manually updated citation lookup

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+key,name,title,paper_id

+10k_US_adult_faces,10K US Adult Faces,The intrinsic memorability of face images,8b2dd5c61b23ead5ae5508bb8ce808b5ea266730

+3d_rma,3D-RMA,Automatic 3D Face Authentication,2160788824c4c29ffe213b2cbeb3f52972d73f37

+3dddb_unconstrained,3D Dynamic,A 3D Dynamic Database for Unconstrained Face Recognition,370b5757a5379b15e30d619e4d3fb9e8e13f3256

+3dpes,3DPeS,3DPes: 3D People Dataset for Surveillance and Forensics,2e8d0f1802e50cccfd3c0aabac0d0beab3a7846e

+4dfab,4DFAB,4DFAB: A Large Scale 4D Facial Expression Database for Biometric Applications,9696ad8b164f5e10fcfe23aacf74bd6168aebb15

+50_people_one_question,50 People One Question,Merging Pose Estimates Across Space and Time,5753b2b5e442eaa3be066daa4a2ca8d8a0bb1725

+a_pascal_yahoo,aPascal,Describing Objects by their Attributes,2e384f057211426ac5922f1b33d2aa8df5d51f57

+adience,Adience,Age and Gender Estimation of Unfiltered Faces,1be498d4bbc30c3bfd0029114c784bc2114d67c0

+afad,AFAD,Ordinal Regression with a Multiple Output CNN for Age Estimation,6618cff7f2ed440a0d2fa9e74ad5469df5cdbe4c

+afew_va,AFEW-VA,AFEW-VA database for valence and arousal estimation in-the-wild,b1f4423c227fa37b9680787be38857069247a307

+afew_va,AFEW-VA,"Collecting Large, Richly Annotated Facial-Expression Databases from Movies",b1f4423c227fa37b9680787be38857069247a307

+affectnet,AffectNet,"AffectNet: A New Database for Facial Expression, Valence, and Arousal Computation in the Wild",f152b6ee251cca940dd853c54e6a7b78fbc6b235

+aflw,AFLW,"Annotated Facial Landmarks in the Wild: A Large-scale, Real-world Database for Facial Landmark Localization",a74251efa970b92925b89eeef50a5e37d9281ad0

+afw,AFW,"Face detection, pose estimation and landmark localization in the wild",0e986f51fe45b00633de9fd0c94d082d2be51406

+agedb,AgeDB,"AgeDB: the first manually collected, in-the-wild age database",6dcf418c778f528b5792104760f1fbfe90c6dd6a

+alert_airport,ALERT Airport,"A Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets",6403117f9c005ae81f1e8e6d1302f4a045e3d99d

+am_fed,AM-FED,Affectiva MIT Facial Expression Dataset (AM-FED): Naturalistic and Spontaneous Facial Expressions Collected “In the Wild”,47aeb3b82f54b5ae8142b4bdda7b614433e69b9a

+apis,APiS1.0,Pedestrian Attribute Classification in Surveillance: Database and Evaluation,488e475eeb3bb39a145f23ede197cd3620f1d98a

+ar_facedb,AR Face,The AR Face Database,370b5757a5379b15e30d619e4d3fb9e8e13f3256

+awe_ears,AWE Ears,Ear Recognition: More Than a Survey,84fe5b4ac805af63206012d29523a1e033bc827e

+b3d_ac,B3D(AC),A 3-D Audio-Visual Corpus of Affective Communication,d08cc366a4a0192a01e9a7495af1eb5d9f9e73ae

+bbc_pose,BBC Pose,Automatic and Efficient Human Pose Estimation for Sign Language Videos,213a579af9e4f57f071b884aa872651372b661fd

+berkeley_pose,BPAD,Describing People: A Poselet-Based Approach to Attribute Classification,7808937b46acad36e43c30ae4e9f3fd57462853d

+bfm,BFM,A 3D Face Model for Pose and Illumination Invariant Face Recognition,639937b3a1b8bded3f7e9a40e85bd3770016cf3c

+bio_id,BioID Face,Robust Face Detection Using the Hausdorff Distance,4053e3423fb70ad9140ca89351df49675197196a

+bjut_3d,BJUT-3D,The BJUT-3D Large-Scale Chinese Face Database,1ed1a49534ad8dd00f81939449f6389cfbc25321

