Institutions

From 2fd066e9c3cb0e45d7a055d090084f941a40fadb Mon Sep 17 00:00:00 2001 From: Jules Laplace Date: Thu, 8 Nov 2018 19:25:04 +0100 Subject: taking another look at the papers --- reports/institutions_missing.html | 2679 ++++++++++++++++++++++++++++++++++--- 1 file changed, 2527 insertions(+), 152 deletions(-) (limited to 'reports/institutions_missing.html') diff --git a/reports/institutions_missing.html b/reports/institutions_missing.html index 6266cffe..93a26238 100644 --- a/reports/institutions_missing.html +++ b/reports/institutions_missing.html @@ -1,4 +1,7 @@ -Institutions

Institutions

61084a25ebe736e8f6d7a6e53b2c20d9723c4608

614a7c42aae8946c7ad4c36b53290860f6256441

1 +Institutions

Institutions

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) : @@ -24,7 +27,13 @@
0dd72887465046b0f8fc655793c6eaaac9c03a3d	Real-time Head Orientation from a Monocular Camera using Deep Neural Network KAIST, Republic of Korea -
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0d2dd4fc016cb6a517d8fb43a7cc3ff62964832e
956317de62bd3024d4ea5a62effe8d6623a64e53	Lighting Analysis and Texture Modification of 3D Human +
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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 @@ -54,12 +63,23 @@ 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 @@ -92,11 +112,18 @@
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 @@ -184,6 +211,17 @@
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 @@ -199,20 +237,29 @@
0c53ef79bb8e5ba4e6a8ebad6d453ecf3672926d	SUBMITTED TO JOURNAL Weakly Supervised PatchNets: Describing and Aggregating Local Patches for Scene Recognition -
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66d512342355fb77a4450decc89977efe7e55fa2	Under review as a conference paper at ICLR 2018 +
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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
66a9935e958a779a3a2267c85ecb69fbbb75b8dc	FAST AND ROBUST FIXED-RANK MATRIX RECOVERY +
66886997988358847615375ba7d6e9eb0f1bb27f
66837add89caffd9c91430820f49adb5d3f40930
66a9935e958a779a3a2267c85ecb69fbbb75b8dc	FAST AND ROBUST FIXED-RANK MATRIX RECOVERY Fast and Robust Fixed-Rank Matrix Recovery Antonio Lopez @@ -378,7 +425,9 @@ sive review of both approaches is given in [5].
3edb0fa2d6b0f1984e8e2c523c558cb026b2a983	Automatic Age Estimation Based on Facial Aging Patterns -
3ee7a8107a805370b296a53e355d111118e96b7c
3ea8a6dc79d79319f7ad90d663558c664cf298d4
3e4f84ce00027723bdfdb21156c9003168bc1c80	1979 +
3ee7a8107a805370b296a53e355d111118e96b7c
3e4acf3f2d112fc6516abcdddbe9e17d839f5d9b	Deep Value Networks Learn to + Evaluate and Iteratively Reﬁne Structured Outputs +
3ea8a6dc79d79319f7ad90d663558c664cf298d4
3e4f84ce00027723bdfdb21156c9003168bc1c80	1979 © EURASIP, 2011 - ISSN 2076-1465 19th European Signal Processing Conference (EUSIPCO 2011) INTRODUCTION @@ -419,7 +468,7 @@ K.U.Leuven, Belgium Dept. of Computer Science K.U.Leuven, Belgium -
50d15cb17144344bb1879c0a5de7207471b9ff74	Divide, Share, and Conquer: Multi-task +
50a0930cb8cc353e15a5cb4d2f41b365675b5ebf
50d15cb17144344bb1879c0a5de7207471b9ff74	Divide, Share, and Conquer: Multi-task Attribute Learning with Selective Sharing
5028c0decfc8dd623c50b102424b93a8e9f2e390	Published as a conference paper at ICLR 2017 REVISITING CLASSIFIER TWO-SAMPLE TESTS @@ -433,7 +482,9 @@ Part and Attribute Discovery from Relative Annotations Received: 25 February 2013 / Accepted: 14 March 2014 / Published online: 26 April 2014 © Springer Science+Business Media New York 2014 -
68a3f12382003bc714c51c85fb6d0557dcb15467
68d4056765c27fbcac233794857b7f5b8a6a82bf	Example-Based Face Shape Recovery Using the +
68d2afd8c5c1c3a9bbda3dd209184e368e4376b9	Representation Learning by Rotating Your Faces +
68a3f12382003bc714c51c85fb6d0557dcb15467
68d08ed9470d973a54ef7806318d8894d87ba610	Drive Video Analysis for the Detection of Trafﬁc Near-Miss Incidents +
68caf5d8ef325d7ea669f3fb76eac58e0170fff0
68d4056765c27fbcac233794857b7f5b8a6a82bf	Example-Based Face Shape Recovery Using the Zenith Angle of the Surface Normal Mario Castel´an1, Ana J. Almaz´an-Delf´ın2, Marco I. Ram´ırez-Sosa-Mor´an3, and Luz A. Torres-M´endez1 @@ -441,6 +492,9 @@ 2 Universidad Veracruzana, Facultad de F´ısica e Inteligencia Artiﬁcial, Xalapa 91000, 3 ITESM, Campus Saltillo, Saltillo 25270, Coahuila, M´exico Veracruz, M´exico +
684f5166d8147b59d9e0938d627beff8c9d208dd	IEEE TRANS. NNLS, JUNE 2017 + Discriminative Block-Diagonal Representation + Learning for Image Recognition
68cf263a17862e4dd3547f7ecc863b2dc53320d8
68e9c837431f2ba59741b55004df60235e50994d	Detecting Faces Using Region-based Fully Convolutional Networks Tencent AI Lab, China @@ -833,7 +887,12 @@ [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. -
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57d37ad025b5796457eee7392d2038910988655a	GEERATVEEETATF +
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574ad7ef015995efb7338829a021776bf9daaa08	AdaScan: Adaptive Scan Pooling in Deep Convolutional Neural Networks + for Human Action Recognition in Videos + 1IIT Kanpur‡ + 2SRI International + 3UCSD +
57d37ad025b5796457eee7392d2038910988655a	GEERATVEEETATF ERARCCAVETYDETECTR by DagaEha @@ -942,9 +1001,17 @@ 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 -
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6f75697a86d23d12a14be5466a41e5a7ffb79fad
6f6b4e2885ea1d9bea1bb2ed388b099a5a6d9b81	Structured Output SVM Prediction of Apparent Age, +
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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 @@ -975,12 +1042,20 @@
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 -
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03f7041515d8a6dcb9170763d4f6debd50202c2b	Clustering Millions of Faces by Identity +
03c56c176ec6377dddb6a96c7b2e95408db65a7a	A Novel Geometric Framework on Gram Matrix + Trajectories for Human Behavior Understanding +
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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 @@ -1195,8 +1270,10 @@ approaches, 1991) and -
9bcfadd22b2c84a717c56a2725971b6d49d3a804	How to Detect a Loss of Attention in a Tutoring System +
9bc01fa9400c231e41e6a72ec509d76ca797207c
9bcfadd22b2c84a717c56a2725971b6d49d3a804	How to Detect a Loss of Attention in a Tutoring System using Facial Expressions and Gaze Direction +
9bac481dc4171aa2d847feac546c9f7299cc5aa0	Matrix Product State for Higher-Order Tensor + Compression and Classiﬁcation
9b7974d9ad19bb4ba1ea147c55e629ad7927c5d7	Faical Expression Recognition by Combining Texture and Geometrical Features
9ea73660fccc4da51c7bc6eb6eedabcce7b5cead	Talking Head Detection by Likelihood-Ratio Test† @@ -1209,7 +1286,11 @@ 
9e0285debd4b0ba7769b389181bd3e0fd7a02af6	From face images and attributes to attributes Computer Vision Laboratory, ETH Zurich, Switzerland -
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0447bdb71490c24dd9c865e187824dee5813a676	Manifold Estimation in View-based Feature +
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04bb3fa0824d255b01e9db4946ead9f856cc0b59
040dc119d5ca9ea3d5fc39953a91ec507ed8cc5d	Noname manuscript No. + (will be inserted by the editor) + Large-scale Bisample Learning on ID vs. Spot Face Recognition + Received: date / Accepted: date +
04470861408d14cc860f24e73d93b3bb476492d0
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 @@ -1231,10 +1312,18 @@
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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 -
6a184f111d26787703f05ce1507eef5705fdda83
6a16b91b2db0a3164f62bfd956530a4206b23fea	A Method for Real-Time Eye Blink Detection and Its Application +
6ad107c08ac018bfc6ab31ec92c8a4b234f67d49
6a184f111d26787703f05ce1507eef5705fdda83
6a16b91b2db0a3164f62bfd956530a4206b23fea	A Method for Real-Time Eye Blink Detection and Its Application Mahidol Wittayanusorn School Puttamonton, Nakornpatom 73170, Thailand
6a806978ca5cd593d0ccd8b3711b6ef2a163d810	Facial feature tracking for Emotional Dynamic @@ -1353,7 +1442,9 @@
32df63d395b5462a8a4a3c3574ae7916b0cd4d1d	978-1-4577-0539-7/11/$26.00 ©2011 IEEE 1489 ICASSP 2011 -
35308a3fd49d4f33bdbd35fefee39e39fe6b30b7
3538d2b5f7ab393387ce138611ffa325b6400774	A DSP-BASED APPROACH FOR THE IMPLEMENTATION OF FACE RECOGNITION +
35308a3fd49d4f33bdbd35fefee39e39fe6b30b7
352d61eb66b053ae5689bd194840fd5d33f0e9c0	Analysis Dictionary Learning based + Classiﬁcation: Structure for Robustness +
3538d2b5f7ab393387ce138611ffa325b6400774	A DSP-BASED APPROACH FOR THE IMPLEMENTATION OF FACE RECOGNITION ALGORITHMS A. U. Batur B. E. Flinchbaugh @@ -1372,6 +1463,11 @@ Unconstrained Still/Video-Based Face Veriﬁcation with Deep Convolutional Neural Networks Received: date / Accepted: date +
35b1c1f2851e9ac4381ef41b4d980f398f1aad68	Geometry Guided Convolutional Neural Networks for + Self-Supervised Video Representation Learning +
351c02d4775ae95e04ab1e5dd0c758d2d80c3ddd	ActionSnapping: Motion-based Video + Synchronization + Disney Research
35e4b6c20756cd6388a3c0012b58acee14ffa604	Gender Classiﬁcation in Large Databases E. Ram´on-Balmaseda, J. Lorenzo-Navarro, and M. Castrill´on-Santana (cid:63) Universidad de Las Palmas de Gran Canaria @@ -1399,6 +1495,8 @@
353a89c277cca3e3e4e8c6a199ae3442cdad59b5
352110778d2cc2e7110f0bf773398812fd905eb1	TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. X, NO. X, JUNE 2014 Matrix Completion for Weakly-supervised Multi-label Image Classiﬁcation +
6964af90cf8ac336a2a55800d9c510eccc7ba8e1	Temporal Relational Reasoning in Videos + MIT CSAIL
697b0b9630213ca08a1ae1d459fabc13325bdcbb
69d29012d17cdf0a2e59546ccbbe46fa49afcd68	Subspace clustering of dimensionality-reduced data ETH Zurich, Switzerland
69de532d93ad8099f4d4902c4cad28db958adfea
69526cdf6abbfc4bcd39616acde544568326d856	636 @@ -1435,7 +1533,7 @@ 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 -
3c03d95084ccbe7bf44b6d54151625c68f6e74d0
3ce2ecf3d6ace8d80303daf67345be6ec33b3a93
3c374cb8e730b64dacb9fbf6eb67f5987c7de3c8	Measuring Gaze Orientation for Human-Robot +
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3cd7b15f5647e650db66fbe2ce1852e00c05b2e4
3ce2ecf3d6ace8d80303daf67345be6ec33b3a93
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 @@ -1471,7 +1569,9 @@ 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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3cd5da596060819e2b156e8b3a28331ef633036b
3c8da376576938160cbed956ece838682fa50e9f	Chapter 4 +
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3cd5da596060819e2b156e8b3a28331ef633036b
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 @@ -1517,16 +1617,31 @@
566038a3c2867894a08125efe41ef0a40824a090	978-1-4244-2354-5/09/$25.00 ©2009 IEEE 1945 ICASSP 2009 +
56dca23481de9119aa21f9044efd7db09f618704	Riemannian Dictionary Learning and Sparse + Coding for Positive Deﬁnite Matrices +
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5161e38e4ea716dcfb554ccb88901b3d97778f64	SSPP-DAN: DEEP DOMAIN ADAPTATION NETWORK FOR + FACE RECOGNITION WITH SINGLE SAMPLE PER PERSON + School of Computing, KAIST, Republic of Korea +
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58bf72750a8f5100e0c01e55fd1b959b31e7dbce	PyramidBox: A Context-assisted Single Shot + Face Detector. + Baidu Inc. +
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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677585ccf8619ec2330b7f2d2b589a37146ffad7	A ﬂexible model for training action localization + with varying levels of supervision +
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675b2caee111cb6aa7404b4d6aa371314bf0e647	AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions + Carl Vondrick∗ +
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67484723e0c2cbeb936b2e863710385bdc7d5368	Anchor Cascade for Efﬁcient Face Detection +
6742c0a26315d7354ab6b1fa62a5fffaea06da14	BAS AND SMITH: WHAT DOES 2D GEOMETRIC INFORMATION REALLY TELL US ABOUT 3D FACE SHAPE? What does 2D geometric information really tell us about 3D face shape?
