experiments use only English captions. On average, each video has 40 captions, and the over- all dataset has about 80, 000 unique video-caption pairs. The average clip duration is roughly 10 sec- onds. We used the standard split as stated in Venu- gopalan et al. (2015), i.e., 1200 videos for training, 100 videos for validation, and 670 for testing. MSR-VTT MSR-VTT is a recent collection of 10, 000 video clips of 41.2 hours duration (i.e., average duration of 15 seconds), which are an- notated by AMT workers. It has 200, 000 video clip-sentence pairs covering diverse content from a commercial video search engine. On average, each clip is annotated with 20 natural language captions. We used the standard split as provided in (Xu et al., 2016), i.e., 6, 513 video clips for training, 497 for validation, and 2, 990 for testing. M-VAD M-VAD is a movie description dataset with 49, 000 video clips collected from 92 movies, with the average clip duration being 6 seconds. Alignment of descriptions to video clips is done through an automatic procedure using Descrip- tive Video Service (DVS) provided for the movies. Each video clip description has only 1 or 2 sen- tences, making most evaluation metrics (except paraphrase-based METEOR) infeasible. Again, we used the standard train/val/test split as pro- vided in Torabi et al. (2015). 1.1.2 Video Prediction Dataset For our unsupervised video representation learn- ing task, we use the UCF-101 action videos dataset (Soomro et al., 2012), which contains 13, 320 video clips of 101 action categories and with an average clip length of 7.21 seconds each. This dataset suits our video captioning task well because both contain short video clips of a sin- gle action or few actions, and hence using future frame prediction on UCF-101 helps learn more ro- bust and context-aware video representations for our short clip video captioning task. We use the standard split of 9, 500 videos for training (we don’t need any validation set in our setup because we directly tune on the validation set of the video captioning task). the three video captioning 1.2 Pre-trained Visual Frame Features For datasets (Youtube2Text, MSR-VTT, M-VAD) and the unsupervised video prediction dataset (UCF-101), we fix our sampling rate to 3f ps to bring uni- formity in the temporal representation of actions across all videos. These sampled frames are then converted into features using several state-of-the- art pre-trained models on ImageNet (Deng et al., 2009) – VGGNet (Simonyan and Zisserman, 2015), GoogLeNet (Szegedy et al., 2015; Ioffe and Szegedy, 2015), and Inception-v4 (Szegedy et al., 2016). For VGGNet, we use its f c7 layer features with dimension 4096. For GoogLeNet and Inception-v4, we use the layer before the fully connected layer with dimensions 1024 and 1536, respectively. We follow standard preprocessing and convert all the natural language descriptions to lower case and tokenize the sentences and remove punctuations. |
| e096b11b3988441c0995c13742ad188a80f2b461 | Noname manuscript No.
(will be inserted by the editor) DeepProposals: Hunting Objects and Actions by Cascading Deep Convolutional Layers Van Gool Received: date / Accepted: date |
| e0c081a007435e0c64e208e9918ca727e2c1c44e | |
| e00d4e4ba25fff3583b180db078ef962bf7d6824 | Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 20 March 2017 doi:10.20944/preprints201703.0152.v1
Article Face Verification with Multi-Task and Multi-Scale Features Fusion |
| e0939b4518a5ad649ba04194f74f3413c793f28e | Technical Report
UCAM-CL-TR-636 ISSN 1476-2986 Number 636 Computer Laboratory Mind-reading machines: automated inference of complex mental states July 2005 15 JJ Thomson Avenue Cambridge CB3 0FD United Kingdom phone +44 1223 763500 http://www.cl.cam.ac.uk/ |
| e0765de5cabe7e287582532456d7f4815acd74c1 | |
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Robust Image Analysis With Sparse Representation on Quantized Visual Features |
| 46a4551a6d53a3cd10474ef3945f546f45ef76ee | 2014 IEEE Intelligent Vehicles Symposium (IV)
June 8-11, 2014. Dearborn, Michigan, USA 978-1-4799-3637-3/14/$31.00 ©2014 IEEE 344 |
| 4686bdcee01520ed6a769943f112b2471e436208 | 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 |
| 4688787d064e59023a304f7c9af950d192ddd33e | Investigating the Discriminative Power of Keystroke
Sound and Dimitris Metaxas, Member, IEEE |
| 46e86cdb674440f61b6658ef3e84fea95ea51fb4 | |
| 464de30d3310123644ab81a1f0adc51598586fd2 | |
| 466a5add15bb5f91e0cfd29a55f5fb159a7980e5 | Video Repeat Recognition and Mining by Visual
Features |
| 46538b0d841654a0934e4c75ccd659f6c5309b72 | 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 1P.G. Student, Department of Computer Engineering, MCERC, Nashik (M.S.), India. 2Associate Professor, Department of Computer Engineering, MCERC, Nashik (M.S.), India |
| 46196735a201185db3a6d8f6e473baf05ba7b68f | |
| 4682fee7dc045aea7177d7f3bfe344aabf153bd5 | Tabula Rasa: Model Transfer for
Object Category Detection Department of Engineering Science Oxford (Presented by Elad Liebman) |
| 2cbb4a2f8fd2ddac86f8804fd7ffacd830a66b58 | |
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Seattle, Washington, May 26-30, 2015 978-1-4799-6922-7/15/$31.00 ©2015 IEEE 3039 |
| 2c61a9e26557dd0fe824909adeadf22a6a0d86b0 | |
| 2c93c8da5dfe5c50119949881f90ac5a0a4f39fe | Advanced local motion patterns for macro and micro facial
expression recognition B. Allaerta,∗, IM. Bilascoa, C. Djerabaa aUniv. Lille, CNRS, Centrale Lille, UMR 9189 - CRIStAL - Centre de Recherche en Informatique Signal et Automatique de Lille, F-59000 Lille, France |
| 2c2786ea6386f2d611fc9dbf209362699b104f83 | |
| 2c848cc514293414d916c0e5931baf1e8583eabc | An automatic facial expression recognition system
evaluated by different classifiers ∗Programa de P´os-Graduac¸˜ao em Mecatrˆonica Universidade Federal da Bahia, †Department of Electrical Engineering - EESC/USP |
| 2cdd9e445e7259117b995516025fcfc02fa7eebb | 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 2008 URL 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 component of this work in other works must be obtained from the IEEE. |
| 2c5d1e0719f3ad7f66e1763685ae536806f0c23b | AENet: Learning Deep Audio Features for Video
Analysis |
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Order-aware Convolutional Pooling for Video Based Action Recognition |
| 2c7c3a74da960cc76c00965bd3e343958464da45 | |
| 2cf5f2091f9c2d9ab97086756c47cd11522a6ef3 | MPIIGaze: Real-World Dataset and Deep
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Occlusion Handling in Expression Recognition and Action Unit detection 1 Sorbonne Universités, UPMC Univ Paris 06, CNRS, ISIR UMR 7222 4 place Jussieu 75005 Paris |
| 79f6a8f777a11fd626185ab549079236629431ac | Copyright
by 2013 |
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| 79dc84a3bf76f1cb983902e2591d913cee5bdb0e | |
| 79b669abf65c2ca323098cf3f19fa7bdd837ff31 | Deakin Research Online
This is the published version: Rana, Santu, Liu, Wanquan, Lazarescu, Mihai and Venkatesh, Svetha 2008, Efficient tensor based 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 |
| 79c3a7131c6c176b02b97d368cd0cd0bc713ff7e | |
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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# |
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Facial Landmark Detection and Xiao-Jun Wu |
| 2d71e0464a55ef2f424017ce91a6bcc6fd83f6c3 | 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 Assistant Professor SPPU, Pune Department of Computer Engg Department of Computer Engg Department of Computer Engg BE Student SPPU, Pune BE Student SPPU, Pune BE Student Department of Computer Engg SPPU, Pune additional analysis for information |
| 2d8d089d368f2982748fde93a959cf5944873673 | Proceedings of NAACL-HLT 2018, pages 788–794
New Orleans, Louisiana, June 1 - 6, 2018. c(cid:13)2018 Association for Computational Linguistics 788 |
| 2df4d05119fe3fbf1f8112b3ad901c33728b498a | 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 1LITIS EA 4108, INSA de Rouen, Saint ´Etienne du Rouvray 76800, France 2LITIS EA 4108, UFR des Sciences, Universit´e de Rouen, France. September 24, 2015 |
| 4188bd3ef976ea0dec24a2512b44d7673fd4ad26 | 1050
Nonlinear Non-Negative Component Analysis Algorithms |
| 41000c3a3344676513ef4bfcd392d14c7a9a7599 | A NOVEL APPROACH FOR GENERATING FACE
TEMPLATE USING BDA 1P.G. Student, Department of Computer Engineering, MCERC, Nashik (M.S.), India. 2Associate Professor, Department of Computer Engineering, MCERC, Nashik (M.S.), India |
| 414715421e01e8c8b5743c5330e6d2553a08c16d | PoTion: Pose MoTion Representation for Action Recognition
1Inria∗ 2NAVER LABS Europe |
| 41ab4939db641fa4d327071ae9bb0df4a612dc89 | Interpreting Face Images by Fitting a Fast
Illumination-Based 3D Active Appearance Model 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 |
| 41a6196f88beced105d8bc48dd54d5494cc156fb | 2015 International Conference on
Communications, Signal Processing, and their Applications (ICCSPA 2015) Sharjah, United Arab Emirates 17-19 February 2015 IEEE Catalog Number: ISBN: CFP1574T-POD 978-1-4799-6533-5 |
| 41de109bca9343691f1d5720df864cdbeeecd9d0 | Article
Facial Emotion Recognition: A Survey and Real-World User Experiences in Mixed Reality Received: 10 December 2017; Accepted: 26 January 2018; Published: 1 Febuary 2018 |
| 41d9a240b711ff76c5448d4bf4df840cc5dad5fc | JOURNAL DRAFT, VOL. X, NO. X, APR 2013
Image Similarity Using Sparse Representation and Compression Distance |
| 419a6fca4c8d73a1e43003edc3f6b610174c41d2 | A Component Based Approach Improves Classification of Discrete
Facial Expressions Over a Holistic Approach |
| 4180978dbcd09162d166f7449136cb0b320adf1f | Real-time head pose classification in uncontrolled environments
with Spatio-Temporal Active Appearance Models ∗ 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 |
| 41b997f6cec7a6a773cd09f174cb6d2f036b36cd | |
| 413a184b584dc2b669fbe731ace1e48b22945443 | Human Pose Co-Estimation and Applications |
| 83ca4cca9b28ae58f461b5a192e08dffdc1c76f3 | DETECTING EMOTIONAL STRESS FROM FACIAL EXPRESSIONS FOR DRIVING SAFETY
Signal Processing Laboratory (LTS5), ´Ecole Polytechnique F´ed´erale de Lausanne, Switzerland |
| 831fbef657cc5e1bbf298ce6aad6b62f00a5b5d9 | |
| 832e1d128059dd5ed5fa5a0b0f021a025903f9d5 | Pairwise Conditional Random Forests for Facial Expression Recognition
S´everine Dubuisson1 1 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 Institut f¨ur Informationsverarbeitung (tnt) Leibniz Universit¨at Hannover, Germany |
| 83b4899d2899dd6a8d956eda3c4b89f27f1cd308 | 1-4244-1437-7/07/$20.00 ©2007 IEEE
I - 377 ICIP 2007 |
| 830e5b1043227fe189b3f93619ef4c58868758a7 | |
| 8395cf3535a6628c3bdc9b8d0171568d551f5ff0 | Entropy Non-increasing Games for the
Improvement of Dataflow Programming Norbert B´atfai, Ren´at´o Besenczi, Gerg˝o Bogacsovics, February 16, 2017 |
| 83ac942d71ba908c8d76fc68de6173151f012b38 | |
| 834f5ab0cb374b13a6e19198d550e7a32901a4b2 | Face Translation between Images and Videos using Identity-aware CycleGAN
†Computer Vision Lab, ETH Zurich, Switzerland ‡VISICS, KU Leuven, Belgium |
| 834b15762f97b4da11a2d851840123dbeee51d33 | Landmark-free smile intensity estimation
IMAGO Research Group - Universidade Federal do Paran´a Fig. 1. Overview of our method for smile intensity estimation |
| 833f6ab858f26b848f0d747de502127406f06417 | 978-1-4244-5654-3/09/$26.00 ©2009 IEEE
61 ICIP 2009 |
| 8309e8f27f3fb6f2ac1b4343a4ad7db09fb8f0ff | Generic versus Salient Region-based Partitioning
for Local Appearance Face Recognition Computer Science Depatment, Universit¨at Karlsruhe (TH) Am Fasanengarten 5, Karlsruhe 76131, Germany http://isl.ira.uka.de/cvhci |
| 1b55c4e804d1298cbbb9c507497177014a923d22 | Incremental Class Representation
Learning for Face Recognition Degree’s Thesis Audiovisual Systems Engineering Author: Universitat Politècnica de Catalunya (UPC) 2016 - 2017 |
| 1bd50926079e68a6e32dc4412e9d5abe331daefb | |