+bosphorus,The Bosphorus,Bosphorus Database for 3D Face Analysis,2acf7e58f0a526b957be2099c10aab693f795973

+bp4d_plus,BP4D+,Multimodal Spontaneous Emotion Corpus for Human Behavior Analysis,53ae38a6bb2b21b42bac4f0c4c8ed1f9fa02f9d4

+bp4d_spontanous,BP4D-Spontanous,A high resolution spontaneous 3D dynamic facial expression database,b91f54e1581fbbf60392364323d00a0cd43e493c

+brainwash,Brainwash,Brainwash dataset,214c966d1f9c2a4b66f4535d9a0d4078e63a5867

+bu_3dfe,BU-3DFE,A 3D Facial Expression Database For Facial Behavior Research,cc589c499dcf323fe4a143bbef0074c3e31f9b60

+buhmap_db,BUHMAP-DB ,Facial Feature Tracking and Expression Recognition for Sign Language,014b8df0180f33b9fea98f34ae611c6447d761d2

+cafe,CAFE,The Child Affective Facial Expression (CAFE) Set: Validity and reliability from untrained adults,20388099cc415c772926e47bcbbe554e133343d1

+caltech_10k_web_faces,Caltech 10K Web Faces,Pruning Training Sets for Learning of Object Categories,636b8ffc09b1b23ff714ac8350bb35635e49fa3c

+caltech_pedestrians,Caltech Pedestrians,Pedestrian Detection: A Benchmark,f72f6a45ee240cc99296a287ff725aaa7e7ebb35

+caltech_pedestrians,Caltech Pedestrians,Pedestrian Detection: An Evaluation of the State of the Art,f72f6a45ee240cc99296a287ff725aaa7e7ebb35

+camel,CAMEL,CAMEL Dataset for Visual and Thermal Infrared Multiple Object Detection and Tracking,5801690199c1917fa58c35c3dead177c0b8f9f2d

+cas_peal,CAS-PEAL,The CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations,2485c98aa44131d1a2f7d1355b1e372f2bb148ad

+casablanca,Casablanca,Context-aware {CNNs} for person head detection,0ceda9dae8b9f322df65ca2ef02caca9758aec6f

+casia_webface,CASIA Webface,Learning Face Representation from Scratch,853bd61bc48a431b9b1c7cab10c603830c488e39

+celeba,CelebA,Deep Learning Face Attributes in the Wild,6424b69f3ff4d35249c0bb7ef912fbc2c86f4ff4

+celeba_plus,CelebFaces+,"Deep Learning Face Representation from Predicting 10,000 Classes",69a68f9cf874c69e2232f47808016c2736b90c35

+cfd,CFD,The Chicago face database: A free stimulus set of faces and norming data,4df3143922bcdf7db78eb91e6b5359d6ada004d2

+chalearn,ChaLearn,ChaLearn Looking at People: A Review of Events and Resources,8d5998cd984e7cce307da7d46f155f9db99c6590

+chokepoint,ChokePoint,Patch-based Probabilistic Image Quality Assessment for Face Selection and Improved Video-based Face Recognition,0486214fb58ee9a04edfe7d6a74c6d0f661a7668

+cityscapes,Cityscapes,The Cityscapes Dataset for Semantic Urban Scene Understanding,32cde90437ab5a70cf003ea36f66f2de0e24b3ab

+cityscapes,Cityscapes,The Cityscapes Dataset,32cde90437ab5a70cf003ea36f66f2de0e24b3ab

+clothing_co_parsing,CCP,Clothing Co-Parsing by Joint Image Segmentation and Labeling,2bf8541199728262f78d4dced6fb91479b39b738

+cmdp,CMDP,Distance Estimation of an Unknown Person from a Portrait,56ae6d94fc6097ec4ca861f0daa87941d1c10b70

+cmu_pie,CMU PIE,"The CMU Pose, Illumination, and Expression Database",4d423acc78273b75134e2afd1777ba6d3a398973

+coco,COCO,Microsoft COCO: Common Objects in Context,696ca58d93f6404fea0fc75c62d1d7b378f47628

+coco_action,COCO-a,Describing Common Human Visual Actions in Images,4946ba10a4d5a7d0a38372f23e6622bd347ae273