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6769cfbd85329e4815bb1332b118b01119975a95	Tied factor analysis for face recognition across large pose changes +
0be43cf4299ce2067a0435798ef4ca2fbd255901	Title + A temporal latent topic model for facial expression recognition + Author(s) + Shang, L; Chan, KP + Citation + The 10th Asian Conference on Computer Vision (ACCV 2010), + Queenstown, New Zealand, 8-12 November 2010. In Lecture + Notes in Computer Science, 2010, v. 6495, p. 51-63 + Issued Date + 2011 + URL + http://hdl.handle.net/10722/142604 + Rights + Creative Commons: Attribution 3.0 Hong Kong License
0b2277a0609565c30a8ee3e7e193ce7f79ab48b0	944 Cost-Sensitive Semi-Supervised Discriminant Analysis for Face Recognition @@ -1601,10 +1746,14 @@ April 13, 2015
0b20f75dbb0823766d8c7b04030670ef7147ccdd	1 Feature selection using nearest attributes +
0b5a82f8c0ee3640503ba24ef73e672d93aeebbf	On Learning 3D Face Morphable Model + from In-the-wild Images
0b174d4a67805b8796bfe86cd69a967d357ba9b6	Research Journal of Recent Sciences _________________________________________________ ISSN 2277-2502 Vol. 3(4), 56-62, April (2014) Res.J.Recent Sci. -
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60c24e44fce158c217d25c1bae9f880a8bd19fc3	Controllable Image-to-Video Translation: + A Case Study on Facial Expression Generation + MIT CSAIL + Wenbing Huang + Tencent AI Lab + MIT-Waston Lab + Tencent AI Lab + Tencent AI Lab +
60e2b9b2e0db3089237d0208f57b22a3aac932c1	Frankenstein: Learning Deep Face Representations + using Small Data +
60ce4a9602c27ad17a1366165033fe5e0cf68078	TECHNICAL NOTE DIGITAL & MULTIMEDIA SCIENCES J Forensic Sci, 2015 doi: 10.1111/1556-4029.12800 @@ -1674,13 +1836,20 @@
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34ec83c8ff214128e7a4a4763059eebac59268a6	Action Anticipation By Predicting Future + Dynamic Images + Australian Centre for Robotic Vision, ANU, Canberra, Australia +
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340d1a9852747b03061e5358a8d12055136599b0	Audio-Visual Recognition System Insusceptible to Illumination Variation over Internet Protocol +
5a3da29970d0c3c75ef4cb372b336fc8b10381d7	CNN-based Real-time Dense Face Reconstruction + with Inverse-rendered Photo-realistic Face Images
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5aad5e7390211267f3511ffa75c69febe3b84cc7	Driver Gaze Estimation Without Using Eye Movement MIT AgeLab -
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5a7520380d9960ff3b4f5f0fe526a00f63791e99	The Indian Spontaneous Expression +
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5a7520380d9960ff3b4f5f0fe526a00f63791e99	The Indian Spontaneous Expression Database for Emotion Recognition +
5fff61302adc65d554d5db3722b8a604e62a8377	Additive Margin Softmax for Face Veriﬁcation + UESTC + Georgia Tech + UESTC + UESTC +
5fa6e4a23da0b39e4b35ac73a15d55cee8608736	IJCV special issue (Best papers of ECCV 2016) manuscript No. + (will be inserted by the editor) + RED-Net: + A Recurrent Encoder-Decoder Network for Video-based Face Alignment + Submitted: April 19 2017 / Revised: December 12 2017
5f871838710a6b408cf647aacb3b198983719c31	1716 Locally Linear Regression for Pose-Invariant Face Recognition
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5f5906168235613c81ad2129e2431a0e5ef2b6e4	Noname manuscript No. +
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5f27ed82c52339124aa368507d66b71d96862cb7	Semi-supervised Learning of Classiﬁers: Theory, Algorithms + and Their Application to Human-Computer Interaction + This work has been partially funded by NSF Grant IIS 00-85980. + DRAFT +
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5f5906168235613c81ad2129e2431a0e5ef2b6e4	Noname manuscript No. (will be inserted by the editor) A Uniﬁed Framework for Compositional Fitting of Active Appearance Models @@ -1731,7 +1915,9 @@ A Face and Palmprint Recognition Approach Based on Discriminant DCT Feature Extraction
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3352426a67eabe3516812cb66a77aeb8b4df4d1b	JOURNAL OF LATEX CLASS FILES, VOL. 4, NO. 5, APRIL 2015 + Joint Multi-view Face Alignment in the Wild +
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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, +
9c1664f69d0d832e05759e8f2f001774fad354d6	Action representations in robotics: A + 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/ +
9c065dfb26ce280610a492c887b7f6beccf27319	Learning from Video and Text via Large-Scale Discriminative Clustering + 1 ´Ecole Normale Sup´erieure + 2Inria + 3CIIRC +
02601d184d79742c7cd0c0ed80e846d95def052e	Graphical Representation for Heterogeneous Face Recognition
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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 @@ -1956,11 +2174,16 @@ 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
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a3a6a6a2eb1d32b4dead9e702824375ee76e3ce7	Multiple Local Curvature Gabor Binary Patterns for Facial Action Recognition Signal Processing Laboratory (LTS5), ´Ecole Polytechnique F´ed´erale de Lausanne, Switzerland -
a3d78bc94d99fdec9f44a7aa40c175d5a106f0b9	Recognizing Violence in Movies +
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.
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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 -
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b51e3d59d1bcbc023f39cec233f38510819a2cf9	CBMM Memo No. 003 +
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b51e3d59d1bcbc023f39cec233f38510819a2cf9	CBMM Memo No. 003 March 27, 2014 Can a biologically-plausible hierarchy eﬀectively replace face detection, alignment, and @@ -2068,6 +2294,10 @@ 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,
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b2c25af8a8e191c000f6a55d5f85cf60794c2709	Noname manuscript No. (will be inserted by the editor) @@ -2075,15 +2305,29 @@ 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
ac6a9f80d850b544a2cbfdde7002ad5e25c05ac6	779 +
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
ac820d67b313c38b9add05abef8891426edd5afb
acb83d68345fe9a6eb9840c6e1ff0e41fa373229	Kernel Methods in Computer Vision: +
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 @@ -2121,7 +2365,73 @@ Submitted for the degree of Doctor of Philosophy Department of Computer Science 20th February 2007 -
ad6745dd793073f81abd1f3246ba4102046da022
bba281fe9c309afe4e5cc7d61d7cff1413b29558	Social Cognitive and Affective Neuroscience, 2017, 984–992 +
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 @@ -2142,7 +2452,9 @@ ==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
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bbf1396eb826b3826c5a800975047beabde2f0de
d73d2c9a6cef79052f9236e825058d5d9cdc1321	2014-ENST-0040 +
bbe1332b4d83986542f5db359aee1fd9b9ba9967
bb7f2c5d84797742f1d819ea34d1f4b4f8d7c197	TO APPEAR IN TPAMI + From Images to 3D Shape Attributes +
bbf01aa347982592b3e4c9e4f433e05d30e71305
bbf1396eb826b3826c5a800975047beabde2f0de
bbd1eb87c0686fddb838421050007e934b2d74ab
d73d2c9a6cef79052f9236e825058d5d9cdc1321	2014-ENST-0040 EDITE - ED 130 Doctorat ParisTech T H È S E @@ -2259,11 +2571,36 @@ 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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d03baf17dff5177d07d94f05f5791779adf3cd5f
d0a21f94de312a0ff31657fd103d6b29db823caa	Facial Expression Analysis + Downloaded From: http://electronicimaging.spiedigitallibrary.org/ on 05/28/2015 Terms of Use: http://spiedl.org/terms
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d03baf17dff5177d07d94f05f5791779adf3cd5f
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 @@ -2277,8 +2614,35 @@ 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 +
b3cb91a08be4117d6efe57251061b62417867de9	T. Swearingen and A. Ross. "A label propagation approach for predicting missing biographic labels in + A Label Propagation Approach for + Predicting Missing Biographic Labels + in Face-Based Biometric Records
b3c60b642a1c64699ed069e3740a0edeabf1922c	Max-Margin Object Detection
b3f7c772acc8bc42291e09f7a2b081024a172564	www.ijmer.com Vol. 3, Issue. 5, Sep - Oct. 2013 pp-3225-3230 ISSN: 2249-6645 International Journal of Modern Engineering Research (IJMER) @@ -2287,6 +2651,15 @@
b32631f456397462b3530757f3a73a2ccc362342	Proceedings of the Twenty-Sixth International Joint Conference on Artiﬁcial Intelligence (IJCAI-17) 3069 +
b3afa234996f44852317af382b98f5f557cab25a
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) International Conference on Recent Trends in engineering & Technology - 2013(ICRTET'2013) Face Recognition using PCA and Eigen Face @@ -2295,6 +2668,8 @@ Sinhgad Academy of Engineering EXTC Department Pune, India +
df577a89830be69c1bfb196e925df3055cafc0ed	Shift: A Zero FLOP, Zero Parameter Alternative to Spatial Convolutions + UC Berkeley
dfabe7ef245ca68185f4fcc96a08602ee1afb3f7
df51dfe55912d30fc2f792561e9e0c2b43179089	Face Hallucination using Linear Models of Coupled Sparse Support grid and fuse them to suppress the aliasing caused by under- @@ -2309,6 +2684,11 @@ Learning Deep Sharable and Structural Detectors for Face Alignment
dfa80e52b0489bc2585339ad3351626dee1a8395	Human Action Forecasting by Learning Task Grammars +
dfecaedeaf618041a5498cd3f0942c15302e75c3	Noname manuscript No. + (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) @@ -2319,15 +2699,22 @@ 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
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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
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. +
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
b40290a694075868e0daef77303f2c4ca1c43269	第 40 卷第 4 期 +
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 @@ -2345,6 +2732,9 @@ 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 @@ -2379,7 +2769,9 @@ Driver Assistance: Issues, Algorithms, and On-Road Evaluations Mohan Manubhai Trivedi, Fellow, IEEE -
a5c04f2ad6a1f7c50b6aa5b1b71c36af76af06be
a503eb91c0bce3a83bf6f524545888524b29b166
bd9eb65d9f0df3379ef96e5491533326e9dde315
bd07d1f68486052b7e4429dccecdb8deab1924db
bd8e2d27987be9e13af2aef378754f89ab20ce10
bd2d7c7f0145028e85c102fe52655c2b6c26aeb5	Attribute-based People Search: Lessons Learnt from a +
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 @@ -2389,20 +2781,91 @@ 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 -
d6102a7ddb19a185019fd2112d2f29d9258f6dec	Proceedings of the Twenty-Sixth International Joint Conference on Artiﬁcial Intelligence (IJCAI-17) +
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
bcc172a1051be261afacdd5313619881cbe0f676	978-1-5090-4117-6/17/$31.00 ©2017 IEEE +
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