| 1b150248d856f95da8316da868532a4286b9d58e | Analyzing 3D Objects in Cluttered Images
UC Irvine UC Irvine |
| 1be498d4bbc30c3bfd0029114c784bc2114d67c0 | Age and Gender Estimation of Unfiltered Faces |
| 1b300a7858ab7870d36622a51b0549b1936572d4 | 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 |
| 1b1173a3fb33f9dfaf8d8cc36eb0bf35e364913d | DICTA
#147 000 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048 049 050 051 052 053 DICTA 2010 Submission #147. CONFIDENTIAL REVIEW COPY. DO NOT DISTRIBUTE. Registration Invariant Representations for Expression Detection Anonymous DICTA submission Paper ID 147 |
| 1b0a071450c419138432c033f722027ec88846ea | Windsor Oceanico Hotel, Rio de Janeiro, Brazil, November 1-4, 2016
978-1-5090-1889-5/16/$31.00 ©2016 IEEE 649 |
| 1b3b01513f99d13973e631c87ffa43904cd8a821 | HMM RECOGNITION OF EXPRESSIONS IN UNRESTRAINED VIDEO INTERVALS
Universitat Politècnica de Catalunya, Barcelona, Spain |
| 1bc214c39536c940b12c3a2a6b78cafcbfddb59a | |
| 1be18a701d5af2d8088db3e6aaa5b9b1d54b6fd3 | ENHANCEMENT OF FAST FACE DETECTION ALGORITHM BASED ON A CASCADE OF
DECISION TREES Commission II, WG II/5 KEY WORDS: Face Detection, Cascade Algorithm, Decision Trees. |
| 1b79628af96eb3ad64dbb859dae64f31a09027d5 | |
| 1b4f6f73c70353869026e5eec1dd903f9e26d43f | Robust Subjective Visual Property Prediction
from Crowdsourced Pairwise Labels |
| 1bc23c771688109bed9fd295ce82d7e702726327 | |
| 1b589016fbabe607a1fb7ce0c265442be9caf3a9 | |
| 1b27ca161d2e1d4dd7d22b1247acee5c53db5104 | |
| 7711a7404f1f1ac3a0107203936e6332f50ac30c | Action Classification and Highlighting in Videos
Disney Research Pittsburgh Disney Research Pittsburgh |
| 778c9f88839eb26129427e1b8633caa4bd4d275e | Pose Pooling Kernels for Sub-category Recognition
ICSI & UC Berkeley ICSI & UC Berkeley Trever Darrell ICSI & UC Berkeley |
| 7789a5d87884f8bafec8a82085292e87d4e2866f | A Unified Tensor-based Active Appearance Face
Model Member, IEEE |
| 776835eb176ed4655d6e6c308ab203126194c41e | |
| 778bff335ae1b77fd7ec67404f71a1446624331b | Hough Forest-based Facial Expression Recognition from
Video Sequences 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
*Disney Research Zurich †ETH Zurich |
| 7754b708d6258fb8279aa5667ce805e9f925dfd0 | Facial Action Unit Recognition by Exploiting
Their Dynamic and Semantic Relationships |
| 77db171a523fc3d08c91cea94c9562f3edce56e1 | 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 |
| 77037a22c9b8169930d74d2ce6f50f1a999c1221 | Robust Face Recognition With Kernelized
Locality-Sensitive Group Sparsity Representation |
| 77d31d2ec25df44781d999d6ff980183093fb3de | The Multiverse Loss for Robust Transfer Learning
Supplementary 1. Omitted proofs for which the joint loss: m(cid:88) r=1 L(F r, br, D, y) (2) 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 (3) l=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) c , b∗ + s1c are also minimizers of L. c , s = s1c, i v+byi +s i v+bj +s i fyi +byi i v+sed(cid:62) i fj +bj i=1 log( L(F ∗ + V, b∗ + s, D, y) = i fyi +d(cid:62) ed(cid:62) i fj +d(cid:62) j=1 ed(cid:62) i v+sed(cid:62) ed(cid:62) j=1 ed(cid:62) i v+sed(cid:62) ed(cid:62) (cid:80)c (cid:80)c i v+s(cid:80)c − n(cid:88) = − n(cid:88) = − n(cid:88) (cid:80)c = − n(cid:88) ed(cid:62) i fyi +byi j=1 ed(cid:62) i fj +bj ed(cid:62) log( log( log( i=1 i=1 i=1 i fj +bj i fyi +byi j=1 ed(cid:62) ) = L(F ∗, b∗, D, y) The following simple lemma was not part of the paper. However, it is the reasoning behind the statement at the end of the proof of Thm. 1. “Since ∀i, j pi(j) > 0 and since rank(D) is full,(cid:80)n Lemma 2. Let K =(cid:80)n such that ∀i qi > 0, the matrix ˆK =(cid:80)n i be a full rank d×d matrix, i.e., it is PD and not just PSD, then for all vector q ∈ Rn is also i pi(j)pi(j(cid:48)) is PD.” i=1 did(cid:62) i=1 did(cid:62) i=1 qidid(cid:62) full rank. Proof. For (miniqi)v(cid:62)Kv > 0. every vector v (cid:2)f 1 (cid:3) , b1, F 2 = (cid:2)f 2 Theorem 3. There exist a set of weights F 1 = j ⊥ f s C ] , bm which are orthogonal ∀jrs f r 2 , ..., f 1 2 , ..., f m 1 , f 1 1 , f m 2 , ..., f 2 1 , f 2 [f m (cid:3) , b2...F m = Proof. We again prove the theorem by constructing such a solution. Denoting by vd−m+2...vd the eigenvectors of K corresponding to λd−m+2 . . . λd. Given F 1 = F ∗, b1 = b∗, we can construct each pair F r, br as follows: (1) ∀j, r fj r = f1 1 + m−1(cid:88) l=1 αjlrvd−l+1 br = b1 (4) The tensor of parameters αjlr is constructed to insure the orthogonality condition. Formally, αjlr has to satisfy: Rd, v(cid:62) ˆKv ∀j, r (cid:54)= s (f 1 j + m−1(cid:88) l=1 αjlrvd−l+1)(cid:62)f s j = 0 (5) 2 m(m− 1) equations, it Noticing that 5 constitutes a set of 1 can be satisfied by the tensor αjlr which contains m(m − c ] = F r − 1)c parameters. Defining Ψr = [ψr 1, ψr 2, . . . , ψr |
| 486840f4f524e97f692a7f6b42cd19019ee71533 | DeepVisage: Making face recognition simple yet with powerful generalization
skills 1Laboratoire LIRIS, ´Ecole centrale de Lyon, 69134 Ecully, France. 2Safran Identity & Security, 92130 Issy-les-Moulineaux, France. |
| 48186494fc7c0cc664edec16ce582b3fcb5249c0 | P-CNN: Pose-based CNN Features for Action Recognition
Guilhem Ch´eron∗ † INRIA |
| 48499deeaa1e31ac22c901d115b8b9867f89f952 | Interim Report of Final Year Project
HKU-Face: A Large Scale Dataset for Deep Face Recognition 3035140108 Haoyu Li 3035141841 COMP4801 Final Year Project Project Code: 17007 |
| 486a82f50835ea888fbc5c6babf3cf8e8b9807bc | MSU TECHNICAL REPORT MSU-CSE-15-11, JULY 24, 2015
Face Search at Scale: 80 Million Gallery |
| 4866a5d6d7a40a26f038fc743e16345c064e9842 | |
| 487df616e981557c8e1201829a1d0ec1ecb7d275 | Acoustic Echo Cancellation Using a Vector-Space-Based
Adaptive Filtering Algorithm |
| 48f211a9764f2bf6d6dda4a467008eda5680837a | |
| 4858d014bb5119a199448fcd36746c413e60f295 | |
| 48cfc5789c246c6ad88ff841701204fc9d6577ed | 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 |
| 70f189798c8b9f2b31c8b5566a5cf3107050b349 | The Challenge of Face Recognition from Digital Point-and-Shoot Cameras
David Bolme‡ |
| 70109c670471db2e0ede3842cbb58ba6be804561 | Noname manuscript No.
(will be inserted by the editor) Zero-Shot Visual Recognition via Bidirectional Latent Embedding Received: date / Accepted: date |
| 703890b7a50d6535900a5883e8d2a6813ead3a03 | |
| 706236308e1c8d8b8ba7749869c6b9c25fa9f957 | Crowdsourced Data Collection of Facial Responses
MIT Media Lab Cambridge 02139, USA Rosalind Picard MIT Media Lab Cambridge 02139, USA MIT Media Lab Cambridge 02139, USA |
| 70569810e46f476515fce80a602a210f8d9a2b95 | Apparent Age Estimation from Face Images Combining General and
Children-Specialized Deep Learning Models 1Orange Labs – France Telecom, 4 rue Clos Courtel, 35512 Cesson-S´evign´e, France 2Eurecom, 450 route des Chappes, 06410 Biot, France |
| 70e79d7b64f5540d309465620b0dab19d9520df1 | International Journal of Scientific & Engineering Research, Volume 8, Issue 3, March-2017
ISSN 2229-5518 Facial Expression Recognition System Using Extreme Learning Machine |
| 7003d903d5e88351d649b90d378f3fc5f211282b | International Journal of Computer Applications (0975 – 8887)
Volume 68– No.23, April 2013 Facial Expression Recognition using Gabor Wavelet ENTC SVERI’S COE (Poly), Pandharpur, Solapur, India ENTC SVERI’S COE, Pandharpur, Solapur, India ENTC SVERI’S COE (Poly), Pandharpur, Solapur, India |
| 70bf1769d2d5737fc82de72c24adbb7882d2effd | Face detection in intelligent ambiences with colored illumination
Department of Intelligent Systems TU Delft Delft, The Netherlands |
| 1e799047e294267087ec1e2c385fac67074ee5c8 | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 21, NO. 12, DECEMBER 1999
1357 Short Papers___________________________________________________________________________________________________ Automatic Classification of Single Facial Images |
| 1ef4815f41fa3a9217a8a8af12cc385f6ed137e1 | Rendering of Eyes for Eye-Shape Registration and Gaze Estimation |
| 1e7ae86a78a9b4860aa720fb0fd0bdc199b092c3 | Article
A Brief Review of Facial Emotion Recognition Based on Visual Information Byoung Chul Ko ID Tel.: +82-10-3559-4564 Received: 6 December 2017; Accepted: 25 January 2018; Published: 30 January 2018 |
| 1e8eee51fd3bf7a9570d6ee6aa9a09454254689d | 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 |
| 1ea8085fe1c79d12adffb02bd157b54d799568e4 | |
| 1ebdfceebad642299e573a8995bc5ed1fad173e3 | |
| 1eec03527703114d15e98ef9e55bee5d6eeba736 | UNIVERSITÄT KARLSRUHE (TH)
FAKULTÄT FÜR INFORMATIK INTERACTIVE SYSTEMS LABS DIPLOMA THESIS Automatic identification of persons in TV series SUBMITTED BY MAY 2008 ADVISORS |
| 1e8394cc9fe7c2392aa36fb4878faf7e78bbf2de | TO APPEAR IN IEEE THMS
Zero-Shot Object Recognition System based on Topic Model |
| 1ef4aac0ebc34e76123f848c256840d89ff728d0 | |
| 1ecb56e7c06a380b3ce582af3a629f6ef0104457 | 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). . 1 Elmer P. Dadios Papers: o A New Way of Discovery of Belief, Desire and Intention in the BDI Agent-Based Software Modeling . . 2 o Integration of Distributed Robotic Systems . 7 Fakhri Karray, Rogelio Soto, Federico Guedea, and Insop Song o A Searching and Tracking Framework for Multi-Robot Observation of Multiple Moving Targets . . 14 Zheng Liu, Marcelo H. Ang Jr., and Winston Khoon Guan Seah Development Paper: o Possibilistic Uncertainty Propagation and Compromise Programming in the Life Cycle Analysis of Alternative Motor Vehicle Fuels Raymond R. Tan, Alvin B. Culaba, and Michael R. I. Purvis . 23 Logic . Napoleon H. Reyes, and Elmer P. Dadios . 29 o A Optical Coordinate Measuring Machine for Nanoscale Dimensional Metrology . . 39 Eric Kirkland, Thomas R. Kurfess, and Steven Y. Liang o Humanoid Robot HanSaRam: Recent Progress and Developments . . 45 Jong-Hwan Kim, Dong-Han Kim, Yong-Jae Kim, Kui-Hong Park, Jae-Ho Park, Choon-Kyoung Moon, Jee-Hwan Ryu, Kiam Tian Seow, and Kyoung-Chul Koh o Generalized Associative Memory Models: Their Memory Capacities and Potential Application . 56 Teddy N. Yap, Jr., and Arnulfo P. Azcarraga o Hybrid Fuzzy Logic Strategy for Soccer Robot Game. . 65 Elmer A. Maravillas , Napoleon H. Reyes, and Elmer P. Dadios o Image Compression and Reconstruction Based on Fuzzy Relation and Soft Computing Technology . . 72 Kaoru Hirota, Hajime Nobuhara, Kazuhiko Kawamoto, and Shin’ichi Yoshida Vol.8 No.2, March 2004 Editorial: o Special Issue on Pattern Recognition . . 83 Papers: o Operation of Spatiotemporal Patterns Stored in Osamu Hasegawa Review: o Support Vector Machine and Generalization . 84 Takio Kurita o Bayesian Network: Probabilistic Reasoning, Statistical Learning, and Applications . . 93 Yoichi Motomura Living Neuronal Networks Cultured on a Microelectrode Array . Suguru N. Kudoh, and Takahisa Taguchi o Rapid Discriminative Learning . . 100 . 108 Jun Rokui o Robust Fuzzy Clustering Based on Similarity between Data . Kohei Inoue, and Kiichi Urahama Vol.8 No.6, 2004 Journal of Advanced Computational Intelligence and Intelligent Informatics . 115 I-1 |
| 1e64b2d2f0a8a608d0d9d913c4baee6973995952 | DOMINANT AND
COMPLEMENTARY MULTI- EMOTIONAL FACIAL EXPRESSION RECOGNITION USING C-SUPPORT VECTOR CLASSIFICATION |
| 1e21b925b65303ef0299af65e018ec1e1b9b8d60 | Under review as a conference paper at ICLR 2017
UNSUPERVISED CROSS-DOMAIN IMAGE GENERATION Facebook AI Research Tel-Aviv, Israel |
| 1ee27c66fabde8ffe90bd2f4ccee5835f8dedbb9 | Entropy Regularization
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- aration, without any modeling of the density of input features. The contribution of unlabeled data to the learning criterion induces local optima, but this problem can 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 situations. 9.1 Introduction semi-supervised induction This chapter addresses semi-supervised induction, which refers to the learning of a decision rule, on the entire input domain X, from labeled and unlabeled data. The objective is identical to the one of supervised classi(cid:12)cation: generalize from examples. The problem di(cid:11)ers in the respect that the supervisor’s responses are missing for some training examples. This characteristic is shared with transduction, which has however a di(cid:11)erent goal, that is, of predicting labels on a set of prede(cid:12)ned |