+coco_qa,COCO QA,Exploring Models and Data for Image Question Answering,35b0331dfcd2897abd5749b49ff5e2b8ba0f7a62

+cofw,COFW,Robust face landmark estimation under occlusion,2724ba85ec4a66de18da33925e537f3902f21249

+cohn_kanade,CK,Comprehensive Database for Facial Expression Analysis,23fc83c8cfff14a16df7ca497661264fc54ed746

+cohn_kanade_plus,CK+,The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression,4d9a02d080636e9666c4d1cc438b9893391ec6c7

+columbia_gaze,Columbia Gaze,Gaze Locking: Passive Eye Contact Detection for Human–Object Interaction,c34532fe6bfbd1e6df477c9ffdbb043b77e7804d

+complex_activities,Ongoing Complex Activities,Recognition of Ongoing Complex Activities by Sequence Prediction over a Hierarchical Label Space,65355cbb581a219bd7461d48b3afd115263ea760

+cuhk01,CUHK01,Human Reidentification with Transferred Metric Learning,44484d2866f222bbb9b6b0870890f9eea1ffb2d0

+cuhk02,CUHK02,Locally Aligned Feature Transforms across Views,38b55d95189c5e69cf4ab45098a48fba407609b4

+cuhk03,CUHK03,DeepReID: Deep Filter Pairing Neural Network for Person Re-identification,6bd36e9fd0ef20a3074e1430a6cc601e6d407fc3

+cvc_01_barcelona,CVC-01,Adaptive Image Sampling and Windows Classification for On-board Pedestrian Detection,57fe081950f21ca03b5b375ae3e84b399c015861

+czech_news_agency,UFI,Unconstrained Facial Images: Database for Face Recognition under Real-world Conditions,4b4106614c1d553365bad75d7866bff0de6056ed

+d3dfacs,D3DFACS,A FACS Valid 3D Dynamic Action Unit database with Applications to 3D Dynamic Morphable Facial Modelling,070de852bc6eb275d7ca3a9cdde8f6be8795d1a3

+dartmouth_children,Dartmouth Children,The Dartmouth Database of Children's Faces: Acquisition and validation of a new face stimulus set,4e6ee936eb50dd032f7138702fa39b7c18ee8907

+data_61,Data61 Pedestrian,A Multi-Modal Graphical Model for Scene Analysis,563c940054e4b456661762c1ab858e6f730c3159

+deep_fashion,DeepFashion,DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations,4fefd1bc8dc4e0ab37ee3324ddfa43ad9d6a04a7

+deep_fashion,DeepFashion,Fashion Landmark Detection in the Wild,4fefd1bc8dc4e0ab37ee3324ddfa43ad9d6a04a7

+disfa,DISFA,DISFA: A Spontaneous Facial Action Intensity Database,a5acda0e8c0937bfed013e6382da127103e41395

+distance_nighttime,Long Distance Heterogeneous Face,Nighttime Face Recognition at Long Distance: Cross-distance and Cross-spectral Matching,4156b7e88f2e0ab0a7c095b9bab199ae2b23bd06

+duke_mtmc,Duke MTMC,"Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking",27a2fad58dd8727e280f97036e0d2bc55ef5424c

+emotio_net,EmotioNet Database,"EmotioNet: An Accurate, Real-Time Algorithm for the Automatic Annotation of a Million Facial Expressions in the Wild",c900e0ad4c95948baaf0acd8449fde26f9b4952a

+eth_andreas_ess,ETHZ Pedestrian,Depth and Appearance for Mobile Scene Analysis,13f06b08f371ba8b5d31c3e288b4deb61335b462

+europersons,EuroCity Persons,The EuroCity Persons Dataset: A Novel Benchmark for Object Detection,f0e17f27f029db4ad650ff278fe3c10ecb6cb0c4

+expw,ExpW,Learning Social Relation Traits from Face Images,22f656d0f8426c84a33a267977f511f127bfd7f3

+expw,ExpW,From Facial Expression Recognition to Interpersonal Relation Prediction,22f656d0f8426c84a33a267977f511f127bfd7f3

+face_research_lab,Face Research Lab London,Face Research Lab London Set. figshare,c6526dd3060d63a6c90e8b7ff340383c4e0e0dd8

+face_scrub,FaceScrub,A data-driven approach to cleaning large face datasets,0d3bb75852098b25d90f31d2f48fd0cb4944702b