bcc172a1051be261afacdd5313619881cbe0f676	978-1-5090-4117-6/17/$31.00 ©2017 IEEE 2197 ICASSP 2017 -
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bcb99d5150d792001a7d33031a3bd1b77bea706b
bcac3a870501c5510df80c2a5631f371f2f6f74a	CVPR +
bcfeac1e5c31d83f1ed92a0783501244dde5a471
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 @@ -2464,7 +2927,17 @@ Structured Face Hallucination Anonymous CVPR submission Paper ID 1387 -
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ae936628e78db4edb8e66853f59433b8cc83594f
aebb9649bc38e878baef082b518fa68f5cda23a5	 +
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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 @@ -2532,12 +3005,50 @@
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 @@ -2555,11 +3066,18 @@ GENÈVE Repro-Mail - Université de Genève 2011 -
e5737ffc4e74374b0c799b65afdbf0304ff344cb
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e200c3f2849d56e08056484f3b6183aa43c0f13a
f437b3884a9e5fab66740ca2a6f1f3a5724385ea	Human Identification Technical Challenges +
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 -
f4c01fc79c7ead67899f6fe7b79dd1ad249f71b0
f4373f5631329f77d85182ec2df6730cbd4686a9	Soft Computing manuscript No. +
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 @@ -2571,8 +3089,38 @@
f3fcaae2ea3e998395a1443c87544f203890ae15
f3d9e347eadcf0d21cb0e92710bc906b22f2b3e7	NosePose: a competitive, landmark-free methodology for head pose estimation in the wild IMAGO Research Group - Universidade Federal do Paran´a -
f355e54ca94a2d8bbc598e06e414a876eb62ef99
ebedc841a2c1b3a9ab7357de833101648281ff0e
eb526174fa071345ff7b1fad1fad240cd943a6d7	Deeply Vulnerable – A Study of the Robustness of Face Recognition to +
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 @@ -2583,7 +3131,9 @@ representation learning using various deep networks School of Electrical Engineering, KAIST, Guseong-dong, Yuseong-gu, Dajeon, Rep. of Korea -
ebb9d53668205c5797045ba130df18842e3eadef
c7e4c7be0d37013de07b6d829a3bf73e1b95ad4e	The International Journal of Multimedia & Its Applications (IJMA) Vol.5, No.5, October 2013 +
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 @@ -2593,9 +3143,28 @@ 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 @@ -2625,6 +3194,9 @@ 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 @@ -2665,6 +3237,22 @@ 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 @@ -2689,8 +3277,14 @@ 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 @@ -2708,7 +3302,51 @@ 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. -
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f519723238701849f1160d5a9cedebd31017da89	Impact of multi-focused images on recognition of soft biometric traits +
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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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 @@ -2719,17 +3357,35 @@ SEARCH #Student,Cse, CIET, Lam,Guntur, India * Assistant Professort,Cse, CIET, Lam,Guntur , India -
e3657ab4129a7570230ff25ae7fbaccb4ba9950c
e315959d6e806c8fbfc91f072c322fb26ce0862b	An Efficient Face Recognition System Based on Sub-Window +
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
cf875336d5a196ce0981e2e2ae9602580f3f6243	7 What 1 +
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 @@ -2775,6 +3431,17 @@ ´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 @@ -2782,7 +3449,18 @@ Detecting Social Relationships in First-Person Views Universit`a degli Studi di Modena e Reggio Emilia Via Vignolese 905, 41125 Modena - Italy -
cac8bb0e393474b9fb3b810c61efdbc2e2c25c29
cadba72aa3e95d6dcf0acac828401ddda7ed8924	THÈSE PRÉSENTÉE À LA FACULTÉ DES SCIENCES +
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 @@ -2820,6 +3498,20 @@ 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 @@ -2833,6 +3525,10 @@
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 @@ -2848,7 +3544,13 @@ 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
c8db8764f9d8f5d44e739bbcb663fbfc0a40fb3d	Modeling for part-based visual object +
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 @@ -2863,17 +3565,66 @@ 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 -
c82c147c4f13e79ad49ef7456473d86881428b89
c8adbe00b5661ab9b3726d01c6842c0d72c8d997	Deep Architectures for Face Attributes +
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
edef98d2b021464576d8d28690d29f5431fd5828	Pixel-Level Alignment of Facial Images for High Accuracy Recognition Using Ensemble of Patches @@ -2958,14 +3709,33 @@ Subspace Regression: Predicting a Subspace from one Sample Anonymous CVPR submission Paper ID 1369 +
c11eb653746afa8148dc9153780a4584ea529d28	Global and Local Consistent Wavelet-domain Age + Synthesis +
c1ebbdb47cb6a0ed49c4d1cf39d7565060e6a7ee	Robust Facial Landmark Localization Based on
c17a332e59f03b77921942d487b4b102b1ee73b6	Learning an appearance-based gaze estimator from one million synthesised images Tadas Baltruˇsaitis2 -
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ec54000c6c0e660dd99051bdbd7aed2988e27ab8	TWO IN ONE: JOINT POSE ESTIMATION AND FACE RECOGNITION WITH P2CA1 +
c1e76c6b643b287f621135ee0c27a9c481a99054
c6f3399edb73cfba1248aec964630c8d54a9c534	A Comparison of CNN-based Face and Head Detectors for + Real-Time Video Surveillance Applications + 1 ´Ecole de technologie sup´erieure, Universit´e du Qu´ebec, Montreal, Canada + 2 Genetec Inc., Montreal, Canada +
c62c07de196e95eaaf614fb150a4fa4ce49588b4	Proceedings of the Twenty-Seventh International Joint Conference on Artiﬁcial Intelligence (IJCAI-18) + 1078 +
ec1e03ec72186224b93b2611ff873656ed4d2f74	JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015 + 3D Reconstruction of “In-the-Wild” Faces in + Images and Videos +
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ec54000c6c0e660dd99051bdbd7aed2988e27ab8	TWO IN ONE: JOINT POSE ESTIMATION AND FACE RECOGNITION WITH P2CA1 *Dept. Teoria del Senyal i Comunicacions - Universitat Politècnica de Catalunya, Barcelona, Spain +Dipartimento di Elettronica e Informazione - Politecnico di Milano, Meiland, Italy
ec0104286c96707f57df26b4f0a4f49b774c486b	758 An Ensemble CNN2ELM for Age Estimation +
4e32fbb58154e878dd2fd4b06398f85636fd0cf4	A Hierarchical Matcher using Local Classiﬁer Chains + L. Zhang and I.A. Kakadiaris + Computational Biomedicine Lab, 4849 Calhoun Rd, Rm 373, Houston, TX 77204 +
4e27fec1703408d524d6b7ed805cdb6cba6ca132	SSD-Sface: Single shot multibox detector for small faces + C. Thuis +
4e6c9be0b646d60390fe3f72ce5aeb0136222a10	Long-term Temporal Convolutions + for Action Recognition
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4ef0a6817a7736c5641dc52cbc62737e2e063420	International Journal of Advanced Computer Research (ISSN (Print): 2249-7277 ISSN (Online): 2277-7970) Volume-4 Number-4 Issue-17 December-2014 Study of Face Recognition Techniques @@ -2994,7 +3764,35 @@ http://www.informatik.uni-hamburg.de/WTM
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20532b1f80b509f2332b6cfc0126c0f80f438f10	A deep matrix factorization method for learning +
20ade100a320cc761c23971d2734388bfe79f7c5	IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE + Subspace Clustering via Good Neighbors +
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206e24f7d4b3943b35b069ae2d028143fcbd0704	Learning Structure and Strength of CNN Filters for Small Sample Size Training + IIIT-Delhi, India +
2059d2fecfa61ddc648be61c0cbc9bc1ad8a9f5b	TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, VOL. 23, NO. 4, APRIL 2015 + Co-Localization of Audio Sources in Images Using + Binaural Features and Locally-Linear Regression + ∗ INRIA Grenoble Rhˆone-Alpes, Montbonnot Saint-Martin, France + † Univ. Grenoble Alpes, GIPSA-Lab, France + ‡ Dept. Electrical Eng., Technion-Israel Inst. of Technology, Haifa, Israel +
206fbe6ab6a83175a0ef6b44837743f8d5f9b7e8
20111924fbf616a13d37823cd8712a9c6b458cd6	International Journal of Computer Applications (0975 – 8887) + Volume 130 – No.11, November2015 + Linear Regression Line based Partial Face Recognition + Naveena M. + Department of Studies in + Computer Science, + Manasagagothri, + Mysore. + Department of Studies in + Computer Science, + Manasagagothri, + Mysore. + P. Nagabhushan + Department of Studies in + Computer Science, + Manasagagothri, + Mysore. + images. In +
20532b1f80b509f2332b6cfc0126c0f80f438f10	A deep matrix factorization method for learning attribute representations Bj¨orn W. Schuller, Senior member, IEEE
205af28b4fcd6b569d0241bb6b255edb325965a4	Intel Serv Robotics (2008) 1:143–157 @@ -3031,6 +3829,20 @@ ADVISERS:
18d5b0d421332c9321920b07e0e8ac4a240e5f1f	Collaborative Representation Classiﬁcation Ensemble for Face Recognition +
18d51a366ce2b2068e061721f43cb798177b4bb7	Cognition and Emotion + ISSN: 0269-9931 (Print) 1464-0600 (Online) Journal homepage: http://www.tandfonline.com/loi/pcem20 + Looking into your eyes: observed pupil size + influences approach-avoidance responses + eyes: observed pupil size influences approach-avoidance responses, Cognition and Emotion, DOI: + 10.1080/02699931.2018.1472554 + To link to this article: https://doi.org/10.1080/02699931.2018.1472554 + View supplementary material + Published online: 11 May 2018. + Submit your article to this journal + View related articles + View Crossmark data + Full Terms & Conditions of access and use can be found at + http://www.tandfonline.com/action/journalInformation?journalCode=pcem20
1885acea0d24e7b953485f78ec57b2f04e946eaf	Combining Local and Global Features for 3D Face Tracking Megvii (face++) Research
184750382fe9b722e78d22a543e852a6290b3f70
18a849b1f336e3c3b7c0ee311c9ccde582d7214f	Int J Comput Vis @@ -3045,7 +3857,38 @@ THE BASICS
185360fe1d024a3313042805ee201a75eac50131	299 Person De-Identiﬁcation in Videos -
18dfc2434a95f149a6cbb583cca69a98c9de9887
27d709f7b67204e1e5e05fe2cfac629afa21699d
27cccf992f54966feb2ab4831fab628334c742d8	International Journal of Computer Applications (0975 – 8887) +
18dfc2434a95f149a6cbb583cca69a98c9de9887
27d709f7b67204e1e5e05fe2cfac629afa21699d
275b5091c50509cc8861e792e084ce07aa906549	Institut für Informatik + der Technischen + Universität München + Dissertation + Leveraging the User’s Face as a Known Object + in Handheld Augmented Reality + Sebastian Bernhard Knorr +
270733d986a1eb72efda847b4b55bc6ba9686df4	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 +
27da432cf2b9129dce256e5bf7f2f18953eef5a5
2770b095613d4395045942dc60e6c560e882f887	GridFace: Face Rectiﬁcation via Learning Local + Homography Transformations + Face++, Megvii Inc. +
27cccf992f54966feb2ab4831fab628334c742d8	International Journal of Computer Applications (0975 – 8887) Volume 64– No.18, February 2013 Facial Expression Recognition by Statistical, Spatial Features and using Decision Tree @@ -3086,6 +3929,8 @@
4b04247c7f22410681b6aab053d9655cf7f3f888	Robust Face Recognition by Constrained Part-based Alignment