| 1ee3b4ba04e54bfbacba94d54bf8d05fd202931d | 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 1,2,3Faculty of Computer and Mathematical Sciences, UniversitiTeknologi MARA (UiTM), 4Faculty 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 Face recognition GoogLeNet |
| 1e41a3fdaac9f306c0ef0a978ae050d884d77d2a | 411
Robust Object Recognition with Cortex-Like Mechanisms Tomaso Poggio, Member, IEEE |
| 1e1e66783f51a206509b0a427e68b3f6e40a27c8 | SEMI-SUPERVISED ESTIMATION OF PERCEIVED AGE
FROM FACE IMAGES VALWAY Technology Center, NEC Soft, Ltd., Tokyo, Japan Keywords: |
| 1efaa128378f988965841eb3f49d1319a102dc36 | JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015
Hierarchical binary CNNs for landmark localization with limited resources |
| 8451bf3dd6bcd946be14b1a75af8bbb65a42d4b2 | Consensual and Privacy-Preserving Sharing of
Multi-Subject and Interdependent Data EPFL, UNIL–HEC Lausanne K´evin Huguenin UNIL–HEC Lausanne EPFL EPFL |
| 84fe5b4ac805af63206012d29523a1e033bc827e | |
| 84e4b7469f9c4b6c9e73733fa28788730fd30379 | 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 Open Access |
| 84dcf04802743d9907b5b3ae28b19cbbacd97981 | |
| 84fa126cb19d569d2f0147bf6f9e26b54c9ad4f1 | Improved Boosting Performance by Explicit
Handling of Ambiguous Positive Examples |
| 841a5de1d71a0b51957d9be9d9bebed33fb5d9fa | 5017
PCANet: A Simple Deep Learning Baseline for Image Classification? |
| 849f891973ad2b6c6f70d7d43d9ac5805f1a1a5b | Detecting Faces Using Region-based Fully
Convolutional Networks Tencent AI Lab, China |
| 4adca62f888226d3a16654ca499bf2a7d3d11b71 | 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 572 |
| 4a2d54ea1da851151d43b38652b7ea30cdb6dfb2 | Direct Recognition of Motion Blurred Faces |
| 4a3758f283b7c484d3f164528d73bc8667eb1591 | Attribute Enhanced Face Aging with Wavelet-based Generative Adversarial
Networks Center for Research on Intelligent Perception and Computing, CASIA National Laboratory of Pattern Recognition, CASIA |
| 4abd49538d04ea5c7e6d31701b57ea17bc349412 | Recognizing Fine-Grained and Composite Activities
using Hand-Centric Features and Script Data |
| 4a0f98d7dbc31497106d4f652968c708f7da6692 | Real-time Eye Gaze Direction Classification Using
Convolutional Neural Network |
| 4acd683b5f91589002e6f50885df51f48bc985f4 | BRIDGING COMPUTER VISION AND SOCIAL SCIENCE : A MULTI-CAMERA VISION
SYSTEM FOR SOCIAL INTERACTION TRAINING ANALYSIS Peter Tu GE Global Research, Niskayuna NY USA |
| 4aeb87c11fb3a8ad603311c4650040fd3c088832 | Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17)
1816 |
| 4a3d96b2a53114da4be3880f652a6eef3f3cc035 | 2666
A Dictionary Learning-Based 3D Morphable Shape Model |
| 4a6fcf714f663618657effc341ae5961784504c7 | Scaling up Class-Specific Kernel Discriminant
Analysis for large-scale Face Verification |
| 24115d209e0733e319e39badc5411bbfd82c5133 | Long-term Recurrent Convolutional Networks for
Visual Recognition and Description |
| 24c442ac3f6802296d71b1a1914b5d44e48b4f29 | Pose and expression-coherent face recovery in the wild
Technicolor, Cesson-S´evign´e, France Franc¸ois Le Clerc Patrick P´erez |
| 24aac045f1e1a4c13a58eab4c7618dccd4c0e671 | |
| 240d5390af19bb43761f112b0209771f19bfb696 | |
| 24e099e77ae7bae3df2bebdc0ee4e00acca71250 | Robust face alignment under occlusion via regional predictive power
estimation. © 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 |
| 2450c618cca4cbd9b8cdbdb05bb57d67e63069b1 | A Connexionist Approach for Robust and Precise Facial Feature Detection in
Complex Scenes Stefan Duffner and Christophe Garcia France Telecom Research & Development 4, rue du Clos Courtel 35512 Cesson-S´evign´e, France |
| 244b57cc4a00076efd5f913cc2833138087e1258 | Warped Convolutions: Efficient Invariance to Spatial Transformations |
| 24869258fef8f47623b5ef43bd978a525f0af60e | UNIVERSITÉDEGRENOBLENoattribuéparlabibliothèqueTHÈSEpourobtenirlegradedeDOCTEURDEL’UNIVERSITÉDEGRENOBLESpécialité:MathématiquesetInformatiquepréparéeauLaboratoireJeanKuntzmanndanslecadredel’ÉcoleDoctoraleMathématiques,SciencesetTechnologiesdel’Information,InformatiqueprésentéeetsoutenuepubliquementparMatthieuGuillauminle27septembre2010ExploitingMultimodalDataforImageUnderstandingDonnéesmultimodalespourl’analysed’imageDirecteursdethèse:CordeliaSchmidetJakobVerbeekJURYM.ÉricGaussierUniversitéJosephFourierPrésidentM.AntonioTorralbaMassachusettsInstituteofTechnologyRapporteurMmeTinneTuytelaarsKatholiekeUniversiteitLeuvenRapporteurM.MarkEveringhamUniversityofLeedsExaminateurMmeCordeliaSchmidINRIAGrenobleExaminatriceM.JakobVerbeekINRIAGrenobleExaminateur |
| 24d376e4d580fb28fd66bc5e7681f1a8db3b6b78 | |
| 24ff832171cb774087a614152c21f54589bf7523 | Beat-Event Detection in Action Movie Franchises
Jerome Revaud Zaid Harchaoui |
| 24bf94f8090daf9bda56d54e42009067839b20df | |
| 230527d37421c28b7387c54e203deda64564e1b7 | Person Re-identification: System Design and
Evaluation Overview |
| 23fdbef123bcda0f07d940c72f3b15704fd49a98 | |
| 23ebbbba11c6ca785b0589543bf5675883283a57 | |
| 23172f9a397f13ae1ecb5793efd81b6aba9b4537 | 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. 10 |
| 236a4f38f79a4dcc2183e99b568f472cf45d27f4 | 1632
Randomized Clustering Forests for Image Classification Frederic Jurie, Member, IEEE Computer Society |
| 230c4a30f439700355b268e5f57d15851bcbf41f | EM Algorithms for Weighted-Data Clustering
with Application to Audio-Visual Scene Analysis |
| 237fa91c8e8098a0d44f32ce259ff0487aec02cf | IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART B: CYBERNETICS, VOL. 36, NO. 4, AUGUST 2006
863 Bidirectional PCA With Assembled Matrix Distance Metric for Image Recognition |
| 23ba9e462151a4bf9dfc3be5d8b12dbcfb7fe4c3 | CS 229 Project, Fall 2014
Determining Mood from Facial Expressions Introduction I 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 authenticity. 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 counseling. This could also be useful for gauging reactions of large audiences in various contexts, 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 are endless. II Data and Features 2.1 Data Our data consists of 1166 frontal images of people’s faces from three databases, with each image labeled with one of eight emotions: anger, contempt, disgust, fear, happiness, neutral, sadness, and surprise. The TFEID [1], CK+ [2], and JAFFE [3] databases primarily consist of Taiwanese, Caucasian, and Japanese subjects, respectively. The TFEID and JAFFE images are both cropped with the faces centered. Each image has a subject posing with one of the emotions. The JAFFE database does not have any images for contempt. 2.2 Features On each face, there are many different facial landmarks. While some of these landmarks (pupil position, nose tip, and face contour) are not as indicative of emotion, others (eyebrow, mouth, and eye shape) are. To extract landmark data from images, we used Happiness Figure 1 Anger |
| 238fc68b2e0ef9f5ec043d081451902573992a03 | 2656
Enhanced Local Gradient Order Features and Discriminant Analysis for Face Recognition role in robust face recognition [5]. Many algorithms have been proposed to deal with the effectiveness of feature design and 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 are 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 a 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. Fisherface [12] will suffer more due to the estimation of inverse within-class covariance matrix [13], thus the per- formance will degenerate rapidly in the cases of occlusion and small sample size. Laplacianfaces [14] refer to another appearance-based approach which learns a locality preserv- ing subspace and seeks to capture the intrinsic geometry and local structure of the data. Other methods such as those in [5] and [15] also provide valuable approaches to supervised or unsupervised dimension reduction tasks. A fundamental problem of appearance-based methods for face recognition, however, is that they are sensitive to imag- ing conditions [10]. As for data corrupted by illumination changes, occlusions, and inaccurate alignment, the estimated subspace will be biased, thus much of the efforts concentrate on removing/shrinking the noise components. In contrast, local feature descriptors [15]–[19] have certain advantages as they are more stable to local changes. In the view of image pro- cessing and vision, the basic imaging system can be simply formulated as (x, y) = A(x, y) × L(x, y) (1) |
| 23d55061f7baf2ffa1c847d356d8f76d78ebc8c1 | 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 |
| 23a8d02389805854cf41c9e5fa56c66ee4160ce3 | Multimed Tools Appl
DOI 10.1007/s11042-013-1568-8 Influence of low resolution of images on reliability of face detection and recognition © The Author(s) 2013. This article is published with open access at SpringerLink.com |
| 4fd29e5f4b7186e349ba34ea30738af7860cf21f | |
| 4f051022de100241e5a4ba8a7514db9167eabf6e | Face Parsing via a Fully-Convolutional Continuous
CRF Neural Network |
| 4f6adc53798d9da26369bea5a0d91ed5e1314df2 | IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. , NO. , 2016
Online Nonnegative Matrix Factorization with General Divergences |
| 4fbef7ce1809d102215453c34bf22b5f9f9aab26 | |
| 4fa0d73b8ba114578744c2ebaf610d2ca9694f45 | |
| 4f591e243a8f38ee3152300bbf42899ac5aae0a5 | SUBMITTED TO TPAMI
Understanding Higher-Order Shape via 3D Shape Attributes |
| 4f9958946ad9fc71c2299847e9ff16741401c591 | Facial Expression Recognition with Recurrent Neural Networks
Robotics and Embedded Systems Lab, Department of Computer Science Image Understanding and Knowledge-Based Systems, Department of Computer Science Technische Universit¨at M¨unchen, Germany |
| 4f0bf2508ae801aee082b37f684085adf0d06d23 | |
| 4f4f920eb43399d8d05b42808e45b56bdd36a929 | 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 two |
| 8d71872d5877c575a52f71ad445c7e5124a4b174 | |
| 8de06a584955f04f399c10f09f2eed77722f6b1c | Author manuscript, published in "International Conference on Computer Vision Theory and Applications (VISAPP 2013) (2013)" |
| 8d4f0517eae232913bf27f516101a75da3249d15 | ARXIV SUBMISSION, MARCH 2018
Event-based Dynamic Face Detection and Tracking Based on Activity |
| 8de2dbe2b03be8a99628ffa000ac78f8b66a1028 | ´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 3e 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 38 334 Montbonnot France Responsable de stage Mme. Cordelia Schmid Tuteur ´ecole Jury |
| 8d42a24d570ad8f1e869a665da855628fcb1378f | CVPR
#987 000 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048 049 050 051 052 053 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 CSE Department,CSVTU RIT, Raipur, Chhattisgarh , INDIA |
| 8dbe79830713925affc48d0afa04ed567c54724b | |
| 8d1adf0ac74e901a94f05eca2f684528129a630a | Facial Expression Recognition Using Facial
Movement Features |
| 8d712cef3a5a8a7b1619fb841a191bebc2a17f15 | |
| 8dffbb6d75877d7d9b4dcde7665888b5675deee1 | Emotion Recognition with Deep-Belief
