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author: jules@lens <julescarbon@gmail.com> 2019-02-20 18:36:25 +0100
committer: jules@lens <julescarbon@gmail.com> 2019-02-20 18:36:25 +0100
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-<!doctype html><html><head><meta charset='utf-8'><title>Paper Title Sanity Check</title><link rel='stylesheet' href='reports.css'></head><body><h2>Paper Title Sanity Check</h2><table border='1' cellpadding='3' cellspacing='3'><th>key</th><th>name</th><th>our title</th><th>found title</th><th></th><th></th><th>address</th><th>s2 id</th><tr><td>yfcc_100m</td><td>YFCC100M</td><td>YFCC100M: The New Data in Multimedia Research</td><td>YFCC100M: the new data in multimedia research</td><td><a href="https://arxiv.org/pdf/1503.01817.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=yfcc100m: the new data in multimedia research&sort=relevance" target="_blank">[s2]</a></td><td></td><td>010f0f4929e6a6644fb01f0e43820f91d0fad292</td></tr><tr><td>fiw_300</td><td>300-W</td><td>A semi-automatic methodology for facial landmark annotation</td><td>A Semi-automatic Methodology for Facial Landmark Annotation</td><td><a href="http://ibug.doc.ic.ac.uk/media/uploads/documents/sagonas_cvpr_2013_amfg_w.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a semi-automatic methodology for facial landmark annotation&sort=relevance" target="_blank">[s2]</a></td><td></td><td>013909077ad843eb6df7a3e8e290cfd5575999d2</td></tr><tr><td>buhmap_db</td><td>BUHMAP-DB</td><td>Facial Feature Tracking and Expression Recognition for Sign Language</td><td>Facial feature tracking and expression recognition for sign language</td><td><a href="https://www.cmpe.boun.edu.tr/~ari/files/ari2008iscis.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=facial feature tracking and expression recognition for sign language&sort=relevance" target="_blank">[s2]</a></td><td></td><td>014b8df0180f33b9fea98f34ae611c6447d761d2</td></tr><tr><td>vqa</td><td>VQA</td><td>VQA: Visual Question Answering</td><td>VQA: Visual Question Answering</td><td><a href="https://arxiv.org/pdf/1505.00468.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=vqa: visual question answering&sort=relevance" target="_blank">[s2]</a></td><td></td><td>01959ef569f74c286956024866c1d107099199f7</td></tr><tr><td>kitti</td><td>KITTI</td><td>Vision meets Robotics: The KITTI Dataset</td><td>Vision meets robotics: The KITTI dataset</td><td><a href="https://pdfs.semanticscholar.org/026e/3363b7f76b51cc711886597a44d5f1fd1de2.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=vision meets robotics: the kitti dataset&sort=relevance" target="_blank">[s2]</a></td><td></td><td>026e3363b7f76b51cc711886597a44d5f1fd1de2</td></tr><tr><td>ilids_mcts</td><td>i-LIDS</td><td>Imagery Library for Intelligent Detection Systems: The i-LIDS User Guide</td><td>Imagery Library for Intelligent Detection Systems (i-LIDS); A Standard for Testing Video Based Detection Systems</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=imagery library for intelligent detection systems: the i-lids user guide&sort=relevance" target="_blank">[s2]</a></td><td></td><td>0297448f3ed948e136bb06ceff10eccb34e5bb77</td></tr><tr><td>ucf_selfie</td><td>UCF Selfie</td><td>How to Take a Good Selfie?</td><td>How to Take a Good Selfie?</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=how to take a good selfie?&sort=relevance" target="_blank">[s2]</a></td><td></td><td>041d3eedf5e45ce5c5229f0181c5c576ed1fafd6</td></tr><tr><td>fiw_300</td><td>300-W</td><td>300 Faces in-the-Wild Challenge: The first facial landmark localization Challenge</td><td>300 Faces in-the-Wild Challenge: The First Facial Landmark Localization Challenge</td><td><a href="http://ibug.doc.ic.ac.uk/media/uploads/documents/sagonas_iccv_2013_300_w.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=300 faces in-the-wild challenge: the first facial landmark localization challenge&sort=relevance" target="_blank">[s2]</a></td><td></td><td>044d9a8c61383312cdafbcc44b9d00d650b21c70</td></tr><tr><td>chokepoint</td><td>ChokePoint</td><td>Patch-based Probabilistic Image Quality Assessment for Face Selection and Improved Video-based Face Recognition</td><td>Patch-based probabilistic image quality assessment for face selection and improved video-based face recognition</td><td><a href="https://arxiv.org/pdf/1304.0869.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=patch-based probabilistic image quality assessment for face selection and improved video-based face recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>0486214fb58ee9a04edfe7d6a74c6d0f661a7668</td></tr><tr><td>hipsterwars</td><td>Hipsterwars</td><td>Hipster Wars: Discovering Elements of Fashion Styles</td><td>Hipster Wars: Discovering Elements of Fashion Styles</td><td><a href="http://acberg.com/papers/hipster_eccv14.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=hipster wars: discovering elements of fashion styles&sort=relevance" target="_blank">[s2]</a></td><td></td><td>04c2cda00e5536f4b1508cbd80041e9552880e67</td></tr><tr><td>psu</td><td>PSU</td><td>Vision-based Analysis of Small Groups in Pedestrian Crowds</td><td>Vision-Based Analysis of Small Groups in Pedestrian Crowds</td><td><a href="http://vc.cs.nthu.edu.tw/home/paper/codfiles/htchiang/201212250411/newp12.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=vision-based analysis of small groups in pedestrian crowds&sort=relevance" target="_blank">[s2]</a></td><td></td><td>066000d44d6691d27202896691f08b27117918b9</td></tr><tr><td>ifdb</td><td>IFDB</td><td>Iranian Face Database and Evaluation with a New Detection Algorithm</td><td>Iranian Face Database and Evaluation with a New Detection Algorithm</td><td><a href="https://pdfs.semanticscholar.org/066d/71fcd997033dce4ca58df924397dfe0b5fd1.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=iranian face database and evaluation with a new detection algorithm&sort=relevance" target="_blank">[s2]</a></td><td></td><td>066d71fcd997033dce4ca58df924397dfe0b5fd1</td></tr><tr><td>columbia_gaze</td><td>Columbia Gaze</td><td>Gaze Locking: Passive Eye Contact Detection for Human–Object Interaction</td><td>Gaze locking: passive eye contact detection for human-object interaction</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=gaze locking: passive eye contact detection for human–object interaction&sort=relevance" target="_blank">[s2]</a></td><td>Columbia University</td><td>06f02199690961ba52997cde1527e714d2b3bf8f</td></tr><tr><td>d3dfacs</td><td>D3DFACS</td><td>A FACS Valid 3D Dynamic Action Unit database with Applications to 3D Dynamic Morphable Facial Modelling</td><td>A FACS valid 3D dynamic action unit database with applications to 3D dynamic morphable facial modeling</td><td><a href="http://www.cs.bath.ac.uk/~dpc/D3DFACS/ICCV_final_2011.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a facs valid 3d dynamic action unit database with applications to 3d dynamic morphable facial modelling&sort=relevance" target="_blank">[s2]</a></td><td></td><td>070de852bc6eb275d7ca3a9cdde8f6be8795d1a3</td></tr><tr><td>mit_cbcl</td><td>MIT CBCL</td><td>Component-based Face Recognition with 3D Morphable Models</td><td>Component-Based Face Recognition with 3D Morphable Models</td><td><a href="http://cbcl.mit.edu/cbcl/publications/theses/thesis-huang.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=component-based face recognition with 3d morphable models&sort=relevance" target="_blank">[s2]</a></td><td></td><td>079a0a3bf5200994e1f972b1b9197bf2f90e87d4</td></tr><tr><td>uccs</td><td>UCCS</td><td>Large scale unconstrained open set face database</td><td>Large scale unconstrained open set face database</td><td><a href="http://vast.uccs.edu/~tboult/PAPERS/BTAS13-Sapkota-Boult-UCCSFaceDB.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=large scale unconstrained open set face database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>07fcbae86f7a3ad3ea1cf95178459ee9eaf77cb1</td></tr><tr><td>kin_face</td><td>UB KinFace</td><td>Understanding Kin Relationships in a Photo</td><td>Understanding Kin Relationships in a Photo</td><td><a href="http://www1.ece.neu.edu/~yunfu/papers/Kinship-TMM.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=understanding kin relationships in a photo&sort=relevance" target="_blank">[s2]</a></td><td></td><td>08f6745bc6c1b0fb68953ea61054bdcdde6d2fc7</td></tr><tr><td>raid</td><td>RAiD</td><td>Consistent Re-identification in a Camera Network</td><td>Consistent Re-identification in a Camera Network</td><td><a href="http://cs-people.bu.edu/dasabir/papers/ECCV14_Poster.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=consistent re-identification in a camera network&sort=relevance" target="_blank">[s2]</a></td><td></td><td>09d78009687bec46e70efcf39d4612822e61cb8c</td></tr><tr><td>pipa</td><td>PIPA</td><td>Beyond Frontal Faces: Improving Person Recognition Using Multiple Cues</td><td>Beyond frontal faces: Improving Person Recognition using multiple cues</td><td><a href="https://arxiv.org/pdf/1501.05703.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=beyond frontal faces: improving person recognition using multiple cues&sort=relevance" target="_blank">[s2]</a></td><td></td><td>0a85bdff552615643dd74646ac881862a7c7072d</td></tr><tr><td>kinectface</td><td>KinectFaceDB</td><td>KinectFaceDB: A Kinect Database for Face Recognition</td><td>KinectFaceDB: A Kinect Database for Face Recognition</td><td><a href="http://www.eurecom.fr/fr/publication/4393/download/mm-publi-4393.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=kinectfacedb: a kinect database for face recognition&sort=relevance" target="_blank">[s2]</a></td><td>University of North Carolina at Chapel Hill</td><td>0b440695c822a8e35184fb2f60dcdaa8a6de84ae</td></tr><tr><td>prw</td><td>PRW</td><td>Person Re-identification in the Wild</td><td>Person Re-identification in the Wild</td><td><a href="https://arxiv.org/pdf/1604.02531.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=person re-identification in the wild&sort=relevance" target="_blank">[s2]</a></td><td></td><td>0b84f07af44f964817675ad961def8a51406dd2e</td></tr><tr><td>feret</td><td>FERET</td><td>The FERET Verification Testing Protocol for Face Recognition Algorithms</td><td>The FERET Verification Testing Protocol for Face Recognition Algorithms</td><td><a href="https://pdfs.semanticscholar.org/8d2a/1c768fce6f71584dd993fb97e7b6419aaf60.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the feret verification testing protocol for face recognition algorithms&sort=relevance" target="_blank">[s2]</a></td><td></td><td>0c4a139bb87c6743c7905b29a3cfec27a5130652</td></tr><tr><td>graz</td><td>Graz Pedestrian</td><td>Weak Hypotheses and Boosting for Generic Object Detection and Recognition</td><td>Weak Hypotheses and Boosting for Generic Object Detection and Recognition</td><td><a href="https://pdfs.semanticscholar.org/0c91/808994a250d7be332400a534a9291ca3b60e.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=weak hypotheses and boosting for generic object detection and recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>0c91808994a250d7be332400a534a9291ca3b60e</td></tr><tr><td>ijb_c</td><td>IJB-B</td><td>IARPA Janus Benchmark-B Face Dataset</td><td>IARPA Janus Benchmark-B Face Dataset</td><td><a href="http://biometrics.cse.msu.edu/Publications/Face/Whitelametal_IARPAJanusBenchmark-BFaceDataset_CVPRW17.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=iarpa janus benchmark-b face dataset&sort=relevance" target="_blank">[s2]</a></td><td></td><td>0cb2dd5f178e3a297a0c33068961018659d0f443</td></tr><tr><td>casablanca</td><td>Casablanca</td><td>Context-aware {CNNs} for person head detection</td><td>Context-Aware CNNs for Person Head Detection</td><td><a href="https://arxiv.org/pdf/1511.07917.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=context-aware {cnns} for person head detection&sort=relevance" target="_blank">[s2]</a></td><td></td><td>0ceda9dae8b9f322df65ca2ef02caca9758aec6f</td></tr><tr><td>hollywood_headset</td><td>HollywoodHeads</td><td>Context-aware CNNs for person head detection</td><td>Context-Aware CNNs for Person Head Detection</td><td><a href="https://arxiv.org/pdf/1511.07917.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=context-aware cnns for person head detection&sort=relevance" target="_blank">[s2]</a></td><td></td><td>0ceda9dae8b9f322df65ca2ef02caca9758aec6f</td></tr><tr><td>lag</td><td>LAG</td><td>Large Age-Gap Face Verification by Feature Injection in Deep Networks</td><td>Large age-gap face verification by feature injection in deep networks</td><td><a href="https://arxiv.org/pdf/1602.06149.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=large age-gap face verification by feature injection in deep networks&sort=relevance" target="_blank">[s2]</a></td><td></td><td>0d2dd4fc016cb6a517d8fb43a7cc3ff62964832e</td></tr><tr><td>face_scrub</td><td>FaceScrub</td><td>A data-driven approach to cleaning large face datasets</td><td>A data-driven approach to cleaning large face datasets</td><td><a href="http://stefan.winkler.net/Publications/icip2014a.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a data-driven approach to cleaning large face datasets&sort=relevance" target="_blank">[s2]</a></td><td></td><td>0d3bb75852098b25d90f31d2f48fd0cb4944702b</td></tr><tr><td>stickmen_family</td><td>We Are Family Stickmen</td><td>We Are Family: Joint Pose Estimation of Multiple Persons</td><td>We Are Family: Joint Pose Estimation of Multiple Persons</td><td><a href="http://eprints.pascal-network.org/archive/00007964/01/eichner10eccv.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=we are family: joint pose estimation of multiple persons&sort=relevance" target="_blank">[s2]</a></td><td></td><td>0dc11a37cadda92886c56a6fb5191ded62099c28</td></tr><tr><td>mpii_gaze</td><td>MPIIGaze</td><td>Appearance-based Gaze Estimation in the Wild</td><td>Appearance-based gaze estimation in the wild</td><td><a href="https://arxiv.org/pdf/1504.02863.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=appearance-based gaze estimation in the wild&sort=relevance" target="_blank">[s2]</a></td><td></td><td>0df0d1adea39a5bef318b74faa37de7f3e00b452</td></tr><tr><td>afw</td><td>AFW</td><td>Face detection, pose estimation and landmark localization in the wild</td><td>Face detection, pose estimation, and landmark localization in the wild</td><td><a href="http://crcv.ucf.edu/courses/CAP6412/Spring2013/papers/zhu-ramanan-face-cvpr12.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=face detection, pose estimation and landmark localization in the wild&sort=relevance" target="_blank">[s2]</a></td><td></td><td>0e986f51fe45b00633de9fd0c94d082d2be51406</td></tr><tr><td>voc</td><td>VOC</td><td>The PASCAL Visual Object Classes (VOC) Challenge</td><td>The Pascal Visual Object Classes (VOC) Challenge</td><td><a href="http://eprints.pascal-network.org/archive/00006187/01/PascalVOC_IJCV2009.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the pascal visual object classes (voc) challenge&sort=relevance" target="_blank">[s2]</a></td><td></td><td>0ee1916a0cb2dc7d3add086b5f1092c3d4beb38a</td></tr><tr><td>feret</td><td>FERET</td><td>The FERET Evaluation Methodology for Face-Recognition Algorithms</td><td>The FERET Evaluation Methodology for Face-Recognition Algorithms</td><td><a href="https://pdfs.semanticscholar.org/5099/7a5605c1f61e09e9a96789ed7495be6625aa.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the feret evaluation methodology for face-recognition algorithms&sort=relevance" target="_blank">[s2]</a></td><td></td><td>0f0fcf041559703998abf310e56f8a2f90ee6f21</td></tr><tr><td>imdb_wiki</td><td>IMDB</td><td>Deep expectation of real and apparent age from a single image without facial landmarks</td><td>Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks</td><td><a href="http://www.vision.ee.ethz.ch/en/publications/papers/articles/eth_biwi_01299.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=deep expectation of real and apparent age from a single image without facial landmarks&sort=relevance" target="_blank">[s2]</a></td><td></td><td>10195a163ab6348eef37213a46f60a3d87f289c5</td></tr><tr><td>inria_person</td><td>INRIA Pedestrian</td><td>Histograms of Oriented Gradients for Human Detection</td><td>Histograms of oriented gradients for human detection</td><td><a href="http://courses.cs.washington.edu/courses/cse576/12sp/notes/CVPR2005_HOG.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=histograms of oriented gradients for human detection&sort=relevance" target="_blank">[s2]</a></td><td>INRIA Rhone-Alps, Montbonnot, France</td><td>10d6b12fa07c7c8d6c8c3f42c7f1c061c131d4c5</td></tr><tr><td>graz</td><td>Graz Pedestrian</td><td>Object Recognition Using Segmentation for Feature Detection</td><td>Object recognition using segmentation for feature detection</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=object recognition using segmentation for feature detection&sort=relevance" target="_blank">[s2]</a></td><td>Inst. of Comput. Sci., Univ. of Leoben, Austria</td><td>12ad3b5bbbf407f8e54ea692c07633d1a867c566</td></tr><tr><td>lfw_a</td><td>LFW-a</td><td>Effective Unconstrained Face Recognition by Combining Multiple Descriptors and Learned Background Statistics</td><td>Effective Unconstrained Face Recognition by Combining Multiple Descriptors and Learned Background Statistics</td><td><a href="http://www.cs.tau.ac.il/~wolf/papers/jpatchlbp.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=effective unconstrained face recognition by combining multiple descriptors and learned background statistics&sort=relevance" target="_blank">[s2]</a></td><td></td><td>133f01aec1534604d184d56de866a4bd531dac87</td></tr><tr><td>hrt_transgender</td><td>HRT Transgender</td><td>Is the Eye Region More Reliable Than the Face? A Preliminary Study of Face-based Recognition on a Transgender Dataset</td><td>Is the eye region more reliable than the face? A preliminary study of face-based recognition on a transgender dataset</td><td><a href="http://www.csis.pace.edu/~ctappert/dps/2013BTAS/Papers/Paper%20139/PID2859389.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=is the eye region more reliable than the face? a preliminary study of face-based recognition on a transgender dataset&sort=relevance" target="_blank">[s2]</a></td><td></td><td>137aa2f891d474fce1e7a1d1e9b3aefe21e22b34</td></tr><tr><td>eth_andreas_ess</td><td>ETHZ Pedestrian</td><td>Depth and Appearance for Mobile Scene Analysis</td><td>Depth and Appearance for Mobile Scene Analysis</td><td><a href="http://www.mmp.rwth-aachen.de/publications/pdf/ess-depthandappearance-iccv07-poster.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=depth and appearance for mobile scene analysis&sort=relevance" target="_blank">[s2]</a></td><td>ETH Zurich, Zurich, Switzerland. aess@vision.ee.ethz.ch</td><td>13f06b08f371ba8b5d31c3e288b4deb61335b462</td></tr><tr><td>lfw_p</td><td>LFWP</td><td>Localizing Parts of Faces Using a Consensus of Exemplars</td><td>Localizing Parts of Faces Using a Consensus of Exemplars</td><td><a href="http://neerajkumar.org/projects/face-parts/base/papers/nk_cvpr2011_faceparts.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=localizing parts of faces using a consensus of exemplars&sort=relevance" target="_blank">[s2]</a></td><td></td><td>140438a77a771a8fb656b39a78ff488066eb6b50</td></tr><tr><td>ijb_c</td><td>IJB-A</td><td>Pushing the Frontiers of Unconstrained Face Detection and Recognition: IARPA Janus Benchmark A</td><td>Pushing the frontiers of unconstrained face detection and recognition: IARPA Janus Benchmark A</td><td><a href="http://biometrics.cse.msu.edu/Publications/Face/Klareetal_UnconstrainedFaceDetectionRecognitionJanus_CVPR15.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=pushing the frontiers of unconstrained face detection and recognition: iarpa janus benchmark a&sort=relevance" target="_blank">[s2]</a></td><td></td><td>140c95e53c619eac594d70f6369f518adfea12ef</td></tr><tr><td>vgg_faces</td><td>VGG Face</td><td>Deep Face Recognition</td><td>Deep Face Recognition</td><td><a href="https://pdfs.semanticscholar.org/f372/ab9b3270d4e4f6a0258c83c2736c3a5c0454.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=deep face recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>162ea969d1929ed180cc6de9f0bf116993ff6e06</td></tr><tr><td>prid</td><td>PRID</td><td>Person Re-Identification by Descriptive and Discriminative Classification</td><td>Person Re-identification by Descriptive and Discriminative Classification</td><td><a href="https://pdfs.semanticscholar.org/4c1b/f0592be3e535faf256c95e27982db9b3d3d3.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=person re-identification by descriptive and discriminative classification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>16c7c31a7553d99f1837fc6e88e77b5ccbb346b8</td></tr><tr><td>umb</td><td>UMB</td><td>UMB-DB: A Database of Partially Occluded 3D Faces</td><td>UMB-DB: A database of partially occluded 3D faces</td><td><a href="http://face.cs.kit.edu/befit/workshop2011/pdf/slides/claudio_cusano-slides.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=umb-db: a database of partially occluded 3d faces&sort=relevance" target="_blank">[s2]</a></td><td></td><td>16e8b0a1e8451d5f697b94c0c2b32a00abee1d52</td></tr><tr><td>celeba_plus</td><td>CelebFaces+</td><td>Deep Learning Face Representation from Predicting 10,000 Classes</td><td>Deep Learning Face Representation from Predicting 10,000 Classes</td><td><a href="http://mmlab.ie.cuhk.edu.hk/pdf/YiSun_CVPR14.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=deep learning face representation from predicting 10,000 classes&sort=relevance" target="_blank">[s2]</a></td><td></td><td>177bc509dd0c7b8d388bb47403f28d6228c14b5c</td></tr><tr><td>geofaces</td><td>GeoFaces</td><td>GeoFaceExplorer: Exploring the Geo-Dependence of Facial Attributes</td><td>GeoFaceExplorer: exploring the geo-dependence of facial attributes</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=geofaceexplorer: exploring the geo-dependence of facial attributes&sort=relevance" target="_blank">[s2]</a></td><td></td><td>17b46e2dad927836c689d6787ddb3387c6159ece</td></tr><tr><td>deep_fashion</td><td>DeepFashion</td><td>DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations</td><td>DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations</td><td><a href="http://personal.ie.cuhk.edu.hk/~lz013/papers/deepfashion_poster.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=deepfashion: powering robust clothes recognition and retrieval with rich annotations&sort=relevance" target="_blank">[s2]</a></td><td></td><td>18010284894ed0edcca74e5bf768ee2e15ef7841</td></tr><tr><td>pilot_parliament</td><td>PPB</td><td>Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification</td><td>Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification</td><td><a href="https://pdfs.semanticscholar.org/03c1/fc9c3339813ed81ad0de540132f9f695a0f8.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=gender shades: intersectional accuracy disparities in commercial gender classification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>18858cc936947fc96b5c06bbe3c6c2faa5614540</td></tr><tr><td>frgc</td><td>FRGC</td><td>Overview of the Face Recognition Grand Challenge</td><td>Overview of the face recognition grand challenge</td><td><a href="http://ivizlab.sfu.ca/arya/Papers/IEEE/Proceedings/C%20V%20P%20R-%2005/Face%20Recognition%20Grand%20Challenge.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=overview of the face recognition grand challenge&sort=relevance" target="_blank">[s2]</a></td><td>NIST</td><td>18ae7c9a4bbc832b8b14bc4122070d7939f5e00e</td></tr><tr><td>yale_faces</td><td>YaleFaces</td><td>From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose</td><td>From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose</td><td><a href="https://pdfs.semanticscholar.org/97bb/c2b439a79d4dc0dc7199d71ed96ad5e3fd0e.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=from few to many: illumination cone models for face recognition under variable lighting and pose&sort=relevance" target="_blank">[s2]</a></td><td></td><td>18c72175ddbb7d5956d180b65a96005c100f6014</td></tr><tr><td>york_3d</td><td>UOY 3D Face Database</td><td>Three-Dimensional Face Recognition: An Eigensurface Approach</td><td>Three-dimensional face recognition: an eigensurface approach</td><td><a href="http://www-users.cs.york.ac.uk/~nep/research/3Dface/tomh/3DFaceRecognition-Eigensurface-ICIP(web)2.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=three-dimensional face recognition: an eigensurface approach&sort=relevance" target="_blank">[s2]</a></td><td></td><td>19d1b811df60f86cbd5e04a094b07f32fff7a32a</td></tr><tr><td>sports_videos_in_the_wild</td><td>SVW</td><td>Sports Videos in the Wild (SVW): A Video Dataset for Sports Analysis</td><td>Sports Videos in the Wild (SVW): A video dataset for sports analysis</td><td><a href="http://cse.msu.edu/~liuxm/publication/Safdarnejad_Liu_Udpa_Andrus_Wood_Craven_FG2015.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=sports videos in the wild (svw): a video dataset for sports analysis&sort=relevance" target="_blank">[s2]</a></td><td></td><td>1a40092b493c6b8840257ab7f96051d1a4dbfeb2</td></tr><tr><td>jpl_pose</td><td>JPL-Interaction dataset</td><td>First-Person Activity Recognition: What Are They Doing to Me?</td><td>First-Person Activity Recognition: What Are They Doing to Me?</td><td><a href="http://michaelryoo.com/papers/cvpr2013_ryoo.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=first-person activity recognition: what are they doing to me?&sort=relevance" target="_blank">[s2]</a></td><td></td><td>1aad2da473888cb7ebc1bfaa15bfa0f1502ce005</td></tr><tr><td>adience</td><td>Adience</td><td>Age and Gender Estimation of Unfiltered Faces</td><td>Age and Gender Estimation of Unfiltered Faces</td><td><a href="http://www.openu.ac.il/home/hassner/Adience/EidingerEnbarHassner_tifs.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=age and gender estimation of unfiltered faces&sort=relevance" target="_blank">[s2]</a></td><td></td><td>1be498d4bbc30c3bfd0029114c784bc2114d67c0</td></tr><tr><td>youtube_poses</td><td>YouTube Pose</td><td>Personalizing Human Video Pose Estimation</td><td>Personalizing Human Video Pose Estimation</td><td><a href="https://arxiv.org/pdf/1511.06676.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=personalizing human video pose estimation&sort=relevance" target="_blank">[s2]</a></td><td></td><td>1c2802c2199b6d15ecefe7ba0c39bfe44363de38</td></tr><tr><td>caltech_pedestrians</td><td>Caltech Pedestrians</td><td>Pedestrian Detection: A Benchmark</td><td>Pedestrian detection: A benchmark</td><td><a href="http://vision.lbl.gov/Conferences/cvpr/Papers/data/papers/1378.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=pedestrian detection: a benchmark&sort=relevance" target="_blank">[s2]</a></td><td></td><td>1dc35905a1deff8bc74688f2d7e2f48fd2273275</td></tr><tr><td>immediacy</td><td>Immediacy</td><td>Multi-task Recurrent Neural Network for Immediacy Prediction</td><td>Multi-task Recurrent Neural Network for Immediacy Prediction</td><td><a href="http://openaccess.thecvf.com/content_iccv_2015/papers/Chu_Multi-Task_Recurrent_Neural_ICCV_2015_paper.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=multi-task recurrent neural network for immediacy prediction&sort=relevance" target="_blank">[s2]</a></td><td></td><td>1e3df3ca8feab0b36fd293fe689f93bb2aaac591</td></tr><tr><td>cafe</td><td>CAFE</td><td>The Child Affective Facial Expression (CAFE) Set: Validity and reliability from untrained adults</td><td>The Child Affective Facial Expression (CAFE) set: validity and reliability from untrained adults</td><td><a href="https://pdfs.semanticscholar.org/2038/8099cc415c772926e47bcbbe554e133343d1.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the child affective facial expression (cafe) set: validity and reliability from untrained adults&sort=relevance" target="_blank">[s2]</a></td><td></td><td>20388099cc415c772926e47bcbbe554e133343d1</td></tr><tr><td>bbc_pose</td><td>BBC Pose</td><td>Automatic and Efficient Human Pose Estimation for Sign Language Videos</td><td>Automatic and Efficient Human Pose Estimation for Sign Language Videos</td><td><a href="http://tomas.pfister.fi/files/charles13ijcv.