4b60e45b6803e2e155f25a2270a28be9f8bec130	Attribute Based Object Identiﬁcation +
4b48e912a17c79ac95d6a60afed8238c9ab9e553	JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015 + Minimum Margin Loss for Deep Face Recognition
4b5eeea5dd8bd69331bd4bd4c66098b125888dea	Human Activity Recognition Using Conditional Random Fields and Privileged Information submitted to @@ -3111,9 +3956,15 @@ JUNE 2008 Tied Factor Analysis for Face Recognition across Large Pose Differences -
111a9645ad0108ad472b2f3b243ed3d942e7ff16	Facial Expression Classification Using +
112780a7fe259dc7aff2170d5beda50b2bfa7bda
111a9645ad0108ad472b2f3b243ed3d942e7ff16	Facial Expression Classification Using Combined Neural Networks DEE/PUC-Rio, Marquês de São Vicente 225, Rio de Janeiro – RJ - Brazil +
111d0b588f3abbbea85d50a28c0506f74161e091	International Journal of Computer Applications (0975 – 8887) + Volume 134 – No.10, January 2016 + Facial Expression Recognition from Visual Information + using Curvelet Transform + Surabhi Group of Institution Bhopal + systems. Further applications
7de6e81d775e9cd7becbfd1bd685f4e2a5eebb22	Labeled Faces in the Wild: A Survey
7d73adcee255469aadc5e926066f71c93f51a1a5	978-1-4799-9988-0/16/$31.00 ©2016 IEEE 1283 @@ -3160,6 +4011,8 @@ Published online: 14 January 2010 © Springer Science+Business Media, LLC 2010
290136947fd44879d914085ee51d8a4f433765fa	On a Taxonomy of Facial Features +
2957715e96a18dbb5ed5c36b92050ec375214aa6	Improving Face Attribute Detection with Race and Gender Diversity + InclusiveFaceNet:
291265db88023e92bb8c8e6390438e5da148e8f5	MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition Microsoft Research @@ -3228,20 +4081,114 @@
7c7b0550ec41e97fcfc635feffe2e53624471c59	1051-4651/14 $31.00 © 2014 IEEE DOI 10.1109/ICPR.2014.124 660 -
7ce03597b703a3b6754d1adac5fbc98536994e8f
7c1e1c767f7911a390d49bed4f73952df8445936	NON-RIGID OBJECT DETECTION WITH LOCAL INTERLEAVED SEQUENTIAL ALIGNMENT (LISA) +
7ce03597b703a3b6754d1adac5fbc98536994e8f
7c9a65f18f7feb473e993077d087d4806578214e	SpringerLink - Zeitschriftenbeitrag + http://www.springerlink.com/content/93hr862660nl1164/?p=abe5352... + Deutsch + Deutsch + Go + Vorherige Beitrag Nächste Beitrag + Beitrag markieren + In den Warenkorb legen + Zu gespeicherten Artikeln + hinzufügen + Permissions & Reprints + Diesen Artikel empfehlen + Ergebnisse + finden + Erweiterte Suche + Go + im gesamten Inhalt + in dieser Zeitschrift + in diesem Heft + Diesen Beitrag exportieren + Diesen Beitrag exportieren als RIS + \| Text + Text + PDF + PDF ist das gebräuchliche Format + für Online Publikationen. Die Größe + dieses Dokumentes beträgt 564 + Kilobyte. Je nach Art Ihrer + Internetverbindung kann der + Download einige Zeit in Anspruch + nehmen. + öffnen: Gesamtdokument + Publikationsart Subject Collections + Zurück zu: Journal Issue + Athens Authentication Point + Zeitschriftenbeitrag + Willkommen! + Um unsere personalisierten + Angebote nutzen zu können, + müssen Sie angemeldet sein. + Login + Jetzt registrieren + Zugangsdaten vergessen? + Hilfe. + Mein Menü + Markierte Beiträge + Alerts + Meine Bestellungen + Private emotions versus social interaction: a data-driven approach towards + analysing emotion in speech + Zeitschrift + Verlag + ISSN + Heft + Kategorie + DOI + Seiten + Subject Collection + SpringerLink Date + User Modeling and User-Adapted Interaction + Springer Netherlands + 0924-1868 (Print) 1573-1391 (Online) + Volume 18, Numbers 1-2 / Februar 2008 + Original Paper + 10.1007/s11257-007-9039-4 + 175-206 + Informatik + Freitag, 12. Oktober 2007 + Gespeicherte Beiträge + Alle + Favoriten + (1) Lehrstuhl für Mustererkennung, FAU Erlangen – Nürnberg, Martensstr. 3, 91058 Erlangen, + Germany + Received: 3 July 2006 Accepted: 14 January 2007 Published online: 12 October 2007 +
7c1e1c767f7911a390d49bed4f73952df8445936	NON-RIGID OBJECT DETECTION WITH LOCAL INTERLEAVED SEQUENTIAL ALIGNMENT (LISA) Non-Rigid Object Detection with Local Interleaved Sequential Alignment (LISA) and Tom´aˇs Svoboda, Member, IEEE
7c349932a3d083466da58ab1674129600b12b81c
1648cf24c042122af2f429641ba9599a2187d605	Boosting Cross-Age Face Veriﬁcation via Generative Age Normalization (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
16671b2dc89367ce4ed2a9c241246a0cec9ec10e	2006 Detecting the Number of Clusters in n-Way Probabilistic Clustering -
16892074764386b74b6040fe8d6946b67a246a0b
16395b40e19cbc6d5b82543039ffff2a06363845	Action Recognition in Video Using Sparse Coding and Relative Features +
16de1324459fe8fdcdca80bba04c3c30bb789bdf
16892074764386b74b6040fe8d6946b67a246a0b
16395b40e19cbc6d5b82543039ffff2a06363845	Action Recognition in Video Using Sparse Coding and Relative Features Anal´ı Alfaro P. Universidad Catolica de Chile P. Universidad Catolica de Chile @@ -3256,6 +4203,25 @@ and Timing of Smiles Perceived as Amused, Polite, and Embarrassed/Nervous Ó Springer Science+Business Media, LLC 2008 +
166186e551b75c9b5adcc9218f0727b73f5de899	Volume 4, Issue 2, February 2016 + International Journal of Advance Research in + Computer Science and Management Studies + Research Article / Survey Paper / Case Study + Available online at: www.ijarcsms.com + ISSN: 2321-7782 (Online) + Automatic Age and Gender Recognition in Human Face Image + Dataset using Convolutional Neural Network System + Subhani Shaik1 + Assoc. Prof & Head of the Department + Department of CSE, + Associate Professor + Department of CSE, + St.Mary’s Group of Institutions Guntur + St.Mary’s Group of Institutions Guntur + Chebrolu(V&M),Guntur(Dt), + Andhra Pradesh - India + Chebrolu(V&M),Guntur(Dt), + Andhra Pradesh - India
16d9b983796ffcd151bdb8e75fc7eb2e31230809	EUROGRAPHICS 2018 / D. Gutierrez and A. Sheffer (Guest Editors) Volume 37 (2018), Number 2 @@ -3264,6 +4230,10 @@
1679943d22d60639b4670eba86665371295f52c3
169076ffe5e7a2310e98087ef7da25aceb12b62d
161eb88031f382e6a1d630cd9a1b9c4bc6b47652	1 Automatic Facial Expression Recognition Using Features of Salient Facial Patches +
4209783b0cab1f22341f0600eed4512155b1dee6	Accurate and Efﬁcient Similarity Search for Large Scale Face Recognition + BUPT + BUPT + BUPT
42e3dac0df30d754c7c7dab9e1bb94990034a90d	PANDA: Pose Aligned Networks for Deep Attribute Modeling 2EECS, UC Berkeley 1Facebook AI Research @@ -3302,7 +4272,23 @@ Factorization in the Presence of Outliers and Missing Data
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8913a5b7ed91c5f6dec95349fbc6919deee4fc75	BigBIRD: A Large-Scale 3D Database of Object Instances -
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4571626d4d71c0d11928eb99a3c8b10955a74afe	Geometry Guided Adversarial Facial Expression Synthesis +
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 +
45f3bf505f1ce9cc600c867b1fb2aa5edd5feed8
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 @@ -3317,7 +4303,19 @@ © EURASIP, 2011 - ISSN 2076-1465 19th European Signal Processing Conference (EUSIPCO 2011) INTRODUCTION -
4511e09ee26044cb46073a8c2f6e1e0fbabe33e8
1f8304f4b51033d2671147b33bb4e51b9a1e16fe	Noname manuscript No. +
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 @@ -3364,7 +4362,27 @@ 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 +
7323b594d3a8508f809e276aa2d224c4e7ec5a80	JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015 + An Experimental Evaluation of Covariates + Effects on Unconstrained Face Veriﬁcation
732e8d8f5717f8802426e1b9debc18a8361c1782	Unimodal Probability Distributions for Deep Ordinal Classiﬁcation +
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 +
734cdda4a4de2a635404e4c6b61f1b2edb3f501d	Tie and Guan EURASIP Journal on Image and Video Processing 2013, 2013:8 + http://jivp.eurasipjournals.com/content/2013/1/8 + R ES EAR CH + Open Access + Automatic landmark point detection and tracking + for human facial expressions
732686d799d760ccca8ad47b49a8308b1ab381fb	Running head: TEACHERS’ DIFFERING BEHAVIORS 1 Graduate School of Psychology @@ -3377,27 +4395,62 @@
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871f5f1114949e3ddb1bca0982086cc806ce84a8	Discriminative Learning of Apparel Features +
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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 +
878301453e3d5cb1a1f7828002ea00f59cbeab06	Faceness-Net: Face Detection through + Deep Facial Part Responses +
87e592ee1a7e2d34e6b115da08700a1ae02e9355	Deep Pictorial Gaze Estimation + AIT Lab, Department of Computer Science, ETH Zurich
87bb183d8be0c2b4cfceb9ee158fee4bbf3e19fd	Craniofacial Image Analysis -
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804b4c1b553d9d7bae70d55bf8767c603c1a09e3	978-1-4799-9988-0/16/$31.00 ©2016 IEEE +
8006219efb6ab76754616b0e8b7778dcfb46603d	CONTRIBUTIONSTOLARGE-SCALELEARNINGFORIMAGECLASSIFICATIONZeynepAkataPhDThesisl’´EcoleDoctoraleMath´ematiques,SciencesetTechnologiesdel’Information,InformatiquedeGrenoble
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800cbbe16be0f7cb921842d54967c9a94eaa2a65	MULTIMODAL RECOGNITION OF EMOTIONS +
803c92a3f0815dbf97e30c4ee9450fd005586e1a	Max-Mahalanobis Linear Discriminant Analysis Networks +
80345fbb6bb6bcc5ab1a7adcc7979a0262b8a923	Research Article + Soft Biometrics for a Socially Assistive Robotic + Platform + Open Access
80a6bb337b8fdc17bffb8038f3b1467d01204375	Proceedings of the International Conference on Computer and Information Science and Technology Ottawa, Ontario, Canada, May 11 – 12, 2015 Paper No. 126 Subspace LDA Methods for Solving the Small Sample Size Problem in Face Recognition 101 KwanFu Rd., Sec. 2, Hsinchu, Taiwan +
80097a879fceff2a9a955bf7613b0d3bfa68dc23	Active Self-Paced Learning for Cost-Effective and + Progressive Face Identiﬁcation
74408cfd748ad5553cba8ab64e5f83da14875ae8	Facial Expressions Tracking and Recognition: Database Protocols for Systems Validation and Evaluation -
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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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7480d8739eb7ab97c12c14e75658e5444b852e9f	NEGREL ET AL.: REVISITED MLBOOST FOR FACE RETRIEVAL +
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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 +
7480d8739eb7ab97c12c14e75658e5444b852e9f	NEGREL ET AL.: REVISITED MLBOOST FOR FACE RETRIEVAL MLBoost Revisited: A Faster Metric Learning Algorithm for Identity-Based Face Retrieval @@ -3426,6 +4479,24 @@ J. Paone, D. Bolme, R. Ferrell, Member, IEEE, D. Aykac, and T. Karnowski, Member, IEEE Oak Ridge National Laboratory, Oak Ridge, TN +