+face_tracer,FaceTracer,FaceTracer: A Search Engine for Large Collections of Images with Faces,670637d0303a863c1548d5b19f705860a23e285c

+face_tracer,FaceTracer,Face Swapping: Automatically Replacing Faces in Photographs,670637d0303a863c1548d5b19f705860a23e285c

+facebook_100,Facebook100,Scaling Up Biologically-Inspired Computer Vision: A Case Study in Unconstrained Face Recognition on Facebook,9c23859ec7313f2e756a3e85575735e0c52249f4

+faceplace,Face Place,Recognizing disguised faces,25474c21613607f6bb7687a281d5f9d4ffa1f9f3

+families_in_the_wild,FIW,Visual Kinship Recognition of Families in the Wild,dd65f71dac86e36eecbd3ed225d016c3336b4a13

+fddb,FDDB,FDDB: A Benchmark for Face Detection in Unconstrained Settings,75da1df4ed319926c544eefe17ec8d720feef8c0

+fei,FEI,Captura e Alinhamento de Imagens: Um Banco de Faces Brasileiro,b6b1b0632eb9d4ab1427278f5e5c46f97753c73d

+feret,FERET,The FERET Verification Testing Protocol for Face Recognition Algorithms,0c4a139bb87c6743c7905b29a3cfec27a5130652

+feret,FERET,The FERET database and evaluation procedure for face-recognition algorithms,dc8b25e35a3acb812beb499844734081722319b4

+feret,FERET,FERET ( Face Recognition Technology ) Recognition Algorithm Development and Test Results,31de9b3dd6106ce6eec9a35991b2b9083395fd0b

+feret,FERET,The FERET Evaluation Methodology for Face-Recognition Algorithms,0f0fcf041559703998abf310e56f8a2f90ee6f21

+ferplus,FER+,Training Deep Networks for Facial Expression Recognition with Crowd-Sourced Label Distribution,298cbc3dfbbb3a20af4eed97906650a4ea1c29e0

+fia,CMU FiA,The CMU Face In Action (FIA) Database,47662d1a368daf70ba70ef2d59eb6209f98b675d

+fiw_300,300-W,300 faces In-the-wild challenge: Database and results,013909077ad843eb6df7a3e8e290cfd5575999d2

+fiw_300,300-W,300 Faces in-the-Wild Challenge: The first facial landmark localization Challenge,013909077ad843eb6df7a3e8e290cfd5575999d2

+fiw_300,300-W,A semi-automatic methodology for facial landmark annotation,013909077ad843eb6df7a3e8e290cfd5575999d2

+frav3d,FRAV3D,"MULTIMODAL 2D, 2.5D & 3D FACE VERIFICATION",2f5d44dc3e1b5955942133ff872ebd31716ec604

+frgc,FRGC,Overview of the Face Recognition Grand Challenge,18ae7c9a4bbc832b8b14bc4122070d7939f5e00e

+gallagher,Gallagher,Clothing Cosegmentation for Recognizing People,6dbe8e5121c534339d6e41f8683e85f87e6abf81

+gavab_db,Gavab,GavabDB: a 3D face database,42505464808dfb446f521fc6ff2cfeffd4d68ff1

+geofaces,GeoFaces,GeoFaceExplorer: Exploring the Geo-Dependence of Facial Attributes,17b46e2dad927836c689d6787ddb3387c6159ece

+georgia_tech_face_database,Georgia Tech Face,Maximum likelihood training of the embedded HMM for face detection and recognition,3dc3f0b64ef80f573e3a5f96e456e52ee980b877

+graz,Graz Pedestrian,Generic Object Recognition with Boosting,12ad3b5bbbf407f8e54ea692c07633d1a867c566

+graz,Graz Pedestrian,Weak Hypotheses and Boosting for Generic Object Detection and Recognition,12ad3b5bbbf407f8e54ea692c07633d1a867c566

+graz,Graz Pedestrian,Object Recognition Using Segmentation for Feature Detection,12ad3b5bbbf407f8e54ea692c07633d1a867c566

+h3d,H3D,Poselets: Body Part Detectors Trained Using 3D Human Pose Annotations,2830fb5282de23d7784b4b4bc37065d27839a412