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=automatic and efficient human pose estimation for sign language videos&sort=relevance" target="_blank">[s2]</a></td><td></td><td>213a579af9e4f57f071b884aa872651372b661fd</td></tr><tr><td>3d_rma</td><td>3D-RMA</td><td>Automatic 3D Face Authentication</td><td>Automatic 3D face authentication</td><td><a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.31.9190&rep=rep1&type=pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=automatic 3d face authentication&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2160788824c4c29ffe213b2cbeb3f52972d73f37</td></tr><tr><td>large_scale_person_search</td><td>Large Scale Person Search</td><td>End-to-End Deep Learning for Person Search</td><td>End-to-End Deep Learning for Person Search</td><td><a href="https://pdfs.semanticscholar.org/2161/f6b7ee3c0acc81603b01dc0df689683577b9.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=end-to-end deep learning for person search&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2161f6b7ee3c0acc81603b01dc0df689683577b9</td></tr><tr><td>images_of_groups</td><td>Images of Groups</td><td>Understanding Groups of Images of People</td><td>Understanding images of groups of people</td><td><a href="http://chenlab.ece.cornell.edu/people/Andy/Andy_files/cvpr09.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=understanding groups of images of people&sort=relevance" target="_blank">[s2]</a></td><td>Carnegie Mellon University</td><td>21d9d0deed16f0ad62a4865e9acf0686f4f15492</td></tr><tr><td>rap_pedestrian</td><td>RAP</td><td>A Richly Annotated Dataset for Pedestrian Attribute Recognition</td><td>A Richly Annotated Dataset for Pedestrian Attribute Recognition</td><td><a href="https://arxiv.org/pdf/1603.07054.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a richly annotated dataset for pedestrian attribute recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>221c18238b829c12b911706947ab38fd017acef7</td></tr><tr><td>mot</td><td>MOT</td><td>Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics</td><td>Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics</td><td><a href="https://pdfs.semanticscholar.org/2e0b/00f4043e2d4b04c59c88bb54bcd907d0dcd4.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=evaluating multiple object tracking performance: the clear mot metrics&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2258e01865367018ed6f4262c880df85b94959f8</td></tr><tr><td>saivt</td><td>SAIVT SoftBio</td><td>A Database for Person Re-Identification in Multi-Camera Surveillance Networks</td><td>A Database for Person Re-Identification in Multi-Camera Surveillance Networks</td><td><a href="http://eprints.qut.edu.au/53437/3/Bialkowski_Database4PersonReID_DICTA.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a database for person re-identification in multi-camera surveillance networks&sort=relevance" target="_blank">[s2]</a></td><td></td><td>22646e00a7ba34d1b5fbe3b1efcd91a1e1be3c2b</td></tr><tr><td>pets</td><td>PETS 2017</td><td>PETS 2017: Dataset and Challenge</td><td>PETS 2017: Dataset and Challenge</td><td><a href="http://tahirnawaz.com/papers/2017_CVPRW_PETS2017Dataset_Luis_Nawaz_Cane_Ferryman.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=pets 2017: dataset and challenge&sort=relevance" target="_blank">[s2]</a></td><td></td><td>22909dd19a0ec3b6065334cb5be5392cb24d839d</td></tr><tr><td>gallagher</td><td>Gallagher</td><td>Clothing Cosegmentation for Recognizing People</td><td>Clothing cosegmentation for recognizing people</td><td><a href="http://amp.ece.cmu.edu/people/Andy/Andy_files/2670CVPR08Gallagher.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=clothing cosegmentation for recognizing people&sort=relevance" target="_blank">[s2]</a></td><td>Carnegie Mellon University</td><td>22ad2c8c0f4d6aa4328b38d894b814ec22579761</td></tr><tr><td>expw</td><td>ExpW</td><td>From Facial Expression Recognition to Interpersonal Relation Prediction</td><td>From Facial Expression Recognition to Interpersonal Relation Prediction</td><td><a href="https://arxiv.org/pdf/1609.06426.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=from facial expression recognition to interpersonal relation prediction&sort=relevance" target="_blank">[s2]</a></td><td></td><td>22f656d0f8426c84a33a267977f511f127bfd7f3</td></tr><tr><td>mifs</td><td>MIFS</td><td>Spoofing Faces Using Makeup: An Investigative Study</td><td>Spoofing faces using makeup: An investigative study</td><td><a href="http://www.cse.msu.edu/~rossarun/pubs/ChenFaceMakeupSpoof_ISBA2017.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=spoofing faces using makeup: an investigative study&sort=relevance" target="_blank">[s2]</a></td><td>INRIA Méditerranée</td><td>23e824d1dfc33f3780dd18076284f07bd99f1c43</td></tr><tr><td>cohn_kanade</td><td>CK</td><td>Comprehensive Database for Facial Expression Analysis</td><td>Comprehensive Database for Facial Expression Analysis</td><td><a href="https://pdfs.semanticscholar.org/23fc/83c8cfff14a16df7ca497661264fc54ed746.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=comprehensive database for facial expression analysis&sort=relevance" target="_blank">[s2]</a></td><td></td><td>23fc83c8cfff14a16df7ca497661264fc54ed746</td></tr><tr><td>cas_peal</td><td>CAS-PEAL</td><td>The CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations</td><td>The CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations</td><td><a href="http://www.jdl.ac.cn/peal/files/ieee_smc_a_gao_cas-peal.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the cas-peal large-scale chinese face database and baseline evaluations&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2485c98aa44131d1a2f7d1355b1e372f2bb148ad</td></tr><tr><td>faceplace</td><td>Face Place</td><td>Recognizing disguised faces</td><td>Recognizing disguised faces</td><td><a href="https://pdfs.semanticscholar.org/d936/7ceb0be378c3a9ddf7cb741c678c1a3c574c.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=recognizing disguised faces&sort=relevance" target="_blank">[s2]</a></td><td></td><td>25474c21613607f6bb7687a281d5f9d4ffa1f9f3</td></tr><tr><td>afew_va</td><td>AFEW-VA</td><td>AFEW-VA database for valence and arousal estimation in-the-wild</td><td>AFEW-VA database for valence and arousal estimation in-the-wild</td><td><a href="https://ibug.doc.ic.ac.uk/media/uploads/documents/afew-va.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=afew-va database for valence and arousal estimation in-the-wild&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2624d84503bc2f8e190e061c5480b6aa4d89277a</td></tr><tr><td>cofw</td><td>COFW</td><td>Robust face landmark estimation under occlusion</td><td>Robust Face Landmark Estimation under Occlusion</td><td><a href="http://authors.library.caltech.edu/45988/1/ICCV13%20Burgos-Artizzu.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=robust face landmark estimation under occlusion&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2724ba85ec4a66de18da33925e537f3902f21249</td></tr><tr><td>duke_mtmc</td><td>Duke MTMC</td><td>Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking</td><td>Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking</td><td><a href="https://arxiv.org/pdf/1609.01775.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=performance measures and a data set for multi-target, multi-camera tracking&sort=relevance" target="_blank">[s2]</a></td><td></td><td>27a2fad58dd8727e280f97036e0d2bc55ef5424c</td></tr><tr><td>mot</td><td>MOT</td><td>Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking</td><td>Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking</td><td><a href="https://arxiv.org/pdf/1609.01775.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=performance measures and a data set for multi-target, multi-camera tracking&sort=relevance" target="_blank">[s2]</a></td><td></td><td>27a2fad58dd8727e280f97036e0d2bc55ef5424c</td></tr><tr><td>h3d</td><td>H3D</td><td>Poselets: Body Part Detectors Trained Using 3D Human Pose Annotations</td><td>Poselets: Body part detectors trained using 3D human pose annotations</td><td><a href="http://http.cs.berkeley.edu/Research/Projects/CS/vision/human/poselets_iccv09.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=poselets: body part detectors trained using 3d human pose annotations&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2830fb5282de23d7784b4b4bc37065d27839a412</td></tr><tr><td>megaface</td><td>MegaFace</td><td>Level Playing Field for Million Scale Face Recognition</td><td>Level Playing Field for Million Scale Face Recognition</td><td><a href="https://arxiv.org/pdf/1705.00393.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=level playing field for million scale face recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>28d4e027c7e90b51b7d8908fce68128d1964668a</td></tr><tr><td>msceleb</td><td>MsCeleb</td><td>MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition</td><td>MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition</td><td><a href="https://arxiv.org/pdf/1607.08221.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=ms-celeb-1m: a dataset and benchmark for large-scale face recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>291265db88023e92bb8c8e6390438e5da148e8f5</td></tr><tr><td>ferplus</td><td>FER+</td><td>Training Deep Networks for Facial Expression Recognition with Crowd-Sourced Label Distribution</td><td>Training deep networks for facial expression recognition with crowd-sourced label distribution</td><td><a href="https://arxiv.org/pdf/1608.01041.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=training deep networks for facial expression recognition with crowd-sourced label distribution&sort=relevance" target="_blank">[s2]</a></td><td></td><td>298cbc3dfbbb3a20af4eed97906650a4ea1c29e0</td></tr><tr><td>scface</td><td>SCface</td><td>SCface – surveillance cameras face database</td><td>SCface – surveillance cameras face database</td><td><a href="http://scface.org/SCface%20-%20Surveillance%20Cameras%20Face%20Database.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=scface – surveillance cameras face database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>29a705a5fa76641e0d8963f1fdd67ee4c0d92d3d</td></tr><tr><td>social_relation</td><td>Social Relation</td><td>Learning Social Relation Traits from Face Images</td><td>Learning Social Relation Traits from Face Images</td><td><a href="https://arxiv.org/pdf/1509.03936.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=learning social relation traits from face images&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2a171f8d14b6b8735001a11c217af9587d095848</td></tr><tr><td>peta</td><td>PETA</td><td>Pedestrian Attribute Recognition At Far Distance</td><td>Pedestrian Attribute Recognition At Far Distance</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=pedestrian attribute recognition at far distance&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2a4bbee0b4cf52d5aadbbc662164f7efba89566c</td></tr><tr><td>mmi_facial_expression</td><td>MMI Facial Expression Dataset</td><td>WEB-BASED DATABASE FOR FACIAL EXPRESSION ANALYSIS</td><td>Web-based database for facial expression analysis</td><td><a href="http://dev.pubs.doc.ic.ac.uk/Pantic-ICME05-2/Pantic-ICME05-2.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=web-based database for facial expression analysis&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2a75f34663a60ab1b04a0049ed1d14335129e908</td></tr><tr><td>bosphorus</td><td>The Bosphorus</td><td>Bosphorus Database for 3D Face Analysis</td><td>Bosphorus Database for 3D Face Analysis</td><td><a href="https://pdfs.semanticscholar.org/4254/fbba3846008f50671edc9cf70b99d7304543.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=bosphorus database for 3d face analysis&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2acf7e58f0a526b957be2099c10aab693f795973</td></tr><tr><td>yale_faces</td><td>YaleFaces</td><td>Acquiring Linear Subspaces for Face Recognition under Variable Lighting</td><td>Acquiring linear subspaces for face recognition under variable lighting</td><td><a href="http://vision.cornell.edu/se3/wp-content/uploads/2014/09/pami05.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=acquiring linear subspaces for face recognition under variable lighting&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2ad0ee93d029e790ebb50574f403a09854b65b7e</td></tr><tr><td>frav3d</td><td>FRAV3D</td><td>MULTIMODAL 2D, 2.5D & 3D FACE VERIFICATION</td><td>Multimodal 2D, 2.5D & 3D Face Verification</td><td><a href="http://www.researchgate.net/profile/Enrique_Cabello/publication/224057733_Multimodal_2D_2.5D__3D_Face_Verification/links/0912f50f522298fa95000000.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=multimodal 2d, 2.5d & 3d face verification&sort=relevance" target="_blank">[s2]</a></td><td>Face Recognition and Artificial Vision Group, Universidad Rey Juan Carlos, C/ Tulipán, s/n, Móstoles E-28933 Madrid (Spain). angel.serrano@urjc.es</td><td>2b926b3586399d028b46315d7d9fb9d879e4f79c</td></tr><tr><td>clothing_co_parsing</td><td>CCP</td><td>Clothing Co-Parsing by Joint Image Segmentation and Labeling</td><td>Clothing Co-parsing by Joint Image Segmentation and Labeling</td><td><a href="https://arxiv.org/pdf/1502.00739.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=clothing co-parsing by joint image segmentation and labeling&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2bf8541199728262f78d4dced6fb91479b39b738</td></tr><tr><td>texas_3dfrd</td><td>Texas 3DFRD</td><td>Anthropometric 3D Face Recognition</td><td>Anthropometric 3D Face Recognition</td><td><a href="http://live.ece.utexas.edu/publications/2010/sg_ijcv_june10.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=anthropometric 3d face recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2ce2560cf59db59ce313bbeb004e8ce55c5ce928</td></tr><tr><td>lfw</td><td>LFW</td><td>Labeled Faces in the Wild: Updates and New Reporting Procedures</td><td>Labeled Faces in the Wild : Updates and New Reporting Procedures</td><td><a href="https://pdfs.semanticscholar.org/2d34/82dcff69c7417c7b933f22de606a0e8e42d4.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=labeled faces in the wild: updates and new reporting procedures&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2d3482dcff69c7417c7b933f22de606a0e8e42d4</td></tr><tr><td>a_pascal_yahoo</td><td>aPascal</td><td>Describing Objects by their Attributes</td><td>Describing objects by their attributes</td><td><a href="http://vision.lbl.gov/Conferences/cvpr/Papers/data/papers/0468.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=describing objects by their attributes&sort=relevance" target="_blank">[s2]</a></td><td>University of Illinois, Urbana-Champaign</td><td>2e384f057211426ac5922f1b33d2aa8df5d51f57</td></tr><tr><td>3dpes</td><td>3DPeS</td><td>3DPes: 3D People Dataset for Surveillance and Forensics</td><td>3DPeS: 3D people dataset for surveillance and forensics</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=3dpes: 3d people dataset for surveillance and forensics&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2e8d0f1802e50cccfd3c0aabac0d0beab3a7846e</td></tr><tr><td>kin_face</td><td>UB KinFace</td><td>Genealogical Face Recognition based on UB KinFace Database</td><td>Genealogical face recognition based on UB KinFace database</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=genealogical face recognition based on ub kinface database&sort=relevance" target="_blank">[s2]</a></td><td>SUNY Buffalo</td><td>2eb84aaba316b095d4bb51da1a3e4365bbf9ab1d</td></tr><tr><td>qmul_grid</td><td>GRID</td><td>Time-delayed correlation analysis for multi-camera activity understanding</td><td>Time-Delayed Correlation Analysis for Multi-Camera Activity Understanding</td><td><a href="http://www.eecs.qmul.ac.uk/~ccloy/files/ijcv_2010.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=time-delayed correlation analysis for multi-camera activity understanding&sort=relevance" target="_blank">[s2]</a></td><td>Queen Mary University of London</td><td>2edb87494278ad11641b6cf7a3f8996de12b8e14</td></tr><tr><td>graz</td><td>Graz Pedestrian</td><td>Generic Object Recognition with Boosting</td><td>Generic object recognition with boosting</td><td><a href="http://www.cse.unr.edu/~bebis/CS773C/ObjectRecognition/Papers/Opelt06.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=generic object recognition with boosting&sort=relevance" target="_blank">[s2]</a></td><td>TU Graz</td><td>2eed184680edcdec8a3b605ad1a3ba8e8f7cc2e9</td></tr><tr><td>hrt_transgender</td><td>HRT Transgender</td><td>Investigating the Periocular-Based Face Recognition Across Gender Transformation</td><td>Investigating the Periocular-Based Face Recognition Across Gender Transformation</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=investigating the periocular-based face recognition across gender transformation&sort=relevance" target="_blank">[s2]</a></td><td>University of North Carolina at Wilmington</td><td>2f43b614607163abf41dfe5d17ef6749a1b61304</td></tr><tr><td>names_and_faces_news</td><td>News Dataset</td><td>Names and Faces</td><td>Names and faces in the news</td><td><a href="http://ttic.uchicago.edu/~mmaire/papers/pdf/names_faces_cvpr2004.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=names and faces&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2fda164863a06a92d3a910b96eef927269aeb730</td></tr><tr><td>umd_faces</td><td>UMD</td><td>UMDFaces: An Annotated Face Dataset for Training Deep Networks</td><td>UMDFaces: An annotated face dataset for training deep networks</td><td><a href="https://arxiv.org/pdf/1611.01484.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=umdfaces: an annotated face dataset for training deep networks&sort=relevance" target="_blank">[s2]</a></td><td></td><td>31b05f65405534a696a847dd19c621b7b8588263</td></tr><tr><td>tiny_images</td><td>Tiny Images</td><td>80 million tiny images: a large dataset for non-parametric object and scene recognition</td><td>80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition</td><td><a href="http://cvcl.mit.edu/SUNSeminar/Torralba_80M_PAMI08.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=80 million tiny images: a large dataset for non-parametric object and scene recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>31b58ced31f22eab10bd3ee2d9174e7c14c27c01</td></tr><tr><td>feret</td><td>FERET</td><td>FERET ( Face Recognition Technology ) Recognition Algorithm Development and Test Results</td><td>FERET ( Face Recognition Technology ) Recognition Algorithm Development and Test Results</td><td><a href="https://pdfs.semanticscholar.org/31de/9b3dd6106ce6eec9a35991b2b9083395fd0b.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=feret ( face recognition technology ) recognition algorithm development and test results&sort=relevance" target="_blank">[s2]</a></td><td></td><td>31de9b3dd6106ce6eec9a35991b2b9083395fd0b</td></tr><tr><td>ucf_crowd</td><td>UCF-CC-50</td><td>Multi-Source Multi-Scale Counting in Extremely Dense Crowd Images</td><td>Multi-source Multi-scale Counting in Extremely Dense Crowd Images</td><td><a href="http://crcv-web.eecs.ucf.edu/papers/cvpr2013/Counting_V3o.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=multi-source multi-scale counting in extremely dense crowd images&sort=relevance" target="_blank">[s2]</a></td><td></td><td>32c801cb7fbeb742edfd94cccfca4934baec71da</td></tr><tr><td>cityscapes</td><td>Cityscapes</td><td>The Cityscapes Dataset for Semantic Urban Scene Understanding</td><td>The Cityscapes Dataset for Semantic Urban Scene Understanding</td><td><a href="https://arxiv.org/pdf/1604.01685.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the cityscapes dataset for semantic urban scene understanding&sort=relevance" target="_blank">[s2]</a></td><td></td><td>32cde90437ab5a70cf003ea36f66f2de0e24b3ab</td></tr><tr><td>tud_campus</td><td>TUD-Campus</td><td>People-Tracking-by-Detection and People-Detection-by-Tracking</td><td>People-tracking-by-detection and people-detection-by-tracking</td><td><a href="http://mplab.ucsd.edu/wp-content/uploads/CVPR2008/Conference/data/papers/243.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=people-tracking-by-detection and people-detection-by-tracking&sort=relevance" target="_blank">[s2]</a></td><td></td><td>3316521a5527c7700af8ae6aef32a79a8b83672c</td></tr><tr><td>tud_crossing</td><td>TUD-Crossing</td><td>People-Tracking-by-Detection and People-Detection-by-Tracking</td><td>People-tracking-by-detection and people-detection-by-tracking</td><td><a href="http://mplab.ucsd.edu/wp-content/uploads/CVPR2008/Conference/data/papers/243.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=people-tracking-by-detection and people-detection-by-tracking&sort=relevance" target="_blank">[s2]</a></td><td></td><td>3316521a5527c7700af8ae6aef32a79a8b83672c</td></tr><tr><td>tud_pedestrian</td><td>TUD-Pedestrian</td><td>People-Tracking-by-Detection and People-Detection-by-Tracking</td><td>People-tracking-by-detection and people-detection-by-tracking</td><td><a href="http://mplab.ucsd.edu/wp-content/uploads/CVPR2008/Conference/data/papers/243.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=people-tracking-by-detection and people-detection-by-tracking&sort=relevance" target="_blank">[s2]</a></td><td></td><td>3316521a5527c7700af8ae6aef32a79a8b83672c</td></tr><tr><td>mpii_human_pose</td><td>MPII Human Pose</td><td>2D Human Pose Estimation: New Benchmark and State of the Art Analysis</td><td>2D Human Pose Estimation: New Benchmark and State of the Art Analysis</td><td><a href="http://ei.is.tuebingen.mpg.de/uploads_file/attachment/attachment/168/andriluka14benchmark.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=2d human pose estimation: new benchmark and state of the art analysis&sort=relevance" target="_blank">[s2]</a></td><td></td><td>3325860c0c82a93b2eac654f5324dd6a776f609e</td></tr><tr><td>penn_fudan</td><td>Penn Fudan</td><td>Object Detection Combining Recognition and Segmentation</td><td>Object Detection Combining Recognition and Segmentation</td><td><a href="https://pdfs.semanticscholar.org/3394/168ff0719b03ff65bcea35336a76b21fe5e4.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=object detection combining recognition and segmentation&sort=relevance" target="_blank">[s2]</a></td><td></td><td>3394168ff0719b03ff65bcea35336a76b21fe5e4</td></tr><tr><td>wider</td><td>WIDER</td><td>Recognize Complex Events from Static Images by Fusing Deep Channels</td><td>Recognize complex events from static images by fusing deep channels</td><td><a href="http://openaccess.thecvf.com/content_cvpr_2015/supplemental/Xiong_Recognize_Complex_Events_2015_CVPR_supplemental.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=recognize complex events from static images by fusing deep channels&sort=relevance" target="_blank">[s2]</a></td><td></td><td>356b431d4f7a2a0a38cf971c84568207dcdbf189</td></tr><tr><td>coco_qa</td><td>COCO QA</td><td>Exploring Models and Data for Image Question Answering</td><td>Exploring Models and Data for Image Question Answering</td><td><a href="https://arxiv.org/pdf/1505.02074.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=exploring models and data for image question answering&sort=relevance" target="_blank">[s2]</a></td><td></td><td>35b0331dfcd2897abd5749b49ff5e2b8ba0f7a62</td></tr><tr><td>lfw</td><td>LFW</td><td>Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments</td><td>Labeled Faces in the Wild : A Database for Studying Face Recognition in Unconstrained Environments</td><td><a href="https://pdfs.semanticscholar.org/c6b3/ca4f939e36a9679a70e14ce8b1bbbc5618f3.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=labeled faces in the wild: a database for studying face recognition in unconstrained environments&sort=relevance" target="_blank">[s2]</a></td><td></td><td>370b5757a5379b15e30d619e4d3fb9e8e13f3256</td></tr><tr><td>rafd</td><td>RaFD</td><td>Presentation and validation of the Radboud Faces Database</td><td>Presentation and validation of the Radboud Faces Database</td><td><a href="https://pdfs.semanticscholar.org/3765/df816dc5a061bc261e190acc8bdd9d47bec0.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=presentation and validation of the radboud faces database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>3765df816dc5a061bc261e190acc8bdd9d47bec0</td></tr><tr><td>ufdd</td><td>UFDD</td><td>Pushing the Limits of Unconstrained Face Detection: a Challenge Dataset and Baseline Results</td><td>Pushing the Limits of Unconstrained Face Detection: a Challenge Dataset and Baseline Results</td><td><a href="https://arxiv.org/pdf/1804.10275.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=pushing the limits of unconstrained face detection: a challenge dataset and baseline results&sort=relevance" target="_blank">[s2]</a></td><td></td><td>377f2b65e6a9300448bdccf678cde59449ecd337</td></tr><tr><td>vmu</td><td>VMU</td><td>Can Facial Cosmetics Affect the Matching Accuracy of Face Recognition Systems?</td><td>Can facial cosmetics affect the matching accuracy of face recognition systems?</td><td><a href="http://www.cse.msu.edu/~climer/DantchevaChenRossFaceCosmetics_BTAS2012.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=can facial cosmetics affect the matching accuracy of face recognition systems?&sort=relevance" target="_blank">[s2]</a></td><td>West Virginia University</td><td>37d6f0eb074d207b53885bd2eb78ccc8a04be597</td></tr><tr><td>youtube_makeup</td><td>YMU</td><td>Can Facial Cosmetics Affect the Matching Accuracy of Face Recognition Systems?</td><td>Can facial cosmetics affect the matching accuracy of face recognition systems?</td><td><a href="http://www.cse.msu.edu/~climer/DantchevaChenRossFaceCosmetics_BTAS2012.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=can facial cosmetics affect the matching accuracy of face recognition systems?&sort=relevance" target="_blank">[s2]</a></td><td>West Virginia University</td><td>37d6f0eb074d207b53885bd2eb78ccc8a04be597</td></tr><tr><td>cuhk02</td><td>CUHK02</td><td>Locally Aligned Feature Transforms across Views</td><td>Locally Aligned Feature Transforms across Views</td><td><a href="http://vigir.missouri.edu/~gdesouza/Research/Conference_CDs/IEEE_CVPR2013/data/Papers/4989d594.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=locally aligned feature transforms across views&sort=relevance" target="_blank">[s2]</a></td><td></td><td>38b55d95189c5e69cf4ab45098a48fba407609b4</td></tr><tr><td>sdu_vid</td><td>SDU-VID</td><td>A Spatio-Temporal Appearance Representation for Video-Based Pedestrian Re-Identification</td><td>A Spatio-Temporal Appearance Representation for Video-Based Pedestrian Re-Identification</td><td><a href="http://openaccess.thecvf.com/content_iccv_2015/papers/Liu_A_Spatio-Temporal_Appearance_ICCV_2015_paper.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a spatio-temporal appearance representation for video-based pedestrian re-identification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>3b4ec8af470948a72a6ed37a9fd226719a874ebc</td></tr><tr><td>qmul_grid</td><td>GRID</td><td>Multi-Camera Activity Correlation Analysis</td><td>Multi-camera activity correlation analysis</td><td><a href="http://vision.lbl.gov/Conferences/cvpr/Papers/data/papers/0163.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=multi-camera activity correlation analysis&sort=relevance" target="_blank">[s2]</a></td><td>Queen Mary University of London</td><td>3b5b6d19d4733ab606c39c69a889f9e67967f151</td></tr><tr><td>tvhi</td><td>TVHI</td><td>High Five: Recognising human interactions in TV shows</td><td>High Five: Recognising human interactions in TV shows</td><td><a href="https://pdfs.semanticscholar.org/3cd4/0bfa1ff193a96bde0207e5140a399476466c.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=high five: recognising human interactions in tv shows&sort=relevance" target="_blank">[s2]</a></td><td></td><td>3cd40bfa1ff193a96bde0207e5140a399476466c</td></tr><tr><td>georgia_tech_face_database</td><td>Georgia Tech Face</td><td>Maximum likelihood training of the embedded HMM for face detection and recognition</td><td>MAXIMUM LIKELIHOOD TRAINING OF THE EMBEDDED HMM FOR FACE DETECTION AND RECOGNITION Ara V. Ne an and Monson H. Hayes III Center for Signal and Image Processing School of Electrical and Computer Engineering</td><td><a href="https://pdfs.semanticscholar.org/3dc3/f0b64ef80f573e3a5f96e456e52ee980b877.