Networks 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 approach 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 Yale Face Database (YALE) [4]. We tested several different database configurations, image pre-processing settings, and DBN parameters, and obtained test errors as low as 20% on a limited subset of the emotion labels. Finally, we created a real-time system which takes images of a single subject using a computer webcam and classifies the emotion shown by the subject. Part 1: Exploration of SVM-based approaches To set a baseline for comparison, we applied an SVM classifier to the emotion images in the CK+ database, using the LIBLINEAR library and its MATLAB interface [5]. This database contains 593 image sequences across 123 human subjects, beginning with a “neutral “expression and showing the progression to one of seven “peak” emotions. When given both a neutral and an expressive face to compare, the SVM obtained accuracy as high as 90%. This the implementation of the SVM classifier. For additional details on this stage of the project, please see our Milestone document. Part 1.1 Choice of labels (emotion numbers vs. FACS features) The CK+ database offers two sets of emotion features: “emotion numbers” and FACS features. Emotion numbers are integer values representing the main emotion shown in the “peak emotion” image. The emotions are coded as follows: 1=anger, 2=contempt, 3=disgust, 4=fear, 5=happiness, 6=sadness, and 7=surprise. The other labeling option is called FACS, or the Facial Action Coding System. FACS decomposes every summarizes section facial emotion into a set of Action Units (AUs), which describe the specific muscle groups involved in forming the emotion. We chose not to use FACS because accurate labeling currently requires trained human experts [8], and we are interesting in creating an automated system. Part 1.2 Features Part 1.2.1 Norm of differences between neutral face and full emotion Each of the CK+ images has been hand-labeled with 68 standard Active Appearance Models (AAM) face landmarks that describe the X and Y position of these landmarks on the image (Figure 1). Figure 1. AAM Facial Landmarks We initially trained the SVM on the norm of the vector differences in landmark positions between the neutral and peak expressions. With this approach, the training error was approximately 35% for hold out cross validation (see Figure 2). with Figure 3. Accuracy of SVM with separate X, Y displacement features. Figure 2. Accuracy of SVM norm- displacement features. Part 1.2.2 Separate X and Y differences between neutral face and full emotion Because the initial approach did not differentiate between displacements of in different directions, we also provided the differences in the X and Y components of each landmark separately. This doubled the size of our feature vector, and resulting in a significant (about 20%) improvement in accuracy (Figure 3). Part 1.2.3 Feature Selection landmarks Finally, we visualized which features were the most important for classifying each emotion; the results can be seen in Figure 4. The figure shows the X and Y |
| 153f5ad54dd101f7f9c2ae17e96c69fe84aa9de4 | Overview of algorithms for face detection and
tracking Nenad Markuˇs |
| 15136c2f94fd29fc1cb6bedc8c1831b7002930a6 | Deep Learning Architectures for Face
Recognition in Video Surveillance |
| 153e5cddb79ac31154737b3e025b4fb639b3c9e7 | PREPRINT SUBMITTED TO IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
Active Dictionary Learning in Sparse Representation Based Classification |
| 15e0b9ba3389a7394c6a1d267b6e06f8758ab82b | 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 |
| 15aa6c457678e25f6bc0e818e5fc39e42dd8e533 | |
| 15f3d47b48a7bcbe877f596cb2cfa76e798c6452 | Automatic face analysis tools for interactive digital games
Anonymised for blind review Anonymous Anonymous Anonymous |
| 15728d6fd5c9fc20b40364b733228caf63558c31 | |
| 1513949773e3a47e11ab87d9a429864716aba42d | |
| 153c8715f491272b06dc93add038fae62846f498 | |
| 122ee00cc25c0137cab2c510494cee98bd504e9f | The Application of
Active Appearance Models to Comprehensive Face Analysis Technical Report TU M¨unchen April 5, 2007 |
| 1287bfe73e381cc8042ac0cc27868ae086e1ce3b | |
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expressions is comparable to expert ratings. Graduation research project, june 2012 Supervised by: Dr. Joost Broekens |
| 12cd96a419b1bd14cc40942b94d9c4dffe5094d2 | 29
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 |
| 12055b8f82d5411f9ad196b60698d76fbd07ac1e | 1475
Multiview Facial Landmark Localization in RGB-D Images via Hierarchical Regression With Binary Patterns |
| 120785f9b4952734818245cc305148676563a99b | 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- blique 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 classifieur neuronal à extraire des prototypes de visages selon différentes valeurs du score de dépression de Beck (BDI-II). |
| 12c713166c46ac87f452e0ae383d04fb44fe4eb2 | |
| 12150d8b51a2158e574e006d4fbdd3f3d01edc93 | Deep End2End Voxel2Voxel Prediction
Presented by: Ahmed Osman Ahmed Osman |
| 8c13f2900264b5cf65591e65f11e3f4a35408b48 | A GENERIC FACE REPRESENTATION APPROACH FOR
LOCAL APPEARANCE BASED FACE VERIFICATION Interactive Systems Labs, Universität Karlsruhe (TH) 76131 Karlsruhe, Germany web: http://isl.ira.uka.de/face_recognition/ |
| 8cb3f421b55c78e56c8a1c1d96f23335ebd4a5bf | |
| 8c955f3827a27e92b6858497284a9559d2d0623a | 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 |
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Affinity Learning and Subspace Clustering Framework |
| 8c6c0783d90e4591a407a239bf6684960b72f34e | SESSION
KNOWLEDGE ENGINEERING AND MANAGEMENT + KNOWLEDGE ACQUISITION Chair(s) TBA Int'l Conf. Information and Knowledge Engineering | IKE'13 |1 |
| 8509abbde2f4b42dc26a45cafddcccb2d370712f | Improving precision and recall of face recognition in SIPP with combination of
modified mean search and LSH Xihua.Li |
| 855bfc17e90ec1b240efba9100fb760c068a8efa | |
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Approaches for Facial Expression Recognition: History, Trends, and Affect-related Applications |
| 858901405086056361f8f1839c2f3d65fc86a748 | ON TENSOR TUCKER DECOMPOSITION: THE CASE FOR AN
ADJUSTABLE CORE SIZE |
| 85188c77f3b2de3a45f7d4f709b6ea79e36bd0d9 | Author manuscript, published in "Workshop on Faces in 'Real-Life' Images: Detection, Alignment, and Recognition, Marseille :
France (2008)" |
| 8518b501425f2975ea6dcbf1e693d41e73d0b0af | Relative Hidden Markov Models for Evaluating Motion Skills
Computer Science and Engineering Arizona State Univerisity, Tempe, AZ 85281 |
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Spatial-Temporal Recurrent Neural Network for Emotion Recognition |
| 1dbbec4ad8429788e16e9f3a79a80549a0d7ac7b | |
| 1d7ecdcb63b20efb68bcc6fd99b1c24aa6508de9 | 1860
The Hidden Sides of Names—Face Modeling with First Name Attributes |
| 1d846934503e2bd7b8ea63b2eafe00e29507f06a | |
| 1d0dd20b9220d5c2e697888e23a8d9163c7c814b | NEGREL ET AL.: BOOSTED METRIC LEARNING FOR FACE RETRIEVAL
Boosted Metric Learning for Efficient Identity-Based Face Retrieval 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 AitoeLabs (www.aitoelabs.com) |
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DOI 10.1007/s11263-017-0989-7 A Branch-and-Bound Framework for Unsupervised Common Event Discovery Received: 3 June 2016 / Accepted: 12 January 2017 © Springer Science+Business Media New York 2017 |
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Metric Learning |
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| 1d58d83ee4f57351b6f3624ac7e727c944c0eb8d | Enhanced Local Texture
Feature Sets for Face Recognition under Difficult Lighting Conditions INRIA & Laboratoire Jean Kuntzmann, 655 avenue de l'Europe, Montbonnot 38330, France |
| 71b376dbfa43a62d19ae614c87dd0b5f1312c966 | The Temporal Connection Between Smiles and Blinks |
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Fujitsu Research & Development Center, Beijing, China. |
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London, UK, April 15-17 2015. c(cid:13)2015 Association for Computational Linguistics 76 |
| 76dc11b2f141314343d1601635f721fdeef86fdb | Weighted Decoding ECOC for Facial
Action Unit Classification |
| 760a712f570f7a618d9385c0cee7e4d0d6a78ed2 | |
| 76b9fe32d763e9abd75b427df413706c4170b95c | |
| 76d9f5623d3a478677d3f519c6e061813e58e833 | FAST ALGORITHMS FOR THE GENERALIZED FOLEY-SAMMON
DISCRIMINANT ANALYSIS |
| 765b2cb322646c52e20417c3b44b81f89860ff71 | PoseShop: Human Image Database
Construction and Personalized Content Synthesis |
| 7644d90efef157e61fe4d773d8a3b0bad5feccec | |
| 760ba44792a383acd9ca8bef45765d11c55b48d4 | ~
I . INTRODUCTION AND BACKGROUND The purpose of this article is to introduce the reader to the basic principles of classification with class-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 the decision funetions are projected back to the common raw data space. CSM eflectively extends the classical classification theory to handle multiple feature spaw.. It is completely general, and requires no s i m p l i n g assumption such as Gaussianity or that data lies in linear subspaces. Manuscript received September 26, 2W2; revised February 12, 2003. This work was supported by the Office of Naval Research. Author’s address: US. Naval Undersea Warfare Center, Newport Classification is the process of assigning data to one of a set of pre-determined class labels [2]. Classification is a fundamental problem that has to be solved if machines are to approximate the human functions of recognizing sounds, images, or other sensory inputs. This is why classification is so important for automation in today’s commercial and military arenas. Many of us have first-hand knowledge of successful automated recognition systems from cameras that recognize faces in airports to computers that can scan and read printed and handwritten text, or systems that can recognize human speech. These systems are becoming more and more reliable and accurate. Given reasonably clean input data, the performance is often quite good if not perfect. But many of these systems fail in applications where clean, uncorrupted data is not available or if the problem is complicated by variability of conditions or by proliferation of inputs from unknown sources. In military environments, the targets to he recognized are often uncooperative and hidden in clutter and interference. In short, military uses of such systems still fall far short of what a well-trained alert human operator can achieve. We are often perplexed by the wide gap of as a car door slamming. From performance between humans and automated systems. Allow a human listener to hear two or three examples of a sound-such these few examples, the human can recognize the sound again and not confuse it with similar interfering sounds. But try the same experiment with general-purpose classifiers using neural networks and the story is quite different. Depending on the problem, the automated system may require hundreds, thousands, even millions of examples for training before it becomes both robust and reliable. Why? The answer lies in what is known as the “curse of dimensionality.” General-purpose classifiers need to extract a large number of measurements, or features, from the data to account for all the different possibilities of data types. The large collection of features form a high-dimensional space that the classifier has to sub-divide into decision boundaries. It is well-known that the complexity of a high-dimensional space increases exponentially with the number of measurements [31-and so does the difficulty of finding the hest decision boundaries from a fixed amount of training data. Unless a lot EEE A&E SYSTEMS MAGAZINE VOL. 19, NO. 1 JANUARY 2004 PART 2: TUTORIALS-BAGGENSTOSS 37 |
| 766728bac030b169fcbc2fbafe24c6e22a58ef3c | A survey of deep facial landmark detection
Yongzhe Yan1,2 Thierry Chateau1 1 Université Clermont Auvergne, France 2 Wisimage, France 3 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- tré sur la détection 2D dans une image fixe, et les mé- thodes spécifiques pour la vidéo. Nous présentons ensuite les corpus existants pour ces trois tâches, ainsi que les mé- triques d’évaluations associées. Nous exposons finalement quelques résultats, ainsi que quelques pistes de recherche. Mots Clef Détection de landmark facial, Alignement de visage, Deep learning |
| 7697295ee6fc817296bed816ac5cae97644c2d5b | Detecting and Recognizing Human-Object Interactions
Facebook AI Research (FAIR) |
| 1c80bc91c74d4984e6422e7b0856cf3cf28df1fb | Noname manuscript No.