+hda_plus,HDA+,The HDA+ data set for research on fully automated re-identification systems,bd88bb2e4f351352d88ee7375af834360e223498

+hda_plus,HDA+,A Multi-camera video data set for research on High-Definition surveillance,bd88bb2e4f351352d88ee7375af834360e223498

+helen,Helen,Interactive Facial Feature Localization,95f12d27c3b4914e0668a268360948bce92f7db3

+hi4d_adsip,Hi4D-ADSIP,Hi4D-ADSIP 3-D dynamic facial articulation database,24830e3979d4ed01b9fd0feebf4a8fd22e0c35fd

+hipsterwars,Hipsterwars,Hipster Wars: Discovering Elements of Fashion Styles,04c2cda00e5536f4b1508cbd80041e9552880e67

+hollywood_headset,HollywoodHeads,Context-aware CNNs for person head detection,0ceda9dae8b9f322df65ca2ef02caca9758aec6f

+hrt_transgender,HRT Transgender,Is the Eye Region More Reliable Than the Face? A Preliminary Study of Face-based Recognition on a Transgender Dataset,28312c3a47c1be3a67365700744d3d6665b86f22

+hrt_transgender,HRT Transgender,Investigating the Periocular-Based Face Recognition Across Gender Transformation,28312c3a47c1be3a67365700744d3d6665b86f22

+hrt_transgender,HRT Transgender,Face recognition across gender transformation using SVM Classifier,28312c3a47c1be3a67365700744d3d6665b86f22

+ifad,IFAD,Indian Face Age Database: A Database for Face Recognition with Age Variation,55c40cbcf49a0225e72d911d762c27bb1c2d14aa

+ifdb,IFDB,"Iranian Face Database with age, pose and expression",066d71fcd997033dce4ca58df924397dfe0b5fd1

+ifdb,IFDB,Iranian Face Database and Evaluation with a New Detection Algorithm,066d71fcd997033dce4ca58df924397dfe0b5fd1

+iit_dehli_ear,IIT Dehli Ear,Automated human identification using ear imaging,faf40ce28857aedf183e193486f5b4b0a8c478a2

+ijb_a,IJB-A,Pushing the Frontiers of Unconstrained Face Detection and Recognition: IARPA Janus Benchmark A,140c95e53c619eac594d70f6369f518adfea12ef

+ijb_b,IJB-B,IARPA Janus Benchmark-B Face Dataset,0cb2dd5f178e3a297a0c33068961018659d0f443

+ijb_c,IJB-C,IARPA Janus Benchmark C,57178b36c21fd7f4529ac6748614bb3374714e91

+ilids_mcts,,"Imagery Library for Intelligent Detection Systems: 
+The i-LIDS User Guide",0297448f3ed948e136bb06ceff10eccb34e5bb77

+ilids_vid_reid,iLIDS-VID,Person Re-Identication by Video Ranking,99eb4cea0d9bc9fe777a5c5172f8638a37a7f262

+images_of_groups,Images of Groups,Understanding Groups of Images of People,21d9d0deed16f0ad62a4865e9acf0686f4f15492

+imdb_wiki,IMDB,Deep expectation of real and apparent age from a single image without facial landmarks,10195a163ab6348eef37213a46f60a3d87f289c5

+imdb_wiki,IMDB,DEX: Deep EXpectation of apparent age from a single image,8355d095d3534ef511a9af68a3b2893339e3f96b

+imfdb,IMFDB,Indian Movie Face Database: A Benchmark for Face Recognition Under Wide Variations,ca3e88d87e1344d076c964ea89d91a75c417f5ee

+imm_face,IMM Face Dataset,The IMM Face Database - An Annotated Dataset of 240 Face Images,a74251efa970b92925b89eeef50a5e37d9281ad0

+immediacy,Immediacy,Multi-task Recurrent Neural Network for Immediacy Prediction,1e3df3ca8feab0b36fd293fe689f93bb2aaac591

+imsitu,imSitu,Situation Recognition: Visual Semantic Role Labeling for Image Understanding,51eba481dac6b229a7490f650dff7b17ce05df73

+inria_person,INRIA Pedestrian,Histograms of Oriented Gradients for Human Detection,10d6b12fa07c7c8d6c8c3f42c7f1c061c131d4c5