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=maximum likelihood training of the embedded hmm for face detection and recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>3dc3f0b64ef80f573e3a5f96e456e52ee980b877</td></tr><tr><td>bio_id</td><td>BioID Face</td><td>Robust Face Detection Using the Hausdorff Distance</td><td>Robust Face Detection Using the Hausdorff Distance</td><td><a href="http://facedetection.homepage.t-online.de/downloads/AVBPA01BioID.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=robust face detection using the hausdorff distance&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4053e3423fb70ad9140ca89351df49675197196a</td></tr><tr><td>distance_nighttime</td><td>Long Distance Heterogeneous Face</td><td>Nighttime Face Recognition at Long Distance: Cross-distance and Cross-spectral Matching</td><td>Nighttime Face Recognition at Long Distance: Cross-Distance and Cross-Spectral Matching</td><td><a href="https://pdfs.semanticscholar.org/4156/b7e88f2e0ab0a7c095b9bab199ae2b23bd06.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=nighttime face recognition at long distance: cross-distance and cross-spectral matching&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4156b7e88f2e0ab0a7c095b9bab199ae2b23bd06</td></tr><tr><td>market_1501</td><td>Market 1501</td><td>Scalable Person Re-identification: A Benchmark</td><td>Scalable Person Re-identification: A Benchmark</td><td><a href="http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Zheng_Scalable_Person_Re-Identification_ICCV_2015_paper.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=scalable person re-identification: a benchmark&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4308bd8c28e37e2ed9a3fcfe74d5436cce34b410</td></tr><tr><td>tud_multiview</td><td>TUD-Multiview</td><td>Monocular 3D Pose Estimation and Tracking by Detection</td><td>Monocular 3D pose estimation and tracking by detection</td><td><a href="http://videolectures.net/site/normal_dl/tag=81522/cvpr2010_andriluka_m3de_01.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=monocular 3d pose estimation and tracking by detection&sort=relevance" target="_blank">[s2]</a></td><td>TU Darmstadt</td><td>436f798d1a4e54e5947c1e7d7375c31b2bdb4064</td></tr><tr><td>tud_stadtmitte</td><td>TUD-Stadtmitte</td><td>Monocular 3D Pose Estimation and Tracking by Detection</td><td>Monocular 3D pose estimation and tracking by detection</td><td><a href="http://videolectures.net/site/normal_dl/tag=81522/cvpr2010_andriluka_m3de_01.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=monocular 3d pose estimation and tracking by detection&sort=relevance" target="_blank">[s2]</a></td><td>TU Darmstadt</td><td>436f798d1a4e54e5947c1e7d7375c31b2bdb4064</td></tr><tr><td>cuhk01</td><td>CUHK01</td><td>Human Reidentification with Transferred Metric Learning</td><td>Human Reidentification with Transferred Metric Learning</td><td><a href="https://pdfs.semanticscholar.org/4448/4d2866f222bbb9b6b0870890f9eea1ffb2d0.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=human reidentification with transferred metric learning&sort=relevance" target="_blank">[s2]</a></td><td></td><td>44484d2866f222bbb9b6b0870890f9eea1ffb2d0</td></tr><tr><td>wider_attribute</td><td>WIDER Attribute</td><td>Human Attribute Recognition by Deep Hierarchical Contexts</td><td>Human Attribute Recognition by Deep Hierarchical Contexts</td><td><a href="https://pdfs.semanticscholar.org/8e28/07f2dd53b03a759e372e07f7191cae65c9fd.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=human attribute recognition by deep hierarchical contexts&sort=relevance" target="_blank">[s2]</a></td><td></td><td>44d23df380af207f5ac5b41459c722c87283e1eb</td></tr><tr><td>vadana</td><td>VADANA</td><td>VADANA: A dense dataset for facial image analysis</td><td>VADANA: A dense dataset for facial image analysis</td><td><a href="http://vims.cis.udel.edu/publications/VADANA_BeFIT2011.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=vadana: a dense dataset for facial image analysis&sort=relevance" target="_blank">[s2]</a></td><td>University of Delaware</td><td>4563b46d42079242f06567b3f2e2f7a80cb3befe</td></tr><tr><td>jaffe</td><td>JAFFE</td><td>Coding Facial Expressions with Gabor Wavelets</td><td>Coding Facial Expressions with Gabor Wavelets</td><td><a href="https://pdfs.semanticscholar.org/45c3/1cde87258414f33412b3b12fc5bec7cb3ba9.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=coding facial expressions with gabor wavelets&sort=relevance" target="_blank">[s2]</a></td><td></td><td>45c31cde87258414f33412b3b12fc5bec7cb3ba9</td></tr><tr><td>malf</td><td>MALF</td><td>Fine-grained Evaluation on Face Detection in the Wild.</td><td>Fine-grained evaluation on face detection in the wild</td><td><a href="http://www.cs.toronto.edu/~byang/papers/malf_fg15.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=fine-grained evaluation on face detection in the wild.&sort=relevance" target="_blank">[s2]</a></td><td></td><td>45e616093a92e5f1e61a7c6037d5f637aa8964af</td></tr><tr><td>sdu_vid</td><td>SDU-VID</td><td>Local descriptors encoded by Fisher vectors for person re-identification</td><td>Local Descriptors Encoded by Fisher Vectors for Person Re-identification</td><td><a href="https://pdfs.semanticscholar.org/a105/f1ef67b4b02da38eadce8ffb4e13aa301a93.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=local descriptors encoded by fisher vectors for person re-identification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>46a01565e6afe7c074affb752e7069ee3bf2e4ef</td></tr><tr><td>fia</td><td>CMU FiA</td><td>The CMU Face In Action (FIA) Database</td><td>The CMU Face In Action (FIA) Database</td><td><a href="https://pdfs.semanticscholar.org/4766/2d1a368daf70ba70ef2d59eb6209f98b675d.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the cmu face in action (fia) database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>47662d1a368daf70ba70ef2d59eb6209f98b675d</td></tr><tr><td>kin_face</td><td>UB KinFace</td><td>Kinship Verification through Transfer Learning</td><td>Kinship verification through transfer learning</td><td><a href="http://ijcai.org/Proceedings/11/Papers/422.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=kinship verification through transfer learning&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4793f11fbca4a7dba898b9fff68f70d868e2497c</td></tr><tr><td>am_fed</td><td>AM-FED</td><td>Affectiva MIT Facial Expression Dataset (AM-FED): Naturalistic and Spontaneous Facial Expressions Collected “In the Wild”</td><td>Affectiva-MIT Facial Expression Dataset (AM-FED): Naturalistic and Spontaneous Facial Expressions Collected "In-the-Wild"</td><td><a href="http://affect.media.mit.edu/pdfs/13.McDuff-etal-AMFED.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=affectiva mit facial expression dataset (am-fed): naturalistic and spontaneous facial expressions collected “in the wild”&sort=relevance" target="_blank">[s2]</a></td><td></td><td>47aeb3b82f54b5ae8142b4bdda7b614433e69b9a</td></tr><tr><td>apis</td><td>APiS1.0</td><td>Pedestrian Attribute Classification in Surveillance: Database and Evaluation</td><td>Pedestrian Attribute Classification in Surveillance: Database and Evaluation</td><td><a href="http://www.cbsr.ia.ac.cn/english/APiS_1.0_paper.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=pedestrian attribute classification in surveillance: database and evaluation&sort=relevance" target="_blank">[s2]</a></td><td></td><td>488e475eeb3bb39a145f23ede197cd3620f1d98a</td></tr><tr><td>svs</td><td>SVS</td><td>Pedestrian Attribute Classification in Surveillance: Database and Evaluation</td><td>Pedestrian Attribute Classification in Surveillance: Database and Evaluation</td><td><a href="http://www.cbsr.ia.ac.cn/english/APiS_1.0_paper.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=pedestrian attribute classification in surveillance: database and evaluation&sort=relevance" target="_blank">[s2]</a></td><td></td><td>488e475eeb3bb39a145f23ede197cd3620f1d98a</td></tr><tr><td>coco_action</td><td>COCO-a</td><td>Describing Common Human Visual Actions in Images</td><td>Describing Common Human Visual Actions in Images</td><td><a href="https://arxiv.org/pdf/1506.02203.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=describing common human visual actions in images&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4946ba10a4d5a7d0a38372f23e6622bd347ae273</td></tr><tr><td>leeds_sports_pose</td><td>Leeds Sports Pose</td><td>Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation</td><td>Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation</td><td><a href="https://pdfs.semanticscholar.org/c327/15b5106f46eb6761531704cd2a9b5571832e.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=clustered pose and nonlinear appearance models for human pose estimation&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4b1d23d17476fcf78f4cbadf69fb130b1aa627c0</td></tr><tr><td>stickmen_buffy</td><td>Buffy Stickmen</td><td>Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation</td><td>Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation</td><td><a href="https://pdfs.semanticscholar.org/c327/15b5106f46eb6761531704cd2a9b5571832e.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=clustered pose and nonlinear appearance models for human pose estimation&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4b1d23d17476fcf78f4cbadf69fb130b1aa627c0</td></tr><tr><td>czech_news_agency</td><td>UFI</td><td>Unconstrained Facial Images: Database for Face Recognition under Real-world Conditions</td><td>Unconstrained Facial Images: Database for Face Recognition Under Real-World Conditions</td><td><a href="https://pdfs.semanticscholar.org/4b41/06614c1d553365bad75d7866bff0de6056ed.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=unconstrained facial images: database for face recognition under real-world conditions&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4b4106614c1d553365bad75d7866bff0de6056ed</td></tr><tr><td>face_tracer</td><td>FaceTracer</td><td>FaceTracer: A Search Engine for Large Collections of Images with Faces</td><td>FaceTracer: A Search Engine for Large Collections of Images with Faces</td><td><a href="https://pdfs.semanticscholar.org/73a8/1d311eedac8dea3ca24dc15b6990fa4a725e.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=facetracer: a search engine for large collections of images with faces&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4c170a0dcc8de75587dae21ca508dab2f9343974</td></tr><tr><td>cmu_pie</td><td>CMU PIE</td><td>The CMU Pose, Illumination, and Expression Database</td><td>The CMU Pose, Illumination, and Expression (PIE) Database</td><td><a href="http://www.comp.nus.edu.sg/~tsim/piedb.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the cmu pose, illumination, and expression database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4d423acc78273b75134e2afd1777ba6d3a398973</td></tr><tr><td>multi_pie</td><td>MULTIPIE</td><td>Multi-PIE</td><td>The CMU Pose, Illumination, and Expression (PIE) Database</td><td><a href="http://www.comp.nus.edu.sg/~tsim/piedb.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=multi-pie&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4d423acc78273b75134e2afd1777ba6d3a398973</td></tr><tr><td>3dddb_unconstrained</td><td>3D Dynamic</td><td>A 3D Dynamic Database for Unconstrained Face Recognition</td><td>A 3 D Dynamic Database for Unconstrained Face Recognition</td><td><a href="https://pdfs.semanticscholar.org/4d4b/b462c9f1d4e4ab1e4aa6a75cc0bc71b38461.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a 3d dynamic database for unconstrained face recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4d4bb462c9f1d4e4ab1e4aa6a75cc0bc71b38461</td></tr><tr><td>texas_3dfrd</td><td>Texas 3DFRD</td><td>Texas 3D Face Recognition Database</td><td>Texas 3D Face Recognition Database</td><td><a href="http://live.ece.utexas.edu/publications/2010/sg_ssiai_may10.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=texas 3d face recognition database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4d58f886f5150b2d5e48fd1b5a49e09799bf895d</td></tr><tr><td>cohn_kanade_plus</td><td>CK+</td><td>The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression</td><td>The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression</td><td><a href="http://www.iainm.com/iainm/Publications_files/2010_The%20Extended.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the extended cohn-kanade dataset (ck+): a complete dataset for action unit and emotion-specified expression&sort=relevance" target="_blank">[s2]</a></td><td>University of Pittsburgh</td><td>4d9a02d080636e9666c4d1cc438b9893391ec6c7</td></tr><tr><td>cfd</td><td>CFD</td><td>The Chicago face database: A free stimulus set of faces and norming data</td><td>The Chicago face database: A free stimulus set of faces and norming data.</td><td><a href="https://pdfs.semanticscholar.org/4df3/143922bcdf7db78eb91e6b5359d6ada004d2.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the chicago face database: a free stimulus set of faces and norming data&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4df3143922bcdf7db78eb91e6b5359d6ada004d2</td></tr><tr><td>leeds_sports_pose_extended</td><td>Leeds Sports Pose Extended</td><td>Learning Effective Human Pose Estimation from Inaccurate Annotation</td><td>Learning effective human pose estimation from inaccurate annotation</td><td><a href="http://www.comp.leeds.ac.uk/mat4saj/publications/johnson11cvpr.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=learning effective human pose estimation from inaccurate annotation&sort=relevance" target="_blank">[s2]</a></td><td>University of Leeds</td><td>4e4746094bf60ee83e40d8597a6191e463b57f76</td></tr><tr><td>dartmouth_children</td><td>Dartmouth Children</td><td>The Dartmouth Database of Children's Faces: Acquisition and validation of a new face stimulus set</td><td>The Dartmouth Database of Children’s Faces: Acquisition and Validation of a New Face Stimulus Set</td><td><a href="https://pdfs.semanticscholar.org/4e6e/e936eb50dd032f7138702fa39b7c18ee8907.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the dartmouth database of children's faces: acquisition and validation of a new face stimulus set&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4e6ee936eb50dd032f7138702fa39b7c18ee8907</td></tr><tr><td>soton</td><td>SOTON HiD</td><td>On a Large Sequence-Based Human Gait Database</td><td>On a large sequence-based human gait database</td><td><a href="https://eprints.soton.ac.uk/257901/1/Shutler_2002.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=on a large sequence-based human gait database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4f93cd09785c6e77bf4bc5a788e079df524c8d21</td></tr><tr><td>deep_fashion</td><td>DeepFashion</td><td>Fashion Landmark Detection in the Wild</td><td>Fashion Landmark Detection in the Wild</td><td><a href="https://arxiv.org/pdf/1608.03049.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=fashion landmark detection in the wild&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4fefd1bc8dc4e0ab37ee3324ddfa43ad9d6a04a7</td></tr><tr><td>violent_flows</td><td>Violent Flows</td><td>Violent Flows: Real-Time Detection of Violent Crowd Behavior</td><td>Violent flows: Real-time detection of violent crowd behavior</td><td><a href="http://www.openu.ac.il/home/hassner/data/violentflows/violent_flows.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=violent flows: real-time detection of violent crowd behavior&sort=relevance" target="_blank">[s2]</a></td><td>Open University of Israel</td><td>5194cbd51f9769ab25260446b4fa17204752e799</td></tr><tr><td>imsitu</td><td>imSitu</td><td>Situation Recognition: Visual Semantic Role Labeling for Image Understanding</td><td>Situation Recognition: Visual Semantic Role Labeling for Image Understanding</td><td><a href="http://allenai.org/content/publications/SituationRecognition.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=situation recognition: visual semantic role labeling for image understanding&sort=relevance" target="_blank">[s2]</a></td><td></td><td>51eba481dac6b229a7490f650dff7b17ce05df73</td></tr><tr><td>wider_face</td><td>WIDER FACE</td><td>WIDER FACE: A Face Detection Benchmark</td><td>WIDER FACE: A Face Detection Benchmark</td><td><a href="https://arxiv.org/pdf/1511.06523.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=wider face: a face detection benchmark&sort=relevance" target="_blank">[s2]</a></td><td></td><td>52d7eb0fbc3522434c13cc247549f74bb9609c5d</td></tr><tr><td>bp4d_plus</td><td>BP4D+</td><td>Multimodal Spontaneous Emotion Corpus for Human Behavior Analysis</td><td>Multimodal Spontaneous Emotion Corpus for Human Behavior Analysis</td><td><a href="http://openaccess.thecvf.com/content_cvpr_2016/papers/Zhang_Multimodal_Spontaneous_Emotion_CVPR_2016_paper.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=multimodal spontaneous emotion corpus for human behavior analysis&sort=relevance" target="_blank">[s2]</a></td><td></td><td>53ae38a6bb2b21b42bac4f0c4c8ed1f9fa02f9d4</td></tr><tr><td>reseed</td><td>ReSEED</td><td>ReSEED: Social Event dEtection Dataset</td><td>ReSEED: social event dEtection dataset</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=reseed: social event detection dataset&sort=relevance" target="_blank">[s2]</a></td><td></td><td>54983972aafc8e149259d913524581357b0f91c3</td></tr><tr><td>orl</td><td>ORL</td><td>Parameterisation of a Stochastic Model for Human Face Identification</td><td>Parameterisation of a stochastic model for human face identification</td><td><a href="https://pdfs.semanticscholar.org/5520/6f0b5f57ce17358999145506cd01e570358c.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=parameterisation of a stochastic model for human face identification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>55206f0b5f57ce17358999145506cd01e570358c</td></tr><tr><td>ifad</td><td>IFAD</td><td>Indian Face Age Database: A Database for Face Recognition with Age Variation</td><td>Indian Face Age Database : A Database for Face Recognition with Age Variation</td><td><a href="https://pdfs.semanticscholar.org/55c4/0cbcf49a0225e72d911d762c27bb1c2d14aa.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=indian face age database: a database for face recognition with age variation&sort=relevance" target="_blank">[s2]</a></td><td></td><td>55c40cbcf49a0225e72d911d762c27bb1c2d14aa</td></tr><tr><td>youtube_faces</td><td>YouTubeFaces</td><td>Face Recognition in Unconstrained Videos with Matched Background Similarity</td><td>Face recognition in unconstrained videos with matched background similarity</td><td><a href="http://www.cs.tau.ac.il/thesis/thesis/Maoz.Itay-MSc.Thesis.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=face recognition in unconstrained videos with matched background similarity&sort=relevance" target="_blank">[s2]</a></td><td></td><td>560e0e58d0059259ddf86fcec1fa7975dee6a868</td></tr><tr><td>data_61</td><td>Data61 Pedestrian</td><td>A Multi-Modal Graphical Model for Scene Analysis</td><td>A Multi-modal Graphical Model for Scene Analysis</td><td><a href="http://www.nicta.com.au/wp-content/uploads/2015/02/TaghaviNaminetalWACV15.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a multi-modal graphical model for scene analysis&sort=relevance" target="_blank">[s2]</a></td><td></td><td>563c940054e4b456661762c1ab858e6f730c3159</td></tr><tr><td>cmdp</td><td>CMDP</td><td>Distance Estimation of an Unknown Person from a Portrait</td><td>Distance Estimation of an Unknown Person from a Portrait</td><td><a href="https://pdfs.semanticscholar.org/56ae/6d94fc6097ec4ca861f0daa87941d1c10b70.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=distance estimation of an unknown person from a portrait&sort=relevance" target="_blank">[s2]</a></td><td></td><td>56ae6d94fc6097ec4ca861f0daa87941d1c10b70</td></tr><tr><td>unbc_shoulder_pain</td><td>UNBC-McMaster Pain</td><td>PAINFUL DATA: The UNBC-McMaster Shoulder Pain Expression Archive Database</td><td>Painful data: The UNBC-McMaster shoulder pain expression archive database</td><td><a href="http://www.iainm.com/iainm/Publications_files/2011_PAINFUL.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=painful data: the unbc-mcmaster shoulder pain expression archive database&sort=relevance" target="_blank">[s2]</a></td><td>Carnegie Mellon University</td><td>56ffa7d906b08d02d6d5a12c7377a57e24ef3391</td></tr><tr><td>stanford_drone</td><td>Stanford Drone</td><td>Learning Social Etiquette: Human Trajectory Prediction In Crowded Scenes</td><td>Social LSTM: Human Trajectory Prediction in Crowded Spaces</td><td><a href="http://cs.stanford.edu/groups/vision/pdf/CVPR16_N_LSTM.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=learning social etiquette: human trajectory prediction in crowded scenes&sort=relevance" target="_blank">[s2]</a></td><td></td><td>570f37ed63142312e6ccdf00ecc376341ec72b9f</td></tr><tr><td>ijb_c</td><td>IJB-C</td><td>IARPA Janus Benchmark C</td><td>IARPA Janus Benchmark - C: Face Dataset and Protocol</td><td><a href="http://biometrics.cse.msu.edu/Publications/Face/Mazeetal_IARPAJanusBenchmarkCFaceDatasetAndProtocol_ICB2018.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=iarpa janus benchmark c&sort=relevance" target="_blank">[s2]</a></td><td></td><td>57178b36c21fd7f4529ac6748614bb3374714e91</td></tr><tr><td>50_people_one_question</td><td>50 People One Question</td><td>Merging Pose Estimates Across Space and Time</td><td>Merging Pose Estimates Across Space and Time</td><td><a href="https://pdfs.semanticscholar.org/63b2/f5348af0f969dfc2afb4977732393c6459ec.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=merging pose estimates across space and time&sort=relevance" target="_blank">[s2]</a></td><td></td><td>5753b2b5e442eaa3be066daa4a2ca8d8a0bb1725</td></tr><tr><td>mr2</td><td>MR2</td><td>The MR2: A multi-racial mega-resolution database of facial stimuli</td><td>The MR2: A multi-racial, mega-resolution database of facial stimuli.</td><td><a href="https://pdfs.semanticscholar.org/be5b/455abd379240460d022a0e246615b0b86c14.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the mr2: a multi-racial mega-resolution database of facial stimuli&sort=relevance" target="_blank">[s2]</a></td><td></td><td>578d4ad74818086bb64f182f72e2c8bd31e3d426</td></tr><tr><td>cvc_01_barcelona</td><td>CVC-01</td><td>Adaptive Image Sampling and Windows Classification for On-board Pedestrian Detection</td><td>Adaptive Image Sampling and Windows Classification for On – board Pedestrian Detection</td><td><a href="https://pdfs.semanticscholar.org/57fe/081950f21ca03b5b375ae3e84b399c015861.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=adaptive image sampling and windows classification for on-board pedestrian detection&sort=relevance" target="_blank">[s2]</a></td><td></td><td>57fe081950f21ca03b5b375ae3e84b399c015861</td></tr><tr><td>mot</td><td>MOT</td><td>Learning to associate: HybridBoosted multi-target tracker for crowded scene</td><td>Learning to associate: HybridBoosted multi-target tracker for crowded scene</td><td><a href="http://iris.usc.edu/Outlines/papers/2009/yuan-chang-nevatia-cvpr09.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=learning to associate: hybridboosted multi-target tracker for crowded scene&sort=relevance" target="_blank">[s2]</a></td><td>University of Southern California</td><td>5981e6479c3fd4e31644db35d236bfb84ae46514</td></tr><tr><td>disfa</td><td>DISFA</td><td>DISFA: A Spontaneous Facial Action Intensity Database</td><td>DISFA: A Spontaneous Facial Action Intensity Database</td><td><a href="http://mohammadmahoor.com/wp-content/uploads/2017/06/DiSFA_Paper_andAppendix_Final_OneColumn1-1.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=disfa: a spontaneous facial action intensity database&sort=relevance" target="_blank">[s2]</a></td><td>University of Denver</td><td>5a5f0287484f0d480fed1ce585dbf729586f0edc</td></tr><tr><td>wlfdb</td><td>WLFDB</td><td>WLFDB: Weakly Labeled Face Databases</td><td>WLFDB: Weakly Labeled Face Databases</td><td><a href="https://pdfs.semanticscholar.org/5ad4/e9f947c1653c247d418f05dad758a3f9277b.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=wlfdb: weakly labeled face databases&sort=relevance" target="_blank">[s2]</a></td><td></td><td>5ad4e9f947c1653c247d418f05dad758a3f9277b</td></tr><tr><td>coco</td><td>COCO</td><td>Microsoft COCO: Common Objects in Context</td><td>Microsoft COCO: Common Objects in Context</td><td><a href="https://arxiv.org/pdf/1405.0312.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=microsoft coco: common objects in context&sort=relevance" target="_blank">[s2]</a></td><td></td><td>5e0f8c355a37a5a89351c02f174e7a5ddcb98683</td></tr><tr><td>cityscapes</td><td>Cityscapes</td><td>The Cityscapes Dataset</td><td>The Cityscapes Dataset</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the cityscapes dataset&sort=relevance" target="_blank">[s2]</a></td><td></td><td>5ffd74d2873b7cba2cbc5fd295cc7fbdedca22a2</td></tr><tr><td>viper</td><td>VIPeR</td><td>Evaluating Appearance Models for Recognition, Reacquisition, and Tracking</td><td>Evaluating Appearance Models for Recognition , Reacquisition , and Tracking</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=evaluating appearance models for recognition, reacquisition, and tracking&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6273b3491e94ea4dd1ce42b791d77bdc96ee73a8</td></tr><tr><td>caltech_10k_web_faces</td><td>Caltech 10K Web Faces</td><td>Pruning Training Sets for Learning of Object Categories</td><td>Pruning training sets for learning of object categories</td><td><a href="http://authors.library.caltech.edu/11469/1/ANGcvpr05.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=pruning training sets for learning of object categories&sort=relevance" target="_blank">[s2]</a></td><td></td><td>636b8ffc09b1b23ff714ac8350bb35635e49fa3c</td></tr><tr><td>bfm</td><td>BFM</td><td>A 3D Face Model for Pose and Illumination Invariant Face Recognition</td><td>A 3D Face Model for Pose and Illumination Invariant Face Recognition</td><td><a href="http://gravis.cs.unibas.ch/publications/2009/BFModel09.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a 3d face model for pose and illumination invariant face recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>639937b3a1b8bded3f7e9a40e85bd3770016cf3c</td></tr><tr><td>alert_airport</td><td>ALERT Airport</td><td>A Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets</td><td>A Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets.</td><td><a href="https://arxiv.org/pdf/1605.09653.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a systematic evaluation and benchmark for person re-identification: features, metrics, and datasets&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6403117f9c005ae81f1e8e6d1302f4a045e3d99d</td></tr><tr><td>celeba</td><td>CelebA</td><td>Deep Learning Face Attributes in the Wild</td><td>Deep Learning Face Attributes in the Wild</td><td><a href="https://arxiv.org/pdf/1411.7766.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=deep learning face attributes in the wild&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6424b69f3ff4d35249c0bb7ef912fbc2c86f4ff4</td></tr><tr><td>complex_activities</td><td>Ongoing Complex Activities</td><td>Recognition of Ongoing Complex Activities by Sequence Prediction over a Hierarchical Label Space</td><td>Recognition of ongoing complex activities by sequence prediction over a hierarchical label space</td><td><a href="https://scalable.mpi-inf.mpg.de/files/2016/01/main_wacv.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=recognition of ongoing complex activities by sequence prediction over a hierarchical label space&sort=relevance" target="_blank">[s2]</a></td><td></td><td>65355cbb581a219bd7461d48b3afd115263ea760</td></tr><tr><td>afad</td><td>AFAD</td><td>Ordinal Regression with a Multiple Output CNN for Age Estimation</td><td>Ordinal Regression with Multiple Output CNN for Age Estimation</td><td><a href="http://openaccess.thecvf.com/content_cvpr_2016/papers/Niu_Ordinal_Regression_With_CVPR_2016_paper.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=ordinal regression with a multiple output cnn for age estimation&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6618cff7f2ed440a0d2fa9e74ad5469df5cdbe4c</td></tr><tr><td>sun_attributes</td><td>SUN</td><td>The SUN Attribute Database: Beyond Categories for Deeper Scene Understanding</td><td>The SUN Attribute Database: Beyond Categories for Deeper Scene Understanding</td><td><a href="http://www.cc.gatech.edu/~hays/papers/attribute_ijcv.