(will be inserted by the editor) Hierarchical Adaptive Structural SVM for Domain Adaptation Received: date / Accepted: date |
| 1ce4587e27e2cf8ba5947d3be7a37b4d1317fbee | Deep fusion of visual signatures
for client-server facial analysis Normandie Univ, UNICAEN, ENSICAEN, CNRS, GREYC 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 a 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 combination) of visual features; from very simple and effi- cient features, like Local Binary Patterns, to more complex and computationally heavy, like Fisher Vectors and CNN based, depending on the computing resources available. The challenge addressed in this paper is to design a common uni- versal representation such that a single merged signature is transmitted to the server, whatever be the type and num- ber of features computed by the client, ensuring nonetheless an optimal performance. Our solution is based on learn- ing of a common optimal subspace for aligning the different face features and merging them into a universal signature. We have validated the proposed method on the challenging CelebA dataset, on which our method outperforms existing state-of-art methods when rich representation is available at test time, while giving competitive performance when only simple signatures (like LBP) are available at test time due to resource constraints on the client. 1. INTRODUCTION We propose a novel method in a heterogeneous server- client framework for the challenging and important task of analyzing images of faces. Facial analysis is a key ingredient for assistive computer vision and human-machine interaction methods, and systems and incorporating high-performing methods in daily life devices is a challenging task. The ob- jective of the present paper is to develop state-of-the-art technologies for recognizing facial expressions and facial at- tributes on mobile and low cost devices. Depending on their computing resources, the clients (i.e. the devices on which the face image is taken) are capable of computing different types of face signatures, from the simplest ones (e.g. LPB) to the most complex ones (e.g. very deep CNN features), and should be able to eventually combine them into a single rich signature. Moreover, it is convenient if the face analyzer, which might require significant computing resources, is im- plemented on a server receiving face signatures and comput- ing facial expressions and attributes from these signatures. Keeping the computation of the signatures on the client is safer in terms of privacy, as the original images are not trans- mitted, and keeping the analysis part on the server is also beneficial for easy model upgrades in the future. To limit the transmission costs, the signatures have to be made as compact as possible. In summary, the technology needed for this scenario has to be able to merge the different avail- able features – the number of features available at test time is not known in advance but is dependent on the computing resources available on the client – producing a unique rich and compact signature of the face, which can be transmitted and analyzed by a server. Ideally, we would like the univer- sal signature to have the following properties: when all the features are available, we would like the performance of the signature to be better than the one of a system specifically optimized for any single type of feature. In addition, we would like to have reasonable performance when only one type of feature is available at test time. For developing such a system, we propose a hybrid deep neural network and give a method to carefully fine-tune the network parameters while learning with all or a subset of features available. Thus, the proposed network can process a number of wide ranges of feature types such as hand-crafted LBP and FV, or even CNN features which are learned end- to-end. While CNNs have been quite successful in computer vi- sion [1], representing images with CNN features is relatively time consuming, much more than some simple hand-crafted features such as LBP. Thus, the use of CNN in real-time ap- plications is still not feasible. In addition, the use of robust hand-crafted features such as FV in hybrid architectures can give performance comparable to Deep CNN features [2]. The main advantage of learning hybrid architectures is to avoid having large numbers of convolutional and pooling layers. Again from [2], we can also observe that hybrid architec- tures improve the performance of hand-crafted features e.g. FVs. Therefore, hybrid architectures are useful for the cases where only hand-crafted features, and not the original im- ages, are available during training and testing time. This scenario is useful when it is not possible to share training images due to copyright or privacy issues. Hybrid networks are particularly adapted to our client- |
| 1c3073b57000f9b6dbf1c5681c52d17c55d60fd7 | THÈSEprésentéepourl’obtentiondutitredeDOCTEURDEL’ÉCOLENATIONALEDESPONTSETCHAUSSÉESSpécialité:InformatiqueparCharlotteGHYSAnalyse,Reconstruction3D,&AnimationduVisageAnalysis,3DReconstruction,&AnimationofFacesSoutenancele19mai2010devantlejurycomposéde:Rapporteurs:MajaPANTICDimitrisSAMARASExaminateurs:MichelBARLAUDRenaudKERIVENDirectiondethèse:NikosPARAGIOSBénédicteBASCLE |
| 1c93b48abdd3ef1021599095a1a5ab5e0e020dd5 | JOURNAL OF LATEX CLASS FILES, VOL. *, NO. *, JANUARY 2009
A Compositional and Dynamic Model for Face Aging |
| 1c6be6874e150898d9db984dd546e9e85c85724e | |
| 1c65f3b3c70e1ea89114f955624d7adab620a013 | |
| 1c6e22516ceb5c97c3caf07a9bd5df357988ceda | |
| 82bef8481207de9970c4dc8b1d0e17dced706352 | |
| 825f56ff489cdd3bcc41e76426d0070754eab1a8 | Making Convolutional Networks Recurrent for Visual Sequence Learning
NVIDIA |
| 82d2af2ffa106160a183371946e466021876870d | A Novel Space-Time Representation on the Positive Semidefinite Cone
for Facial Expression Recognition 1IMT Lille Douai, Univ. Lille, CNRS, UMR 9189 – CRIStAL – Centre de Recherche en Informatique Signal et Automatique de Lille, F-59000 Lille, France 2Univ. Lille, CNRS, UMR 8524, Laboratoire Paul Painlev´e, F-59000 Lille, France. |
| 8210fd10ef1de44265632589f8fc28bc439a57e6 | 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 S´ebastien Ouellet |
| 82f4e8f053d20be64d9318529af9fadd2e3547ef | Technical Report:
Multibiometric Cryptosystems |
| 82d781b7b6b7c8c992e0cb13f7ec3989c8eafb3d | 141
REFERENCES 1. 2. 3. 4. 5. 6. 7. 8. 9. Adler A., Youmaran R. and Loyka S., “Towards a Measure of Biometric Information”, Canadian Conference on Electrical and Computer Engineering, pp. 210-213, 2006. Military Academy, West Point, New York, pp. 452-458, 2005. Security and Trust, St. Andrews, New Brunswick, Canada, pp. 1-8, 2005. Structural Model for Biometric Sketch Recognition”, Proceedings of DAGM, Magdeburg, Germany, Vol. 2781, pp. 187-195, 2003. of Security”, The First UAE International Conference on Biological and Medical Physics, pp. 1-4, 2005. Avraam Kasapis., “MLPs and Pose, Expression Classification”, Proceedings of UNiS Report, pp. 1-87, 2003. Detection for Storage Area Networks (SANs)”, Proceedings of 22nd IEEE / 13th NASA Goddard Conference on Mass Storage Systems and Technologies, pp. 118-127, 2005. Black M.J. and Yacoob Y., “Recognizing Facial Expressions in Image Sequences using Local Parameterized Models of Image Motion”, Int. Journal Computer Vision, Vol. 25, No. 1, pp. 23-48, 1997. 10. Recognition using a State-Based Model of Spatially-Localized Facial |
| 82417d8ec8ac6406f2d55774a35af2a1b3f4b66e | Some faces are more equal than others:
Hierarchical organization for accurate and efficient large-scale identity-based face retrieval GREYC, CNRS UMR 6072, Universit´e de Caen Basse-Normandie, France1 Technicolor, Rennes, France2 |
| 826c66bd182b54fea3617192a242de1e4f16d020 | 978-1-5090-4117-6/17/$31.00 ©2017 IEEE
1602 ICASSP 2017 |
| 4972aadcce369a8c0029e6dc2f288dfd0241e144 | Multi-target Unsupervised Domain Adaptation
without Exactly Shared Categories |
| 49dd4b359f8014e85ed7c106e7848049f852a304 | |
| 49e85869fa2cbb31e2fd761951d0cdfa741d95f3 | 253
Adaptive Manifold Learning |
| 49659fb64b1d47fdd569e41a8a6da6aa76612903 | |
| 49a7949fabcdf01bbae1c2eb38946ee99f491857 | A CONCATENATING FRAMEWORK OF SHORTCUT
CONVOLUTIONAL NEURAL NETWORKS |
| 49e1aa3ecda55465641b2c2acc6583b32f3f1fc6 | 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 1Assistant Professor, CSE, RSR RCET, Kohka Bhilai 2,3 Sr. Assistant Professor, CSE, SSCET, Junwani Bhilai |
| 49df381ea2a1e7f4059346311f1f9f45dd997164 | 2018
On the Use of Client-Specific Information for Face Presentation Attack Detection Based on Anomaly Detection |
| 40205181ed1406a6f101c5e38c5b4b9b583d06bc | Using Context to Recognize People in Consumer Images |
| 40dab43abef32deaf875c2652133ea1e2c089223 | Noname manuscript No.
(will be inserted by the editor) Facial Communicative Signals Valence Recognition in Task-Oriented Human-Robot Interaction Received: date / Accepted: date |
| 405b43f4a52f70336ac1db36d5fa654600e9e643 | What can we learn about CNNs from a large scale controlled object dataset?
UWM AUT USC |
| 40b86ce698be51e36884edcc8937998979cd02ec | 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 ait resimler daha çok olup, bu resimler birbirine diğerlerine olduğundan daha çok benzeyeceklerdir. Bu nedenle, yüzler arasındaki benzerlikler çizgesel olarak betimlendiğinde , birbirine en çok benzeyen yüzler bu çizgede en yoğun bileşen olacaktır. Bu çalışmada, sorgu ismiyle ilişkilendirilmiş, yüzler arasında birbirine en çok benzeyen alt kümeyi bulan, çizgeye dayalı bir yöntem sunulmaktadır. |
| 402f6db00251a15d1d92507887b17e1c50feebca | 3D Facial Action Units Recognition for Emotional
Expression 1Department of Information Technology and Communication, Politeknik Kuching, Sarawak, Malaysia 2Faculty 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 and testing phase. Keywords: Facial action units recognition, 3D AU recognition, facial expression |
| 40fb4e8932fb6a8fef0dddfdda57a3e142c3e823 | A Mixed Generative-Discriminative Framework for Pedestrian Classification
Dariu M. Gavrila2,3 1 Image & Pattern Analysis Group, Dept. of Math. and Comp. Sc., Univ. of Heidelberg, Germany 2 Environment Perception, Group Research, Daimler AG, Ulm, Germany 3 Intelligent Systems Lab, Faculty of Science, Univ. of Amsterdam, The Netherlands |
| 40cd062438c280c76110e7a3a0b2cf5ef675052c | |
| 40a1935753cf91f29ffe25f6c9dde2dc49bf2a3a | 80 |
| 40a34d4eea5e32dfbcef420ffe2ce7c1ee0f23cd | Bridging Heterogeneous Domains With Parallel Transport For Vision and
Multimedia Applications Dept. of Video and Multimedia Technologies Research AT&T Labs-Research San Francisco, CA 94108 |
| 40389b941a6901c190fb74e95dc170166fd7639d | Automatic Facial Expression Recognition
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: a 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 applications. For example, in pharmacology, the effect of new anti-depression drugs could be assessed more accurately based on daily records of the patients’ facial expressions than asking 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 expression recognition will be able to assess the states and intentions of humans and respond accordingly [41]. Smart phones with expression analysis may help people to prepare for important meetings and job interviews. Thanks to the introduction of machine learning methods, recent years have seen great progress in the field of automatic facial expression recognition. Commercial real-time ex- pression recognition systems are starting to be used in consumer applications, e.g., smile detectors embedded in digital cameras [62]. Nonetheless, considerable progress has yet to be made: Methods for face detection and tracking (the first step of automated face analysis) work well for frontal views of adult Caucasian and Asian faces [50], but their performance |
| 40273657e6919455373455bd9a5355bb46a7d614 | Anonymizing k-Facial Attributes via Adversarial Perturbations
1 IIIT Delhi, New Delhi, India 2 Ministry of Electronics and Information Technology, New Delhi, India |
| 40b10e330a5511a6a45f42c8b86da222504c717f | Implementing the Viola-Jones
Face Detection Algorithm Kongens Lyngby 2008 IMM-M.Sc.-2008-93 |
| 40ca925befa1f7e039f0cd40d57dbef6007b4416 | Sampling Matters in Deep Embedding Learning
UT Austin A9/Amazon Amazon Philipp Kr¨ahenb¨uhl UT Austin |
| 4042bbb4e74e0934f4afbedbe92dd3e37336b2f4 | |
| 40f127fa4459a69a9a21884ee93d286e99b54c5f | Optimizing Apparent Display Resolution
Enhancement for Arbitrary Videos |
| 401e6b9ada571603b67377b336786801f5b54eee | Active Image Clustering: Seeking Constraints from
Humans to Complement Algorithms November 22, 2011 |
| 2e20ed644e7d6e04dd7ab70084f1bf28f93f75e9 | |
| 2e8e6b835e5a8f55f3b0bdd7a1ff765a0b7e1b87 | International Journal of Computer Vision manuscript No.