+jaffe,JAFFE,Coding Facial Expressions with Gabor Wavelets,45c31cde87258414f33412b3b12fc5bec7cb3ba9

+jiku_mobile,Jiku Mobile Video Dataset,The Jiku Mobile Video Dataset,ad62c6e17bc39b4dec20d32f6ac667ae42d2c118

+jpl_pose,JPL-Interaction dataset,First-Person Activity Recognition: What Are They Doing to Me?,1aad2da473888cb7ebc1bfaa15bfa0f1502ce005

+kdef,KDEF,The Karolinska Directed Emotional Faces – KDEF,93884e46c49f7ae1c7c34046fbc28882f2bd6341

+kin_face,UB KinFace,Genealogical Face Recognition based on UB KinFace Database,08f6745bc6c1b0fb68953ea61054bdcdde6d2fc7

+kin_face,UB KinFace,Kinship Verification through Transfer Learning,08f6745bc6c1b0fb68953ea61054bdcdde6d2fc7

+kin_face,UB KinFace,Understanding Kin Relationships in a Photo,08f6745bc6c1b0fb68953ea61054bdcdde6d2fc7

+kinectface,KinectFaceDB,KinectFaceDB: A Kinect Database for Face Recognition,0b440695c822a8e35184fb2f60dcdaa8a6de84ae

+kitti,KITTI,Vision meets Robotics: The KITTI Dataset,35ba4ebfd017a56b51e967105af9ae273c9b0178

+lag,LAG,Large Age-Gap Face Verification by Feature Injection in Deep Networks,0d2dd4fc016cb6a517d8fb43a7cc3ff62964832e

+large_scale_person_search,Large Scale Person Search,End-to-End Deep Learning for Person Search,2161f6b7ee3c0acc81603b01dc0df689683577b9

+leeds_sports_pose,Leeds Sports Pose,Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation,4b1d23d17476fcf78f4cbadf69fb130b1aa627c0

+leeds_sports_pose_extended,Leeds Sports Pose Extended,Learning Effective Human Pose Estimation from Inaccurate Annotation,4e4746094bf60ee83e40d8597a6191e463b57f76

+lfw,LFW,Labeled Faces in the Wild: A Survey,7de6e81d775e9cd7becbfd1bd685f4e2a5eebb22

+lfw,LFW,Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments,370b5757a5379b15e30d619e4d3fb9e8e13f3256

+lfw,LFW,Labeled Faces in the Wild: Updates and New Reporting Procedures,2d3482dcff69c7417c7b933f22de606a0e8e42d4

+lfw_a,LFW-a,"Effective Unconstrained Face Recognition by
+ Combining Multiple Descriptors and Learned
+ Background Statistics",133f01aec1534604d184d56de866a4bd531dac87

+lfw_p,LFWP,Localizing Parts of Faces Using a Consensus of Exemplars,140438a77a771a8fb656b39a78ff488066eb6b50

+m2vts,m2vts,The M2VTS Multimodal Face Database (Release 1.00),2485c98aa44131d1a2f7d1355b1e372f2bb148ad

+m2vtsdb_extended,xm2vtsdb,XM2VTSDB: The Extended M2VTS Database,370b5757a5379b15e30d619e4d3fb9e8e13f3256

+mafl,MAFL,Facial Landmark Detection by Deep Multi-task Learning,a0fd85b3400c7b3e11122f44dc5870ae2de9009a

+mafl,MAFL,Learning Deep Representation for Face Alignment with Auxiliary Attributes,a0fd85b3400c7b3e11122f44dc5870ae2de9009a

+malf,MALF,Fine-grained Evaluation on Face Detection in the Wild.,45e616093a92e5f1e61a7c6037d5f637aa8964af

+mapillary,Mapillary,The Mapillary Vistas Dataset for Semantic Understanding of Street Scenes,79828e6e9f137a583082b8b5a9dfce0c301989b8

+market_1501,Market 1501,Scalable Person Re-identification: A Benchmark,4308bd8c28e37e2ed9a3fcfe74d5436cce34b410

+market1203,Market 1203,Orientation Driven Bag of Appearances for Person Re-identification,a7fe834a0af614ce6b50dc093132b031dd9a856b

+mars,MARS,MARS: A Video Benchmark for Large-Scale Person Re-identification,c0387e788a52f10bf35d4d50659cfa515d89fbec