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the sun attribute database: beyond categories for deeper scene understanding&sort=relevance" target="_blank">[s2]</a></td><td></td><td>66e6f08873325d37e0ec20a4769ce881e04e964e</td></tr><tr><td>face_tracer</td><td>FaceTracer</td><td>Face Swapping: Automatically Replacing Faces in Photographs</td><td>Face swapping: automatically replacing faces in photographs</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=face swapping: automatically replacing faces in photographs&sort=relevance" target="_blank">[s2]</a></td><td></td><td>670637d0303a863c1548d5b19f705860a23e285c</td></tr><tr><td>tud_brussels</td><td>TUD-Brussels</td><td>Multi-Cue Onboard Pedestrian Detection</td><td>Multi-cue onboard pedestrian detection</td><td><a href="http://vision.lbl.gov/Conferences/cvpr/Papers/data/papers/1454.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=multi-cue onboard pedestrian detection&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6ad5a38df8dd4cdddd74f31996ce096d41219f72</td></tr><tr><td>tud_motionpairs</td><td>TUD-Motionparis</td><td>Multi-Cue Onboard Pedestrian Detection</td><td>Multi-cue onboard pedestrian detection</td><td><a href="http://vision.lbl.gov/Conferences/cvpr/Papers/data/papers/1454.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=multi-cue onboard pedestrian detection&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6ad5a38df8dd4cdddd74f31996ce096d41219f72</td></tr><tr><td>cuhk03</td><td>CUHK03</td><td>DeepReID: Deep Filter Pairing Neural Network for Person Re-identification</td><td>DeepReID: Deep Filter Pairing Neural Network for Person Re-identification</td><td><a href="http://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Li_DeepReID_Deep_Filter_2014_CVPR_paper.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=deepreid: deep filter pairing neural network for person re-identification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6bd36e9fd0ef20a3074e1430a6cc601e6d407fc3</td></tr><tr><td>ar_facedb</td><td>AR Face</td><td>The AR Face Database</td><td>The AR face database</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the ar face database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6d96f946aaabc734af7fe3fc4454cf8547fcd5ed</td></tr><tr><td>agedb</td><td>AgeDB</td><td>AgeDB: the first manually collected, in-the-wild age database</td><td>AgeDB: The First Manually Collected, In-the-Wild Age Database</td><td><a href="https://ibug.doc.ic.ac.uk/media/uploads/documents/agedb.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=agedb: the first manually collected, in-the-wild age database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6dcf418c778f528b5792104760f1fbfe90c6dd6a</td></tr><tr><td>stickmen_buffy</td><td>Buffy Stickmen</td><td>Learning to Parse Images of Articulated Objects</td><td>Learning to parse images of articulated bodies</td><td><a href="https://pdfs.semanticscholar.org/9cd7/4c43dbf9be0b9caae4606ee53e6d45850471.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=learning to parse images of articulated objects&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6dd0597f8513dc100cd0bc1b493768cde45098a9</td></tr><tr><td>stickmen_pascal</td><td>Stickmen PASCAL</td><td>Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation</td><td>Learning to parse images of articulated bodies</td><td><a href="https://pdfs.semanticscholar.org/9cd7/4c43dbf9be0b9caae4606ee53e6d45850471.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=clustered pose and nonlinear appearance models for human pose estimation&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6dd0597f8513dc100cd0bc1b493768cde45098a9</td></tr><tr><td>stickmen_pascal</td><td>Stickmen PASCAL</td><td>Learning to Parse Images of Articulated Objects</td><td>Learning to parse images of articulated bodies</td><td><a href="https://pdfs.semanticscholar.org/9cd7/4c43dbf9be0b9caae4606ee53e6d45850471.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=learning to parse images of articulated objects&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6dd0597f8513dc100cd0bc1b493768cde45098a9</td></tr><tr><td>ward</td><td>WARD</td><td>Re-identify people in wide area camera network</td><td>Re-identify people in wide area camera network</td><td><a href="http://users.dimi.uniud.it/~niki.martinel/data/publications/2012/CVPR/MarMicCVPR2012.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=re-identify people in wide area camera network&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6f3c76b7c0bd8e1d122c6ea808a271fd4749c951</td></tr><tr><td>umd_faces</td><td>UMD</td><td>The Do's and Don'ts for CNN-based Face Verification</td><td>The Do’s and Don’ts for CNN-Based Face Verification</td><td><a href="https://arxiv.org/pdf/1705.07426.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the do's and don'ts for cnn-based face verification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>71b7fc715e2f1bb24c0030af8d7e7b6e7cd128a6</td></tr><tr><td>affectnet</td><td>AffectNet</td><td>AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild</td><td>AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild</td><td><a href="https://arxiv.org/pdf/1708.03985.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=affectnet: a database for facial expression, valence, and arousal computing in the wild&sort=relevance" target="_blank">[s2]</a></td><td></td><td>758d7e1be64cc668c59ef33ba8882c8597406e53</td></tr><tr><td>pubfig</td><td>PubFig</td><td>Attribute and Simile Classifiers for Face Verification</td><td>Attribute and simile classifiers for face verification</td><td><a href="http://acberg.com/papers/kbbn09iccv.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=attribute and simile classifiers for face verification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>759a3b3821d9f0e08e0b0a62c8b693230afc3f8d</td></tr><tr><td>fddb</td><td>FDDB</td><td>FDDB: A Benchmark for Face Detection in Unconstrained Settings</td><td>A Benchmark for Face Detection in Unconstrained Settings</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=fddb: a benchmark for face detection in unconstrained settings&sort=relevance" target="_blank">[s2]</a></td><td></td><td>75da1df4ed319926c544eefe17ec8d720feef8c0</td></tr><tr><td>urban_tribes</td><td>Urban Tribes</td><td>From Bikers to Surfers: Visual Recognition of Urban Tribes</td><td>From Bikers to Surfers: Visual Recognition of Urban Tribes</td><td><a href="https://pdfs.semanticscholar.org/774c/bb45968607a027ae4729077734db000a1ec5.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=from bikers to surfers: visual recognition of urban tribes&sort=relevance" target="_blank">[s2]</a></td><td></td><td>774cbb45968607a027ae4729077734db000a1ec5</td></tr><tr><td>wildtrack</td><td>WildTrack</td><td>WILDTRACK: A Multi-camera HD Dataset for Dense Unscripted Pedestrian Detection</td><td>WILDTRACK : A Multi-camera HD Dataset for Dense Unscripted Pedestrian Detection</td><td><a href="https://pdfs.semanticscholar.org/fe1c/ec4e4995b8615855572374ae3efc94949105.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=wildtrack: a multi-camera hd dataset for dense unscripted pedestrian detection&sort=relevance" target="_blank">[s2]</a></td><td></td><td>77c81c13a110a341c140995bedb98101b9e84f7f</td></tr><tr><td>berkeley_pose</td><td>BPAD</td><td>Describing People: A Poselet-Based Approach to Attribute Classification</td><td>Describing people: A poselet-based approach to attribute classification</td><td><a href="http://ttic.uchicago.edu/~smaji/papers/attributes-iccv11.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=describing people: a poselet-based approach to attribute classification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>7808937b46acad36e43c30ae4e9f3fd57462853d</td></tr><tr><td>mapillary</td><td>Mapillary</td><td>The Mapillary Vistas Dataset for Semantic Understanding of Street Scenes</td><td>The Mapillary Vistas Dataset for Semantic Understanding of Street Scenes</td><td><a href="http://openaccess.thecvf.com/content_ICCV_2017/papers/Neuhold_The_Mapillary_Vistas_ICCV_2017_paper.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the mapillary vistas dataset for semantic understanding of street scenes&sort=relevance" target="_blank">[s2]</a></td><td></td><td>79828e6e9f137a583082b8b5a9dfce0c301989b8</td></tr><tr><td>nudedetection</td><td>Nude Detection</td><td>A Bag-of-Features Approach based on Hue-SIFT Descriptor for Nude Detection</td><td>A bag-of-features approach based on Hue-SIFT descriptor for nude detection</td><td><a href="http://www.eurasip.org/Proceedings/Eusipco/Eusipco2009/contents/papers/1569191772.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a bag-of-features approach based on hue-sift descriptor for nude detection&sort=relevance" target="_blank">[s2]</a></td><td></td><td>7ace44190729927e5cb0dd5d363fcae966fe13f7</td></tr><tr><td>lfw</td><td>LFW</td><td>Labeled Faces in the Wild: A Survey</td><td>Labeled Faces in the Wild : A Survey</td><td><a href="https://pdfs.semanticscholar.org/7de6/e81d775e9cd7becbfd1bd685f4e2a5eebb22.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=labeled faces in the wild: a survey&sort=relevance" target="_blank">[s2]</a></td><td></td><td>7de6e81d775e9cd7becbfd1bd685f4e2a5eebb22</td></tr><tr><td>vgg_celebs_in_places</td><td>CIP</td><td>Faces in Places: Compound Query Retrieval</td><td>Faces in Places: compound query retrieval</td><td><a href="https://pdfs.semanticscholar.org/7ebb/153704706e457ab57b432793d2b6e5d12592.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=faces in places: compound query retrieval&sort=relevance" target="_blank">[s2]</a></td><td></td><td>7ebb153704706e457ab57b432793d2b6e5d12592</td></tr><tr><td>nova_emotions</td><td>Novaemötions Dataset</td><td>Competitive affective gamming: Winning with a smile</td><td>Competitive affective gaming: winning with a smile</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=competitive affective gamming: winning with a smile&sort=relevance" target="_blank">[s2]</a></td><td>Universidade NOVA de Lisboa, Caparica, Portugal</td><td>7f4040b482d16354d5938c1d1b926b544652bf5b</td></tr><tr><td>sun_attributes</td><td>SUN</td><td>SUN Attribute Database: 
+<!doctype html><html><head><meta charset='utf-8'><title>Paper Title Sanity Check</title><link rel='stylesheet' href='reports.css'></head><body><h2>Paper Title Sanity Check</h2><table border='1' cellpadding='3' cellspacing='3'><th>key</th><th>name</th><th>our title</th><th>found title</th><th></th><th></th><th>address</th><th>s2 id</th><tr><td>yfcc_100m</td><td>YFCC100M</td><td>YFCC100M: The New Data in Multimedia Research</td><td>YFCC100M: the new data in multimedia research</td><td><a href="https://arxiv.org/pdf/1503.01817.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=yfcc100m: the new data in multimedia research&sort=relevance" target="_blank">[s2]</a></td><td></td><td>010f0f4929e6a6644fb01f0e43820f91d0fad292</td></tr><tr><td>fiw_300</td><td>300-W</td><td>A semi-automatic methodology for facial landmark annotation</td><td>A Semi-automatic Methodology for Facial Landmark Annotation</td><td><a href="http://ibug.doc.ic.ac.uk/media/uploads/documents/sagonas_cvpr_2013_amfg_w.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a semi-automatic methodology for facial landmark annotation&sort=relevance" target="_blank">[s2]</a></td><td></td><td>013909077ad843eb6df7a3e8e290cfd5575999d2</td></tr><tr><td>buhmap_db</td><td>BUHMAP-DB</td><td>Facial Feature Tracking and Expression Recognition for Sign Language</td><td>Facial feature tracking and expression recognition for sign language</td><td><a href="https://www.cmpe.boun.edu.tr/~ari/files/ari2008iscis.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=facial feature tracking and expression recognition for sign language&sort=relevance" target="_blank">[s2]</a></td><td></td><td>014b8df0180f33b9fea98f34ae611c6447d761d2</td></tr><tr><td>vqa</td><td>VQA</td><td>VQA: Visual Question Answering</td><td>VQA: Visual Question Answering</td><td><a href="https://arxiv.org/pdf/1505.00468.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=vqa: visual question answering&sort=relevance" target="_blank">[s2]</a></td><td></td><td>01959ef569f74c286956024866c1d107099199f7</td></tr><tr><td>kitti</td><td>KITTI</td><td>Vision meets Robotics: The KITTI Dataset</td><td>Vision meets robotics: The KITTI dataset</td><td><a href="https://pdfs.semanticscholar.org/026e/3363b7f76b51cc711886597a44d5f1fd1de2.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=vision meets robotics: the kitti dataset&sort=relevance" target="_blank">[s2]</a></td><td></td><td>026e3363b7f76b51cc711886597a44d5f1fd1de2</td></tr><tr><td>ilids_mcts</td><td>i-LIDS</td><td>Imagery Library for Intelligent Detection Systems: The i-LIDS User Guide</td><td>Imagery Library for Intelligent Detection Systems (i-LIDS); A Standard for Testing Video Based Detection Systems</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=imagery library for intelligent detection systems: the i-lids user guide&sort=relevance" target="_blank">[s2]</a></td><td></td><td>0297448f3ed948e136bb06ceff10eccb34e5bb77</td></tr><tr><td>ucf_selfie</td><td>UCF Selfie</td><td>How to Take a Good Selfie?</td><td>How to Take a Good Selfie?</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=how to take a good selfie?&sort=relevance" target="_blank">[s2]</a></td><td></td><td>041d3eedf5e45ce5c5229f0181c5c576ed1fafd6</td></tr><tr><td>fiw_300</td><td>300-W</td><td>300 Faces in-the-Wild Challenge: The first facial landmark localization Challenge</td><td>300 Faces in-the-Wild Challenge: The First Facial Landmark Localization Challenge</td><td><a href="http://ibug.doc.ic.ac.uk/media/uploads/documents/sagonas_iccv_2013_300_w.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=300 faces in-the-wild challenge: the first facial landmark localization challenge&sort=relevance" target="_blank">[s2]</a></td><td></td><td>044d9a8c61383312cdafbcc44b9d00d650b21c70</td></tr><tr><td>chokepoint</td><td>ChokePoint</td><td>Patch-based Probabilistic Image Quality Assessment for Face Selection and Improved Video-based Face Recognition</td><td>Patch-based probabilistic image quality assessment for face selection and improved video-based face recognition</td><td><a href="https://arxiv.org/pdf/1304.0869.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=patch-based probabilistic image quality assessment for face selection and improved video-based face recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>0486214fb58ee9a04edfe7d6a74c6d0f661a7668</td></tr><tr><td>hipsterwars</td><td>Hipsterwars</td><td>Hipster Wars: Discovering Elements of Fashion Styles</td><td>Hipster Wars: Discovering Elements of Fashion Styles</td><td><a href="http://acberg.com/papers/hipster_eccv14.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=hipster wars: discovering elements of fashion styles&sort=relevance" target="_blank">[s2]</a></td><td></td><td>04c2cda00e5536f4b1508cbd80041e9552880e67</td></tr><tr><td>psu</td><td>PSU</td><td>Vision-based Analysis of Small Groups in Pedestrian Crowds</td><td>Vision-Based Analysis of Small Groups in Pedestrian Crowds</td><td><a href="http://vc.cs.nthu.edu.tw/home/paper/codfiles/htchiang/201212250411/newp12.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=vision-based analysis of small groups in pedestrian crowds&sort=relevance" target="_blank">[s2]</a></td><td></td><td>066000d44d6691d27202896691f08b27117918b9</td></tr><tr><td>ifdb</td><td>IFDB</td><td>Iranian Face Database and Evaluation with a New Detection Algorithm</td><td>Iranian Face Database and Evaluation with a New Detection Algorithm</td><td><a href="https://pdfs.semanticscholar.org/066d/71fcd997033dce4ca58df924397dfe0b5fd1.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=iranian face database and evaluation with a new detection algorithm&sort=relevance" target="_blank">[s2]</a></td><td></td><td>066d71fcd997033dce4ca58df924397dfe0b5fd1</td></tr><tr><td>columbia_gaze</td><td>Columbia Gaze</td><td>Gaze Locking: Passive Eye Contact Detection for Human–Object Interaction</td><td>Gaze locking: passive eye contact detection for human-object interaction</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=gaze locking: passive eye contact detection for human–object interaction&sort=relevance" target="_blank">[s2]</a></td><td>Columbia University</td><td>06f02199690961ba52997cde1527e714d2b3bf8f</td></tr><tr><td>d3dfacs</td><td>D3DFACS</td><td>A FACS Valid 3D Dynamic Action Unit database with Applications to 3D Dynamic Morphable Facial Modelling</td><td>A FACS valid 3D dynamic action unit database with applications to 3D dynamic morphable facial modeling</td><td><a href="http://www.cs.bath.ac.uk/~dpc/D3DFACS/ICCV_final_2011.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a facs valid 3d dynamic action unit database with applications to 3d dynamic morphable facial modelling&sort=relevance" target="_blank">[s2]</a></td><td></td><td>070de852bc6eb275d7ca3a9cdde8f6be8795d1a3</td></tr><tr><td>mit_cbcl</td><td>MIT CBCL</td><td>Component-based Face Recognition with 3D Morphable Models</td><td>Component-Based Face Recognition with 3D Morphable Models</td><td><a href="http://cbcl.mit.edu/cbcl/publications/theses/thesis-huang.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=component-based face recognition with 3d morphable models&sort=relevance" target="_blank">[s2]</a></td><td></td><td>079a0a3bf5200994e1f972b1b9197bf2f90e87d4</td></tr><tr><td>uccs</td><td>UCCS</td><td>Large scale unconstrained open set face database</td><td>Large scale unconstrained open set face database</td><td><a href="http://vast.uccs.edu/~tboult/PAPERS/BTAS13-Sapkota-Boult-UCCSFaceDB.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=large scale unconstrained open set face database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>07fcbae86f7a3ad3ea1cf95178459ee9eaf77cb1</td></tr><tr><td>uccs</td><td>UCCS</td><td>Unconstrained Face Detection and Open-Set Face Recognition Challenge</td><td>Unconstrained Face Detection and Open-Set Face Recognition Challenge</td><td><a href="https://arxiv.org/pdf/1708.02337.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=unconstrained face detection and open-set face recognition challenge&sort=relevance" target="_blank">[s2]</a></td><td></td><td>d4f1eb008eb80595bcfdac368e23ae9754e1e745</td></tr><tr><td>kin_face</td><td>UB KinFace</td><td>Understanding Kin Relationships in a Photo</td><td>Understanding Kin Relationships in a Photo</td><td><a href="http://www1.ece.neu.edu/~yunfu/papers/Kinship-TMM.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=understanding kin relationships in a photo&sort=relevance" target="_blank">[s2]</a></td><td></td><td>08f6745bc6c1b0fb68953ea61054bdcdde6d2fc7</td></tr><tr><td>raid</td><td>RAiD</td><td>Consistent Re-identification in a Camera Network</td><td>Consistent Re-identification in a Camera Network</td><td><a href="http://cs-people.bu.edu/dasabir/papers/ECCV14_Poster.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=consistent re-identification in a camera network&sort=relevance" target="_blank">[s2]</a></td><td></td><td>09d78009687bec46e70efcf39d4612822e61cb8c</td></tr><tr><td>pipa</td><td>PIPA</td><td>Beyond Frontal Faces: Improving Person Recognition Using Multiple Cues</td><td>Beyond frontal faces: Improving Person Recognition using multiple cues</td><td><a href="https://arxiv.org/pdf/1501.05703.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=beyond frontal faces: improving person recognition using multiple cues&sort=relevance" target="_blank">[s2]</a></td><td></td><td>0a85bdff552615643dd74646ac881862a7c7072d</td></tr><tr><td>kinectface</td><td>KinectFaceDB</td><td>KinectFaceDB: A Kinect Database for Face Recognition</td><td>KinectFaceDB: A Kinect Database for Face Recognition</td><td><a href="http://www.eurecom.fr/fr/publication/4393/download/mm-publi-4393.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=kinectfacedb: a kinect database for face recognition&sort=relevance" target="_blank">[s2]</a></td><td>University of North Carolina at Chapel Hill</td><td>0b440695c822a8e35184fb2f60dcdaa8a6de84ae</td></tr><tr><td>prw</td><td>PRW</td><td>Person Re-identification in the Wild</td><td>Person Re-identification in the Wild</td><td><a href="https://arxiv.org/pdf/1604.02531.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=person re-identification in the wild&sort=relevance" target="_blank">[s2]</a></td><td></td><td>0b84f07af44f964817675ad961def8a51406dd2e</td></tr><tr><td>feret</td><td>FERET</td><td>The FERET Verification Testing Protocol for Face Recognition Algorithms</td><td>The FERET Verification Testing Protocol for Face Recognition Algorithms</td><td><a href="https://pdfs.semanticscholar.org/8d2a/1c768fce6f71584dd993fb97e7b6419aaf60.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the feret verification testing protocol for face recognition algorithms&sort=relevance" target="_blank">[s2]</a></td><td></td><td>0c4a139bb87c6743c7905b29a3cfec27a5130652</td></tr><tr><td>graz</td><td>Graz Pedestrian</td><td>Weak Hypotheses and Boosting for Generic Object Detection and Recognition</td><td>Weak Hypotheses and Boosting for Generic Object Detection and Recognition</td><td><a href="https://pdfs.semanticscholar.org/0c91/808994a250d7be332400a534a9291ca3b60e.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=weak hypotheses and boosting for generic object detection and recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>0c91808994a250d7be332400a534a9291ca3b60e</td></tr><tr><td>ijb_c</td><td>IJB-B</td><td>IARPA Janus Benchmark-B Face Dataset</td><td>IARPA Janus Benchmark-B Face Dataset</td><td><a href="http://biometrics.cse.msu.edu/Publications/Face/Whitelametal_IARPAJanusBenchmark-BFaceDataset_CVPRW17.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=iarpa janus benchmark-b face dataset&sort=relevance" target="_blank">[s2]</a></td><td></td><td>0cb2dd5f178e3a297a0c33068961018659d0f443</td></tr><tr><td>casablanca</td><td>Casablanca</td><td>Context-aware {CNNs} for person head detection</td><td>Context-Aware CNNs for Person Head Detection</td><td><a href="https://arxiv.org/pdf/1511.07917.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=context-aware {cnns} for person head detection&sort=relevance" target="_blank">[s2]</a></td><td></td><td>0ceda9dae8b9f322df65ca2ef02caca9758aec6f</td></tr><tr><td>hollywood_headset</td><td>HollywoodHeads</td><td>Context-aware CNNs for person head detection</td><td>Context-Aware CNNs for Person Head Detection</td><td><a href="https://arxiv.org/pdf/1511.07917.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=context-aware cnns for person head detection&sort=relevance" target="_blank">[s2]</a></td><td></td><td>0ceda9dae8b9f322df65ca2ef02caca9758aec6f</td></tr><tr><td>lag</td><td>LAG</td><td>Large Age-Gap Face Verification by Feature Injection in Deep Networks</td><td>Large age-gap face verification by feature injection in deep networks</td><td><a href="https://arxiv.org/pdf/1602.06149.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=large age-gap face verification by feature injection in deep networks&sort=relevance" target="_blank">[s2]</a></td><td></td><td>0d2dd4fc016cb6a517d8fb43a7cc3ff62964832e</td></tr><tr><td>face_scrub</td><td>FaceScrub</td><td>A data-driven approach to cleaning large face datasets</td><td>A data-driven approach to cleaning large face datasets</td><td><a href="http://stefan.winkler.net/Publications/icip2014a.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a data-driven approach to cleaning large face datasets&sort=relevance" target="_blank">[s2]</a></td><td></td><td>0d3bb75852098b25d90f31d2f48fd0cb4944702b</td></tr><tr><td>stickmen_family</td><td>We Are Family Stickmen</td><td>We Are Family: Joint Pose Estimation of Multiple Persons</td><td>We Are Family: Joint Pose Estimation of Multiple Persons</td><td><a href="http://eprints.pascal-network.org/archive/00007964/01/eichner10eccv.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=we are family: joint pose estimation of multiple persons&sort=relevance" target="_blank">[s2]</a></td><td></td><td>0dc11a37cadda92886c56a6fb5191ded62099c28</td></tr><tr><td>mpii_gaze</td><td>MPIIGaze</td><td>Appearance-based Gaze Estimation in the Wild</td><td>Appearance-based gaze estimation in the wild</td><td><a href="https://arxiv.org/pdf/1504.02863.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=appearance-based gaze estimation in the wild&sort=relevance" target="_blank">[s2]</a></td><td></td><td>0df0d1adea39a5bef318b74faa37de7f3e00b452</td></tr><tr><td>afw</td><td>AFW</td><td>Face detection, pose estimation and landmark localization in the wild</td><td>Face detection, pose estimation, and landmark localization in the wild</td><td><a href="http://crcv.ucf.edu/courses/CAP6412/Spring2013/papers/zhu-ramanan-face-cvpr12.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=face detection, pose estimation and landmark localization in the wild&sort=relevance" target="_blank">[s2]</a></td><td></td><td>0e986f51fe45b00633de9fd0c94d082d2be51406</td></tr><tr><td>voc</td><td>VOC</td><td>The PASCAL Visual Object Classes (VOC) Challenge</td><td>The Pascal Visual Object Classes (VOC) Challenge</td><td><a href="http://eprints.pascal-network.org/archive/00006187/01/PascalVOC_IJCV2009.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the pascal visual object classes (voc) challenge&sort=relevance" target="_blank">[s2]</a></td><td></td><td>0ee1916a0cb2dc7d3add086b5f1092c3d4beb38a</td></tr><tr><td>feret</td><td>FERET</td><td>The FERET Evaluation Methodology for Face-Recognition Algorithms</td><td>The FERET Evaluation Methodology for Face-Recognition Algorithms</td><td><a href="https://pdfs.semanticscholar.org/5099/7a5605c1f61e09e9a96789ed7495be6625aa.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the feret evaluation methodology for face-recognition algorithms&sort=relevance" target="_blank">[s2]</a></td><td></td><td>0f0fcf041559703998abf310e56f8a2f90ee6f21</td></tr><tr><td>imdb_wiki</td><td>IMDB</td><td>Deep expectation of real and apparent age from a single image without facial landmarks</td><td>Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks</td><td><a href="http://www.vision.ee.ethz.ch/en/publications/papers/articles/eth_biwi_01299.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=deep expectation of real and apparent age from a single image without facial landmarks&sort=relevance" target="_blank">[s2]</a></td><td></td><td>10195a163ab6348eef37213a46f60a3d87f289c5</td></tr><tr><td>inria_person</td><td>INRIA Pedestrian</td><td>Histograms of Oriented Gradients for Human Detection</td><td>Histograms of oriented gradients for human detection</td><td><a href="http://courses.cs.washington.edu/courses/cse576/12sp/notes/CVPR2005_HOG.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=histograms of oriented gradients for human detection&sort=relevance" target="_blank">[s2]</a></td><td>INRIA Rhone-Alps, Montbonnot, France</td><td>10d6b12fa07c7c8d6c8c3f42c7f1c061c131d4c5</td></tr><tr><td>graz</td><td>Graz Pedestrian</td><td>Object Recognition Using Segmentation for Feature Detection</td><td>Object recognition using segmentation for feature detection</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=object recognition using segmentation for feature detection&sort=relevance" target="_blank">[s2]</a></td><td>Inst. of Comput. Sci., Univ. of Leoben, Austria</td><td>12ad3b5bbbf407f8e54ea692c07633d1a867c566</td></tr><tr><td>lfw_a</td><td>LFW-a</td><td>Effective Unconstrained Face Recognition by Combining Multiple Descriptors and Learned Background Statistics</td><td>Effective Unconstrained Face Recognition by Combining Multiple Descriptors and Learned Background Statistics</td><td><a href="http://www.cs.tau.ac.il/~wolf/papers/jpatchlbp.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=effective unconstrained face recognition by combining multiple descriptors and learned background statistics&sort=relevance" target="_blank">[s2]</a></td><td></td><td>133f01aec1534604d184d56de866a4bd531dac87</td></tr><tr><td>hrt_transgender</td><td>HRT Transgender</td><td>Is the Eye Region More Reliable Than the Face? A Preliminary Study of Face-based Recognition on a Transgender Dataset</td><td>Is the eye region more reliable than the face? A preliminary study of face-based recognition on a transgender dataset</td><td><a href="http://www.csis.pace.edu/~ctappert/dps/2013BTAS/Papers/Paper%20139/PID2859389.