1a849b694f2d68c3536ed849ed78c82e979d64d5	This is a repository copy of Symmetric Shape Morphing for 3D Face and Head Modelling. + White Rose Research Online URL for this paper: + http://eprints.whiterose.ac.uk/131760/ + Version: Accepted Version + Proceedings Paper: + Dai, Hang, Pears, Nicholas Edwin orcid.org/0000-0001-9513-5634, Smith, William Alfred + Peter orcid.org/0000-0002-6047-0413 et al. (1 more author) (2018) Symmetric Shape + Morphing for 3D Face and Head Modelling. In: The 13th IEEE Conference on Automatic + Face and Gesture Recognition. IEEE . + 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 @@ -3463,7 +4534,13 @@
28bc378a6b76142df8762cd3f80f737ca2b79208	Understanding Objects in Detail with Fine-grained Attributes Ross Girshick5 David Weiss7 -
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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 @@ -3495,7 +4572,10 @@ the name of the author
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17a995680482183f3463d2e01dd4c113ebb31608	IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. X, NO. Y, MONTH Z + Structured Label Inference for + Visual Understanding +
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173657da03e3249f4e47457d360ab83b3cefbe63	HKU-Face: A Large Scale Dataset for + Deep Face Recognition + Final Report + 3035140108 + COMP4801 Final Year Project + Project Code: 17007
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7b9b3794f79f87ca8a048d86954e0a72a5f97758	DOI 10.1515/jisys-2013-0016 Journal of Intelligent Systems 2013; 22(4): 365–415 Passing an Enhanced Turing Test – Interacting with Lifelike Computer Representations of Specific Individuals +
7b0f1fc93fb24630eb598330e13f7b839fb46cce	Learning to Find Eye Region Landmarks for Remote Gaze + Estimation in Unconstrained Settings + ETH Zurich + MPI for Informatics + MPI for Informatics + ETH Zurich
7bdcd85efd1e3ce14b7934ff642b76f017419751	289 Learning Discriminant Face Descriptor
7b3b7769c3ccbdf7c7e2c73db13a4d32bf93d21f	On the Design and Evaluation of Robust Head Pose for @@ -3530,10 +4622,22 @@ Laboratory of Intelligent and Safe Automobiles UCSD - La Jolla, CA, USA -
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8f772d9ce324b2ef5857d6e0b2a420bc93961196	MAHPOD et al.: CFDRNN + Facial Landmark Point Localization using + Coarse-to-Fine Deep Recurrent Neural Network +
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8fda2f6b85c7e34d3e23927e501a4b4f7fc15b2a	Feature Selection with Annealing for Big Data + Learning +
8f9c37f351a91ed416baa8b6cdb4022b231b9085	Generative Adversarial Style Transfer Networks for Face Aging + Sveinn Palsson + D-ITET, ETH Zurich + Eirikur Agustsson + D-ITET, ETH Zurich +
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8fd9c22b00bd8c0bcdbd182e17694046f245335f	Recognizing Facial Expressions in Videos +
8a866bc0d925dfd8bb10769b8b87d7d0ff01774d	WikiArt Emotions: An Annotated Dataset of Emotions Evoked by Art + National Research Council Canada
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8a91ad8c46ca8f4310a442d99b98c80fb8f7625f	2592 2D Segmentation Using a Robust Active Shape Model With the EM Algorithm @@ -3545,7 +4649,7 @@ Detecting Visually Observable Disease Symptoms from Faces Open Access -
7e8016bef2c180238f00eecc6a50eac473f3f138	TECHNISCHE UNIVERSIT ¨AT M ¨UNCHEN +
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7e8016bef2c180238f00eecc6a50eac473f3f138	TECHNISCHE UNIVERSIT ¨AT M ¨UNCHEN Lehrstuhl f¨ur Mensch-Maschine-Kommunikation Immersive Interactive Data Mining and Machine Learning Algorithms for Big Data Visualization @@ -3622,7 +4726,10 @@
102e374347698fe5404e1d83f441630b1abf62d9	Facial Image Analysis for Fully-Automatic Prediction of Difﬁcult Endotracheal Intubation
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101569eeef2cecc576578bd6500f1c2dcc0274e2	Multiaccuracy: Black-Box Post-Processing for Fairness in + Classiﬁcation + James Zou +
106732a010b1baf13c61d0994552aee8336f8c85	Expanded Parts Model for Semantic Description of Humans in Still Images
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102b27922e9bd56667303f986404f0e1243b68ab	Wang et al. Appl Inform (2017) 4:13 DOI 10.1186/s40535-017-0042-5 @@ -3707,6 +4814,9 @@ Google Inc. Google Inc. Google Inc. +
197c64c36e8a9d624a05ee98b740d87f94b4040c	Regularized Greedy Column Subset Selection + aDepartment of Computer Systems, Universidad Polit´ecnica de Madrid + bDepartment of Applied Mathematics, Universidad Polit´ecnica de Madrid
19d4855f064f0d53cb851e9342025bd8503922e2	Learning SURF Cascade for Fast and Accurate Object Detection Intel Labs China
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4c6e1840451e1f86af3ef1cb551259cb259493ba	HAND POSTURE DATASET CREATION FOR GESTURE @@ -3717,7 +4827,7 @@ 38271 Universidad de La Laguna, Spain Keywords: Image understanding, Gesture recognition, Hand dataset. -
4c815f367213cc0fb8c61773cd04a5ca8be2c959	978-1-4244-4296-6/10/$25.00 ©2010 IEEE +
4c29e1f31660ba33e46d7e4ffdebb9b8c6bd5adc
4c815f367213cc0fb8c61773cd04a5ca8be2c959	978-1-4244-4296-6/10/$25.00 ©2010 IEEE 2470 ICASSP 2010
4c4236b62302957052f1bbfbd34dbf71ac1650ec	SEMI-SUPERVISED FACE RECOGNITION WITH LDA SELF-TRAINING @@ -3749,6 +4859,12 @@ Interactions Prepared for: Office of Naval Research +
26e570049aaedcfa420fc8c7b761bc70a195657c	J Sign Process Syst + 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
218b2c5c9d011eb4432be4728b54e39f366354c1	Enhancing Training Collections for Image Annotation: An Instance-Weighted Mixture Modeling Approach @@ -3796,10 +4912,19 @@
4d2975445007405f8cdcd74b7fd1dd547066f9b8	Image and Video Processing for Affective Applications -
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4d7e1eb5d1afecb4e238ba05d4f7f487dff96c11	978-1-5090-4117-6/17/$31.00 ©2017 IEEE +
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4db9e5f19366fe5d6a98ca43c1d113dac823a14d	Combining Crowdsourcing and Face Recognition to Identify Civil War Soldiers + 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 +
4d90bab42806d082e3d8729067122a35bbc15e8d
4d6ad0c7b3cf74adb0507dc886993e603c863e8c	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 +
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 @@ -3807,14 +4932,82 @@ Pre-print of article that appeared at BTAS 2010. The published article can be accessed from: http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=5634517 +
4d47261b2f52c361c09f7ab96fcb3f5c22cafb9f	Deep multi-frame face super-resolution + Evgeniya Ustinova, Victor Lempitsky + October 17, 2017 +
75879ab7a77318bbe506cb9df309d99205862f6c	Analysis Of Emotion Recognition From Facial + Expressions Using Spatial And Transform Domain + Methods +
7574f999d2325803f88c4915ba8f304cccc232d1	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.
75e9a141b85d902224f849ea61ab135ae98e7bfb
75503aff70a61ff4810e85838a214be484a674ba	Improved Facial Expression Recognition via Uni-Hyperplane Classiﬁcation S.W. Chew∗, S. Lucey†, P. Lucey‡, S. Sridharan∗, and J.F. Cohn‡
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75859ac30f5444f0d9acfeff618444ae280d661d	Multibiometric Cryptosystems based on Feature Level Fusion +
758d7e1be64cc668c59ef33ba8882c8597406e53	IEEE TRANSACTIONS ON AFFECTIVE COMPUTING + AffectNet: A Database for Facial Expression, + Valence, and Arousal Computing in the Wild +
754f7f3e9a44506b814bf9dc06e44fecde599878	Quantized Densely Connected U-Nets for + Eﬃcient Landmark Localization +
75249ebb85b74e8932496272f38af274fbcfd696	Face Identiﬁcation in Large Galleries + 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
8147ee02ec5ff3a585dddcd000974896cb2edc53	Angular Embedding: A Robust Quadratic Criterion Stella X. Yu, Member, IEEE +
8199803f476c12c7f6c0124d55d156b5d91314b6	The iNaturalist Species Classiﬁcation and Detection Dataset + 1Caltech + 2Google + 3Cornell Tech + 4iNaturalist
81831ed8e5b304e9d28d2d8524d952b12b4cbf55
81b2a541d6c42679e946a5281b4b9dc603bc171c	Universit¨at Ulm \| 89069 Ulm \| Deutschland Fakult¨at f¨ur Ingenieurwissenschaften und Informatik Institut f¨ur Neuroinformatik @@ -4084,15 +5277,17 @@
86b105c3619a433b6f9632adcf9b253ff98aee87	1424403677/06/$20.00 ©2006 IEEE 1013 ICME 2006 -
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72a00953f3f60a792de019a948174bf680cd6c9f	Stat Comput (2007) 17:57–70 +
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72a00953f3f60a792de019a948174bf680cd6c9f	Stat Comput (2007) 17:57–70 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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72ecaff8b57023f9fbf8b5b2588f3c7019010ca7	Facial Keypoints Detection +
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72ecaff8b57023f9fbf8b5b2588f3c7019010ca7	Facial Keypoints Detection +
72591a75469321074b072daff80477d8911c3af3	Group Component Analysis for Multi-block Data: + Common and Individual Feature Extraction
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729a9d35bc291cc7117b924219bef89a864ce62c	Recognizing Material Properties from Images -
72c0c8deb9ea6f59fde4f5043bff67366b86bd66	Age progression in Human Faces : A Survey +
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72c0c8deb9ea6f59fde4f5043bff67366b86bd66	Age progression in Human Faces : A Survey
445461a34adc4bcdccac2e3c374f5921c93750f8	Emotional Expression Classiﬁcation using Time-Series Kernels∗
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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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2ae139b247057c02cda352f6661f46f7feb38e45	Combining Modality Speciﬁc Deep Neural Networks for +
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2aec012bb6dcaacd9d7a1e45bc5204fac7b63b3c	Robust Registration and Geometry Estimation from Unstructured + Facial Scans +
2ae139b247057c02cda352f6661f46f7feb38e45	Combining Modality Speciﬁc Deep Neural Networks for Emotion Recognition in Video 1École Polytechique de Montréal, Université de Montréal, Montréal, Canada 2Laboratoire d’Informatique des Systèmes Adaptatifs, Université de Montréal, Montréal, Canada +
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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2aea27352406a2066ddae5fad6f3f13afdc90be9
2ad0ee93d029e790ebb50574f403a09854b65b7e	Acquiring Linear Subspaces for Face Recognition under Variable Lighting David Kriegman, Senior Member, IEEE @@ -4165,7 +5400,9 @@
2f16459e2e24dc91b3b4cac7c6294387d4a0eacf
2f59f28a1ca3130d413e8e8b59fb30d50ac020e2	Children Gender Recognition Under Unconstrained Conditions Based on Contextual Information Joint Research Centre, European Commission, Ispra, Italy -
2fda164863a06a92d3a910b96eef927269aeb730	Names and Faces in the News +
2f88d3189723669f957d83ad542ac5c2341c37a5	Downloaded From: https://www.spiedigitallibrary.org/journals/Journal-of-Electronic-Imaging on 9/13/2018 + Terms of Use: https://www.spiedigitallibrary.org/terms-of-use + Attribute-correlatedlocalregionsfordeeprelativeattributeslearningFenZhangXiangweiKongZeJiaFenZhang,XiangweiKong,ZeJia,“Attribute-correlatedlocalregionsfordeeprelativeattributeslearning,”J.Electron.Imaging27(4),043021(2018),doi:10.1117/1.JEI.27.4.043021.