(will be inserted by the editor) Pointly-Supervised Action Localization Received: date / Accepted: date |
| 2eb37a3f362cffdcf5882a94a20a1212dfed25d9 | 4
Local Feature Based Face Recognition R.I.T., Rajaramnagar and S.G.G.S. COE &T, Nanded India 1. 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 becoming 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 acceptability. 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 achieving recognition rates greater than 90%. However real-world scenarios remain a challenge, because face acquisition process can undergo to a wide range of variations. Hence the AFR can be thought as a very complex object recognition problem, where the object to be recognized is the face. This problem becomes even more difficult because the search is done among objects belonging to the same class and very few images of each class are available to train the system. Moreover different problems arise when images are acquired under uncontrolled conditions such as illumination variations, pose changes, occlusion, person appearance at different ages, expression changes and face deformations. The numbers of approaches has been proposed by various researchers to deal with these problems but still reported results cannot suffice the need of the reliable AFR system in presence of all facial image variations. A recent survey paper (Abate et al., 2007) states that the sensibility of the AFR systems to illumination and pose variations are the main problems researchers have been facing up till. 2. Face recognition methods The existing face recognition methods can be divided into two categories: holistic matching methods and local matching methods.The holistic matching methods use complete face region as a input to face recognition system and constructs a lower dimensional subspace using principal component analysis (PCA) (Turk & Pentland, 1991), linear discriminant www.intechopen.com |
| 2e5cfa97f3ecc10ae8f54c1862433285281e6a7c | |
| 2e0e056ed5927a4dc6e5c633715beb762628aeb0 | |
| 2e68190ebda2db8fb690e378fa213319ca915cf8 | Generating Videos with Scene Dynamics
MIT UMBC MIT |
| 2e0d56794379c436b2d1be63e71a215dd67eb2ca | Improving precision and recall of face recognition in SIPP with combination of
modified mean search and LSH Xihua.Li |
| 2ee8900bbde5d3c81b7ed4725710ed46cc7e91cd | |
| 2ef51b57c4a3743ac33e47e0dc6a40b0afcdd522 | Leveraging Billions of Faces to Overcome
Performance Barriers in Unconstrained Face Recognition face.com |
| 2e19371a2d797ab9929b99c80d80f01a1fbf9479 | |
| 2ebc35d196cd975e1ccbc8e98694f20d7f52faf3 | 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 |
| 2e3d081c8f0e10f138314c4d2c11064a981c1327 | |
| 2e86402b354516d0a8392f75430156d629ca6281 | |
| 2e0f5e72ad893b049f971bc99b67ebf254e194f7 | Apparel Classification with Style
1ETH Z¨urich, Switzerland 2Microsoft, Austria 3Kooaba AG, Switzerland 4KU Leuven, Belgium |
| 2ec7d6a04c8c72cc194d7eab7456f73dfa501c8c | 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 1U.G. Scholars, 2Assistant Professor, Dept. of E & C Engg., MIT Moradabad, Ram Ganga Vihar, Phase II, Moradabad, India. |
| 2e1b1969ded4d63b69a5ec854350c0f74dc4de36 | |
| 2b0ff4b82bac85c4f980c40b3dc4fde05d3cc23f | An Effective Approach for Facial Expression Recognition with Local Binary
Pattern and Support Vector Machine |
| 2b3ceb40dced78a824cf67054959e250aeaa573b | |
| 2b1327a51412646fcf96aa16329f6f74b42aba89 | Under review as a conference paper at ICLR 2016
IMPROVING PERFORMANCE OF RECURRENT NEURAL NETWORK WITH RELU NONLINEARITY Qualcomm Research San Diego, CA 92121, USA |
| 2b5cb5466eecb131f06a8100dcaf0c7a0e30d391 | A Comparative Study of Active Appearance Model
Annotation Schemes for the Face Face Aging Group UNCW, USA Face Aging Group UNCW, USA Face Aging Group UNCW, USA |
| 2b632f090c09435d089ff76220fd31fd314838ae | Early Adaptation of Deep Priors in Age Prediction from Face Images
Computer Vision Lab D-ITET, ETH Zurich Computer Vision Lab D-ITET, ETH Zurich CVL, D-ITET, ETH Zurich Merantix GmbH |
| 2b8dfbd7cae8f412c6c943ab48c795514d53c4a7 | 529
2014 IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP) 978-1-4799-2893-4/14/$31.00 ©2014 IEEE RECOGNITION 1. INTRODUCTION (d1,d2)∈[0;d]2 d1+d2≤d |
| 2baec98c19804bf19b480a9a0aa814078e28bb3d | |
| 470dbd3238b857f349ebf0efab0d2d6e9779073a | Unsupervised Simultaneous Orthogonal Basis Clustering Feature Selection
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- based 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 formulation to consider feature selection and clustering together in a sin- gle unified term of the objective function. The proposed objective function has fewer parameters to tune and does not require complicated optimization tools so just a simple optimization algorithm is sufficient. Substantial ex- periments are performed on several publicly available real world datasets, which shows that SOCFS outperforms various unsupervised feature selec- tion methods and that latent cluster information by the target matrix is ef- fective for regularized regression-based feature selection. Problem Formulation: Given training data, let X = [x1, . . . ,xn] ∈ Rd×n denote the data matrix with n instances where dimension is d and T = [t1, . . . ,tn] ∈ Rm×n denote the corresponding target matrix where dimension is m. We start from the regularized regression-based formulation to select maximum r features is minW (cid:107)WT X− T(cid:107)2 s.t. (cid:107)W(cid:107)2,0 ≤ r. To exploit such formulation on unlabeled data more effectively, it is crucial for the tar- get matrix T to have discriminative destinations for projected clusters. To this end, a new type of target matrix T is proposed to conduct clustering di- rectly on the projected data points WT X. We allow extra degrees of freedom to T by decomposing it into two other matrices B ∈ Rm×c and E ∈ Rn×c as T = BET with additional constraints as (1) F + λ(cid:107)W(cid:107)2,1 (cid:107)WT X− BET(cid:107)2 s.t. BT B = I, ET E = I, E ≥ 0, min W,B,E where λ > 0 is a weighting parameter for the relaxed regularizer (cid:107)W(cid:107)2,1 that induces row sparsity of the projection matrix W. The meanings of the constraints BT B = I,ET E = I,E ≥ 0 are as follows: 1) the orthogonal con- straint of B lets each column of B be independent; 2) the orthogonal and the nonnegative constraint of E make each row of E has only one non-zero element [2]. From 1) and 2), we can clearly interpret B as the basis matrix, which has orthogonality and E as the encoding matrix, where the non-zero element of each column of ET selects one column in B. While optimizing problem (1), T = BET acts like clustering of projected data points WT X with orthogonal basis B and encoder E, so T can estimate latent cluster centers of the WT X. Then, W successively projects X close to corresponding latent cluster centers, which are estimated by T. Note that the orthogonal constraint of B makes each projected cluster in WT X be sep- arated (independent of each other), and it helps W to be a better projection matrix for selecting more discriminative features. If the clustering is directly performed on X not on WT X, the orthogonal constraint of B extremely re- stricts the degree of freedom of B. However, since features are selected by W and the clustering is carried out on WT X in our formulation, so the or- thogonal constraint of B is highly reasonable. A schematic illustration of the proposed method is shown in Figure 1. |
| 47541d04ec24662c0be438531527323d983e958e | Affective Information Processing |
| 474b461cd12c6d1a2fbd67184362631681defa9e | 2014 IEEE International
Conference on Systems, Man and 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 1/5 |
| 47d4838087a7ac2b995f3c5eba02ecdd2c28ba14 | JOURNAL OF IEEE TRANSACTIONS ON AFFECTIVE COMPUTING, VOL. XX, NO. X, XXX 2017
Automatic Recognition of Facial Displays of Unfelt Emotions Escalera, Xavier Bar´o, Sylwia Hyniewska, Member, IEEE, J¨uri Allik, |
| 47a2727bd60e43f3253247b6d6f63faf2b67c54b | Semi-supervised Vocabulary-informed Learning
Disney Research |
| 47e3029a3d4cf0a9b0e96252c3dc1f646e750b14 | 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
Microsoft Vineeth N Balasubramanian Indian Institution of Technology Hyderabad |
| 471befc1b5167fcfbf5280aa7f908eff0489c72b | 570
Class-Specific Kernel-Discriminant Analysis for Face Verification class problems ( |
| 47f8b3b3f249830b6e17888df4810f3d189daac1 | |
| 47e8db3d9adb79a87c8c02b88f432f911eb45dc5 | MAGMA: Multi-level accelerated gradient mirror descent algorithm for
large-scale convex composite minimization July 15, 2016 |
| 47aeb3b82f54b5ae8142b4bdda7b614433e69b9a | |
| 477811ff147f99b21e3c28309abff1304106dbbe | |
| 47e14fdc6685f0b3800f709c32e005068dfc8d47 | |
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| 78a4cabf0afc94da123e299df5b32550cd638939 | |
| 78f08cc9f845dc112f892a67e279a8366663e26d | TECHNISCHE UNIVERSIT ¨AT M ¨UNCHEN
Lehrstuhl f¨ur Mensch-Maschine-Kommunikation Semi-Autonomous Data Enrichment and Optimisation for Intelligent Speech Analysis 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. habil. Dr. h.c. Alexander W. Koch Pr¨ufer der Dissertation: 1. Univ.-Prof. Dr.-Ing. habil. Bj¨orn W. Schuller, Universit¨at Passau 2. Univ.-Prof. Gordon Cheng, Ph.D. Die Dissertation wurde am 30.09.2014 bei der Technischen Universit¨at M¨unchen einge- reicht und durch die Fakult¨at f¨ur Elektrotechnik und Informationstechnik am 07.04.2015 angenommen. |
| 783f3fccde99931bb900dce91357a6268afecc52 | 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 1 SUP ´ELEC/IETR, Avenue de la Boulaie, 35511 Cesson-S´evign´e, France 2 Orange Labs—TECH/IRIS, 4 rue du clos courtel, 35 512 Cesson S´evign´e, France 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 controlled. 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 adapted 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 cited. 1. Introduction All applications related to face analysis and synthesis (Man- Machine Interaction, compression in video communication, augmented reality) need to detect and then to align the user’s face. This latest process consists in the precise localization of the eyes, nose, and mouth gravity center. Face detection can now be realized in real time and in a rather efficient manner [1, 2]; the technical bottleneck lies now in the face alignment when it is done in real conditions, which is precisely the object of this paper. Since such Active Appearance Models (AAMs) as those described in [3] exist, it is therefore possible to align faces in real time. The AAMs exploit a set of face examples in order to extract a statistical model. To align an unknown face in new image, the models parameters must be tuned, in order to match the analyzed face features in the best possible way. There is no difficulty to align a face featuring the same characteristics (same morphology, illumination, and pose) as those constituting the example data set. Unfortunately, AAMs are less outstanding when illumination, pose, and face type changes. We suggest in this paper a robust Active Appearance Model allowing a real-time implementation. In the next section, we will survey the different techniques, which aim to increase the AAM robustness. We will see that none of them address at the same time the three types of robustness, we are interested in pose, illumination, and identity. It must be pointed out that we do not consider the robustness against occlusion as [4] does, for example, when a person moves his hand around the face. After a quick introduction of the Active Appearance Models and their limitations (Section 3), we will present our two main contributions in Section 4.1 in order to improve AAM robustness in illumination, pose, and identity. Exper- iments will be conducted and discussed in Section 5 before drawing a conclusion, suggesting new research directions in the last section. 2. State of the Art We propose to classify the methods which lead to an increase of the AAM robustness as follows. The specific types of dedicated robustness are in italic. (i) Preprocess (1) Invariant features (illumination) (2) Canonical representation (illumination) (ii) Parameter space extension (1) Light modeling (illumination) (2) 3D modeling (pose) |
| 7897c8a9361b427f7b07249d21eb9315db189496 | |
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| 78a11b7d2d7e1b19d92d2afd51bd3624eca86c3c | |
| 781c2553c4ed2a3147bbf78ad57ef9d0aeb6c7ed | Int J Comput Vis
DOI 10.1007/s11263-017-1023-9 Tubelets: Unsupervised Action Proposals from Spatiotemporal Super-Voxels Cees G. M. Snoek1 Received: 25 June 2016 / Accepted: 18 May 2017 © The Author(s) 2017. This article is an open access publication |
| 78df7d3fdd5c32f037fb5cc2a7c104ac1743d74e | TEMPORAL PYRAMID POOLING CNN FOR ACTION RECOGNITION
Temporal Pyramid Pooling Based Convolutional Neural Network for Action Recognition |
| 78fdf2b98cf6380623b0e20b0005a452e736181e | |
| 788a7b59ea72e23ef4f86dc9abb4450efefeca41 | |
| 8b7191a2b8ab3ba97423b979da6ffc39cb53f46b | Search Pruning in Video Surveillance Systems: Efficiency-Reliability Tradeoff
EURECOM Sophia Antipolis, France |
| 8b8728edc536020bc4871dc66b26a191f6658f7c | |
| 8b744786137cf6be766778344d9f13abf4ec0683 | 978-1-4799-9988-0/16/$31.00 ©2016 IEEE
2697 ICASSP 2016 |
| 8bf647fed40bdc9e35560021636dfb892a46720e | Learning to Hash-tag Videos with Tag2Vec
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 | Methoden