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=is the eye region more reliable than the face? a preliminary study of face-based recognition on a transgender dataset&sort=relevance" target="_blank">[s2]</a></td><td></td><td>137aa2f891d474fce1e7a1d1e9b3aefe21e22b34</td></tr><tr><td>eth_andreas_ess</td><td>ETHZ Pedestrian</td><td>Depth and Appearance for Mobile Scene Analysis</td><td>Depth and Appearance for Mobile Scene Analysis</td><td><a href="http://www.mmp.rwth-aachen.de/publications/pdf/ess-depthandappearance-iccv07-poster.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=depth and appearance for mobile scene analysis&sort=relevance" target="_blank">[s2]</a></td><td>ETH Zurich</td><td>13f06b08f371ba8b5d31c3e288b4deb61335b462</td></tr><tr><td>lfw_p</td><td>LFWP</td><td>Localizing Parts of Faces Using a Consensus of Exemplars</td><td>Localizing Parts of Faces Using a Consensus of Exemplars</td><td><a href="http://neerajkumar.org/projects/face-parts/base/papers/nk_cvpr2011_faceparts.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=localizing parts of faces using a consensus of exemplars&sort=relevance" target="_blank">[s2]</a></td><td></td><td>140438a77a771a8fb656b39a78ff488066eb6b50</td></tr><tr><td>ijb_c</td><td>IJB-A</td><td>Pushing the Frontiers of Unconstrained Face Detection and Recognition: IARPA Janus Benchmark A</td><td>Pushing the frontiers of unconstrained face detection and recognition: IARPA Janus Benchmark A</td><td><a href="http://biometrics.cse.msu.edu/Publications/Face/Klareetal_UnconstrainedFaceDetectionRecognitionJanus_CVPR15.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=pushing the frontiers of unconstrained face detection and recognition: iarpa janus benchmark a&sort=relevance" target="_blank">[s2]</a></td><td></td><td>140c95e53c619eac594d70f6369f518adfea12ef</td></tr><tr><td>vgg_faces</td><td>VGG Face</td><td>Deep Face Recognition</td><td>Deep Face Recognition</td><td><a href="https://pdfs.semanticscholar.org/f372/ab9b3270d4e4f6a0258c83c2736c3a5c0454.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=deep face recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>162ea969d1929ed180cc6de9f0bf116993ff6e06</td></tr><tr><td>prid</td><td>PRID</td><td>Person Re-Identification by Descriptive and Discriminative Classification</td><td>Person Re-identification by Descriptive and Discriminative Classification</td><td><a href="https://pdfs.semanticscholar.org/4c1b/f0592be3e535faf256c95e27982db9b3d3d3.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=person re-identification by descriptive and discriminative classification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>16c7c31a7553d99f1837fc6e88e77b5ccbb346b8</td></tr><tr><td>umb</td><td>UMB</td><td>UMB-DB: A Database of Partially Occluded 3D Faces</td><td>UMB-DB: A database of partially occluded 3D faces</td><td><a href="http://face.cs.kit.edu/befit/workshop2011/pdf/slides/claudio_cusano-slides.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=umb-db: a database of partially occluded 3d faces&sort=relevance" target="_blank">[s2]</a></td><td></td><td>16e8b0a1e8451d5f697b94c0c2b32a00abee1d52</td></tr><tr><td>celeba_plus</td><td>CelebFaces+</td><td>Deep Learning Face Representation from Predicting 10,000 Classes</td><td>Deep Learning Face Representation from Predicting 10,000 Classes</td><td><a href="http://mmlab.ie.cuhk.edu.hk/pdf/YiSun_CVPR14.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=deep learning face representation from predicting 10,000 classes&sort=relevance" target="_blank">[s2]</a></td><td></td><td>177bc509dd0c7b8d388bb47403f28d6228c14b5c</td></tr><tr><td>geofaces</td><td>GeoFaces</td><td>GeoFaceExplorer: Exploring the Geo-Dependence of Facial Attributes</td><td>GeoFaceExplorer: exploring the geo-dependence of facial attributes</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=geofaceexplorer: exploring the geo-dependence of facial attributes&sort=relevance" target="_blank">[s2]</a></td><td></td><td>17b46e2dad927836c689d6787ddb3387c6159ece</td></tr><tr><td>deep_fashion</td><td>DeepFashion</td><td>DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations</td><td>DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations</td><td><a href="http://personal.ie.cuhk.edu.hk/~lz013/papers/deepfashion_poster.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=deepfashion: powering robust clothes recognition and retrieval with rich annotations&sort=relevance" target="_blank">[s2]</a></td><td></td><td>18010284894ed0edcca74e5bf768ee2e15ef7841</td></tr><tr><td>pilot_parliament</td><td>PPB</td><td>Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification</td><td>Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification</td><td><a href="https://pdfs.semanticscholar.org/03c1/fc9c3339813ed81ad0de540132f9f695a0f8.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=gender shades: intersectional accuracy disparities in commercial gender classification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>18858cc936947fc96b5c06bbe3c6c2faa5614540</td></tr><tr><td>frgc</td><td>FRGC</td><td>Overview of the Face Recognition Grand Challenge</td><td>Overview of the face recognition grand challenge</td><td><a href="http://ivizlab.sfu.ca/arya/Papers/IEEE/Proceedings/C%20V%20P%20R-%2005/Face%20Recognition%20Grand%20Challenge.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=overview of the face recognition grand challenge&sort=relevance" target="_blank">[s2]</a></td><td>NIST</td><td>18ae7c9a4bbc832b8b14bc4122070d7939f5e00e</td></tr><tr><td>yale_faces</td><td>YaleFaces</td><td>From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose</td><td>From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose</td><td><a href="https://pdfs.semanticscholar.org/97bb/c2b439a79d4dc0dc7199d71ed96ad5e3fd0e.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=from few to many: illumination cone models for face recognition under variable lighting and pose&sort=relevance" target="_blank">[s2]</a></td><td></td><td>18c72175ddbb7d5956d180b65a96005c100f6014</td></tr><tr><td>york_3d</td><td>UOY 3D Face Database</td><td>Three-Dimensional Face Recognition: An Eigensurface Approach</td><td>Three-dimensional face recognition: an eigensurface approach</td><td><a href="http://www-users.cs.york.ac.uk/~nep/research/3Dface/tomh/3DFaceRecognition-Eigensurface-ICIP(web)2.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=three-dimensional face recognition: an eigensurface approach&sort=relevance" target="_blank">[s2]</a></td><td></td><td>19d1b811df60f86cbd5e04a094b07f32fff7a32a</td></tr><tr><td>sports_videos_in_the_wild</td><td>SVW</td><td>Sports Videos in the Wild (SVW): A Video Dataset for Sports Analysis</td><td>Sports Videos in the Wild (SVW): A video dataset for sports analysis</td><td><a href="http://cse.msu.edu/~liuxm/publication/Safdarnejad_Liu_Udpa_Andrus_Wood_Craven_FG2015.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=sports videos in the wild (svw): a video dataset for sports analysis&sort=relevance" target="_blank">[s2]</a></td><td></td><td>1a40092b493c6b8840257ab7f96051d1a4dbfeb2</td></tr><tr><td>jpl_pose</td><td>JPL-Interaction dataset</td><td>First-Person Activity Recognition: What Are They Doing to Me?</td><td>First-Person Activity Recognition: What Are They Doing to Me?</td><td><a href="http://michaelryoo.com/papers/cvpr2013_ryoo.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=first-person activity recognition: what are they doing to me?&sort=relevance" target="_blank">[s2]</a></td><td></td><td>1aad2da473888cb7ebc1bfaa15bfa0f1502ce005</td></tr><tr><td>adience</td><td>Adience</td><td>Age and Gender Estimation of Unfiltered Faces</td><td>Age and Gender Estimation of Unfiltered Faces</td><td><a href="http://www.openu.ac.il/home/hassner/Adience/EidingerEnbarHassner_tifs.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=age and gender estimation of unfiltered faces&sort=relevance" target="_blank">[s2]</a></td><td></td><td>1be498d4bbc30c3bfd0029114c784bc2114d67c0</td></tr><tr><td>youtube_poses</td><td>YouTube Pose</td><td>Personalizing Human Video Pose Estimation</td><td>Personalizing Human Video Pose Estimation</td><td><a href="https://arxiv.org/pdf/1511.06676.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=personalizing human video pose estimation&sort=relevance" target="_blank">[s2]</a></td><td></td><td>1c2802c2199b6d15ecefe7ba0c39bfe44363de38</td></tr><tr><td>caltech_pedestrians</td><td>Caltech Pedestrians</td><td>Pedestrian Detection: A Benchmark</td><td>Pedestrian detection: A benchmark</td><td><a href="http://vision.lbl.gov/Conferences/cvpr/Papers/data/papers/1378.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=pedestrian detection: a benchmark&sort=relevance" target="_blank">[s2]</a></td><td></td><td>1dc35905a1deff8bc74688f2d7e2f48fd2273275</td></tr><tr><td>immediacy</td><td>Immediacy</td><td>Multi-task Recurrent Neural Network for Immediacy Prediction</td><td>Multi-task Recurrent Neural Network for Immediacy Prediction</td><td><a href="http://openaccess.thecvf.com/content_iccv_2015/papers/Chu_Multi-Task_Recurrent_Neural_ICCV_2015_paper.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=multi-task recurrent neural network for immediacy prediction&sort=relevance" target="_blank">[s2]</a></td><td></td><td>1e3df3ca8feab0b36fd293fe689f93bb2aaac591</td></tr><tr><td>cafe</td><td>CAFE</td><td>The Child Affective Facial Expression (CAFE) Set: Validity and reliability from untrained adults</td><td>The Child Affective Facial Expression (CAFE) set: validity and reliability from untrained adults</td><td><a href="https://pdfs.semanticscholar.org/2038/8099cc415c772926e47bcbbe554e133343d1.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the child affective facial expression (cafe) set: validity and reliability from untrained adults&sort=relevance" target="_blank">[s2]</a></td><td></td><td>20388099cc415c772926e47bcbbe554e133343d1</td></tr><tr><td>bbc_pose</td><td>BBC Pose</td><td>Automatic and Efficient Human Pose Estimation for Sign Language Videos</td><td>Automatic and Efficient Human Pose Estimation for Sign Language Videos</td><td><a href="http://tomas.pfister.fi/files/charles13ijcv.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=automatic and efficient human pose estimation for sign language videos&sort=relevance" target="_blank">[s2]</a></td><td></td><td>213a579af9e4f57f071b884aa872651372b661fd</td></tr><tr><td>3d_rma</td><td>3D-RMA</td><td>Automatic 3D Face Authentication</td><td>Automatic 3D face authentication</td><td><a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.31.9190&rep=rep1&type=pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=automatic 3d face authentication&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2160788824c4c29ffe213b2cbeb3f52972d73f37</td></tr><tr><td>large_scale_person_search</td><td>Large Scale Person Search</td><td>End-to-End Deep Learning for Person Search</td><td>End-to-End Deep Learning for Person Search</td><td><a href="https://pdfs.semanticscholar.org/2161/f6b7ee3c0acc81603b01dc0df689683577b9.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=end-to-end deep learning for person search&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2161f6b7ee3c0acc81603b01dc0df689683577b9</td></tr><tr><td>images_of_groups</td><td>Images of Groups</td><td>Understanding Groups of Images of People</td><td>Understanding images of groups of people</td><td><a href="http://chenlab.ece.cornell.edu/people/Andy/Andy_files/cvpr09.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=understanding groups of images of people&sort=relevance" target="_blank">[s2]</a></td><td>Carnegie Mellon University</td><td>21d9d0deed16f0ad62a4865e9acf0686f4f15492</td></tr><tr><td>rap_pedestrian</td><td>RAP</td><td>A Richly Annotated Dataset for Pedestrian Attribute Recognition</td><td>A Richly Annotated Dataset for Pedestrian Attribute Recognition</td><td><a href="https://arxiv.org/pdf/1603.07054.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a richly annotated dataset for pedestrian attribute recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>221c18238b829c12b911706947ab38fd017acef7</td></tr><tr><td>mot</td><td>MOT</td><td>Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics</td><td>Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics</td><td><a href="https://pdfs.semanticscholar.org/2e0b/00f4043e2d4b04c59c88bb54bcd907d0dcd4.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=evaluating multiple object tracking performance: the clear mot metrics&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2258e01865367018ed6f4262c880df85b94959f8</td></tr><tr><td>saivt</td><td>SAIVT SoftBio</td><td>A Database for Person Re-Identification in Multi-Camera Surveillance Networks</td><td>A Database for Person Re-Identification in Multi-Camera Surveillance Networks</td><td><a href="http://eprints.qut.edu.au/53437/3/Bialkowski_Database4PersonReID_DICTA.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a database for person re-identification in multi-camera surveillance networks&sort=relevance" target="_blank">[s2]</a></td><td></td><td>22646e00a7ba34d1b5fbe3b1efcd91a1e1be3c2b</td></tr><tr><td>pets</td><td>PETS 2017</td><td>PETS 2017: Dataset and Challenge</td><td>PETS 2017: Dataset and Challenge</td><td><a href="http://tahirnawaz.com/papers/2017_CVPRW_PETS2017Dataset_Luis_Nawaz_Cane_Ferryman.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=pets 2017: dataset and challenge&sort=relevance" target="_blank">[s2]</a></td><td></td><td>22909dd19a0ec3b6065334cb5be5392cb24d839d</td></tr><tr><td>gallagher</td><td>Gallagher</td><td>Clothing Cosegmentation for Recognizing People</td><td>Clothing cosegmentation for recognizing people</td><td><a href="http://amp.ece.cmu.edu/people/Andy/Andy_files/2670CVPR08Gallagher.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=clothing cosegmentation for recognizing people&sort=relevance" target="_blank">[s2]</a></td><td>Carnegie Mellon University</td><td>22ad2c8c0f4d6aa4328b38d894b814ec22579761</td></tr><tr><td>expw</td><td>ExpW</td><td>From Facial Expression Recognition to Interpersonal Relation Prediction</td><td>From Facial Expression Recognition to Interpersonal Relation Prediction</td><td><a href="https://arxiv.org/pdf/1609.06426.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=from facial expression recognition to interpersonal relation prediction&sort=relevance" target="_blank">[s2]</a></td><td></td><td>22f656d0f8426c84a33a267977f511f127bfd7f3</td></tr><tr><td>mifs</td><td>MIFS</td><td>Spoofing Faces Using Makeup: An Investigative Study</td><td>Spoofing faces using makeup: An investigative study</td><td><a href="http://www.cse.msu.edu/~rossarun/pubs/ChenFaceMakeupSpoof_ISBA2017.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=spoofing faces using makeup: an investigative study&sort=relevance" target="_blank">[s2]</a></td><td>INRIA Méditerranée</td><td>23e824d1dfc33f3780dd18076284f07bd99f1c43</td></tr><tr><td>cohn_kanade</td><td>CK</td><td>Comprehensive Database for Facial Expression Analysis</td><td>Comprehensive Database for Facial Expression Analysis</td><td><a href="https://pdfs.semanticscholar.org/23fc/83c8cfff14a16df7ca497661264fc54ed746.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=comprehensive database for facial expression analysis&sort=relevance" target="_blank">[s2]</a></td><td></td><td>23fc83c8cfff14a16df7ca497661264fc54ed746</td></tr><tr><td>cas_peal</td><td>CAS-PEAL</td><td>The CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations</td><td>The CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations</td><td><a href="http://www.jdl.ac.cn/peal/files/ieee_smc_a_gao_cas-peal.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the cas-peal large-scale chinese face database and baseline evaluations&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2485c98aa44131d1a2f7d1355b1e372f2bb148ad</td></tr><tr><td>faceplace</td><td>Face Place</td><td>Recognizing disguised faces</td><td>Recognizing disguised faces</td><td><a href="https://pdfs.semanticscholar.org/d936/7ceb0be378c3a9ddf7cb741c678c1a3c574c.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=recognizing disguised faces&sort=relevance" target="_blank">[s2]</a></td><td></td><td>25474c21613607f6bb7687a281d5f9d4ffa1f9f3</td></tr><tr><td>afew_va</td><td>AFEW-VA</td><td>AFEW-VA database for valence and arousal estimation in-the-wild</td><td>AFEW-VA database for valence and arousal estimation in-the-wild</td><td><a href="https://ibug.doc.ic.ac.uk/media/uploads/documents/afew-va.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=afew-va database for valence and arousal estimation in-the-wild&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2624d84503bc2f8e190e061c5480b6aa4d89277a</td></tr><tr><td>cofw</td><td>COFW</td><td>Robust face landmark estimation under occlusion</td><td>Robust Face Landmark Estimation under Occlusion</td><td><a href="http://authors.library.caltech.edu/45988/1/ICCV13%20Burgos-Artizzu.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=robust face landmark estimation under occlusion&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2724ba85ec4a66de18da33925e537f3902f21249</td></tr><tr><td>duke_mtmc</td><td>Duke MTMC</td><td>Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking</td><td>Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking</td><td><a href="https://arxiv.org/pdf/1609.01775.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=performance measures and a data set for multi-target, multi-camera tracking&sort=relevance" target="_blank">[s2]</a></td><td></td><td>27a2fad58dd8727e280f97036e0d2bc55ef5424c</td></tr><tr><td>mot</td><td>MOT</td><td>Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking</td><td>Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking</td><td><a href="https://arxiv.org/pdf/1609.01775.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=performance measures and a data set for multi-target, multi-camera tracking&sort=relevance" target="_blank">[s2]</a></td><td></td><td>27a2fad58dd8727e280f97036e0d2bc55ef5424c</td></tr><tr><td>h3d</td><td>H3D</td><td>Poselets: Body Part Detectors Trained Using 3D Human Pose Annotations</td><td>Poselets: Body part detectors trained using 3D human pose annotations</td><td><a href="http://http.cs.berkeley.edu/Research/Projects/CS/vision/human/poselets_iccv09.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=poselets: body part detectors trained using 3d human pose annotations&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2830fb5282de23d7784b4b4bc37065d27839a412</td></tr><tr><td>megaface</td><td>MegaFace</td><td>Level Playing Field for Million Scale Face Recognition</td><td>Level Playing Field for Million Scale Face Recognition</td><td><a href="https://arxiv.org/pdf/1705.00393.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=level playing field for million scale face recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>28d4e027c7e90b51b7d8908fce68128d1964668a</td></tr><tr><td>msceleb</td><td>MsCeleb</td><td>MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition</td><td>MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition</td><td><a href="https://arxiv.org/pdf/1607.08221.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=ms-celeb-1m: a dataset and benchmark for large-scale face recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>291265db88023e92bb8c8e6390438e5da148e8f5</td></tr><tr><td>ferplus</td><td>FER+</td><td>Training Deep Networks for Facial Expression Recognition with Crowd-Sourced Label Distribution</td><td>Training deep networks for facial expression recognition with crowd-sourced label distribution</td><td><a href="https://arxiv.org/pdf/1608.01041.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=training deep networks for facial expression recognition with crowd-sourced label distribution&sort=relevance" target="_blank">[s2]</a></td><td></td><td>298cbc3dfbbb3a20af4eed97906650a4ea1c29e0</td></tr><tr><td>scface</td><td>SCface</td><td>SCface – surveillance cameras face database</td><td>SCface – surveillance cameras face database</td><td><a href="http://scface.org/SCface%20-%20Surveillance%20Cameras%20Face%20Database.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=scface – surveillance cameras face database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>29a705a5fa76641e0d8963f1fdd67ee4c0d92d3d</td></tr><tr><td>social_relation</td><td>Social Relation</td><td>Learning Social Relation Traits from Face Images</td><td>Learning Social Relation Traits from Face Images</td><td><a href="https://arxiv.org/pdf/1509.03936.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=learning social relation traits from face images&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2a171f8d14b6b8735001a11c217af9587d095848</td></tr><tr><td>peta</td><td>PETA</td><td>Pedestrian Attribute Recognition At Far Distance</td><td>Pedestrian Attribute Recognition At Far Distance</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=pedestrian attribute recognition at far distance&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2a4bbee0b4cf52d5aadbbc662164f7efba89566c</td></tr><tr><td>mmi_facial_expression</td><td>MMI Facial Expression Dataset</td><td>WEB-BASED DATABASE FOR FACIAL EXPRESSION ANALYSIS</td><td>Web-based database for facial expression analysis</td><td><a href="http://dev.pubs.doc.ic.ac.uk/Pantic-ICME05-2/Pantic-ICME05-2.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=web-based database for facial expression analysis&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2a75f34663a60ab1b04a0049ed1d14335129e908</td></tr><tr><td>bosphorus</td><td>The Bosphorus</td><td>Bosphorus Database for 3D Face Analysis</td><td>Bosphorus Database for 3D Face Analysis</td><td><a href="https://pdfs.semanticscholar.org/4254/fbba3846008f50671edc9cf70b99d7304543.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=bosphorus database for 3d face analysis&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2acf7e58f0a526b957be2099c10aab693f795973</td></tr><tr><td>yale_faces</td><td>YaleFaces</td><td>Acquiring Linear Subspaces for Face Recognition under Variable Lighting</td><td>Acquiring linear subspaces for face recognition under variable lighting</td><td><a href="http://vision.cornell.edu/se3/wp-content/uploads/2014/09/pami05.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=acquiring linear subspaces for face recognition under variable lighting&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2ad0ee93d029e790ebb50574f403a09854b65b7e</td></tr><tr><td>frav3d</td><td>FRAV3D</td><td>MULTIMODAL 2D, 2.5D & 3D FACE VERIFICATION</td><td>Multimodal 2D, 2.5D & 3D Face Verification</td><td><a href="http://www.researchgate.net/profile/Enrique_Cabello/publication/224057733_Multimodal_2D_2.5D__3D_Face_Verification/links/0912f50f522298fa95000000.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=multimodal 2d, 2.5d & 3d face verification&sort=relevance" target="_blank">[s2]</a></td><td>Universidad Rey Juan Carlos, Spain</td><td>2b926b3586399d028b46315d7d9fb9d879e4f79c</td></tr><tr><td>clothing_co_parsing</td><td>CCP</td><td>Clothing Co-Parsing by Joint Image Segmentation and Labeling</td><td>Clothing Co-parsing by Joint Image Segmentation and Labeling</td><td><a href="https://arxiv.org/pdf/1502.00739.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=clothing co-parsing by joint image segmentation and labeling&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2bf8541199728262f78d4dced6fb91479b39b738</td></tr><tr><td>texas_3dfrd</td><td>Texas 3DFRD</td><td>Anthropometric 3D Face Recognition</td><td>Anthropometric 3D Face Recognition</td><td><a href="http://live.ece.utexas.edu/publications/2010/sg_ijcv_june10.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=anthropometric 3d face recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2ce2560cf59db59ce313bbeb004e8ce55c5ce928</td></tr><tr><td>lfw</td><td>LFW</td><td>Labeled Faces in the Wild: Updates and New Reporting Procedures</td><td>Labeled Faces in the Wild : Updates and New Reporting Procedures</td><td><a href="https://pdfs.semanticscholar.org/2d34/82dcff69c7417c7b933f22de606a0e8e42d4.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=labeled faces in the wild: updates and new reporting procedures&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2d3482dcff69c7417c7b933f22de606a0e8e42d4</td></tr><tr><td>a_pascal_yahoo</td><td>aPascal</td><td>Describing Objects by their Attributes</td><td>Describing objects by their attributes</td><td><a href="http://vision.lbl.gov/Conferences/cvpr/Papers/data/papers/0468.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=describing objects by their attributes&sort=relevance" target="_blank">[s2]</a></td><td>University of Illinois, Urbana-Champaign</td><td>2e384f057211426ac5922f1b33d2aa8df5d51f57</td></tr><tr><td>3dpes</td><td>3DPeS</td><td>3DPes: 3D People Dataset for Surveillance and Forensics</td><td>3DPeS: 3D people dataset for surveillance and forensics</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=3dpes: 3d people dataset for surveillance and forensics&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2e8d0f1802e50cccfd3c0aabac0d0beab3a7846e</td></tr><tr><td>kin_face</td><td>UB KinFace</td><td>Genealogical Face Recognition based on UB KinFace Database</td><td>Genealogical face recognition based on UB KinFace database</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=genealogical face recognition based on ub kinface database&sort=relevance" target="_blank">[s2]</a></td><td>SUNY Buffalo</td><td>2eb84aaba316b095d4bb51da1a3e4365bbf9ab1d</td></tr><tr><td>qmul_grid</td><td>GRID</td><td>Time-delayed correlation analysis for multi-camera activity understanding</td><td>Time-Delayed Correlation Analysis for Multi-Camera Activity Understanding</td><td><a href="http://www.eecs.qmul.ac.uk/~ccloy/files/ijcv_2010.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=time-delayed correlation analysis for multi-camera activity understanding&sort=relevance" target="_blank">[s2]</a></td><td>Queen Mary University of London</td><td>2edb87494278ad11641b6cf7a3f8996de12b8e14</td></tr><tr><td>graz</td><td>Graz Pedestrian</td><td>Generic Object Recognition with Boosting</td><td>Generic object recognition with boosting</td><td><a href="http://www.cse.unr.edu/~bebis/CS773C/ObjectRecognition/Papers/Opelt06.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=generic object recognition with boosting&sort=relevance" target="_blank">[s2]</a></td><td>TU Graz</td><td>2eed184680edcdec8a3b605ad1a3ba8e8f7cc2e9</td></tr><tr><td>hrt_transgender</td><td>HRT Transgender</td><td>Investigating the Periocular-Based Face Recognition Across Gender Transformation</td><td>Investigating the Periocular-Based Face Recognition Across Gender Transformation</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=investigating the periocular-based face recognition across gender transformation&sort=relevance" target="_blank">[s2]</a></td><td>University of North Carolina at Wilmington</td><td>2f43b614607163abf41dfe5d17ef6749a1b61304</td></tr><tr><td>names_and_faces_news</td><td>News Dataset</td><td>Names and Faces</td><td>Names and faces in the news</td><td><a href="http://ttic.uchicago.edu/~mmaire/papers/pdf/names_faces_cvpr2004.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=names and faces&sort=relevance" target="_blank">[s2]</a></td><td></td><td>2fda164863a06a92d3a910b96eef927269aeb730</td></tr><tr><td>umd_faces</td><td>UMD</td><td>UMDFaces: An Annotated Face Dataset for Training Deep Networks</td><td>UMDFaces: An annotated face dataset for training deep networks</td><td><a href="https://arxiv.org/pdf/1611.01484.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=umdfaces: an annotated face dataset for training deep networks&sort=relevance" target="_blank">[s2]</a></td><td></td><td>31b05f65405534a696a847dd19c621b7b8588263</td></tr><tr><td>tiny_images</td><td>Tiny Images</td><td>80 million tiny images: a large dataset for non-parametric object and scene recognition</td><td>80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition</td><td><a href="http://cvcl.mit.edu/SUNSeminar/Torralba_80M_PAMI08.