2fda164863a06a92d3a910b96eef927269aeb730	Names and Faces in the News Computer Science Division U.C. Berkeley Berkeley, CA 94720 @@ -4185,7 +5422,7 @@ Convolutional Neural Network Vogt-K¨olln-Straße 30, 22527 Hamburg, Germany http://www.informatik.uni-hamburg.de/WTM/ -
2faa09413162b0a7629db93fbb27eda5aeac54ca	NISTIR 7674 +
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2faa09413162b0a7629db93fbb27eda5aeac54ca	NISTIR 7674 Quantifying How Lighting and Focus Affect Face Recognition Performance Phillips, P. J. @@ -4229,7 +5466,7 @@ Anand, INDIA Anand, INDIA Anand, INDIA -
43476cbf2a109f8381b398e7a1ddd794b29a9a16	A Practical Transfer Learning Algorithm for Face Veriﬁcation +
43e268c118ac25f1f0e984b57bc54f0119ded520
43476cbf2a109f8381b398e7a1ddd794b29a9a16	A Practical Transfer Learning Algorithm for Face Veriﬁcation David Wipf
4353d0dcaf450743e9eddd2aeedee4d01a1be78b	Learning Discriminative LBP-Histogram Bins for Facial Expression Recognition @@ -4250,6 +5487,9 @@ Chennai, India IIT Madras Chennai, India +
43d7d0d0d0e2d6cf5355e60c4fe5b715f0a1101a	Pobrane z czasopisma Annales AI- Informatica http://ai.annales.umcs.pl + Data: 04/05/2018 16:53:32 + U M CS
889bc64c7da8e2a85ae6af320ae10e05c4cd6ce7	174 Using Support Vector Machines to Enhance the Performance of Bayesian Face Recognition @@ -4266,6 +5506,9 @@
883006c0f76cf348a5f8339bfcb649a3e46e2690	Weakly Supervised Pain Localization using Multiple Instance Learning
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 @@ -4288,7 +5531,7 @@ Sarnoff Corporation 201 Washington Rd, Princeton, NJ, 08540 -
6b9aa288ce7740ec5ce9826c66d059ddcfd8dba9
6b089627a4ea24bff193611e68390d1a4c3b3644	CROSS-POLLINATION OF NORMALISATION +
6b333b2c6311e36c2bde920ab5813f8cfcf2b67b
6b9aa288ce7740ec5ce9826c66d059ddcfd8dba9
6b089627a4ea24bff193611e68390d1a4c3b3644	CROSS-POLLINATION OF NORMALISATION TECHNIQUES FROM SPEAKER TO FACE AUTHENTICATION USING GAUSSIAN MIXTURE MODELS @@ -4322,6 +5565,8 @@ 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 @@ -4354,11 +5599,26 @@ 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 -
38861d0d3a0292c1f54153b303b0d791cbba1d50
38192a0f9261d9727b119e294a65f2e25f72d7e6
0077cd8f97cafd2b389783858a6e4ab7887b0b6b	MAI et al.: ON THE RECONSTRUCTION OF DEEP FACE TEMPLATES +
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 @@ -4375,7 +5635,7 @@ Prof. Dr. B. Leibe Betreuer: September 2009 -
00f0ed04defec19b4843b5b16557d8d0ccc5bb42
0037bff7be6d463785d4e5b2671da664cd7ef746	Author manuscript, published in "European Conference on Computer Vision (ECCV '10) 6311 (2010) 634--647" +
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 @@ -4403,8 +5663,23 @@ Preserving Structure in Model-Free Tracking
0059b3dfc7056f26de1eabaafd1ad542e34c2c2e
6e198f6cc4199e1c4173944e3df6f39a302cf787	MORPH-II: Inconsistencies and Cleaning Whitepaper NSF-REU Site at UNC Wilmington, Summer 2017 -
6eaf446dec00536858548fe7cc66025b70ce20eb
6eba25166fe461dc388805cc2452d49f5d1cdadd	Pages 122.1-122.12 +
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 @@ -4421,6 +5696,11 @@ 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 @@ -4558,6 +5838,14 @@ 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 @@ -4565,7 +5853,16 @@ Department of Information Technology, Faculty of Industrial Technology and Management,
9a42c519f0aaa68debbe9df00b090ca446d25bc4	Face Recognition via Centralized Coordinate Learning -
36b40c75a3e53c633c4afb5a9309d10e12c292c7
365f67fe670bf55dc9ccdcd6888115264b2a2c56
36fe39ed69a5c7ff9650fd5f4fe950b5880760b0	Tracking von Gesichtsmimik +
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 @@ -4605,6 +5902,33 @@ 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 @@ -4624,10 +5948,19 @@ 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
09718bf335b926907ded5cb4c94784fd20e5ccd8	875 +
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 @@ -4691,6 +6024,23 @@ 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
097104fc731a15fad07479f4f2c4be2e071054a2
09f853ce12f7361c4b50c494df7ce3b9fad1d221	myjournal manuscript No. (will be inserted by the editor) Random forests for real time 3D face analysis @@ -4715,7 +6065,15 @@ Facial Emotions School of Mechatronic Engineering, Universiti Malaysia Perlis, 02600, Ulu Pauh, Arau, Perlis, West Malaysia
5d7f8eb73b6a84eb1d27d1138965eb7aef7ba5cf	Robust Registration of Dynamic Facial Sequences -
5dcf78de4d3d867d0fd4a3105f0defae2234b9cb
5d5cd6fa5c41eb9d3d2bab3359b3e5eb60ae194e	Face Recognition Algorithms +
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: @@ -4801,7 +6159,9 @@ 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
31c0968fb5f587918f1c49bf7fa51453b3e89cf7	Deep Transfer Learning for Person Re-identiﬁcation +
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 @@ -4827,7 +6187,11 @@ Publisher: Springer http://link.springer.com/content/pdf/10.1007%2F978-3- 642-04146-4_50.pdf -
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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 +
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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 @@ -4835,10 +6199,17 @@ 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
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91e58c39608c6eb97b314b0c581ddaf7daac075e	Pixel-wise Ear Detection with Convolutional Encoder-Decoder Networks -
91d2fe6fdf180e8427c65ffb3d895bf9f0ec4fa0
915d4a0fb523249ecbc88eb62cb150a60cf60fa0	Comparison of Feature Extraction Techniques in Automatic +
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 @@ -4901,7 +6272,7 @@ for Visual Recognition Doctoral Thesis Stockholm, Sweden, 2017 -
65817963194702f059bae07eadbf6486f18f4a0a	http://dx.doi.org/10.1007/s11263-015-0814-0 +
656f05741c402ba43bb1b9a58bcc5f7ce2403d9a
65817963194702f059bae07eadbf6486f18f4a0a	http://dx.doi.org/10.1007/s11263-015-0814-0 WhittleSearch: Interactive Image Search with Relative Attribute Feedback Received: date / Accepted: date @@ -4925,6 +6296,9 @@ 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 @@ -4939,7 +6313,77 @@ for Mathematics Huerta-Pacheco1 *Corresponding author -
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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] + Facebook + Google + 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 +
62a30f1b149843860938de6dd6d1874954de24b7	418 Fast Algorithm for Updating the Discriminant Vectors of Dual-Space LDA
62e0380a86e92709fe2c64e6a71ed94d152c6643	Facial Emotion Recognition With Expression Energy @@ -4976,6 +6420,12 @@ 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 @@ -4989,6 +6439,10 @@
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, @@ -5014,6 +6468,8 @@ 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 @@ -5056,7 +6512,7 @@ M.Tech (CSE) VKIT, Bangalore- 560040 BANGALORE, INDIA -
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5366573e96a1dadfcd4fd592f83017e378a0e185	Böhlen, Chandola and Salunkhe +
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5366573e96a1dadfcd4fd592f83017e378a0e185	Böhlen, Chandola and Salunkhe Server, server in the cloud. Who is the fairest in the crowd?
533bfb82c54f261e6a2b7ed7d31a2fd679c56d18	Technical Report MSU-CSE-14-1 @@ -5084,9 +6540,17 @@ 1 Center for Research in Computer Vision at UCF, Orlando, USA 2 Google Research, Mountain View, USA http://crcv.ucf.edu/projects/DaMN/ +
3fb98e76ffd8ba79e1c22eda4d640da0c037e98a	Convolutional Neural Networks for Crop Yield Prediction using Satellite Images + 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 @@ -5154,6 +6618,7 @@ 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 @@ -5169,15 +6634,22 @@ IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY Unconstrained Face Recognition Using A Set-to-Set Distance Measure -
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5e28673a930131b1ee50d11f69573c17db8fff3e	Author manuscript, published in "Workshop on Faces in 'Real-Life' Images: Detection, Alignment, and Recognition, Marseille : France +
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5e6ba16cddd1797853d8898de52c1f1f44a73279	Face Identiﬁcation with Second-Order Pooling
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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
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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 @@ -5190,7 +6662,7 @@ IIIT-Delhi, New Delhi, India Article history: Received 29 March 2017 -
5be3cc1650c918da1c38690812f74573e66b1d32	Relative Parts: Distinctive Parts for Learning Relative Attributes +
5b2cfee6e81ef36507ebf3c305e84e9e0473575a
5be3cc1650c918da1c38690812f74573e66b1d32	Relative Parts: Distinctive Parts for Learning Relative Attributes Center for Visual Information Technology, IIIT Hyderabad, India - 500032
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37f2e03c7cbec9ffc35eac51578e7e8fdfee3d4e	WACV #394 @@ -5310,6 +6782,16 @@ 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
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088aabe3da627432fdccf5077969e3f6402f0a80	Under review as a conference paper at ICLR 2018 @@ -5317,6 +6799,7 @@ 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 @@ -5325,7 +6808,13 @@ web: http://www.aiia.csd.auth.gr 38031 Grenoble, France web: http://www.lis.inpg.fr -
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081fb4e97d6bb357506d1b125153111b673cc128
08f6745bc6c1b0fb68953ea61054bdcdde6d2fc7	Understanding Kin Relationships in a Photo +
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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
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6dd052df6b0e89d394192f7f2af4a3e3b8f89875	International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249 – 8958, Volume-2, Issue-4, April 2013 A literature survey on Facial Expression @@ -5335,7 +6824,12 @@ vol. 7 (2014), pp. 25-40 A Survey on Newer Prospective Biometric Authentication Modalities -
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6d66c98009018ac1512047e6bdfb525c35683b16	IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 25, NO. 9, SEPTEMBER 2003 +
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 +
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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 @@ -5389,10 +6883,17 @@ 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
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0113b302a49de15a1d41ca4750191979ad756d2f	1424403677/06/$20.00 ©2006 IEEE 537 ICME 2006 @@ -5533,6 +7034,8 @@ Face Recognition
06262d14323f9e499b7c6e2a3dec76ad9877ba04	Real-Time Pose Estimation Piggybacked on Object Detection Brno, Czech Republic +
062c41dad67bb68fefd9ff0c5c4d296e796004dc	Temporal Generative Adversarial Nets with Singular Value Clipping + Preferred Networks inc., Japan
06400a24526dd9d131dfc1459fce5e5189b7baec	Event Recognition in Photo Collections with a Stopwatch HMM 1Computer Vision Lab ETH Z¨urich, Switzerland @@ -5560,6 +7063,9 @@ Activity Analysis
06ad99f19cf9cb4a40741a789e4acbf4433c19ae	SenTion: A framework for Sensing Facial Expressions +
6c304f3b9c3a711a0cca5c62ce221fb098dccff0	Attentive Semantic Video Generation using Captions + IIT Hyderabad + IIT Hyderabad
6c2b392b32b2fd0fe364b20c496fcf869eac0a98	DOI 10.1007/s00138-012-0423-7 ORIGINAL PAPER Fully automatic face recognition framework based @@ -5583,7 +7089,8 @@ by David Lieh-Chiang Chen 2012 -
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3986161c20c08fb4b9b791b57198b012519ea58b	International Journal of Soft Computing and Engineering (IJSCE) +
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39c8b34c1b678235b60b648d0b11d241a34c8e32	Learning to Deblur Images with Exemplars +
3986161c20c08fb4b9b791b57198b012519ea58b	International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume-4 Issue-4, September 2014 An Efficient Method for Face Recognition based on Fusion of Global and Local Feature Extraction @@ -5593,9 +7100,27 @@ 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 -
3958db5769c927cfc2a9e4d1ee33ecfba86fe054	Describable Visual Attributes for +
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3933e323653ff27e68c3458d245b47e3e37f52fd	Evaluation of a 3D-aided Pose Invariant 2D Face Recognition System + Computational Biomedicine Lab + 4800 Calhoun Rd. Houston, TX, USA +
39b452453bea9ce398613d8dd627984fd3a0d53c
3958db5769c927cfc2a9e4d1ee33ecfba86fe054	Describable Visual Attributes for Face Veriﬁcation and Image Search -
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9949ac42f39aeb7534b3478a21a31bc37fe2ffe3	Parametric Stereo for Multi-Pose Face Recognition and +
39b5f6d6f8d8127b2b97ea1a4987732c0db6f9df
994f7c469219ccce59c89badf93c0661aae34264	1 + 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 +
9949ac42f39aeb7534b3478a21a31bc37fe2ffe3	Parametric Stereo for Multi-Pose Face Recognition and 3D-Face Modeling PSI ESAT-KUL Leuven, Belgium @@ -5606,6 +7131,14 @@ A Simple, Fast and Highly-Accurate Algorithm to Recover 3D Shape from 2D Landmarks on a Single Image +
99c20eb5433ed27e70881d026d1dbe378a12b342	ISCA Archive + http://www.isca-speech.org/archive + First Workshop on Speech, Language + and Audio in Multimedia + Marseille, France + August 22-23, 2013 + Proceedings of the First Workshop on Speech, Language and Audio in Multimedia (SLAM), Marseille, France, August 22-23, 2013. + 78
9990e0b05f34b586ffccdc89de2f8b0e5d427067	International Journal of Modeling and Optimization, Vol. 3, No. 2, April 2013 Auto-Optimized Multimodal Expression Recognition Framework Using 3D Kinect Data for ASD Therapeutic @@ -5616,6 +7149,9 @@ and to recognize +
99d7678039ad96ee29ab520ff114bb8021222a91	Political image analysis with deep neural + networks + November 28, 2017
529e2ce6fb362bfce02d6d9a9e5de635bde81191	This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. > TIP-05732-2009< 1 @@ -5625,13 +7161,42 @@ Travel Recommendation by Mining People Attributes and Travel Group Types From Community-Contributed Photos -
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529baf1a79cca813f8c9966ceaa9b3e42748c058	Triangle Wise Mapping Technique to Transform one Face Image into Another Face Image + + {tag} {/tag} + + International Journal of Computer Applications + + © 2014 by IJCA Journal + Volume 87 - Number 6 + + Year of Publication: 2014 + + + + Authors: + + Bhogeswar Borah + + + + + + + + + + + 10.5120/15209-3714 + {bibtex}pxc3893714.bib{/bibtex} +
5239001571bc64de3e61be0be8985860f08d7e7e	SUBMITTED TO IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, JUNE 2016 Deep Appearance Models: A Deep Boltzmann Machine Approach for Face Modeling
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554b9478fd285f2317214396e0ccd81309963efd	Spatio-Temporal Action Localization For Human Action Recognition in Large Dataset 1L2TI, Institut Galil´ee, Universit´e Paris 13, France; 2SERCOM, Ecole Polytechnique de Tunisie +
55c68c1237166679d2cb65f266f496d1ecd4bec6	Learning to Score Figure Skating Sport Videos
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55c40cbcf49a0225e72d911d762c27bb1c2d14aa	Indian Face Age Database: A Database for Face Recognition with Age Variation + {tag} {/tag} + International Journal of Computer Applications + + Foundation of Computer Science (FCS), NY, USA + + + Volume 126 + - + Number 5 + + + Year of Publication: 2015 + + + + + Authors: + + + + + + + + + + + + 10.5120/ijca2015906055 + {bibtex}2015906055.bib{/bibtex}