at 11/2013 (cid:2)(cid:2)(cid:2) Multimodale Interaktion auf einer sozialen Roboterplattform Multimodal Interaction on a Social Robotic Platform Zusammenfassung Dieser Beitrag beschreibt die multimo- dalen Interaktionsmöglichkeiten mit der Forschungsroboter- plattform ELIAS. Zunächst wird ein Überblick über die Ro- boterplattform 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 interaction capabilities of the robotic research plat- form ELIAS. An overview of the robotic platform as well as the developed processing components is presented, the classification of the components follows the concept of sen- sing and acting modalities. Finally, the interplay between those components within a multimodal gaming scenario is described. Schlagwörter Mensch-Roboter-Interaktion, Multimodalität, Gesten, Blick (cid:2)(cid:2)(cid:2) Keywords Human-robot interaction, multimodal, gestures, gaze 1 Einleitung Eine intuitive und natürliche Bedienbarkeit der zuneh- mend komplexeren Technik wird für den Menschen immer wichtiger, da im heutigen Alltag eine Vielzahl an technischen Geräten mit wachsendem Funktionsumfang anzutreffen ist. Unterschiedliche Aktivitäten in der For- schungsgemeinschaft haben sich schon seit längerer Zeit mit verbalen sowie nonverbalen Kommunikationsformen (bspw. Emotions- und Gestenerkennung) in der Mensch- Maschine-Interaktion beschäftigt. Gerade in der jüngeren Zeit trugen auf diesem Forschungsfeld unterschiedliche Innovationen (bspw. Touchscreen, Gestensteuerung im Fernseher) dazu bei, dass intuitive und natürliche Bedien- konzepte mehr und mehr im Alltag Verwendung finden. Auch Möglichkeiten zur Sprach- und Gestensteuerung von Konsolen und Mobiltelefonen finden heute vermehr- ten Einsatz in der Gerätebedienung. Diese natürlicheren und multimodalen Benutzerschnittstellen sind dem Nut- zer schnell zugänglich und erlauben eine intuitivere Interaktion mit komplexen technischen Geräten. Auch für Robotersysteme bietet sich eine multimodale Interaktion an, um die Benutzung und den Zugang zu den Funktionalitäten zu vereinfachen. Der Mensch soll in seiner Kommunikation idealerweise vollkommene Ent- scheidungsfreiheit bei der Wahl der Modalitäten haben, um sein gewünschtes Ziel zu erreichen. Dafür werden in diesem Beitrag die wahrnehmenden und agieren- den Modalitäten einer, rein auf Kommunikationsaspekte reduzierten, Forschungsroboterplattform beispielhaft in einer Spieleanwendung untersucht. 1.1 Struktur des Beitrags In diesem Beitrag wird zunächst ein kurzer Über- blick über die multimodale Interaktion im Allgemeinen gegeben, hierbei erfolgt eine Betrachtung nach wahr- nehmenden und agierenden Modalitäten. Im nächsten Abschnitt werden Arbeiten vorgestellt, die sich auch mit multimodalen Robotersystemen beschäftigen. Im darauf folgenden Abschnitt wird die Roboterplattform ELIAS mit den wahrnehmenden, verarbeitenden und agierenden at – Automatisierungstechnik 61 (2013) 11 / DOI 10.1515/auto.2013.1062 © Oldenbourg Wissenschaftsverlag - 10.1515/auto.2013.1062 Downloaded from De Gruyter Online at 09/27/2016 10:08:34PM via Technische Universität München 737 |
| 8b1db0894a23c4d6535b5adf28692f795559be90 | 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 |
| 134db6ca13f808a848321d3998e4fe4cdc52fbc2 | IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART B: CYBERNETICS, VOL. 36, NO. 2, APRIL 2006
433 Dynamics of Facial Expression: Recognition of Facial Actions and Their Temporal Segments From Face Profile Image Sequences |
| 133dd0f23e52c4e7bf254e8849ac6f8b17fcd22d | 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 |
| 1369e9f174760ea592a94177dbcab9ed29be1649 | Geometrical Facial Modeling for Emotion Recognition |
| 133900a0e7450979c9491951a5f1c2a403a180f0 | JOURNAL OF LATEX CLASS FILES
Social Grouping for Multi-target Tracking and Head Pose Estimation in Video |
| 13141284f1a7e1fe255f5c2b22c09e32f0a4d465 | Object Tracking by
Oversampling Local Features |
| 133da0d8c7719a219537f4a11c915bf74c320da7 | 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 Dept. of CSE ITM Universe Gwalior Dept. of CSE ITM Universe Gwalior two |
| 133f01aec1534604d184d56de866a4bd531dac87 | Effective Unconstrained Face Recognition by
Combining Multiple Descriptors and Learned Background Statistics |
| 13841d54c55bd74964d877b4b517fa94650d9b65 | Generalised Ambient Reflection Models for Lambertian and
Phong Surfaces Author Zhang, Paul, Gao, Yongsheng Published 2009 Conference Title Proceedings of the 2009 IEEE International Conference on Image Processing (ICIP 2009) DOI https://doi.org/10.1109/ICIP.2009.5413812 Copyright Statement © 2009 IEEE. Personal use of this material is permitted. However, permission to reprint/ republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. Downloaded from http://hdl.handle.net/10072/30001 Griffith Research Online https://research-repository.griffith.edu.au |
| 131e395c94999c55c53afead65d81be61cd349a4 | |
| 1384a83e557b96883a6bffdb8433517ec52d0bea | |
| 13fd0a4d06f30a665fc0f6938cea6572f3b496f7 | |
| 13afc4f8d08f766479577db2083f9632544c7ea6 | Multiple Kernel Learning for
Emotion Recognition in the Wild Machine Perception Laboratory UCSD EmotiW Challenge, ICMI, 2013 1 |
| 13d9da779138af990d761ef84556e3e5c1e0eb94 | Int J Comput Vis (2008) 77: 3–24
DOI 10.1007/s11263-007-0093-5 Learning to Locate Informative Features for Visual Identification Received: 18 August 2005 / Accepted: 11 September 2007 / Published online: 9 November 2007 © Springer Science+Business Media, LLC 2007 |
| 7f533bd8f32525e2934a66a5b57d9143d7a89ee1 | Audio-Visual Identity Grounding for Enabling Cross Media Search
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| 7f4bc8883c3b9872408cc391bcd294017848d0cf |
Computer Sciences Department The Multimodal Focused Attribute Model: A Nonparametric Bayesian Approach to Simultaneous Object Classification and Attribute Discovery Technical Report #1697 January 2012 |
| 7f6061c83dc36633911e4d726a497cdc1f31e58a | YouTube-8M: A Large-Scale Video Classification
Benchmark Paul Natsev Google Research |
| 7f36dd9ead29649ed389306790faf3b390dc0aa2 | MOVEMENT DIFFERENCES BETWEEN DELIBERATE
AND SPONTANEOUS FACIAL EXPRESSIONS: ZYGOMATICUS MAJOR ACTION IN SMILING |
| 7f6cd03e3b7b63fca7170e317b3bb072ec9889e0 | 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 |
| 7f3a73babe733520112c0199ff8d26ddfc7038a0 | |
| 7f205b9fca7e66ac80758c4d6caabe148deb8581 | Page 1 of 47
Computing Surveys A Survey on Mobile Social Signal Processing Understanding human behaviour in an automatic but non-intrusive manner is an important area for various applications. This requires the collaboration of information technology with human sciences to transfer existing knowledge of human behaviour into self-acting tools. These tools will reduce human error that is introduced by current obtrusive methods such as questionnaires. To achieve unobtrusiveness, we focus on exploiting the pervasive and ubiquitous character of mobile devices. In this article, a survey of existing techniques for extracting social behaviour through mobile devices is provided. Initially we expose the terminology used in the area and introduce a concrete architecture for social signal processing applications on mobile phones, constituted by sensing, social interaction detection, behavioural cues extraction, social signal inference and social behaviour understanding. Furthermore, we present state-of-the-art techniques applied to each stage of the process. Finally, potential applications are shown while arguing about the main challenges of the area. Categories and Subject Descriptors: General and reference [Document Types]: Surveys and Overviews; Human-centered computing [Collab- orative and social computing, Ubiquitous and mobile computing] General Terms: Design, Theory, Human Factors, Performance Additional Key Words and Phrases: Social Signal Processing, mobile phones, social behaviour ACM Reference Format: Processing. ACM V, N, Article A (January YYYY), 35 pages. DOI:http://dx.doi.org/10.1145/0000000.0000000 1. INTRODUCTION Human behaviour understanding has received a great deal of interest since the beginning of the previous century. People initially conducted research on the way animals behave when they are surrounded by creatures of the same species. Acquiring basic underlying knowledge of animal relations led to extending this information to humans in order to understand social behaviour, social relations etc. Initial experiments were conducted by empirically observing people and retrieving feedback from them. These methods gave rise to well-established psychological approaches for understanding human behaviour, such as surveys, questionnaires, camera recordings and human observers. Nevertheless, these methods introduce several limitations including various sources of error. Complet- ing surveys and questionnaires induces partiality, unconcern etc. [Groves 2004], human error [Reason 1990], and additional restrictions in scalability of the experiments. Accumulating these research problems leads to a common challenge, the lack of automation in an unobtrusive manner. An area that has focussed on detecting social behaviour automatically and has received a great amount of at- tention is Social Signal Processing (SSP). The main target of the field is to model, analyse and synthesise human behaviour with limited user intervention. To achieve these targets, researchers presented three key terms which |
| 7a9ef21a7f59a47ce53b1dff2dd49a8289bb5098 | |
| 7af38f6dcfbe1cd89f2307776bcaa09c54c30a8b | eaig i C e Vii ad Beyd:
Deve e h . Weg Deae f C e Sciece ichiga Sae Uiveiy Ea aig 48824 Abac Thi chae id ce wha i ca ed he deve ea aach c e vii i aic a ad ai(cid:12)cia ie igece i geea . dic e he c e baic aadig f de ve ig a ye ad i f daea iiai. The deve ea aach i ivaed by h a cgiive deve e f ifacy ad hd. A deve ea eaig a g ih i deeied befe he \bih" f he ye. Afe he \bih" i eab e he ye ea ew ak wih a eed f egaig. The aj ga f he deve ea aach i ea ize a ai f geea e eaig ha eab e achie ef deve ea eaig ve a g eid. S ch eaig i cd ced i a de ii a he way aia ad h a ea. The achie ea diec y f ci ey i ea whi e ieacig wih he evie ic dig h a eache. hi eaig de deve ig ie ige ga f vai ak i ea ized h gh ea ie ieac |
| 7a81967598c2c0b3b3771c1af943efb1defd4482 | Do We Need More Training Data? |
| 7ad77b6e727795a12fdacd1f328f4f904471233f | Supervised Local Descriptor Learning
for Human Action Recognition |
| 7a97de9460d679efa5a5b4c6f0b0a5ef68b56b3b | |
| 7aa4c16a8e1481629f16167dea313fe9256abb42 | 978-1-5090-4117-6/17/$31.00 ©2017 IEEE
2981 ICASSP 2017 |
| 7a85b3ab0efb6b6fcb034ce13145156ee9d10598 | |
| 7ab930146f4b5946ec59459f8473c700bcc89233 | |
| 7ad7897740e701eae455457ea74ac10f8b307bed | Random Subspace Two-dimensional LDA for Face Recognition* |
| 7a7b1352d97913ba7b5d9318d4c3d0d53d6fb697 | Attend and Rectify: a Gated Attention
Mechanism for Fine-Grained Recovery †Computer Vision Center and Universitat Aut`onoma de Barcelona (UAB), Campus UAB, 08193 Bellaterra, Catalonia Spain ‡Visual Tagging Services, Parc de Recerca, Campus UAB |
| 1451e7b11e66c86104f9391b80d9fb422fb11c01 | 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 1Multimedia Signal Processing Group, Electrical Engineering Department, EPFL, Station 11, Lausanne, Switzerland |
| 14761b89152aa1fc280a33ea4d77b723df4e3864 | |
| 14fa27234fa2112014eda23da16af606db7f3637 | |
| 1459d4d16088379c3748322ab0835f50300d9a38 | Cross-Domain Visual Matching via Generalized
Similarity Measure and Feature Learning |
| 14e949f5754f9e5160e8bfa3f1364dd92c2bb8d6 | |
| 1450296fb936d666f2f11454cc8f0108e2306741 | Learning to Discover Cross-Domain Relations
with Generative Adversarial Networks |
| 14fdce01c958043140e3af0a7f274517b235adf3 | |
| 141eab5f7e164e4ef40dd7bc19df9c31bd200c5e | |
| 14e759cb019aaf812d6ac049fde54f40c4ed1468 | Subspace Methods
Synonyms { Multiple similarity method Related Concepts { Principal component analysis (PCA) { Subspace analysis { Dimensionality reduction De(cid:12)nition Subspace analysis in computer vision is a generic name to describe a general framework for comparison and classification of subspaces. A typical approach in subspace analysis is the subspace method (SM) that classify an input pattern vector into several classes based on the minimum distance or angle between the input pattern vector and each class subspace, where a class subspace corresponds to the distribution of pattern vectors of the class in high dimensional vector space. Background Comparison and classification of subspaces has been one of the central prob- lems in computer vision, where an image set of an object to be classified is compactly represented by a subspace in high dimensional vector space. The subspace method is one of the most effective classification method in subspace analysis, which was developed by two Japanese researchers, Watanabe and Iijima around 1970, independently [1, 2]. Watanabe and Iijima named their methods the CLAFIC [3] and the multiple similarity method [4], respectively. The concept of the subspace method is derived from the observation that pat- terns belonging to a class forms a compact cluster in high dimensional vector space, where, for example, a w×h pixels image pattern is usually represented as a vector in w×h-dimensional vector space. The compact cluster can be represented by a subspace, which is generated by using Karhunen-Lo`eve (KL) expansion, also known as the principal component analysis (PCA). Note that a subspace is gen- erated for each class, unlike the Eigenface Method [5] in which only one subspace (called eigenspace) is generated. The SM has been known as one of the most useful methods in pattern recog- nition field, since its algorithm is very simple and it can handle classification of multiple classes. However, its classification performance was not sufficient for many applications in practice, because class subspaces are generated indepen- dently of each other [1]. There is no reason to assume a priori that each class |
| 148eb413bede35487198ce7851997bf8721ea2d6 | People Search in Surveillance Videos