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=80 million tiny images: a large dataset for non-parametric object and scene recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>31b58ced31f22eab10bd3ee2d9174e7c14c27c01</td></tr><tr><td>feret</td><td>FERET</td><td>FERET ( Face Recognition Technology ) Recognition Algorithm Development and Test Results</td><td>FERET ( Face Recognition Technology ) Recognition Algorithm Development and Test Results</td><td><a href="https://pdfs.semanticscholar.org/31de/9b3dd6106ce6eec9a35991b2b9083395fd0b.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=feret ( face recognition technology ) recognition algorithm development and test results&sort=relevance" target="_blank">[s2]</a></td><td></td><td>31de9b3dd6106ce6eec9a35991b2b9083395fd0b</td></tr><tr><td>ucf_crowd</td><td>UCF-CC-50</td><td>Multi-Source Multi-Scale Counting in Extremely Dense Crowd Images</td><td>Multi-source Multi-scale Counting in Extremely Dense Crowd Images</td><td><a href="http://crcv-web.eecs.ucf.edu/papers/cvpr2013/Counting_V3o.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=multi-source multi-scale counting in extremely dense crowd images&sort=relevance" target="_blank">[s2]</a></td><td></td><td>32c801cb7fbeb742edfd94cccfca4934baec71da</td></tr><tr><td>cityscapes</td><td>Cityscapes</td><td>The Cityscapes Dataset for Semantic Urban Scene Understanding</td><td>The Cityscapes Dataset for Semantic Urban Scene Understanding</td><td><a href="https://arxiv.org/pdf/1604.01685.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the cityscapes dataset for semantic urban scene understanding&sort=relevance" target="_blank">[s2]</a></td><td></td><td>32cde90437ab5a70cf003ea36f66f2de0e24b3ab</td></tr><tr><td>tud_campus</td><td>TUD-Campus</td><td>People-Tracking-by-Detection and People-Detection-by-Tracking</td><td>People-tracking-by-detection and people-detection-by-tracking</td><td><a href="http://mplab.ucsd.edu/wp-content/uploads/CVPR2008/Conference/data/papers/243.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=people-tracking-by-detection and people-detection-by-tracking&sort=relevance" target="_blank">[s2]</a></td><td></td><td>3316521a5527c7700af8ae6aef32a79a8b83672c</td></tr><tr><td>tud_crossing</td><td>TUD-Crossing</td><td>People-Tracking-by-Detection and People-Detection-by-Tracking</td><td>People-tracking-by-detection and people-detection-by-tracking</td><td><a href="http://mplab.ucsd.edu/wp-content/uploads/CVPR2008/Conference/data/papers/243.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=people-tracking-by-detection and people-detection-by-tracking&sort=relevance" target="_blank">[s2]</a></td><td></td><td>3316521a5527c7700af8ae6aef32a79a8b83672c</td></tr><tr><td>tud_pedestrian</td><td>TUD-Pedestrian</td><td>People-Tracking-by-Detection and People-Detection-by-Tracking</td><td>People-tracking-by-detection and people-detection-by-tracking</td><td><a href="http://mplab.ucsd.edu/wp-content/uploads/CVPR2008/Conference/data/papers/243.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=people-tracking-by-detection and people-detection-by-tracking&sort=relevance" target="_blank">[s2]</a></td><td></td><td>3316521a5527c7700af8ae6aef32a79a8b83672c</td></tr><tr><td>mpii_human_pose</td><td>MPII Human Pose</td><td>2D Human Pose Estimation: New Benchmark and State of the Art Analysis</td><td>2D Human Pose Estimation: New Benchmark and State of the Art Analysis</td><td><a href="http://ei.is.tuebingen.mpg.de/uploads_file/attachment/attachment/168/andriluka14benchmark.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=2d human pose estimation: new benchmark and state of the art analysis&sort=relevance" target="_blank">[s2]</a></td><td></td><td>3325860c0c82a93b2eac654f5324dd6a776f609e</td></tr><tr><td>penn_fudan</td><td>Penn Fudan</td><td>Object Detection Combining Recognition and Segmentation</td><td>Object Detection Combining Recognition and Segmentation</td><td><a href="https://pdfs.semanticscholar.org/3394/168ff0719b03ff65bcea35336a76b21fe5e4.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=object detection combining recognition and segmentation&sort=relevance" target="_blank">[s2]</a></td><td></td><td>3394168ff0719b03ff65bcea35336a76b21fe5e4</td></tr><tr><td>wider</td><td>WIDER</td><td>Recognize Complex Events from Static Images by Fusing Deep Channels</td><td>Recognize complex events from static images by fusing deep channels</td><td><a href="http://openaccess.thecvf.com/content_cvpr_2015/supplemental/Xiong_Recognize_Complex_Events_2015_CVPR_supplemental.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=recognize complex events from static images by fusing deep channels&sort=relevance" target="_blank">[s2]</a></td><td></td><td>356b431d4f7a2a0a38cf971c84568207dcdbf189</td></tr><tr><td>coco_qa</td><td>COCO QA</td><td>Exploring Models and Data for Image Question Answering</td><td>Exploring Models and Data for Image Question Answering</td><td><a href="https://arxiv.org/pdf/1505.02074.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=exploring models and data for image question answering&sort=relevance" target="_blank">[s2]</a></td><td></td><td>35b0331dfcd2897abd5749b49ff5e2b8ba0f7a62</td></tr><tr><td>lfw</td><td>LFW</td><td>Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments</td><td>Labeled Faces in the Wild : A Database for Studying Face Recognition in Unconstrained Environments</td><td><a href="https://pdfs.semanticscholar.org/c6b3/ca4f939e36a9679a70e14ce8b1bbbc5618f3.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=labeled faces in the wild: a database for studying face recognition in unconstrained environments&sort=relevance" target="_blank">[s2]</a></td><td></td><td>370b5757a5379b15e30d619e4d3fb9e8e13f3256</td></tr><tr><td>rafd</td><td>RaFD</td><td>Presentation and validation of the Radboud Faces Database</td><td>Presentation and validation of the Radboud Faces Database</td><td><a href="https://pdfs.semanticscholar.org/3765/df816dc5a061bc261e190acc8bdd9d47bec0.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=presentation and validation of the radboud faces database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>3765df816dc5a061bc261e190acc8bdd9d47bec0</td></tr><tr><td>ufdd</td><td>UFDD</td><td>Pushing the Limits of Unconstrained Face Detection: a Challenge Dataset and Baseline Results</td><td>Pushing the Limits of Unconstrained Face Detection: a Challenge Dataset and Baseline Results</td><td><a href="https://arxiv.org/pdf/1804.10275.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=pushing the limits of unconstrained face detection: a challenge dataset and baseline results&sort=relevance" target="_blank">[s2]</a></td><td></td><td>377f2b65e6a9300448bdccf678cde59449ecd337</td></tr><tr><td>vmu</td><td>VMU</td><td>Can Facial Cosmetics Affect the Matching Accuracy of Face Recognition Systems?</td><td>Can facial cosmetics affect the matching accuracy of face recognition systems?</td><td><a href="http://www.cse.msu.edu/~climer/DantchevaChenRossFaceCosmetics_BTAS2012.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=can facial cosmetics affect the matching accuracy of face recognition systems?&sort=relevance" target="_blank">[s2]</a></td><td>West Virginia University</td><td>37d6f0eb074d207b53885bd2eb78ccc8a04be597</td></tr><tr><td>youtube_makeup</td><td>YMU</td><td>Can Facial Cosmetics Affect the Matching Accuracy of Face Recognition Systems?</td><td>Can facial cosmetics affect the matching accuracy of face recognition systems?</td><td><a href="http://www.cse.msu.edu/~climer/DantchevaChenRossFaceCosmetics_BTAS2012.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=can facial cosmetics affect the matching accuracy of face recognition systems?&sort=relevance" target="_blank">[s2]</a></td><td>West Virginia University</td><td>37d6f0eb074d207b53885bd2eb78ccc8a04be597</td></tr><tr><td>cuhk02</td><td>CUHK02</td><td>Locally Aligned Feature Transforms across Views</td><td>Locally Aligned Feature Transforms across Views</td><td><a href="http://vigir.missouri.edu/~gdesouza/Research/Conference_CDs/IEEE_CVPR2013/data/Papers/4989d594.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=locally aligned feature transforms across views&sort=relevance" target="_blank">[s2]</a></td><td></td><td>38b55d95189c5e69cf4ab45098a48fba407609b4</td></tr><tr><td>sdu_vid</td><td>SDU-VID</td><td>A Spatio-Temporal Appearance Representation for Video-Based Pedestrian Re-Identification</td><td>A Spatio-Temporal Appearance Representation for Video-Based Pedestrian Re-Identification</td><td><a href="http://openaccess.thecvf.com/content_iccv_2015/papers/Liu_A_Spatio-Temporal_Appearance_ICCV_2015_paper.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a spatio-temporal appearance representation for video-based pedestrian re-identification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>3b4ec8af470948a72a6ed37a9fd226719a874ebc</td></tr><tr><td>qmul_grid</td><td>GRID</td><td>Multi-Camera Activity Correlation Analysis</td><td>Multi-camera activity correlation analysis</td><td><a href="http://vision.lbl.gov/Conferences/cvpr/Papers/data/papers/0163.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=multi-camera activity correlation analysis&sort=relevance" target="_blank">[s2]</a></td><td>Queen Mary University of London</td><td>3b5b6d19d4733ab606c39c69a889f9e67967f151</td></tr><tr><td>tvhi</td><td>TVHI</td><td>High Five: Recognising human interactions in TV shows</td><td>High Five: Recognising human interactions in TV shows</td><td><a href="https://pdfs.semanticscholar.org/3cd4/0bfa1ff193a96bde0207e5140a399476466c.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=high five: recognising human interactions in tv shows&sort=relevance" target="_blank">[s2]</a></td><td></td><td>3cd40bfa1ff193a96bde0207e5140a399476466c</td></tr><tr><td>georgia_tech_face_database</td><td>Georgia Tech Face</td><td>Maximum likelihood training of the embedded HMM for face detection and recognition</td><td>MAXIMUM LIKELIHOOD TRAINING OF THE EMBEDDED HMM FOR FACE DETECTION AND RECOGNITION Ara V. Ne an and Monson H. Hayes III Center for Signal and Image Processing School of Electrical and Computer Engineering</td><td><a href="https://pdfs.semanticscholar.org/3dc3/f0b64ef80f573e3a5f96e456e52ee980b877.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=maximum likelihood training of the embedded hmm for face detection and recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>3dc3f0b64ef80f573e3a5f96e456e52ee980b877</td></tr><tr><td>bio_id</td><td>BioID Face</td><td>Robust Face Detection Using the Hausdorff Distance</td><td>Robust Face Detection Using the Hausdorff Distance</td><td><a href="http://facedetection.homepage.t-online.de/downloads/AVBPA01BioID.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=robust face detection using the hausdorff distance&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4053e3423fb70ad9140ca89351df49675197196a</td></tr><tr><td>distance_nighttime</td><td>Long Distance Heterogeneous Face</td><td>Nighttime Face Recognition at Long Distance: Cross-distance and Cross-spectral Matching</td><td>Nighttime Face Recognition at Long Distance: Cross-Distance and Cross-Spectral Matching</td><td><a href="https://pdfs.semanticscholar.org/4156/b7e88f2e0ab0a7c095b9bab199ae2b23bd06.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=nighttime face recognition at long distance: cross-distance and cross-spectral matching&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4156b7e88f2e0ab0a7c095b9bab199ae2b23bd06</td></tr><tr><td>market_1501</td><td>Market 1501</td><td>Scalable Person Re-identification: A Benchmark</td><td>Scalable Person Re-identification: A Benchmark</td><td><a href="http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Zheng_Scalable_Person_Re-Identification_ICCV_2015_paper.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=scalable person re-identification: a benchmark&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4308bd8c28e37e2ed9a3fcfe74d5436cce34b410</td></tr><tr><td>tud_multiview</td><td>TUD-Multiview</td><td>Monocular 3D Pose Estimation and Tracking by Detection</td><td>Monocular 3D pose estimation and tracking by detection</td><td><a href="http://videolectures.net/site/normal_dl/tag=81522/cvpr2010_andriluka_m3de_01.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=monocular 3d pose estimation and tracking by detection&sort=relevance" target="_blank">[s2]</a></td><td>TU Darmstadt</td><td>436f798d1a4e54e5947c1e7d7375c31b2bdb4064</td></tr><tr><td>tud_stadtmitte</td><td>TUD-Stadtmitte</td><td>Monocular 3D Pose Estimation and Tracking by Detection</td><td>Monocular 3D pose estimation and tracking by detection</td><td><a href="http://videolectures.net/site/normal_dl/tag=81522/cvpr2010_andriluka_m3de_01.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=monocular 3d pose estimation and tracking by detection&sort=relevance" target="_blank">[s2]</a></td><td>TU Darmstadt</td><td>436f798d1a4e54e5947c1e7d7375c31b2bdb4064</td></tr><tr><td>cuhk01</td><td>CUHK01</td><td>Human Reidentification with Transferred Metric Learning</td><td>Human Reidentification with Transferred Metric Learning</td><td><a href="https://pdfs.semanticscholar.org/4448/4d2866f222bbb9b6b0870890f9eea1ffb2d0.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=human reidentification with transferred metric learning&sort=relevance" target="_blank">[s2]</a></td><td></td><td>44484d2866f222bbb9b6b0870890f9eea1ffb2d0</td></tr><tr><td>wider_attribute</td><td>WIDER Attribute</td><td>Human Attribute Recognition by Deep Hierarchical Contexts</td><td>Human Attribute Recognition by Deep Hierarchical Contexts</td><td><a href="https://pdfs.semanticscholar.org/8e28/07f2dd53b03a759e372e07f7191cae65c9fd.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=human attribute recognition by deep hierarchical contexts&sort=relevance" target="_blank">[s2]</a></td><td></td><td>44d23df380af207f5ac5b41459c722c87283e1eb</td></tr><tr><td>vadana</td><td>VADANA</td><td>VADANA: A dense dataset for facial image analysis</td><td>VADANA: A dense dataset for facial image analysis</td><td><a href="http://vims.cis.udel.edu/publications/VADANA_BeFIT2011.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=vadana: a dense dataset for facial image analysis&sort=relevance" target="_blank">[s2]</a></td><td>University of Delaware</td><td>4563b46d42079242f06567b3f2e2f7a80cb3befe</td></tr><tr><td>jaffe</td><td>JAFFE</td><td>Coding Facial Expressions with Gabor Wavelets</td><td>Coding Facial Expressions with Gabor Wavelets</td><td><a href="https://pdfs.semanticscholar.org/45c3/1cde87258414f33412b3b12fc5bec7cb3ba9.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=coding facial expressions with gabor wavelets&sort=relevance" target="_blank">[s2]</a></td><td></td><td>45c31cde87258414f33412b3b12fc5bec7cb3ba9</td></tr><tr><td>malf</td><td>MALF</td><td>Fine-grained Evaluation on Face Detection in the Wild.</td><td>Fine-grained evaluation on face detection in the wild</td><td><a href="http://www.cs.toronto.edu/~byang/papers/malf_fg15.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=fine-grained evaluation on face detection in the wild.&sort=relevance" target="_blank">[s2]</a></td><td></td><td>45e616093a92e5f1e61a7c6037d5f637aa8964af</td></tr><tr><td>sdu_vid</td><td>SDU-VID</td><td>Local descriptors encoded by Fisher vectors for person re-identification</td><td>Local Descriptors Encoded by Fisher Vectors for Person Re-identification</td><td><a href="https://pdfs.semanticscholar.org/a105/f1ef67b4b02da38eadce8ffb4e13aa301a93.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=local descriptors encoded by fisher vectors for person re-identification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>46a01565e6afe7c074affb752e7069ee3bf2e4ef</td></tr><tr><td>fia</td><td>CMU FiA</td><td>The CMU Face In Action (FIA) Database</td><td>The CMU Face In Action (FIA) Database</td><td><a href="https://pdfs.semanticscholar.org/4766/2d1a368daf70ba70ef2d59eb6209f98b675d.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the cmu face in action (fia) database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>47662d1a368daf70ba70ef2d59eb6209f98b675d</td></tr><tr><td>kin_face</td><td>UB KinFace</td><td>Kinship Verification through Transfer Learning</td><td>Kinship verification through transfer learning</td><td><a href="http://ijcai.org/Proceedings/11/Papers/422.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=kinship verification through transfer learning&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4793f11fbca4a7dba898b9fff68f70d868e2497c</td></tr><tr><td>am_fed</td><td>AM-FED</td><td>Affectiva MIT Facial Expression Dataset (AM-FED): Naturalistic and Spontaneous Facial Expressions Collected “In the Wild”</td><td>Affectiva-MIT Facial Expression Dataset (AM-FED): Naturalistic and Spontaneous Facial Expressions Collected "In-the-Wild"</td><td><a href="http://affect.media.mit.edu/pdfs/13.McDuff-etal-AMFED.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=affectiva mit facial expression dataset (am-fed): naturalistic and spontaneous facial expressions collected “in the wild”&sort=relevance" target="_blank">[s2]</a></td><td></td><td>47aeb3b82f54b5ae8142b4bdda7b614433e69b9a</td></tr><tr><td>apis</td><td>APiS1.0</td><td>Pedestrian Attribute Classification in Surveillance: Database and Evaluation</td><td>Pedestrian Attribute Classification in Surveillance: Database and Evaluation</td><td><a href="http://www.cbsr.ia.ac.cn/english/APiS_1.0_paper.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=pedestrian attribute classification in surveillance: database and evaluation&sort=relevance" target="_blank">[s2]</a></td><td></td><td>488e475eeb3bb39a145f23ede197cd3620f1d98a</td></tr><tr><td>svs</td><td>SVS</td><td>Pedestrian Attribute Classification in Surveillance: Database and Evaluation</td><td>Pedestrian Attribute Classification in Surveillance: Database and Evaluation</td><td><a href="http://www.cbsr.ia.ac.cn/english/APiS_1.0_paper.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=pedestrian attribute classification in surveillance: database and evaluation&sort=relevance" target="_blank">[s2]</a></td><td></td><td>488e475eeb3bb39a145f23ede197cd3620f1d98a</td></tr><tr><td>coco_action</td><td>COCO-a</td><td>Describing Common Human Visual Actions in Images</td><td>Describing Common Human Visual Actions in Images</td><td><a href="https://arxiv.org/pdf/1506.02203.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=describing common human visual actions in images&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4946ba10a4d5a7d0a38372f23e6622bd347ae273</td></tr><tr><td>leeds_sports_pose</td><td>Leeds Sports Pose</td><td>Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation</td><td>Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation</td><td><a href="https://pdfs.semanticscholar.org/c327/15b5106f46eb6761531704cd2a9b5571832e.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=clustered pose and nonlinear appearance models for human pose estimation&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4b1d23d17476fcf78f4cbadf69fb130b1aa627c0</td></tr><tr><td>stickmen_buffy</td><td>Buffy Stickmen</td><td>Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation</td><td>Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation</td><td><a href="https://pdfs.semanticscholar.org/c327/15b5106f46eb6761531704cd2a9b5571832e.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=clustered pose and nonlinear appearance models for human pose estimation&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4b1d23d17476fcf78f4cbadf69fb130b1aa627c0</td></tr><tr><td>czech_news_agency</td><td>UFI</td><td>Unconstrained Facial Images: Database for Face Recognition under Real-world Conditions</td><td>Unconstrained Facial Images: Database for Face Recognition Under Real-World Conditions</td><td><a href="https://pdfs.semanticscholar.org/4b41/06614c1d553365bad75d7866bff0de6056ed.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=unconstrained facial images: database for face recognition under real-world conditions&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4b4106614c1d553365bad75d7866bff0de6056ed</td></tr><tr><td>face_tracer</td><td>FaceTracer</td><td>FaceTracer: A Search Engine for Large Collections of Images with Faces</td><td>FaceTracer: A Search Engine for Large Collections of Images with Faces</td><td><a href="https://pdfs.semanticscholar.org/73a8/1d311eedac8dea3ca24dc15b6990fa4a725e.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=facetracer: a search engine for large collections of images with faces&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4c170a0dcc8de75587dae21ca508dab2f9343974</td></tr><tr><td>cmu_pie</td><td>CMU PIE</td><td>The CMU Pose, Illumination, and Expression Database</td><td>The CMU Pose, Illumination, and Expression (PIE) Database</td><td><a href="http://www.comp.nus.edu.sg/~tsim/piedb.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the cmu pose, illumination, and expression database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4d423acc78273b75134e2afd1777ba6d3a398973</td></tr><tr><td>multi_pie</td><td>MULTIPIE</td><td>Multi-PIE</td><td>The CMU Pose, Illumination, and Expression (PIE) Database</td><td><a href="http://www.comp.nus.edu.sg/~tsim/piedb.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=multi-pie&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4d423acc78273b75134e2afd1777ba6d3a398973</td></tr><tr><td>3dddb_unconstrained</td><td>3D Dynamic</td><td>A 3D Dynamic Database for Unconstrained Face Recognition</td><td>A 3 D Dynamic Database for Unconstrained Face Recognition</td><td><a href="https://pdfs.semanticscholar.org/4d4b/b462c9f1d4e4ab1e4aa6a75cc0bc71b38461.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a 3d dynamic database for unconstrained face recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4d4bb462c9f1d4e4ab1e4aa6a75cc0bc71b38461</td></tr><tr><td>texas_3dfrd</td><td>Texas 3DFRD</td><td>Texas 3D Face Recognition Database</td><td>Texas 3D Face Recognition Database</td><td><a href="http://live.ece.utexas.edu/publications/2010/sg_ssiai_may10.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=texas 3d face recognition database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4d58f886f5150b2d5e48fd1b5a49e09799bf895d</td></tr><tr><td>cohn_kanade_plus</td><td>CK+</td><td>The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression</td><td>The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression</td><td><a href="http://www.iainm.com/iainm/Publications_files/2010_The%20Extended.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the extended cohn-kanade dataset (ck+): a complete dataset for action unit and emotion-specified expression&sort=relevance" target="_blank">[s2]</a></td><td>University of Pittsburgh</td><td>4d9a02d080636e9666c4d1cc438b9893391ec6c7</td></tr><tr><td>cfd</td><td>CFD</td><td>The Chicago face database: A free stimulus set of faces and norming data</td><td>The Chicago face database: A free stimulus set of faces and norming data.</td><td><a href="https://pdfs.semanticscholar.org/4df3/143922bcdf7db78eb91e6b5359d6ada004d2.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the chicago face database: a free stimulus set of faces and norming data&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4df3143922bcdf7db78eb91e6b5359d6ada004d2</td></tr><tr><td>leeds_sports_pose_extended</td><td>Leeds Sports Pose Extended</td><td>Learning Effective Human Pose Estimation from Inaccurate Annotation</td><td>Learning effective human pose estimation from inaccurate annotation</td><td><a href="http://www.comp.leeds.ac.uk/mat4saj/publications/johnson11cvpr.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=learning effective human pose estimation from inaccurate annotation&sort=relevance" target="_blank">[s2]</a></td><td>University of Leeds</td><td>4e4746094bf60ee83e40d8597a6191e463b57f76</td></tr><tr><td>dartmouth_children</td><td>Dartmouth Children</td><td>The Dartmouth Database of Children's Faces: Acquisition and validation of a new face stimulus set</td><td>The Dartmouth Database of Children’s Faces: Acquisition and Validation of a New Face Stimulus Set</td><td><a href="https://pdfs.semanticscholar.org/4e6e/e936eb50dd032f7138702fa39b7c18ee8907.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the dartmouth database of children's faces: acquisition and validation of a new face stimulus set&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4e6ee936eb50dd032f7138702fa39b7c18ee8907</td></tr><tr><td>soton</td><td>SOTON HiD</td><td>On a Large Sequence-Based Human Gait Database</td><td>On a large sequence-based human gait database</td><td><a href="https://eprints.soton.ac.uk/257901/1/Shutler_2002.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=on a large sequence-based human gait database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4f93cd09785c6e77bf4bc5a788e079df524c8d21</td></tr><tr><td>deep_fashion</td><td>DeepFashion</td><td>Fashion Landmark Detection in the Wild</td><td>Fashion Landmark Detection in the Wild</td><td><a href="https://arxiv.org/pdf/1608.03049.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=fashion landmark detection in the wild&sort=relevance" target="_blank">[s2]</a></td><td></td><td>4fefd1bc8dc4e0ab37ee3324ddfa43ad9d6a04a7</td></tr><tr><td>violent_flows</td><td>Violent Flows</td><td>Violent Flows: Real-Time Detection of Violent Crowd Behavior</td><td>Violent flows: Real-time detection of violent crowd behavior</td><td><a href="http://www.openu.ac.il/home/hassner/data/violentflows/violent_flows.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=violent flows: real-time detection of violent crowd behavior&sort=relevance" target="_blank">[s2]</a></td><td>Open University of Israel</td><td>5194cbd51f9769ab25260446b4fa17204752e799</td></tr><tr><td>imsitu</td><td>imSitu</td><td>Situation Recognition: Visual Semantic Role Labeling for Image Understanding</td><td>Situation Recognition: Visual Semantic Role Labeling for Image Understanding</td><td><a href="http://allenai.org/content/publications/SituationRecognition.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=situation recognition: visual semantic role labeling for image understanding&sort=relevance" target="_blank">[s2]</a></td><td></td><td>51eba481dac6b229a7490f650dff7b17ce05df73</td></tr><tr><td>wider_face</td><td>WIDER FACE</td><td>WIDER FACE: A Face Detection Benchmark</td><td>WIDER FACE: A Face Detection Benchmark</td><td><a href="https://arxiv.org/pdf/1511.06523.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=wider face: a face detection benchmark&sort=relevance" target="_blank">[s2]</a></td><td></td><td>52d7eb0fbc3522434c13cc247549f74bb9609c5d</td></tr><tr><td>bp4d_plus</td><td>BP4D+</td><td>Multimodal Spontaneous Emotion Corpus for Human Behavior Analysis</td><td>Multimodal Spontaneous Emotion Corpus for Human Behavior Analysis</td><td><a href="http://openaccess.thecvf.com/content_cvpr_2016/papers/Zhang_Multimodal_Spontaneous_Emotion_CVPR_2016_paper.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=multimodal spontaneous emotion corpus for human behavior analysis&sort=relevance" target="_blank">[s2]</a></td><td></td><td>53ae38a6bb2b21b42bac4f0c4c8ed1f9fa02f9d4</td></tr><tr><td>reseed</td><td>ReSEED</td><td>ReSEED: Social Event dEtection Dataset</td><td>ReSEED: social event dEtection dataset</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=reseed: social event detection dataset&sort=relevance" target="_blank">[s2]</a></td><td></td><td>54983972aafc8e149259d913524581357b0f91c3</td></tr><tr><td>orl</td><td>ORL</td><td>Parameterisation of a Stochastic Model for Human Face Identification</td><td>Parameterisation of a stochastic model for human face identification</td><td><a href="https://pdfs.semanticscholar.org/5520/6f0b5f57ce17358999145506cd01e570358c.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=parameterisation of a stochastic model for human face identification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>55206f0b5f57ce17358999145506cd01e570358c</td></tr><tr><td>ifad</td><td>IFAD</td><td>Indian Face Age Database: A Database for Face Recognition with Age Variation</td><td>Indian Face Age Database : A Database for Face Recognition with Age Variation</td><td><a href="https://pdfs.semanticscholar.org/55c4/0cbcf49a0225e72d911d762c27bb1c2d14aa.