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97032b13f1371c8a813802ade7558e816d25c73f	Total Recall Final Report Supervisor: Professor Duncan Gillies January 11, 2006 +
97cf04eaf1fc0ac4de0f5ad4a510d57ce12544f5	manuscript No. + (will be inserted by the editor) + Deep Affect Prediction in-the-wild: Aff-Wild Database and Challenge, + Deep Architectures, and Beyond + Zafeiriou4
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631483c15641c3652377f66c8380ff684f3e365c	Sync-DRAW: Automatic Video Generation using Deep Recurrent + A(cid:130)entive Architectures + Gaurav Mi(cid:138)al∗ + IIT Hyderabad + Vineeth N Balasubramanian + IIT Hyderabad +
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632fa986bed53862d83918c2b71ab953fd70d6cc	GÜNEL ET AL.: WHAT FACE AND BODY SHAPES CAN TELL ABOUT HEIGHT + What Face and Body Shapes Can Tell + About Height + CVLab + EPFL, + Lausanne, Switzerland +
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0fd1bffb171699a968c700f206665b2f8837d953	Weakly Supervised Object Localization with + Multi-fold Multiple Instance Learning
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0ae9cc6a06cfd03d95eee4eca9ed77b818b59cb7	Noname manuscript No. +
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bf5940d57f97ed20c50278a81e901ae4656f0f2c	Query-free Clothing Retrieval via Implicit + Relevance Feedback
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d3b73e06d19da6b457924269bb208878160059da	Proceedings of the 5th International Conference on Computing and Informatics, ICOCI 2015 @@ -5815,7 +7434,19 @@ Learning Compact Feature Descriptor and Adaptive Matching Framework for Face Recognition improvements -
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d4b88be6ce77164f5eea1ed2b16b985c0670463a	TECHNICAL REPORT JAN.15.2016 +
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 +
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d33fcdaf2c0bd0100ec94b2c437dccdacec66476	Neurons with Paraboloid Decision Boundaries for + Improved Neural Network Classiﬁcation + Performance +
d444368421f456baf8c3cb089244e017f8d32c41	CNN for IMU Assisted Odometry Estimation using Velodyne LiDAR +
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d4b88be6ce77164f5eea1ed2b16b985c0670463a	TECHNICAL REPORT JAN.15.2016 A Survey of Different 3D Face Reconstruction Methods Department of Computer Science and Engineering @@ -5839,7 +7470,14 @@ Bogot´a, Colombia Bogot´a, Colombia Bogot´a, Colombia -
ba8a99d35aee2c4e5e8a40abfdd37813bfdd0906	ELEKTROTEHNI ˇSKI VESTNIK 78(1-2): 12–17, 2011 +
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 +
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a0f94e9400938cbd05c4b60b06d9ed58c3458303	1118 Value-Directed Human Behavior Analysis from Video Using Partially Observable Markov Decision Processes -
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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
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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, @@ -5959,7 +7637,9 @@ Merantix GmbH D-ITET, ETH Zurich ESAT, KU Leuven -
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a7a6eb53bee5e2224f2ecd56a14e3a5a717e55b9	11th International Symposium of Robotics Research (ISRR2003), pp.192-201, 2003 +
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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 @@ -5968,15 +7648,29 @@ 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 -
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b81cae2927598253da37954fb36a2549c5405cdb	Experiments on Visual Information Extraction with the Faces of Wikipedia +
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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 -
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b1d89015f9b16515735d4140c84b0bacbbef19ac	Too Far to See? Not Really! + — Pedestrian Detection with Scale-aware + Localization Policy +
b14b672e09b5b2d984295dfafb05604492bfaec5	LearningImageClassiﬁcationandRetrievalModelsThomasMensink
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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
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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 @@ -6001,14 +7701,22 @@
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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
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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 -
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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 +
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b613b30a7cbe76700855479a8d25164fa7b6b9f1	1 Identifying User-Specific Facial Affects from Spontaneous Expressions with Minimal Annotation +
b6f682648418422e992e3ef78a6965773550d36b	February 8, 2017
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a9eb6e436cfcbded5a9f4b82f6b914c7f390adbd	(IJARAI) International Journal of Advanced Research in Artificial Intelligence, Vol. 5, No.6, 2016 A Model for Facial Emotion Inference Based on @@ -6029,6 +7737,9 @@ 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 @@ -6066,9 +7777,16 @@ 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 -
d50c6d22449cc9170ab868b42f8c72f8d31f9b6c	Proceedings of the Twenty-Sixth International Joint Conference on Artiﬁcial Intelligence (IJCAI-17) +
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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 @@ -6141,6 +7859,11 @@ 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 @@ -6157,7 +7880,13 @@ Face Synthesis from Visual Attributes via Sketch using Conditional VAEs and GANs Received: date / Accepted: date -
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d24dafe10ec43ac8fb98715b0e0bd8e479985260	J Nonverbal Behav (2018) 42:81–99 +
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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 @@ -6166,6 +7895,27 @@ • 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
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aa52910c8f95e91e9fc96a1aefd406ffa66d797d	FACE RECOGNITION SYSTEM BASED ON 2DFLD AND PCA E&TC Department @@ -6175,6 +7925,8 @@ 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
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aae0e417bbfba701a1183d3d92cc7ad550ee59c3	844 A Statistical Method for 2-D Facial Landmarking @@ -6262,7 +8014,7 @@ Okhla Phase 3 Delhi, 110020, India Delhi, 110020, India -
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afc7092987f0d05f5685e9332d83c4b27612f964	Person-Independent Facial Expression Detection using Constrained +
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af54dd5da722e104740f9b6f261df9d4688a9712
afc7092987f0d05f5685e9332d83c4b27612f964	Person-Independent Facial Expression Detection using Constrained Local Models
b730908bc1f80b711c031f3ea459e4de09a3d324	2024 Active Orientation Models for Face @@ -6289,7 +8041,8 @@ 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
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b7f7a4df251ff26aca83d66d6b479f1dc6cd1085	Bouges et al. EURASIP Journal on Image and Video Processing 2013, 2013:55 +
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 @@ -6304,7 +8057,10 @@
dbaf89ca98dda2c99157c46abd136ace5bdc33b3	Nonlinear Cross-View Sample Enrichment for Action Recognition Institut Mines-T´el´ecom; T´el´ecom ParisTech; CNRS LTCI -
dbab6ac1a9516c360cdbfd5f3239a351a64adde7
dbb0a527612c828d43bcb9a9c41f1bf7110b1dc8	Chapter 7 +
dbab6ac1a9516c360cdbfd5f3239a351a64adde7
dbe255d3d2a5d960daaaba71cb0da292e0af36a7	Evolutionary Cost-sensitive Extreme Learning + Machine + 1 +
dbb0a527612c828d43bcb9a9c41f1bf7110b1dc8	Chapter 7 Machine Learning Techniques for Face Analysis
dbb7f37fb9b41d1aa862aaf2d2e721a470fd2c57	Face Image Analysis With @@ -6317,6 +8073,8 @@ 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 @@ -6373,8 +8131,35 @@ 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 @@ -6383,23 +8168,51 @@
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
b0de0892d2092c8c70aa22500fed31aa7eb4dd3f	(will be inserted by the editor) +
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 -
a6583c8daa7927eedb3e892a60fc88bdfe89a486
a694180a683f7f4361042c61648aa97d222602db	Face Recognition using Scattering Wavelet under Illicit Drug Abuse Variations +
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
b9f2a755940353549e55690437eb7e13ea226bbf	Unsupervised Feature Learning from Videos for Discovering and Recognizing Actions +
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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 @@ -6454,6 +8267,42 @@ 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 @@ -6461,18 +8310,58 @@ 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 +
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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 -
c3418f866a86dfd947c2b548cbdeac8ca5783c15
c32383330df27625592134edd72d69bb6b5cff5c	422 +
c3285a1d6ec6972156fea9e6dc9a8d88cd001617
c3418f866a86dfd947c2b548cbdeac8ca5783c15
c32383330df27625592134edd72d69bb6b5cff5c	422 IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART B: CYBERNETICS, VOL. 42, NO. 2, APRIL 2012 Intrinsic Illumination Subspace for Lighting Insensitive Face Recognition -
c3a3f7758bccbead7c9713cb8517889ea6d04687
c37a971f7a57f7345fdc479fa329d9b425ee02be	A Novice Guide towards Human Motion Analysis and Understanding +
c3a3f7758bccbead7c9713cb8517889ea6d04687
c30e4e4994b76605dcb2071954eaaea471307d80
c37a971f7a57f7345fdc479fa329d9b425ee02be	A Novice Guide towards Human Motion Analysis and Understanding
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c3fb2399eb4bcec22723715556e31c44d086e054	499 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 @@ -6519,9 +8408,73 @@ 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 -
ea85378a6549bb9eb9bcc13e31aa6a61b655a9af	Diplomarbeit +
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 @@ -6543,7 +8496,16 @@ Sabbir Ahmmed TU Berlin TU Berlin -
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e1e6e6792e92f7110e26e27e80e0c30ec36ac9c2	TSINGHUA SCIENCE AND TECHNOLOGY +
e16efd2ae73a325b7571a456618bfa682b51aef8
e19ebad4739d59f999d192bac7d596b20b887f78	Learning Gating ConvNet for Two-Stream based Methods in Action + Recognition +
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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 @@ -6585,6 +8547,8 @@
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 @@ -6604,9 +8568,28 @@ 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 @@ -6616,10 +8599,55 @@ Dimension Reduction 0 = , the linear regression function ( -
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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 +
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f0f4f16d5b5f9efe304369120651fa688a03d495	Temporal Generative Adversarial Nets + Preferred Networks inc., Japan +
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f7de943aa75406fe5568fdbb08133ce0f9a765d4	Project 1.5: Human Identification at a Distance - Hornak, Adjeroh, Cukic, Gautum, & Ross +
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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 @@ -6677,9 +8705,63 @@
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f77c9bf5beec7c975584e8087aae8d679664a1eb	Local Deep Neural Networks for Age and Gender Classiﬁcation March 27, 2017 -
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e8b2a98f87b7b2593b4a046464c1ec63bfd13b51	CMS-RCNN: Contextual Multi-Scale +
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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 @@ -6744,13 +8826,23 @@ 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
ff8315c1a0587563510195356c9153729b533c5b	432 +
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fad895771260048f58d12158a4d4d6d0623f4158	Audio-Visual Emotion + Recognition For Natural + Human-Robot Interaction + Dissertation zur Erlangung des akademischen Grades + Doktor der Ingenieurwissenschaften (Dr.-Ing.) + vorgelegt von + an der Technischen Fakultät der Universität Bielefeld + 15. März 2010 +
ff8315c1a0587563510195356c9153729b533c5b	432 Zapping Index:Using Smile to Measure Advertisement Zapping Likelihood
ff44d8938c52cfdca48c80f8e1618bbcbf91cb2a	Towards Video Captioning with Naming: a Novel Dataset and a Multi-Modal Approach Dipartimento di Ingegneria “Enzo Ferrari” Universit`a degli Studi di Modena e Reggio Emilia +
fffefc1fb840da63e17428fd5de6e79feb726894	Fine-Grained Age Estimation in the wild with + Attention LSTM Networks
ff398e7b6584d9a692e70c2170b4eecaddd78357
ffd81d784549ee51a9b0b7b8aaf20d5581031b74	Performance Analysis of Retina and DoG Filtering Applied to Face Images for Training Correlation Filters @@ -6762,7 +8854,9 @@ 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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c50d73557be96907f88b59cfbd1ab1b2fd696d41	JournalofElectronicImaging13(3),474–485(July2004). +
ff60d4601adabe04214c67e12253ea3359f4e082
ff8ef43168b9c8dd467208a0b1b02e223b731254	BreakingNews: Article Annotation by + Image and Text Processing +
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c50d73557be96907f88b59cfbd1ab1b2fd696d41	JournalofElectronicImaging13(3),474–485(July2004). Semiconductor sidewall shape estimation Oak Ridge National Laboratory Oak Ridge, Tennessee 37831-6010 @@ -6797,18 +8891,23 @@
c220f457ad0b28886f8b3ef41f012dd0236cd91a	JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015 Crystal Loss and Quality Pooling for Unconstrained Face Veriﬁcation and Recognition +
c254b4c0f6d5a5a45680eb3742907ec93c3a222b	A Fusion-based Gender Recognition Method + Using Facial Images
c28461e266fe0f03c0f9a9525a266aa3050229f0	Automatic Detection of Facial Feature Points via 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 -
c29e33fbd078d9a8ab7adbc74b03d4f830714cd0
f6ca29516cce3fa346673a2aec550d8e671929a6	International Journal of Engineering and Advanced Technology (IJEAT) +
c29e33fbd078d9a8ab7adbc74b03d4f830714cd0
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f6ca29516cce3fa346673a2aec550d8e671929a6	International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249 – 8958, Volume-2, Issue-4, April 2013 Algorithm for Face Matching Using Normalized Cross-Correlation  -
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f6fa97fbfa07691bc9ff28caf93d0998a767a5c1	k2-means for fast and accurate large scale clustering +
f6c70635241968a6d5fd5e03cde6907022091d64
f6ce34d6e4e445cc2c8a9b8ba624e971dd4144ca	Cross-label Suppression: A Discriminative and Fast + Dictionary Learning with Group Regularization + April 24, 2017 +
f6abecc1f48f6ec6eede4143af33cc936f14d0d0
f6fa97fbfa07691bc9ff28caf93d0998a767a5c1	k2-means for fast and accurate large scale clustering Computer Vision Lab D-ITET ETH Zurich @@ -6822,6 +8921,11 @@ Cognitive Learning for Social Robot through Facial Expression from Video Input 1Department of Automation & Robotics, 2Department of Computer Science & Engg. +
e988be047b28ba3b2f1e4cdba3e8c94026139fcf	Multi-Task Convolutional Neural Network for + Pose-Invariant Face Recognition +
e9d43231a403b4409633594fa6ccc518f035a135	Deformable Part Models with CNN Features + 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: @@ -6833,6 +8937,10 @@ 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 @@ -6856,6 +8964,8 @@ reicht und durch die Fakult¨at f¨ur Elektrotechnik und Informationstechnik am 30.10.2009 angenommen.