Four Eyes Lab, UCSB IBM Research IBM Research IBM Research Four Eyes Lab, UCSB INTRODUCTION 1. In traditional surveillance scenarios, users are required to watch video footage corresponding to extended periods of time in order to find events of interest. However, this pro- cess is resource-consuming, and suffers from high costs of employing security personnel. The field of intelligent vi- sual surveillance [2] seeks to address these issues by applying computer vision techniques to automatically detect specific events in long video streams. The events can then be pre- sented to the user or be indexed into a database to allow queries such as “show me the red cars that entered a given parking lot from 7pm to 9pm on Monday” or “show me the faces of people who left the city’s train station last week.” In this work, we are interested in analyzing people, by ex- tracting information that can be used to search for them in surveillance videos. Current research on this topic focuses on approaches based on face recognition, where the goal is to establish the identity of a person given an image of a face. However, face recognition is still a very challenging problem, especially in low resolution images with variations in pose and lighting, which is often the case in surveillance data. State-of-the-art face recognition systems [1] require a fair amount of resolution in order to produce reliable re- sults, but in many cases this level of detail is not available in surveillance applications. We approach the problem in an alternative way, by avoiding face recognition and proposing a framework for finding peo- ple based on parsing the human body and exploiting part attributes. Those include visual attributes such as facial hair type (beards, mustaches, absence of facial hair), type of eye- wear (sunglasses, eyeglasses, absence of glasses), hair type (baldness, hair, wearing a hat), and clothing color. While face recognition is still a difficult problem, accurate and ef- ficient face detectors1 based on learning approaches [6] are available. Those have been demonstrated to work well on challenging low-resolution images, with variations in pose and lighting. In our method, we employ this technology to design detectors for facial attributes from large sets of train- ing data. 1The face detection problem consists of localizing faces in images, while face recognition aims to establish the identity of a person given an image of a face. Face detection is a challenging problem, but it is arguably not as complex as face recognition. Our technique falls into the category of short term recogni- tion methods, taking advantage of features present in brief intervals in time, such as clothing color, hairstyle, and makeup, which are generally considered an annoyance in face recogni- tion methods. There are several applications that naturally fit within a short term recognition framework. An example is in criminal investigation, when the police are interested in locating a suspect. In those cases, eyewitnesses typically fill out a suspect description form, where they indicate personal traits of the suspect as seen at the moment when the crime was committed. Those include facial hair type, hair color, clothing type, etc. Based on that description, the police manually scan the entire video archive looking for a person with similar characteristics. This process is tedious and time consuming, and could be drastically accelerated by the use of our technique. Another application is on finding missing people. Parents looking for their children in an amusement park could provide a description including clothing and eye- wear type, and videos from multiple cameras in the park would then be automatically searched. |
| 1473a233465ea664031d985e10e21de927314c94 | |
| 140c95e53c619eac594d70f6369f518adfea12ef | Pushing the Frontiers of Unconstrained Face Detection and Recognition: IARPA Janus Benchmark A
The development of accurate and scalable unconstrained face recogni- tion algorithms is a long term goal of the biometrics and computer vision communities. The term “unconstrained” implies a system can perform suc- cessful 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 captured 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 are the primary goals of the IARPA Janus program. The current state of the art in unconstrained face recognition is high accuracy (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 accuracy on other faces. Despite the fact that face detection and recognition research generally has advanced somewhat independently, the frontal face detector filtering approach used for key in the wild face recognition datasets means that progress in face recognition is currently hampered by progress in face detection. Hence, a major need exists for a face recognition dataset that captures as wide of a range of variations as possible to offer challenges to both face detection as well as face recognition. In this paper we introduce the IARPA Janus Benchmark A (IJB-A), which is publicly available for download. The IJB-A contains images and videos from 500 subjects captured from “in the wild” environment. All la- belled subjects have been manually localized with bounding boxes for face detection, as well as fiducial landmarks for the center of the two eyes (if visible) and base of the nose. Manual bounding box annotations for all non- labelled subjects (i.e., other persons captured in the imagery) have been cap- tured as well. All imagery is Creative Commons licensed, which is a license that allows open re-distribution provided proper attribution is made to the data creator. The subjects have been intentionally sampled to contain wider geographic distribution than previous datasets. Recognition and detection protocols are provided which are motivated by operational deployments of face recognition systems. An example of images and video from IJB-A can be found in Figure 3. The IJB-A dataset has the following claimed contributions: (i) The most unconstrained database released to date; (ii) The first joint face detection and face recognition benchmark dataset collected in the wild; (iii) Meta-data providing subject gender and skin color, and occlusion (eyes, mouth/nose, and forehead), facial hear, and coarse pose information for each imagery instance; (iv) Widest geographic distribution of any public face dataset; (v) The first in the wild dataset to contain a mixture of images and videos; (vi) Clear authority for re-distribution; (vii) Protocols for identification (search) and verification (compare); (viii) Baseline accuracies from off the shelf de- tectors and recognition algorithms; and (ix) Protocols for both template and model-based face recognition. Every subject in the dataset contains at least five images and one video. IJB-A consists of a total of 5,712 images and 2,085 videos, with an average of 11.4 images and 4.2 videos per subject. |
| 142dcfc3c62b1f30a13f1f49c608be3e62033042 | Adaptive Region Pooling for Object Detection
UC Merced Qualcomm Research, San Diego UC Merced |
| 14e428f2ff3dc5cf96e5742eedb156c1ea12ece1 | Facial Expression Recognition Using Neural Network Trained with Zernike
Moments Dept. Génie-Electrique Université M.C.M Souk-Ahras Souk-Ahras, Algeria |
| 14a5feadd4209d21fa308e7a942967ea7c13b7b6 | 978-1-4673-0046-9/12/$26.00 ©2012 IEEE
1025 ICASSP 2012 |
| 14fee990a372bcc4cb6dc024ab7fc4ecf09dba2b | Modeling Spatio-Temporal Human Track Structure for Action
Localization |
| 14ee4948be56caeb30aa3b94968ce663e7496ce4 | 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 |
| 8ee62f7d59aa949b4a943453824e03f4ce19e500 | Robust Head-Pose Estimation Based on
Partially-Latent Mixture of Linear Regression ∗INRIA Grenoble Rhˆone-Alpes, Montbonnot Saint-Martin, France †INRIA Rennes Bretagne Atlantique, Rennes, France |
| 8e33183a0ed7141aa4fa9d87ef3be334727c76c0 | – COS429 Written Report, Fall 2017 –
Robustness of Face Recognition to Image Manipulations 1. 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 algorithms 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 about whether the impact of image manipulations on computer algorithms’ face recognition ability mirrors related insights from neuroscience about humans’ face recognition abilities. 2. Goal In this project, we implement both face recognition algorithms and image manipulations. We then analyze the impact of each image manipulation on the recognition accuracy each algorithm, and how these influences depend on the accuracy of each algorithm on non-manipulated images. 3. Background and Related Work Researchers have developed a wide variety of face recognition algorithms, such as traditional statistical methods such as PCA, more opaque methods such as deep neural networks, and proprietary systems used by governments and corporations [1][13][14]. Similarly, others have developed image manipulations using principles from linear algebra, such as mimicking distortions from lens distortions, as well as using neural networks, such as a system for transforming images according to specified characteristics [12][16]. Furthermore, researchers in psychology have studied face recognition in humans. A study of “super-recognizers” (people with extraordinarily high powers of face recognition) and “developmen- tal prosopagnosics” (people with severely impaired face recognition abilities) found that inverting images of faces impaired recognition ability more for people with stronger face recognition abilities [11]. This could indicate that image manipulations tend to equalize face recognition abilities, and we investigate whether this is the case with the manipulations and face recognition algorithms we test. |
| 8e3d0b401dec8818cd0245c540c6bc032f169a1d | McGan: Mean and Covariance Feature Matching GAN |
| 8e94ed0d7606408a0833e69c3185d6dcbe22bbbe | © 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, creating 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
Marian Bartlett1,∗,‡ 1UCSD, USA 2MPI for Informatics, Germany 3IIT Kanpur, India |
| 8ea30ade85880b94b74b56a9bac013585cb4c34b | FROM TURBO HIDDEN MARKOV MODELS TO TURBO STATE-SPACE MODELS
Institut Eur´ecom Multimedia Communications Department BP 193, 06904 Sophia Antipolis Cedex, France |
| 8ed32c8fad924736ebc6d99c5c319312ba1fa80b | |
| 8e8e3f2e66494b9b6782fb9e3f52aeb8e1b0d125 | 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 copyrighted component of this work in other works. Pre-print of article that will appear at BTAS 2012.!! |
| 8e378ef01171b33c59c17ff5798f30293fe30686 | 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: 1. Prof. Dr.-Ing. habil. Gerhard Rigoll 2. 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 am 18.09.2008 angenommen. |
| 8ed051be31309a71b75e584bc812b71a0344a019 | Class-based feature matching across unrestricted
transformations |
| 8e36100cb144685c26e46ad034c524b830b8b2f2 | Modeling Facial Geometry using Compositional VAEs
1 ´Ecole Polytechnique F´ed´erale de Lausanne 2Facebook Reality Labs, Pittsburgh |
| 8e0becfc5fe3ecdd2ac93fabe34634827b21ef2b | 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 Savvides · Tien D. Bui Received: date / Accepted: date |
| 225fb9181545f8750061c7693661b62d715dc542 | |
| 22043cbd2b70cb8195d8d0500460ddc00ddb1a62 | Separability-Oriented Subclass Discriminant
Analysis |
| 22137ce9c01a8fdebf92ef35407a5a5d18730dde | |
| 22dada4a7ba85625824489375184ba1c3f7f0c8f | |
| 223ec77652c268b98c298327d42aacea8f3ce23f | TR-CS-11-02
Acted Facial Expressions In The Wild Database September 2011 ANU Computer Science Technical Report Series |
| 227b18fab568472bf14f9665cedfb95ed33e5fce | Compositional Dictionaries for Domain Adaptive
Face Recognition |
| 227b1a09b942eaf130d1d84cdcabf98921780a22 | 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 alignment Open Access |
| 22dabd4f092e7f3bdaf352edd925ecc59821e168 | 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 |
| 22f656d0f8426c84a33a267977f511f127bfd7f3 | |
| 2271d554787fdad561fafc6e9f742eea94d35518 | TECHNISCHE UNIVERSIT ¨AT M ¨UNCHEN
Lehrstuhl f¨ur Mensch-Maschine-Kommunikation Multimodale Mensch-Roboter-Interaktion f¨ur Ambient Assisted Living Tobias F. Rehrl 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. Vorsitzende: Pr¨ufer der Dissertation: 1. Univ.-Prof. Dr.-Ing. habil. Gerhard Rigoll 2. Univ.-Prof. Dr.-Ing. Horst-Michael Groß Univ.-Prof. Dr.-Ing. Sandra Hirche (Technische Universit¨at Ilmenau) Die Dissertation wurde am 17. April 2013 bei der Technischen Universit¨at M¨unchen eingereicht und durch die Fakult¨at f¨ur Elektrotechnik und Informationstechnik am 8. Oktober 2013 angenommen. |
| 22ec256400e53cee35f999244fb9ba6ba11c1d06 | |
| 22a7f1aebdb57eecd64be2a1f03aef25f9b0e9a7 | |
| 22e189a813529a8f43ad76b318207d9a4b6de71a | What will Happen Next?
Forecasting Player Moves in Sports Videos UC Berkeley, STATS UC Berkeley UC Berkeley |
| 25d514d26ecbc147becf4117512523412e1f060b | Annotated Crowd Video Face Database
IIIT-Delhi, India |
| 25c19d8c85462b3b0926820ee5a92fc55b81c35a | Noname manuscript No.
(will be inserted by the editor) Pose-Invariant Facial Expression Recognition Using Variable-Intensity Templates Received: date / Accepted: date |
| 258a8c6710a9b0c2dc3818333ec035730062b1a5 | 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 |
| 25695abfe51209798f3b68fb42cfad7a96356f1f | AN INVESTIGATION INTO COMBINING
BOTH FACIAL DETECTION AND LANDMARK LOCALISATION INTO A UNIFIED PROCEDURE USING GPU COMPUTING MSc by Research 2016 |
| 25d3e122fec578a14226dc7c007fb1f05ddf97f7 | The First Facial Expression Recognition and Analysis Challenge |
| 2597b0dccdf3d89eaffd32e202570b1fbbedd1d6 | Towards predicting the likeability of fashion images |
| 25982e2bef817ebde7be5bb80b22a9864b979fb0 | |
| 25e05a1ea19d5baf5e642c2a43cca19c5cbb60f8 | Label Distribution Learning |
| 2559b15f8d4a57694a0a33bdc4ac95c479a3c79a | 570
Contextual Object Localization With Multiple Kernel Nearest Neighbor Gert Lanckriet, Member, IEEE |
| 2574860616d7ffa653eb002bbaca53686bc71cdd | |
| 25f1f195c0efd84c221b62d1256a8625cb4b450c | 1-4244-1017-7/07/$25.00 ©2007 IEEE
1091 ICME 2007 |
| 25885e9292957feb89dcb4a30e77218ffe7b9868 | JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2016
Analyzing the Affect of a Group of People Using Multi-modal Framework |
| 259706f1fd85e2e900e757d2656ca289363e74aa | Improving People Search Using Query Expansions
How Friends Help To Find People LEAR - INRIA Rhˆone Alpes - Grenoble, France |
| 25728e08b0ee482ee6ced79c74d4735bb5478e29 | |
| 258a2dad71cb47c71f408fa0611a4864532f5eba | 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 | 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 Faculty of Computer of Computer Science, Universitas Indonesia, Kampus UI Depok, Indonesia |