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=indian face age database: a database for face recognition with age variation&sort=relevance" target="_blank">[s2]</a></td><td></td><td>55c40cbcf49a0225e72d911d762c27bb1c2d14aa</td></tr><tr><td>youtube_faces</td><td>YouTubeFaces</td><td>Face Recognition in Unconstrained Videos with Matched Background Similarity</td><td>Face recognition in unconstrained videos with matched background similarity</td><td><a href="http://www.cs.tau.ac.il/thesis/thesis/Maoz.Itay-MSc.Thesis.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=face recognition in unconstrained videos with matched background similarity&sort=relevance" target="_blank">[s2]</a></td><td></td><td>560e0e58d0059259ddf86fcec1fa7975dee6a868</td></tr><tr><td>data_61</td><td>Data61 Pedestrian</td><td>A Multi-Modal Graphical Model for Scene Analysis</td><td>A Multi-modal Graphical Model for Scene Analysis</td><td><a href="http://www.nicta.com.au/wp-content/uploads/2015/02/TaghaviNaminetalWACV15.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a multi-modal graphical model for scene analysis&sort=relevance" target="_blank">[s2]</a></td><td></td><td>563c940054e4b456661762c1ab858e6f730c3159</td></tr><tr><td>cmdp</td><td>CMDP</td><td>Distance Estimation of an Unknown Person from a Portrait</td><td>Distance Estimation of an Unknown Person from a Portrait</td><td><a href="https://pdfs.semanticscholar.org/56ae/6d94fc6097ec4ca861f0daa87941d1c10b70.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=distance estimation of an unknown person from a portrait&sort=relevance" target="_blank">[s2]</a></td><td></td><td>56ae6d94fc6097ec4ca861f0daa87941d1c10b70</td></tr><tr><td>unbc_shoulder_pain</td><td>UNBC-McMaster Pain</td><td>PAINFUL DATA: The UNBC-McMaster Shoulder Pain Expression Archive Database</td><td>Painful data: The UNBC-McMaster shoulder pain expression archive database</td><td><a href="http://www.iainm.com/iainm/Publications_files/2011_PAINFUL.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=painful data: the unbc-mcmaster shoulder pain expression archive database&sort=relevance" target="_blank">[s2]</a></td><td>Carnegie Mellon University</td><td>56ffa7d906b08d02d6d5a12c7377a57e24ef3391</td></tr><tr><td>stanford_drone</td><td>Stanford Drone</td><td>Learning Social Etiquette: Human Trajectory Prediction In Crowded Scenes</td><td>Social LSTM: Human Trajectory Prediction in Crowded Spaces</td><td><a href="http://cs.stanford.edu/groups/vision/pdf/CVPR16_N_LSTM.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=learning social etiquette: human trajectory prediction in crowded scenes&sort=relevance" target="_blank">[s2]</a></td><td></td><td>570f37ed63142312e6ccdf00ecc376341ec72b9f</td></tr><tr><td>ijb_c</td><td>IJB-C</td><td>IARPA Janus Benchmark C</td><td>IARPA Janus Benchmark - C: Face Dataset and Protocol</td><td><a href="http://biometrics.cse.msu.edu/Publications/Face/Mazeetal_IARPAJanusBenchmarkCFaceDatasetAndProtocol_ICB2018.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=iarpa janus benchmark c&sort=relevance" target="_blank">[s2]</a></td><td></td><td>57178b36c21fd7f4529ac6748614bb3374714e91</td></tr><tr><td>50_people_one_question</td><td>50 People One Question</td><td>Merging Pose Estimates Across Space and Time</td><td>Merging Pose Estimates Across Space and Time</td><td><a href="https://pdfs.semanticscholar.org/63b2/f5348af0f969dfc2afb4977732393c6459ec.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=merging pose estimates across space and time&sort=relevance" target="_blank">[s2]</a></td><td></td><td>5753b2b5e442eaa3be066daa4a2ca8d8a0bb1725</td></tr><tr><td>mr2</td><td>MR2</td><td>The MR2: A multi-racial mega-resolution database of facial stimuli</td><td>The MR2: A multi-racial, mega-resolution database of facial stimuli.</td><td><a href="https://pdfs.semanticscholar.org/be5b/455abd379240460d022a0e246615b0b86c14.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the mr2: a multi-racial mega-resolution database of facial stimuli&sort=relevance" target="_blank">[s2]</a></td><td></td><td>578d4ad74818086bb64f182f72e2c8bd31e3d426</td></tr><tr><td>cvc_01_barcelona</td><td>CVC-01</td><td>Adaptive Image Sampling and Windows Classification for On-board Pedestrian Detection</td><td>Adaptive Image Sampling and Windows Classification for On – board Pedestrian Detection</td><td><a href="https://pdfs.semanticscholar.org/57fe/081950f21ca03b5b375ae3e84b399c015861.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=adaptive image sampling and windows classification for on-board pedestrian detection&sort=relevance" target="_blank">[s2]</a></td><td></td><td>57fe081950f21ca03b5b375ae3e84b399c015861</td></tr><tr><td>mot</td><td>MOT</td><td>Learning to associate: HybridBoosted multi-target tracker for crowded scene</td><td>Learning to associate: HybridBoosted multi-target tracker for crowded scene</td><td><a href="http://iris.usc.edu/Outlines/papers/2009/yuan-chang-nevatia-cvpr09.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=learning to associate: hybridboosted multi-target tracker for crowded scene&sort=relevance" target="_blank">[s2]</a></td><td>University of Southern California</td><td>5981e6479c3fd4e31644db35d236bfb84ae46514</td></tr><tr><td>disfa</td><td>DISFA</td><td>DISFA: A Spontaneous Facial Action Intensity Database</td><td>DISFA: A Spontaneous Facial Action Intensity Database</td><td><a href="http://mohammadmahoor.com/wp-content/uploads/2017/06/DiSFA_Paper_andAppendix_Final_OneColumn1-1.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=disfa: a spontaneous facial action intensity database&sort=relevance" target="_blank">[s2]</a></td><td>University of Denver</td><td>5a5f0287484f0d480fed1ce585dbf729586f0edc</td></tr><tr><td>wlfdb</td><td>WLFDB</td><td>WLFDB: Weakly Labeled Face Databases</td><td>WLFDB: Weakly Labeled Face Databases</td><td><a href="https://pdfs.semanticscholar.org/5ad4/e9f947c1653c247d418f05dad758a3f9277b.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=wlfdb: weakly labeled face databases&sort=relevance" target="_blank">[s2]</a></td><td></td><td>5ad4e9f947c1653c247d418f05dad758a3f9277b</td></tr><tr><td>coco</td><td>COCO</td><td>Microsoft COCO: Common Objects in Context</td><td>Microsoft COCO: Common Objects in Context</td><td><a href="https://arxiv.org/pdf/1405.0312.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=microsoft coco: common objects in context&sort=relevance" target="_blank">[s2]</a></td><td></td><td>5e0f8c355a37a5a89351c02f174e7a5ddcb98683</td></tr><tr><td>cityscapes</td><td>Cityscapes</td><td>The Cityscapes Dataset</td><td>The Cityscapes Dataset</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the cityscapes dataset&sort=relevance" target="_blank">[s2]</a></td><td></td><td>5ffd74d2873b7cba2cbc5fd295cc7fbdedca22a2</td></tr><tr><td>viper</td><td>VIPeR</td><td>Evaluating Appearance Models for Recognition, Reacquisition, and Tracking</td><td>Evaluating Appearance Models for Recognition , Reacquisition , and Tracking</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=evaluating appearance models for recognition, reacquisition, and tracking&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6273b3491e94ea4dd1ce42b791d77bdc96ee73a8</td></tr><tr><td>caltech_10k_web_faces</td><td>Caltech 10K Web Faces</td><td>Pruning Training Sets for Learning of Object Categories</td><td>Pruning training sets for learning of object categories</td><td><a href="http://authors.library.caltech.edu/11469/1/ANGcvpr05.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=pruning training sets for learning of object categories&sort=relevance" target="_blank">[s2]</a></td><td></td><td>636b8ffc09b1b23ff714ac8350bb35635e49fa3c</td></tr><tr><td>bfm</td><td>BFM</td><td>A 3D Face Model for Pose and Illumination Invariant Face Recognition</td><td>A 3D Face Model for Pose and Illumination Invariant Face Recognition</td><td><a href="http://gravis.cs.unibas.ch/publications/2009/BFModel09.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a 3d face model for pose and illumination invariant face recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>639937b3a1b8bded3f7e9a40e85bd3770016cf3c</td></tr><tr><td>alert_airport</td><td>ALERT Airport</td><td>A Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets</td><td>A Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets.</td><td><a href="https://arxiv.org/pdf/1605.09653.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a systematic evaluation and benchmark for person re-identification: features, metrics, and datasets&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6403117f9c005ae81f1e8e6d1302f4a045e3d99d</td></tr><tr><td>celeba</td><td>CelebA</td><td>Deep Learning Face Attributes in the Wild</td><td>Deep Learning Face Attributes in the Wild</td><td><a href="https://arxiv.org/pdf/1411.7766.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=deep learning face attributes in the wild&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6424b69f3ff4d35249c0bb7ef912fbc2c86f4ff4</td></tr><tr><td>complex_activities</td><td>Ongoing Complex Activities</td><td>Recognition of Ongoing Complex Activities by Sequence Prediction over a Hierarchical Label Space</td><td>Recognition of ongoing complex activities by sequence prediction over a hierarchical label space</td><td><a href="https://scalable.mpi-inf.mpg.de/files/2016/01/main_wacv.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=recognition of ongoing complex activities by sequence prediction over a hierarchical label space&sort=relevance" target="_blank">[s2]</a></td><td></td><td>65355cbb581a219bd7461d48b3afd115263ea760</td></tr><tr><td>afad</td><td>AFAD</td><td>Ordinal Regression with a Multiple Output CNN for Age Estimation</td><td>Ordinal Regression with Multiple Output CNN for Age Estimation</td><td><a href="http://openaccess.thecvf.com/content_cvpr_2016/papers/Niu_Ordinal_Regression_With_CVPR_2016_paper.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=ordinal regression with a multiple output cnn for age estimation&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6618cff7f2ed440a0d2fa9e74ad5469df5cdbe4c</td></tr><tr><td>sun_attributes</td><td>SUN</td><td>The SUN Attribute Database: Beyond Categories for Deeper Scene Understanding</td><td>The SUN Attribute Database: Beyond Categories for Deeper Scene Understanding</td><td><a href="http://www.cc.gatech.edu/~hays/papers/attribute_ijcv.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the sun attribute database: beyond categories for deeper scene understanding&sort=relevance" target="_blank">[s2]</a></td><td></td><td>66e6f08873325d37e0ec20a4769ce881e04e964e</td></tr><tr><td>face_tracer</td><td>FaceTracer</td><td>Face Swapping: Automatically Replacing Faces in Photographs</td><td>Face swapping: automatically replacing faces in photographs</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=face swapping: automatically replacing faces in photographs&sort=relevance" target="_blank">[s2]</a></td><td></td><td>670637d0303a863c1548d5b19f705860a23e285c</td></tr><tr><td>tud_brussels</td><td>TUD-Brussels</td><td>Multi-Cue Onboard Pedestrian Detection</td><td>Multi-cue onboard pedestrian detection</td><td><a href="http://vision.lbl.gov/Conferences/cvpr/Papers/data/papers/1454.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=multi-cue onboard pedestrian detection&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6ad5a38df8dd4cdddd74f31996ce096d41219f72</td></tr><tr><td>tud_motionpairs</td><td>TUD-Motionparis</td><td>Multi-Cue Onboard Pedestrian Detection</td><td>Multi-cue onboard pedestrian detection</td><td><a href="http://vision.lbl.gov/Conferences/cvpr/Papers/data/papers/1454.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=multi-cue onboard pedestrian detection&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6ad5a38df8dd4cdddd74f31996ce096d41219f72</td></tr><tr><td>cuhk03</td><td>CUHK03</td><td>DeepReID: Deep Filter Pairing Neural Network for Person Re-identification</td><td>DeepReID: Deep Filter Pairing Neural Network for Person Re-identification</td><td><a href="http://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Li_DeepReID_Deep_Filter_2014_CVPR_paper.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=deepreid: deep filter pairing neural network for person re-identification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6bd36e9fd0ef20a3074e1430a6cc601e6d407fc3</td></tr><tr><td>ar_facedb</td><td>AR Face</td><td>The AR Face Database</td><td>The AR face database</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the ar face database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6d96f946aaabc734af7fe3fc4454cf8547fcd5ed</td></tr><tr><td>agedb</td><td>AgeDB</td><td>AgeDB: the first manually collected, in-the-wild age database</td><td>AgeDB: The First Manually Collected, In-the-Wild Age Database</td><td><a href="https://ibug.doc.ic.ac.uk/media/uploads/documents/agedb.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=agedb: the first manually collected, in-the-wild age database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6dcf418c778f528b5792104760f1fbfe90c6dd6a</td></tr><tr><td>stickmen_buffy</td><td>Buffy Stickmen</td><td>Learning to Parse Images of Articulated Objects</td><td>Learning to parse images of articulated bodies</td><td><a href="https://pdfs.semanticscholar.org/9cd7/4c43dbf9be0b9caae4606ee53e6d45850471.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=learning to parse images of articulated objects&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6dd0597f8513dc100cd0bc1b493768cde45098a9</td></tr><tr><td>stickmen_pascal</td><td>Stickmen PASCAL</td><td>Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation</td><td>Learning to parse images of articulated bodies</td><td><a href="https://pdfs.semanticscholar.org/9cd7/4c43dbf9be0b9caae4606ee53e6d45850471.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=clustered pose and nonlinear appearance models for human pose estimation&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6dd0597f8513dc100cd0bc1b493768cde45098a9</td></tr><tr><td>stickmen_pascal</td><td>Stickmen PASCAL</td><td>Learning to Parse Images of Articulated Objects</td><td>Learning to parse images of articulated bodies</td><td><a href="https://pdfs.semanticscholar.org/9cd7/4c43dbf9be0b9caae4606ee53e6d45850471.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=learning to parse images of articulated objects&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6dd0597f8513dc100cd0bc1b493768cde45098a9</td></tr><tr><td>ward</td><td>WARD</td><td>Re-identify people in wide area camera network</td><td>Re-identify people in wide area camera network</td><td><a href="http://users.dimi.uniud.it/~niki.martinel/data/publications/2012/CVPR/MarMicCVPR2012.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=re-identify people in wide area camera network&sort=relevance" target="_blank">[s2]</a></td><td></td><td>6f3c76b7c0bd8e1d122c6ea808a271fd4749c951</td></tr><tr><td>umd_faces</td><td>UMD</td><td>The Do's and Don'ts for CNN-based Face Verification</td><td>The Do’s and Don’ts for CNN-Based Face Verification</td><td><a href="https://arxiv.org/pdf/1705.07426.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the do's and don'ts for cnn-based face verification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>71b7fc715e2f1bb24c0030af8d7e7b6e7cd128a6</td></tr><tr><td>affectnet</td><td>AffectNet</td><td>AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild</td><td>AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild</td><td><a href="https://arxiv.org/pdf/1708.03985.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=affectnet: a database for facial expression, valence, and arousal computing in the wild&sort=relevance" target="_blank">[s2]</a></td><td></td><td>758d7e1be64cc668c59ef33ba8882c8597406e53</td></tr><tr><td>pubfig</td><td>PubFig</td><td>Attribute and Simile Classifiers for Face Verification</td><td>Attribute and simile classifiers for face verification</td><td><a href="http://acberg.com/papers/kbbn09iccv.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=attribute and simile classifiers for face verification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>759a3b3821d9f0e08e0b0a62c8b693230afc3f8d</td></tr><tr><td>fddb</td><td>FDDB</td><td>FDDB: A Benchmark for Face Detection in Unconstrained Settings</td><td>A Benchmark for Face Detection in Unconstrained Settings</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=fddb: a benchmark for face detection in unconstrained settings&sort=relevance" target="_blank">[s2]</a></td><td></td><td>75da1df4ed319926c544eefe17ec8d720feef8c0</td></tr><tr><td>urban_tribes</td><td>Urban Tribes</td><td>From Bikers to Surfers: Visual Recognition of Urban Tribes</td><td>From Bikers to Surfers: Visual Recognition of Urban Tribes</td><td><a href="https://pdfs.semanticscholar.org/774c/bb45968607a027ae4729077734db000a1ec5.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=from bikers to surfers: visual recognition of urban tribes&sort=relevance" target="_blank">[s2]</a></td><td></td><td>774cbb45968607a027ae4729077734db000a1ec5</td></tr><tr><td>wildtrack</td><td>WildTrack</td><td>WILDTRACK: A Multi-camera HD Dataset for Dense Unscripted Pedestrian Detection</td><td>WILDTRACK : A Multi-camera HD Dataset for Dense Unscripted Pedestrian Detection</td><td><a href="https://pdfs.semanticscholar.org/fe1c/ec4e4995b8615855572374ae3efc94949105.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=wildtrack: a multi-camera hd dataset for dense unscripted pedestrian detection&sort=relevance" target="_blank">[s2]</a></td><td></td><td>77c81c13a110a341c140995bedb98101b9e84f7f</td></tr><tr><td>berkeley_pose</td><td>BPAD</td><td>Describing People: A Poselet-Based Approach to Attribute Classification</td><td>Describing people: A poselet-based approach to attribute classification</td><td><a href="http://ttic.uchicago.edu/~smaji/papers/attributes-iccv11.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=describing people: a poselet-based approach to attribute classification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>7808937b46acad36e43c30ae4e9f3fd57462853d</td></tr><tr><td>mapillary</td><td>Mapillary</td><td>The Mapillary Vistas Dataset for Semantic Understanding of Street Scenes</td><td>The Mapillary Vistas Dataset for Semantic Understanding of Street Scenes</td><td><a href="http://openaccess.thecvf.com/content_ICCV_2017/papers/Neuhold_The_Mapillary_Vistas_ICCV_2017_paper.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the mapillary vistas dataset for semantic understanding of street scenes&sort=relevance" target="_blank">[s2]</a></td><td></td><td>79828e6e9f137a583082b8b5a9dfce0c301989b8</td></tr><tr><td>nudedetection</td><td>Nude Detection</td><td>A Bag-of-Features Approach based on Hue-SIFT Descriptor for Nude Detection</td><td>A bag-of-features approach based on Hue-SIFT descriptor for nude detection</td><td><a href="http://www.eurasip.org/Proceedings/Eusipco/Eusipco2009/contents/papers/1569191772.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a bag-of-features approach based on hue-sift descriptor for nude detection&sort=relevance" target="_blank">[s2]</a></td><td></td><td>7ace44190729927e5cb0dd5d363fcae966fe13f7</td></tr><tr><td>lfw</td><td>LFW</td><td>Labeled Faces in the Wild: A Survey</td><td>Labeled Faces in the Wild : A Survey</td><td><a href="https://pdfs.semanticscholar.org/7de6/e81d775e9cd7becbfd1bd685f4e2a5eebb22.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=labeled faces in the wild: a survey&sort=relevance" target="_blank">[s2]</a></td><td></td><td>7de6e81d775e9cd7becbfd1bd685f4e2a5eebb22</td></tr><tr><td>vgg_celebs_in_places</td><td>CIP</td><td>Faces in Places: Compound Query Retrieval</td><td>Faces in Places: compound query retrieval</td><td><a href="https://pdfs.semanticscholar.org/7ebb/153704706e457ab57b432793d2b6e5d12592.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=faces in places: compound query retrieval&sort=relevance" target="_blank">[s2]</a></td><td></td><td>7ebb153704706e457ab57b432793d2b6e5d12592</td></tr><tr><td>nova_emotions</td><td>Novaemötions Dataset</td><td>Competitive affective gamming: Winning with a smile</td><td>Competitive affective gaming: winning with a smile</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=competitive affective gamming: winning with a smile&sort=relevance" target="_blank">[s2]</a></td><td>Universidade NOVA de Lisboa, Caparica, Portugal</td><td>7f4040b482d16354d5938c1d1b926b544652bf5b</td></tr><tr><td>sun_attributes</td><td>SUN</td><td>SUN Attribute Database: 
 Discovering, Annotating, and Recognizing Scene Attributes</td><td>SUN attribute database: Discovering, annotating, and recognizing scene attributes</td><td><a href="http://static.cs.brown.edu/~gen/pub_papers/SUN_Attribute_Database_CVPR2012.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=sun attribute database: 
 discovering, annotating, and recognizing scene attributes&sort=relevance" target="_blank">[s2]</a></td><td>Brown University</td><td>833fa04463d90aab4a9fe2870d480f0b40df446e</td></tr><tr><td>imdb_wiki</td><td>IMDB</td><td>DEX: Deep EXpectation of apparent age from a single image</td><td>DEX: Deep EXpectation of Apparent Age from a Single Image</td><td><a href="http://www.cv-foundation.org/openaccess/content_iccv_2015_workshops/w11/papers/Rothe_DEX_Deep_EXpectation_ICCV_2015_paper.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=dex: deep expectation of apparent age from a single image&sort=relevance" target="_blank">[s2]</a></td><td></td><td>8355d095d3534ef511a9af68a3b2893339e3f96b</td></tr><tr><td>awe_ears</td><td>AWE Ears</td><td>Ear Recognition: More Than a Survey</td><td>Ear Recognition: More Than a Survey</td><td><a href="https://arxiv.org/pdf/1611.06203.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=ear recognition: more than a survey&sort=relevance" target="_blank">[s2]</a></td><td></td><td>84fe5b4ac805af63206012d29523a1e033bc827e</td></tr><tr><td>casia_webface</td><td>CASIA Webface</td><td>Learning Face Representation from Scratch</td><td>Learning Face Representation from Scratch</td><td><a href="https://arxiv.org/pdf/1411.7923.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=learning face representation from scratch&sort=relevance" target="_blank">[s2]</a></td><td></td><td>853bd61bc48a431b9b1c7cab10c603830c488e39</td></tr><tr><td>used</td><td>USED Social Event Dataset</td><td>USED: A Large-scale Social Event Detection Dataset</td><td>USED: a large-scale social event detection dataset</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=used: a large-scale social event detection dataset&sort=relevance" target="_blank">[s2]</a></td><td>University of Trento</td><td>8627f019882b024aef92e4eb9355c499c733e5b7</td></tr><tr><td>tiny_faces</td><td>TinyFace</td><td>Low-Resolution Face Recognition</td><td>Low-Resolution Face Recognition</td><td><a href="https://arxiv.org/pdf/1811.08965.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=low-resolution face recognition&sort=relevance" target="_blank">[s2]</a></td><td></td><td>8990cdce3f917dad622e43e033db686b354d057c</td></tr><tr><td>mafl</td><td>MAFL</td><td>Facial Landmark Detection by Deep Multi-task Learning</td><td>Facial Landmark Detection by Deep Multi-task Learning</td><td><a href="http://personal.ie.cuhk.edu.hk/~ccloy/files/eccv_2014_deepfacealign.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=facial landmark detection by deep multi-task learning&sort=relevance" target="_blank">[s2]</a></td><td></td><td>8a3c5507237957d013a0fe0f082cab7f757af6ee</td></tr><tr><td>mtfl</td><td>MTFL</td><td>Facial Landmark Detection by Deep Multi-task Learning</td><td>Facial Landmark Detection by Deep Multi-task Learning</td><td><a href="http://personal.ie.cuhk.edu.hk/~ccloy/files/eccv_2014_deepfacealign.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=facial landmark detection by deep multi-task learning&sort=relevance" target="_blank">[s2]</a></td><td></td><td>8a3c5507237957d013a0fe0f082cab7f757af6ee</td></tr><tr><td>10k_US_adult_faces</td><td>10K US Adult Faces</td><td>The intrinsic memorability of face images</td><td>The intrinsic memorability of face photographs.</td><td><a href="https://pdfs.semanticscholar.org/8b2d/d5c61b23ead5ae5508bb8ce808b5ea266730.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the intrinsic memorability of face images&sort=relevance" target="_blank">[s2]</a></td><td></td><td>8b2dd5c61b23ead5ae5508bb8ce808b5ea266730</td></tr><tr><td>fei</td><td>FEI</td><td>Captura e Alinhamento de Imagens: Um Banco de Faces Brasileiro</td><td>A new ranking method for principal components analysis and its application to face image analysis</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=captura e alinhamento de imagens: um banco de faces brasileiro&sort=relevance" target="_blank">[s2]</a></td><td></td><td>8b56e33f33e582f3e473dba573a16b598ed9bcdc</td></tr><tr><td>chalearn</td><td>ChaLearn</td><td>ChaLearn Looking at People: A Review of Events and Resources</td><td>ChaLearn looking at people: A review of events and resources</td><td><a href="https://arxiv.org/pdf/1701.02664.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=chalearn looking at people: a review of events and resources&sort=relevance" target="_blank">[s2]</a></td><td></td><td>8d5998cd984e7cce307da7d46f155f9db99c6590</td></tr><tr><td>hda_plus</td><td>HDA+</td><td>The HDA+ data set for research on fully automated re-identification systems</td><td>The HDA+ Data Set for Research on Fully Automated Re-identification Systems</td><td><a href="http://vigir.missouri.edu/~gdesouza/Research/Conference_CDs/ECCV_2014/workshops/w19/11%20-%20The%20HDA%20data%20set%20for%20research%20on%20fully.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the hda+ data set for research on fully automated re-identification systems&sort=relevance" target="_blank">[s2]</a></td><td></td><td>8f02ec0be21461fbcedf51d864f944cfc42c875f</td></tr><tr><td>morph</td><td>MORPH Commercial</td><td>MORPH: A Longitudinal Image Database of Normal Adult Age-Progression</td><td>MORPH: a longitudinal image database of normal adult age-progression</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=morph: a longitudinal image database of normal adult age-progression&sort=relevance" target="_blank">[s2]</a></td><td></td><td>9055b155cbabdce3b98e16e5ac9c0edf00f9552f</td></tr><tr><td>morph_nc</td><td>MORPH Non-Commercial</td><td>MORPH: A Longitudinal Image Database of Normal Adult Age-Progression</td><td>MORPH: a longitudinal image database of normal adult age-progression</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=morph: a longitudinal image database of normal adult age-progression&sort=relevance" target="_blank">[s2]</a></td><td></td><td>9055b155cbabdce3b98e16e5ac9c0edf00f9552f</td></tr><tr><td>v47</td><td>V47</td><td>Re-identification of Pedestrians with Variable Occlusion and Scale</td><td>Re-identification of pedestrians with variable occlusion and scale</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=re-identification of pedestrians with variable occlusion and scale&sort=relevance" target="_blank">[s2]</a></td><td>Kingston University</td><td>922e0a51a3b8c67c4c6ac09a577ff674cbd28b34</td></tr><tr><td>towncenter</td><td>TownCenter</td><td>Stable Multi-Target Tracking in Real-Time Surveillance Video</td><td>Stable multi-target tracking in real-time surveillance video</td><td><a href="http://ben.benfold.com/docs/benfold_reid_cvpr2011-preprint.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=stable multi-target tracking in real-time surveillance video&sort=relevance" target="_blank">[s2]</a></td><td></td><td>9361b784e73e9238d5cefbea5ac40d35d1e3103f</td></tr><tr><td>helen</td><td>Helen</td><td>Interactive Facial Feature Localization</td><td>Interactive Facial Feature Localization</td><td><a href="https://pdfs.semanticscholar.org/95f1/2d27c3b4914e0668a268360948bce92f7db3.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=interactive facial feature localization&sort=relevance" target="_blank">[s2]</a></td><td></td><td>95f12d27c3b4914e0668a268360948bce92f7db3</td></tr><tr><td>4dfab</td><td>4DFAB</td><td>4DFAB: A Large Scale 4D Facial Expression Database for Biometric Applications</td><td>4DFAB: A Large Scale 4D Facial Expression Database for Biometric Applications</td><td><a href="https://arxiv.org/pdf/1712.01443.