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e7b6887cd06d0c1aa4902335f7893d7640aef823	Modelling of Facial Aging and Kinship: A Survey +
cb004e9706f12d1de83b88c209ac948b137caae0	Face Aging Effect Simulation using Hidden Factor + Analysis Joint Sparse Representation
cb9092fe74ea6a5b2bb56e9226f1c88f96094388
cb08f679f2cb29c7aa972d66fe9e9996c8dfae00	JOURNAL OF LATEX CLASS FILES, VOL. 13, NO. 9, SEPTEMBER 2014 Action Understanding with Multiple Classes of Actors @@ -6999,10 +9109,30 @@ 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
e013c650c7c6b480a1b692bedb663947cd9d260f	860 Robust Image Analysis With Sparse Representation on Quantized Visual Features @@ -7040,7 +9170,12 @@ Seattle, Washington, May 26-30, 2015 978-1-4799-6922-7/15/$31.00 ©2015 IEEE 3039 -
2c61a9e26557dd0fe824909adeadf22a6a0d86b0
2c2786ea6386f2d611fc9dbf209362699b104f83
2c848cc514293414d916c0e5931baf1e8583eabc	An automatic facial expression recognition system +
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, @@ -7068,6 +9203,8 @@ 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
2c8f24f859bbbc4193d4d83645ef467bcf25adc2	845 Classiﬁcation in the Presence of Label Noise: a Survey @@ -7081,7 +9218,10 @@ Unit detection 1 Sorbonne Universités, UPMC Univ Paris 06, CNRS, ISIR UMR 7222 4 place Jussieu 75005 Paris -
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79b669abf65c2ca323098cf3f19fa7bdd837ff31	Deakin Research Online +
79f6a8f777a11fd626185ab549079236629431ac	Copyright + by + 2013 +
795ea140df2c3d29753f40ccc4952ef24f46576c
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 @@ -7095,7 +9235,7 @@ resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. Copyright : 2008, IEEE -
79dd787b2877cf9ce08762d702589543bda373be	Face Detection Using SURF Cascade +
79c3a7131c6c176b02b97d368cd0cd0bc713ff7e
79dd787b2877cf9ce08762d702589543bda373be	Face Detection Using SURF Cascade Intel Labs China
793e7f1ba18848908da30cbad14323b0389fd2a8
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2d294c58b2afb529b26c49d3c92293431f5f98d0	4413 Maximum Margin Projection Subspace Learning @@ -7130,6 +9270,9 @@ 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 @@ -7145,6 +9288,9 @@ 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 @@ -7208,6 +9354,13 @@ 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 @@ -7285,7 +9438,9 @@ DECISION TREES Commission II, WG II/5 KEY WORDS: Face Detection, Cascade Algorithm, Decision Trees. -
1b79628af96eb3ad64dbb859dae64f31a09027d5
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7711a7404f1f1ac3a0107203936e6332f50ac30c	Action Classiﬁcation and Highlighting in Videos +
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 @@ -7293,6 +9448,9 @@ 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 @@ -7302,6 +9460,13 @@ †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 @@ -7437,6 +9602,17 @@ 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 @@ -7450,6 +9626,10 @@ 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 @@ -7614,6 +9794,10 @@ 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, @@ -7642,6 +9826,26 @@ 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 @@ -7650,6 +9854,16 @@ 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 @@ -7658,13 +9872,22 @@ Projective complex matrix factorization for facial expression recognition Open Access -
84dcf04802743d9907b5b3ae28b19cbbacd97981
841a5de1d71a0b51957d9be9d9bebed33fb5d9fa	5017 +
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 @@ -7680,6 +9903,8 @@ 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 @@ -7698,7 +9923,10 @@ 4, rue du Clos Courtel 35512 Cesson-S´evign´e, France
244b57cc4a00076efd5f913cc2833138087e1258	Warped Convolutions: Efﬁcient Invariance to Spatial Transformations -
24d376e4d580fb28fd66bc5e7681f1a8db3b6b78
24bf94f8090daf9bda56d54e42009067839b20df
230527d37421c28b7387c54e203deda64564e1b7	Person Re-identiﬁcation: System Design and +
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. @@ -7808,7 +10036,9 @@ 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
4f6adc53798d9da26369bea5a0d91ed5e1314df2	IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. , NO. , 2016 +
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 @@ -7818,7 +10048,20 @@ 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
8d71872d5877c575a52f71ad445c7e5124a4b174
8de06a584955f04f399c10f09f2eed77722f6b1c	Author manuscript, published in "International Conference on Computer Vision Theory and Applications (VISAPP 2013) (2013)" +
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 @@ -8002,6 +10245,8 @@
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 @@ -8015,7 +10260,7 @@ The OU-ISIR Gait Database comprising the Large Population Dataset with Age and performance evaluation of age estimation -
15f3d47b48a7bcbe877f596cb2cfa76e798c6452	Automatic face analysis tools for interactive digital games +
15aa6c457678e25f6bc0e818e5fc39e42dd8e533
15f3d47b48a7bcbe877f596cb2cfa76e798c6452	Automatic face analysis tools for interactive digital games Anonymised for blind review Anonymous Anonymous @@ -8026,7 +10271,7 @@ Technical Report TU M¨unchen April 5, 2007 -
12cb3bf6abf63d190f849880b1703ccc183692fe	Guess Who?: A game to crowdsource the labeling of affective facial +
1287bfe73e381cc8042ac0cc27868ae086e1ce3b
12cb3bf6abf63d190f849880b1703ccc183692fe	Guess Who?: A game to crowdsource the labeling of affective facial expressions is comparable to expert ratings. Graduation research project, june 2012 Supervised by: Dr. Joost Broekens @@ -8037,6 +10282,27 @@ 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 @@ -8051,6 +10317,9 @@ Tom 53(67), Fascicola 1-2, 2008 Facial Expression Recognition under Noisy Environment Using Gabor Filters +
8ce9b7b52d05701d5ef4a573095db66ce60a7e1c	Structured Sparse Subspace Clustering: A Joint + Afﬁnity Learning and Subspace Clustering + Framework
8c6c0783d90e4591a407a239bf6684960b72f34e	SESSION KNOWLEDGE ENGINEERING AND MANAGEMENT + KNOWLEDGE ACQUISITION @@ -8082,6 +10351,9 @@ 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)
1d3e01d5e2721dcfafe5a3b39c54ee1c980350bb
1de8f38c35f14a27831130060810cf9471a62b45	Int J Comput Vis DOI 10.1007/s11263-017-0989-7 A Branch-and-Bound Framework for Unsupervised Common @@ -8098,6 +10370,9 @@ Kuntzmann, 655 avenue de l'Europe, Montbonnot 38330, France
71b376dbfa43a62d19ae614c87dd0b5f1312c966	The Temporal Connection Between Smiles and Blinks +
714d487571ca0d676bad75c8fa622d6f50df953b	eBear: An Expressive Bear-Like Robot +
710011644006c18291ad512456b7580095d628a2	Learning Residual Images for Face Attribute Manipulation + Fujitsu Research & Development Center, Beijing, China.
76fd801981fd69ff1b18319c450cb80c4bc78959	Proceedings of the 11th International Conference on Computational Semantics, pages 76–81, London, UK, April 15-17 2015. c(cid:13)2015 Association for Computational Linguistics 76 @@ -8192,6 +10467,32 @@ 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 @@ -8296,7 +10597,9 @@ 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
82bef8481207de9970c4dc8b1d0e17dced706352
82d2af2ffa106160a183371946e466021876870d	A Novel Space-Time Representation on the Positive Semideﬁnite Cone +
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 @@ -8340,9 +10643,16 @@ 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 @@ -8352,6 +10662,10 @@ 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) @@ -8405,6 +10719,11 @@ 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 @@ -8433,6 +10752,9 @@ 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 @@ -8448,7 +10770,11 @@
401e6b9ada571603b67377b336786801f5b54eee	Active Image Clustering: Seeking Constraints from Humans to Complement Algorithms November 22, 2011 -
2e20ed644e7d6e04dd7ab70084f1bf28f93f75e9
2eb37a3f362cffdcf5882a94a20a1212dfed25d9	4 +
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 @@ -8485,10 +10811,13 @@ 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 -
2e0e056ed5927a4dc6e5c633715beb762628aeb0
2e68190ebda2db8fb690e378fa213319ca915cf8	Generating Videos with Scene Dynamics +
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 @@ -8636,7 +10965,7 @@
47f8b3b3f249830b6e17888df4810f3d189daac1
47e8db3d9adb79a87c8c02b88f432f911eb45dc5	MAGMA: Multi-level accelerated gradient mirror descent algorithm for large-scale convex composite minimization July 15, 2016 -
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78f08cc9f845dc112f892a67e279a8366663e26d	TECHNISCHE UNIVERSIT ¨AT M ¨UNCHEN +
47aeb3b82f54b5ae8142b4bdda7b614433e69b9a
477811ff147f99b21e3c28309abff1304106dbbe
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78f08cc9f845dc112f892a67e279a8366663e26d	TECHNISCHE UNIVERSIT ¨AT M ¨UNCHEN Lehrstuhl f¨ur Mensch-Maschine-Kommunikation Semi-Autonomous Data Enrichment and Optimisation for Intelligent Speech Analysis @@ -8732,7 +11061,10 @@
78fdf2b98cf6380623b0e20b0005a452e736181e
788a7b59ea72e23ef4f86dc9abb4450efefeca41
8b7191a2b8ab3ba97423b979da6ffc39cb53f46b	Search Pruning in Video Surveillance Systems: Efﬁciency-Reliability Tradeoff EURECOM Sophia Antipolis, France -
8b8728edc536020bc4871dc66b26a191f6658f7c
8bf647fed40bdc9e35560021636dfb892a46720e	Learning to Hash-tag Videos with Tag2Vec +
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 @@ -8878,6 +11210,17 @@ Sparse Output Coding for Scalable Visual Recognition Received: 15 May 2013 / Accepted: 16 June 2015 / Published online: 26 June 2015 © Springer Science+Business Media New York 2015 +
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 @@ -8940,12 +11283,18 @@ way aia ad h a ea. The achie ea diecy f ci ey i ea whie ieacig wih he evie ic dig h a eache. hi eaig de deveig ieige ga f vai ak i eaized h gh ea ie ieac +
7a81967598c2c0b3b3771c1af943efb1defd4482	Do We Need More Training Data?
7ad77b6e727795a12fdacd1f328f4f904471233f	Supervised Local Descriptor Learning for Human Action Recognition -
7aa4c16a8e1481629f16167dea313fe9256abb42	978-1-5090-4117-6/17/$31.00 ©2017 IEEE +
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 @@ -9132,6 +11481,14 @@
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 @@ -9171,6 +11528,7 @@ [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, @@ -9186,7 +11544,7 @@ Institut Eur´ecom Multimedia Communications Department BP 193, 06904 Sophia Antipolis Cedex, France -
8e8e3f2e66494b9b6782fb9e3f52aeb8e1b0d125	in any current or +
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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 @@ -9217,6 +11575,15 @@ 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
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22043cbd2b70cb8195d8d0500460ddc00ddb1a62	Separability-Oriented Subclass Discriminant Analysis
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223ec77652c268b98c298327d42aacea8f3ce23f	TR-CS-11-02 @@ -9226,6 +11593,14 @@ 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 @@ -9284,7 +11659,7 @@ 2016
25d3e122fec578a14226dc7c007fb1f05ddf97f7	The First Facial Expression Recognition and Analysis Challenge
2597b0dccdf3d89eaffd32e202570b1fbbedd1d6	Towards predicting the likeability of fashion images -
25e05a1ea19d5baf5e642c2a43cca19c5cbb60f8	Label Distribution Learning +
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