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=4dfab: a large scale 4d facial expression database for biometric applications&sort=relevance" target="_blank">[s2]</a></td><td></td><td>9696ad8b164f5e10fcfe23aacf74bd6168aebb15</td></tr><tr><td>megaface</td><td>MegaFace</td><td>The MegaFace Benchmark: 1 Million Faces for Recognition at Scale</td><td>The MegaFace Benchmark: 1 Million Faces for Recognition at Scale</td><td><a href="https://arxiv.org/pdf/1512.00596.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the megaface benchmark: 1 million faces for recognition at scale&sort=relevance" target="_blank">[s2]</a></td><td></td><td>96e0cfcd81cdeb8282e29ef9ec9962b125f379b0</td></tr><tr><td>ilids_vid_reid</td><td>iLIDS-VID</td><td>Person Re-Identication by Video Ranking</td><td>Person Re-identification by Video Ranking</td><td><a href="https://pdfs.semanticscholar.org/98bb/029afe2a1239c3fdab517323066f0957b81b.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=person re-identication by video ranking&sort=relevance" target="_blank">[s2]</a></td><td></td><td>98bb029afe2a1239c3fdab517323066f0957b81b</td></tr><tr><td>sdu_vid</td><td>SDU-VID</td><td>Person reidentification by video ranking</td><td>Person Re-identification by Video Ranking</td><td><a href="https://pdfs.semanticscholar.org/98bb/029afe2a1239c3fdab517323066f0957b81b.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=person reidentification by video ranking&sort=relevance" target="_blank">[s2]</a></td><td></td><td>98bb029afe2a1239c3fdab517323066f0957b81b</td></tr><tr><td>m2vts</td><td>m2vts</td><td>The M2VTS Multimodal Face Database (Release 1.00)</td><td>The M2VTS Multimodal Face Database (Release 1.00)</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the m2vts multimodal face database (release 1.00)&sort=relevance" target="_blank">[s2]</a></td><td>Laboratoire de Télécommunications et Télédétection, UCL, Louvain-La-Neuve, Belgium</td><td>9a9877791945c6fa4c1743ec6d3fb32570ef8481</td></tr><tr><td>who_goes_there</td><td>WGT</td><td>Who Goes There? Approaches to Mapping Facial Appearance Diversity</td><td>Who goes there?: approaches to mapping facial appearance diversity</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=who goes there? approaches to mapping facial appearance diversity&sort=relevance" target="_blank">[s2]</a></td><td>University of Kentucky</td><td>9b9bf5e623cb8af7407d2d2d857bc3f1b531c182</td></tr><tr><td>facebook_100</td><td>Facebook100</td><td>Scaling Up Biologically-Inspired Computer Vision: A Case Study in Unconstrained Face Recognition on Facebook</td><td>Scaling up biologically-inspired computer vision: A case study in unconstrained face recognition on facebook</td><td><a href="http://klab.tch.harvard.edu/academia/classes/Neuro230/2012/lectures/Lecture_11_Reading.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=scaling up biologically-inspired computer vision: a case study in unconstrained face recognition on facebook&sort=relevance" target="_blank">[s2]</a></td><td>Harvard University</td><td>9c23859ec7313f2e756a3e85575735e0c52249f4</td></tr><tr><td>pubfig_83</td><td>pubfig83</td><td>Scaling Up Biologically-Inspired Computer Vision: A Case Study in Unconstrained Face Recognition on Facebook</td><td>Scaling up biologically-inspired computer vision: A case study in unconstrained face recognition on facebook</td><td><a href="http://klab.tch.harvard.edu/academia/classes/Neuro230/2012/lectures/Lecture_11_Reading.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=scaling up biologically-inspired computer vision: a case study in unconstrained face recognition on facebook&sort=relevance" target="_blank">[s2]</a></td><td>Harvard University</td><td>9c23859ec7313f2e756a3e85575735e0c52249f4</td></tr><tr><td>precarious</td><td>Precarious</td><td>Expecting the Unexpected: Training Detectors for Unusual Pedestrians With Adversarial Imposters</td><td>Expecting the Unexpected: Training Detectors for Unusual Pedestrians with Adversarial Imposters</td><td><a href="https://arxiv.org/pdf/1703.06283.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=expecting the unexpected: training detectors for unusual pedestrians with adversarial imposters&sort=relevance" target="_blank">[s2]</a></td><td></td><td>9e5378e7b336c89735d3bb15cf67eff96f86d39a</td></tr><tr><td>mafl</td><td>MAFL</td><td>Learning Deep Representation for Face Alignment with Auxiliary Attributes</td><td>Learning Deep Representation for Face Alignment with Auxiliary Attributes</td><td><a href="https://arxiv.org/pdf/1408.3967.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=learning deep representation for face alignment with auxiliary attributes&sort=relevance" target="_blank">[s2]</a></td><td></td><td>a0fd85b3400c7b3e11122f44dc5870ae2de9009a</td></tr><tr><td>mtfl</td><td>MTFL</td><td>Learning Deep Representation for Face Alignment with Auxiliary Attributes</td><td>Learning Deep Representation for Face Alignment with Auxiliary Attributes</td><td><a href="https://arxiv.org/pdf/1408.3967.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=learning deep representation for face alignment with auxiliary attributes&sort=relevance" target="_blank">[s2]</a></td><td></td><td>a0fd85b3400c7b3e11122f44dc5870ae2de9009a</td></tr><tr><td>moments_in_time</td><td>Moments in Time</td><td>Moments in Time Dataset: one million videos for event understanding</td><td>Moments in Time Dataset: one million videos for event understanding</td><td><a href="https://arxiv.org/pdf/1801.03150.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=moments in time dataset: one million videos for event understanding&sort=relevance" target="_blank">[s2]</a></td><td></td><td>a5a44a32a91474f00a3cda671a802e87c899fbb4</td></tr><tr><td>aflw</td><td>AFLW</td><td>Annotated Facial Landmarks in the Wild: A Large-scale, Real-world Database for Facial Landmark Localization</td><td>Annotated Facial Landmarks in the Wild: A large-scale, real-world database for facial landmark localization</td><td><a href="http://face.cs.kit.edu/befit/workshop2011/pdf/slides/martin_koestinger-slides.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=annotated facial landmarks in the wild: a large-scale, real-world database for facial landmark localization&sort=relevance" target="_blank">[s2]</a></td><td></td><td>a74251efa970b92925b89eeef50a5e37d9281ad0</td></tr><tr><td>market1203</td><td>Market 1203</td><td>Orientation Driven Bag of Appearances for Person Re-identification</td><td>Orientation Driven Bag of Appearances for Person Re-identification</td><td><a href="https://arxiv.org/pdf/1605.02464.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=orientation driven bag of appearances for person re-identification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>a7fe834a0af614ce6b50dc093132b031dd9a856b</td></tr><tr><td>pku_reid</td><td>PKU-Reid</td><td>Orientation driven bag of appearances for person re-identification</td><td>Orientation Driven Bag of Appearances for Person Re-identification</td><td><a href="https://arxiv.org/pdf/1605.02464.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=orientation driven bag of appearances for person re-identification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>a7fe834a0af614ce6b50dc093132b031dd9a856b</td></tr><tr><td>hi4d_adsip</td><td>Hi4D-ADSIP</td><td>Hi4D-ADSIP 3-D dynamic facial articulation database</td><td>Hi4D-ADSIP 3-D dynamic facial articulation database</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=hi4d-adsip 3-d dynamic facial articulation database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>a8d0b149c2eadaa02204d3e4356fbc8eccf3b315</td></tr><tr><td>yawdd</td><td>YawDD</td><td>YawDD: A Yawning Detection Dataset</td><td>YawDD: a yawning detection dataset</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=yawdd: a yawning detection dataset&sort=relevance" target="_blank">[s2]</a></td><td></td><td>a94cae786d515d3450d48267e12ca954aab791c4</td></tr><tr><td>mrp_drone</td><td>MRP Drone</td><td>Investigating Open-World Person Re-identification Using a Drone</td><td>Investigating Open-World Person Re-identification Using a Drone</td><td><a href="https://pdfs.semanticscholar.org/ad01/687649d95cd5b56d7399a9603c4b8e2217d7.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=investigating open-world person re-identification using a drone&sort=relevance" target="_blank">[s2]</a></td><td></td><td>ad01687649d95cd5b56d7399a9603c4b8e2217d7</td></tr><tr><td>put_face</td><td>Put Face</td><td>The PUT face database</td><td>The put face database</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the put face database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>ae0aee03d946efffdc7af2362a42d3750e7dd48a</td></tr><tr><td>afew_va</td><td>AFEW-VA</td><td>Collecting Large, Richly Annotated Facial-Expression Databases from Movies</td><td>Collecting Large, Richly Annotated Facial-Expression Databases from Movies</td><td><a href="http://users.cecs.anu.edu.au/~adhall/Dhall_Goecke_Lucey_Gedeon_M_2012.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=collecting large, richly annotated facial-expression databases from movies&sort=relevance" target="_blank">[s2]</a></td><td>Australian National University</td><td>b1f4423c227fa37b9680787be38857069247a307</td></tr><tr><td>ucf_101</td><td>UCF101</td><td>UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild</td><td>UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild</td><td><a href="https://arxiv.org/pdf/1212.0402.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=ucf101: a dataset of 101 human actions classes from videos in the wild&sort=relevance" target="_blank">[s2]</a></td><td></td><td>b5f2846a506fc417e7da43f6a7679146d99c5e96</td></tr><tr><td>m2vtsdb_extended</td><td>xm2vtsdb</td><td>XM2VTSDB: The Extended M2VTS Database</td><td>Xm2vtsdb: the Extended M2vts Database</td><td><a href="https://pdfs.semanticscholar.org/b626/28ac06bbac998a3ab825324a41a11bc3a988.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=xm2vtsdb: the extended m2vts database&sort=relevance" target="_blank">[s2]</a></td><td></td><td>b62628ac06bbac998a3ab825324a41a11bc3a988</td></tr><tr><td>ifdb</td><td>IFDB</td><td>Iranian Face Database with age, pose and expression</td><td>Iranian Face Database with age, pose and expression</td><td><a href="http://www.iranprc.org/pdf/paper/2007-02.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=iranian face database with age, pose and expression&sort=relevance" target="_blank">[s2]</a></td><td>Islamic Azad University</td><td>b71d1aa90dcbe3638888725314c0d56640c1fef1</td></tr><tr><td>bp4d_spontanous</td><td>BP4D-Spontanous</td><td>A high resolution spontaneous 3D dynamic facial expression database</td><td>A high-resolution spontaneous 3D dynamic facial expression database</td><td><a href="http://www.csee.usf.edu/~scanavan/papers/FG2013.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a high resolution spontaneous 3d dynamic facial expression database&sort=relevance" target="_blank">[s2]</a></td><td>SUNY Binghamton</td><td>b91f54e1581fbbf60392364323d00a0cd43e493c</td></tr><tr><td>pornodb</td><td>Pornography DB</td><td>Pooling in Image Representation: the Visual Codeword Point of View</td><td>Pooling in image representation: The visual codeword point of view</td><td><a href="http://cedric.cnam.fr/~thomen/papers/avila_CVIU2012_final.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=pooling in image representation: the visual codeword point of view&sort=relevance" target="_blank">[s2]</a></td><td></td><td>b92a1ed9622b8268ae3ac9090e25789fc41cc9b8</td></tr><tr><td>scut_fbp</td><td>SCUT-FBP</td><td>SCUT-FBP: A Benchmark Dataset for Facial Beauty Perception</td><td>SCUT-FBP: A Benchmark Dataset for Facial Beauty Perception</td><td><a href="https://arxiv.org/pdf/1511.02459.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=scut-fbp: a benchmark dataset for facial beauty perception&sort=relevance" target="_blank">[s2]</a></td><td></td><td>bd26dabab576adb6af30484183c9c9c8379bf2e0</td></tr><tr><td>hda_plus</td><td>HDA+</td><td>A Multi-camera video data set for research on High-Definition surveillance</td><td>HDA dataset-DRAFT 1 A Multi-camera video data set for research on High-Definition surveillance</td><td><a href="https://pdfs.semanticscholar.org/bd88/bb2e4f351352d88ee7375af834360e223498.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a multi-camera video data set for research on high-definition surveillance&sort=relevance" target="_blank">[s2]</a></td><td></td><td>bd88bb2e4f351352d88ee7375af834360e223498</td></tr><tr><td>mars</td><td>MARS</td><td>MARS: A Video Benchmark for Large-Scale Person Re-identification</td><td>MARS: A Video Benchmark for Large-Scale Person Re-Identification</td><td><a href="http://liangzheng.org/1320.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=mars: a video benchmark for large-scale person re-identification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>c0387e788a52f10bf35d4d50659cfa515d89fbec</td></tr><tr><td>nova_emotions</td><td>Novaemötions Dataset</td><td>Crowdsourcing facial expressions for affective-interaction</td><td>Crowdsourcing facial expressions for affective-interaction</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=crowdsourcing facial expressions for affective-interaction&sort=relevance" target="_blank">[s2]</a></td><td></td><td>c06b13d0ec3f5c43e2782cd22542588e233733c3</td></tr><tr><td>mcgill</td><td>McGill Real World</td><td>Hierarchical Temporal Graphical Model for Head Pose Estimation and Subsequent Attribute Classification in Real-World Videos</td><td>Hierarchical temporal graphical model for head pose estimation and subsequent attribute classification in real-world videos</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=hierarchical temporal graphical model for head pose estimation and subsequent attribute classification in real-world videos&sort=relevance" target="_blank">[s2]</a></td><td></td><td>a5a3bc3e5e9753769163cb30b16dbd12e266b93e</td></tr><tr><td>mcgill</td><td>McGill Real World</td><td>Robust Semi-automatic Head Pose Labeling for Real-World Face Video Sequences</td><td>Robust semi-automatic head pose labeling for real-world face video sequences</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=robust semi-automatic head pose labeling for real-world face video sequences&sort=relevance" target="_blank">[s2]</a></td><td>McGill University</td><td>c570d1247e337f91e555c3be0e8c8a5aba539d9f</td></tr><tr><td>qmul_surv_face</td><td>QMUL-SurvFace</td><td>Surveillance Face Recognition Challenge</td><td>Surveillance Face Recognition Challenge</td><td><a href="https://arxiv.org/pdf/1804.09691.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=surveillance face recognition challenge&sort=relevance" target="_blank">[s2]</a></td><td></td><td>c866a2afc871910e3282fd9498dce4ab20f6a332</td></tr><tr><td>emotio_net</td><td>EmotioNet Database</td><td>EmotioNet: An Accurate, Real-Time Algorithm for the Automatic Annotation of a Million Facial Expressions in the Wild</td><td>EmotioNet: An Accurate, Real-Time Algorithm for the Automatic Annotation of a Million Facial Expressions in the Wild</td><td><a href="http://cbcsl.ece.ohio-state.edu/cvpr16.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=emotionet: an accurate, real-time algorithm for the automatic annotation of a million facial expressions in the wild&sort=relevance" target="_blank">[s2]</a></td><td></td><td>c900e0ad4c95948baaf0acd8449fde26f9b4952a</td></tr><tr><td>imfdb</td><td>IMFDB</td><td>Indian Movie Face Database: A Benchmark for Face Recognition Under Wide Variations</td><td>Indian Movie Face Database: A benchmark for face recognition under wide variations</td><td><a href="http://cdn.iiit.ac.in/cdn/cvit.iiit.ac.in/papers/Shankar2013Indian.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=indian movie face database: a benchmark for face recognition under wide variations&sort=relevance" target="_blank">[s2]</a></td><td>India</td><td>ca3e88d87e1344d076c964ea89d91a75c417f5ee</td></tr><tr><td>bu_3dfe</td><td>BU-3DFE</td><td>A 3D Facial Expression Database For Facial Behavior Research</td><td>A 3D facial expression database for facial behavior research</td><td><a href="http://www.cs.binghamton.edu/~lijun/Research/3DFE/Yin_FGR06_a.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a 3d facial expression database for facial behavior research&sort=relevance" target="_blank">[s2]</a></td><td></td><td>cc589c499dcf323fe4a143bbef0074c3e31f9b60</td></tr><tr><td>b3d_ac</td><td>B3D(AC)</td><td>A 3-D Audio-Visual Corpus of Affective Communication</td><td>A 3-D Audio-Visual Corpus of Affective Communication</td><td><a href="http://files.is.tue.mpg.de/jgall/download/jgall_avcorpus_mm10.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=a 3-d audio-visual corpus of affective communication&sort=relevance" target="_blank">[s2]</a></td><td></td><td>d08cc366a4a0192a01e9a7495af1eb5d9f9e73ae</td></tr><tr><td>jiku_mobile</td><td>Jiku Mobile Video Dataset</td><td>The Jiku Mobile Video Dataset</td><td>The jiku mobile video dataset</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the jiku mobile video dataset&sort=relevance" target="_blank">[s2]</a></td><td></td><td>d178cde92ab3dc0dd2ebee5a76a33d556c39448b</td></tr><tr><td>stair_actions</td><td>STAIR Action</td><td>STAIR Actions: A Video Dataset of Everyday Home Actions</td><td>STAIR Actions: A Video Dataset of Everyday Home Actions</td><td><a href="https://arxiv.org/pdf/1804.04326.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=stair actions: a video dataset of everyday home actions&sort=relevance" target="_blank">[s2]</a></td><td></td><td>d3f5a1848b0028d8ab51d0b0673732cad2e3c8c9</td></tr><tr><td>megaage</td><td>MegaAge</td><td>Quantifying Facial Age by Posterior of Age Comparisons</td><td>Quantifying Facial Age by Posterior of Age Comparisons</td><td><a href="https://arxiv.org/pdf/1708.09687.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=quantifying facial age by posterior of age comparisons&sort=relevance" target="_blank">[s2]</a></td><td></td><td>d80a3d1f3a438e02a6685e66ee908446766fefa9</td></tr><tr><td>feret</td><td>FERET</td><td>The FERET database and evaluation procedure for face-recognition algorithms</td><td>The FERET database and evaluation procedure for face-recognition algorithms</td><td><a href="http://biometrics.nist.gov/cs_links/face/frvt/feret/FERET_Database_evaluation_procedure.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the feret database and evaluation procedure for face-recognition algorithms&sort=relevance" target="_blank">[s2]</a></td><td></td><td>dc8b25e35a3acb812beb499844734081722319b4</td></tr><tr><td>families_in_the_wild</td><td>FIW</td><td>Visual Kinship Recognition of Families in the Wild</td><td>Visual Kinship Recognition of Families in the Wild</td><td><a href="https://web.northeastern.edu/smilelab/fiw/papers/Supplemental_PP.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=visual kinship recognition of families in the wild&sort=relevance" target="_blank">[s2]</a></td><td>University of Massachusetts Dartmouth</td><td>dd65f71dac86e36eecbd3ed225d016c3336b4a13</td></tr><tr><td>scut_head</td><td>SCUT HEAD</td><td>Detecting Heads using Feature Refine Net and Cascaded Multi-scale Architecture</td><td>Detecting Heads using Feature Refine Net and Cascaded Multi-scale Architecture</td><td><a href="https://arxiv.org/pdf/1803.09256.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=detecting heads using feature refine net and cascaded multi-scale architecture&sort=relevance" target="_blank">[s2]</a></td><td></td><td>d3200d49a19a4a4e4e9745ee39649b65d80c834b</td></tr><tr><td>sarc3d</td><td>Sarc3D</td><td>SARC3D: a new 3D body model for People Tracking and Re-identification</td><td>SARC3D: A New 3D Body Model for People Tracking and Re-identification</td><td><a href="https://pdfs.semanticscholar.org/e27e/f52c641c2b5100a1b34fd0b819e84a31b4df.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=sarc3d: a new 3d body model for people tracking and re-identification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>e27ef52c641c2b5100a1b34fd0b819e84a31b4df</td></tr><tr><td>fiw_300</td><td>300-W</td><td>300 faces In-the-wild challenge: Database and results</td><td>300 Faces In-The-Wild Challenge: database and results</td><td><a href="http://ibug.doc.ic.ac.uk/media/uploads/documents/1-s2.0-s0262885616000147-main.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=300 faces in-the-wild challenge: database and results&sort=relevance" target="_blank">[s2]</a></td><td></td><td>e4754afaa15b1b53e70743880484b8d0736990ff</td></tr><tr><td>gfw</td><td>YouTube Pose</td><td>Merge or Not? Learning to Group Faces via Imitation Learning</td><td>Merge or Not? Learning to Group Faces via Imitation Learning</td><td><a href="https://arxiv.org/pdf/1707.03986.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=merge or not? learning to group faces via imitation learning&sort=relevance" target="_blank">[s2]</a></td><td></td><td>e58dd160a76349d46f881bd6ddbc2921f08d1050</td></tr><tr><td>visual_phrases</td><td>Phrasal Recognition</td><td>Recognition using Visual Phrases</td><td>Recognition using visual phrases</td><td><a href="http://vision.cs.uiuc.edu/phrasal/recognition_using_visual_phrases.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=recognition using visual phrases&sort=relevance" target="_blank">[s2]</a></td><td>University of Illinois, Urbana-Champaign</td><td>e8de844fefd54541b71c9823416daa238be65546</td></tr><tr><td>mpi_large</td><td>Large MPI Facial Expression</td><td>The MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial Expressions</td><td>The MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial Expressions</td><td><a href="https://pdfs.semanticscholar.org/ea05/0801199f98a1c7c1df6769f23f658299a3ae.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the mpi facial expression database — a validated database of emotional and conversational facial expressions&sort=relevance" target="_blank">[s2]</a></td><td></td><td>ea050801199f98a1c7c1df6769f23f658299a3ae</td></tr><tr><td>mpi_small</td><td>Small MPI Facial Expression</td><td>The MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial Expressions</td><td>The MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial Expressions</td><td><a href="https://pdfs.semanticscholar.org/ea05/0801199f98a1c7c1df6769f23f658299a3ae.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the mpi facial expression database — a validated database of emotional and conversational facial expressions&sort=relevance" target="_blank">[s2]</a></td><td></td><td>ea050801199f98a1c7c1df6769f23f658299a3ae</td></tr><tr><td>vgg_faces2</td><td>VGG Face2</td><td>VGGFace2: A dataset for recognising faces across pose and age</td><td>VGGFace2: A Dataset for Recognising Faces across Pose and Age</td><td><a href="https://arxiv.org/pdf/1710.08092.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=vggface2: a dataset for recognising faces across pose and age&sort=relevance" target="_blank">[s2]</a></td><td></td><td>eb027969f9310e0ae941e2adee2d42cdf07d938c</td></tr><tr><td>msmt_17</td><td>MSMT17</td><td>Person Transfer GAN to Bridge Domain Gap for Person Re-Identification</td><td>Person Transfer GAN to Bridge Domain Gap for Person Re-Identification</td><td><a href="https://arxiv.org/pdf/1711.08565.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=person transfer gan to bridge domain gap for person re-identification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>ec792ad2433b6579f2566c932ee414111e194537</td></tr><tr><td>europersons</td><td>EuroCity Persons</td><td>The EuroCity Persons Dataset: A Novel Benchmark for Object Detection</td><td>The EuroCity Persons Dataset: A Novel Benchmark for Object Detection</td><td><a href="https://arxiv.org/pdf/1805.07193.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the eurocity persons dataset: a novel benchmark for object detection&sort=relevance" target="_blank">[s2]</a></td><td></td><td>f0e17f27f029db4ad650ff278fe3c10ecb6cb0c4</td></tr><tr><td>mug_faces</td><td>MUG Faces</td><td>The MUG Facial Expression Database</td><td>The MUG facial expression database</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the mug facial expression database&sort=relevance" target="_blank">[s2]</a></td><td>Aristotle University of Thessaloniki</td><td>f1af714b92372c8e606485a3982eab2f16772ad8</td></tr><tr><td>pku</td><td>PKU</td><td>Swiss-System Based Cascade Ranking for Gait-based Person Re-identification</td><td>Swiss-System Based Cascade Ranking for Gait-Based Person Re-Identification</td><td><a href="https://pdfs.semanticscholar.org/f6c8/d5e35d7e4d60a0104f233ac1a3ab757da53f.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=swiss-system based cascade ranking for gait-based person re-identification&sort=relevance" target="_blank">[s2]</a></td><td></td><td>f6c8d5e35d7e4d60a0104f233ac1a3ab757da53f</td></tr><tr><td>caltech_pedestrians</td><td>Caltech Pedestrians</td><td>Pedestrian Detection: An Evaluation of the State of the Art</td><td>Pedestrian Detection: An Evaluation of the State of the Art</td><td><a href="http://vision.caltech.edu/Image_Datasets/CaltechPedestrians/files/PAMI12pedestrians.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=pedestrian detection: an evaluation of the state of the art&sort=relevance" target="_blank">[s2]</a></td><td>California Institute of Technology</td><td>f72f6a45ee240cc99296a287ff725aaa7e7ebb35</td></tr><tr><td>iit_dehli_ear</td><td>IIT Dehli Ear</td><td>Automated human identification using ear imaging</td><td>Automated Human Identification Using Ear Imaging</td><td><a href="https://pdfs.semanticscholar.org/faf4/0ce28857aedf183e193486f5b4b0a8c478a2.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=automated human identification using ear imaging&sort=relevance" target="_blank">[s2]</a></td><td></td><td>faf40ce28857aedf183e193486f5b4b0a8c478a2</td></tr><tr><td>miw</td><td>MIW</td><td>Automatic Facial Makeup Detection with Application in Face Recognition</td><td>Automatic facial makeup detection with application in face recognition</td><td><a href="http://www.cse.msu.edu/~rossarun/pubs/ChenMakeupDetection_ICB2013.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=automatic facial makeup detection with application in face recognition&sort=relevance" target="_blank">[s2]</a></td><td>West Virginia University</td><td>fcc6fe6007c322641796cb8792718641856a22a7</td></tr><tr><td>youtube_makeup</td><td>YMU</td><td>Automatic Facial Makeup Detection with Application in Face Recognition</td><td>Automatic facial makeup detection with application in face recognition</td><td><a href="http://www.cse.msu.edu/~rossarun/pubs/ChenMakeupDetection_ICB2013.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=automatic facial makeup detection with application in face recognition&sort=relevance" target="_blank">[s2]</a></td><td>West Virginia University</td><td>fcc6fe6007c322641796cb8792718641856a22a7</td></tr><tr><td>nd_2006</td><td>ND-2006</td><td>Using a Multi-Instance Enrollment Representation to Improve 3D Face Recognition</td><td>Using a Multi-Instance Enrollment Representation to Improve 3D Face Recognition</td><td><span class="gray">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=using a multi-instance enrollment representation to improve 3d face recognition&sort=relevance" target="_blank">[s2]</a></td><td>University of Notre Dame</td><td>fd8168f1c50de85bac58a8d328df0a50248b16ae</td></tr><tr><td>imdb_face</td><td>IMDB Face</td><td>The Devil of Face Recognition is in the Noise</td><td>The Devil of Face Recognition is in the Noise</td><td><a href="https://arxiv.org/pdf/1807.11649.pdf" target="_blank">[pdf]</a></td><td><a href="https://www.semanticscholar.org/search?q=the devil of face recognition is in the noise&sort=relevance" target="_blank">[s2]</a></td><td></td><td>9e31e77f9543ab42474ba4e9330676e18c242e72</td></tr></table></body></html>
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author	jules@lens <julescarbon@gmail.com>	2019-02-20 18:36:25 +0100
committer	jules@lens <julescarbon@gmail.com>	2019-02-20 18:36:25 +0100
commit	2116027843edad22d87e6a56269b26cd6aafb8e8 (patch)
tree	ae15c70898a3ee28668a154ccdc1e600af51834c /scraper/reports/paper_title_report.html
parent	1ef0b07c0bbd779f3ab9b618a0edb768b927816e (diff)