+mcgill,McGill Real World,Hierarchical Temporal Graphical Model for Head Pose Estimation and Subsequent Attribute Classification in Real-World Videos,c570d1247e337f91e555c3be0e8c8a5aba539d9f

+mcgill,McGill Real World,Robust Semi-automatic Head Pose Labeling for Real-World Face Video Sequences,c570d1247e337f91e555c3be0e8c8a5aba539d9f

+megaage,MegaAge,Quantifying Facial Age by Posterior of Age Comparisons,d80a3d1f3a438e02a6685e66ee908446766fefa9

+megaface,MegaFace,The MegaFace Benchmark: 1 Million Faces for Recognition at Scale,96e0cfcd81cdeb8282e29ef9ec9962b125f379b0

+megaface,MegaFace,Level Playing Field for Million Scale Face Recognition,28d4e027c7e90b51b7d8908fce68128d1964668a

+mifs,MIFS,Spoofing Faces Using Makeup: An Investigative Study,23e824d1dfc33f3780dd18076284f07bd99f1c43

+mit_cbcl,MIT CBCL,Component-based Face Recognition with 3D Morphable Models,079a0a3bf5200994e1f972b1b9197bf2f90e87d4

+miw,MIW,Automatic Facial Makeup Detection with Application in Face Recognition,fcc6fe6007c322641796cb8792718641856a22a7

+mmi_facial_expression,MMI Facial Expression Dataset,WEB-BASED DATABASE FOR FACIAL EXPRESSION ANALYSIS,2a75f34663a60ab1b04a0049ed1d14335129e908

+moments_in_time,Moments in Time,Moments in Time Dataset: one million videos for event understanding,a5a44a32a91474f00a3cda671a802e87c899fbb4

+morph,MORPH Commercial,MORPH: A Longitudinal Image Database of Normal Adult Age-Progression,9055b155cbabdce3b98e16e5ac9c0edf00f9552f

+morph_nc,MORPH Non-Commercial,MORPH: A Longitudinal Image Database of Normal Adult Age-Progression,9055b155cbabdce3b98e16e5ac9c0edf00f9552f

+mot,MOT,Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics,5981e6479c3fd4e31644db35d236bfb84ae46514

+mot,MOT,"Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking",5981e6479c3fd4e31644db35d236bfb84ae46514

+mot,MOT,Learning to associate: HybridBoosted multi-target tracker for crowded scene,5981e6479c3fd4e31644db35d236bfb84ae46514

+mpi_large,Large MPI Facial Expression,The MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial Expressions,ea050801199f98a1c7c1df6769f23f658299a3ae

+mpi_small,Small MPI Facial Expression,The MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial Expressions,ea050801199f98a1c7c1df6769f23f658299a3ae

+mpii_gaze,MPIIGaze,Appearance-based Gaze Estimation in the Wild,0df0d1adea39a5bef318b74faa37de7f3e00b452

+mpii_human_pose,MPII Human Pose,2D Human Pose Estimation: New Benchmark and State of the Art Analysis,3325860c0c82a93b2eac654f5324dd6a776f609e

+mr2,MR2,The MR2: A multi-racial mega-resolution database of facial stimuli,578d4ad74818086bb64f182f72e2c8bd31e3d426

+mrp_drone,MRP Drone,Investigating Open-World Person Re-identification Using a Drone,ad01687649d95cd5b56d7399a9603c4b8e2217d7

+msceleb,MsCeleb,MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition,291265db88023e92bb8c8e6390438e5da148e8f5

+msmt_17,MSMT17,Person Transfer GAN to Bridge Domain Gap for Person Re-Identification,ec792ad2433b6579f2566c932ee414111e194537

+mtfl,MTFL,Facial Landmark Detection by Deep Multi-task Learning,a0fd85b3400c7b3e11122f44dc5870ae2de9009a

+mtfl,MTFL,Learning Deep Representation for Face Alignment with Auxiliary Attributes,a0fd85b3400c7b3e11122f44dc5870ae2de9009a

+muct,MUCT,The MUCT Landmarked Face Database,a74251efa970b92925b89eeef50a5e37d9281ad0

+mug_faces,MUG Faces,The MUG Facial Expression Database,f1af714b92372c8e606485a3982eab2f16772ad8

+multi_pie,MULTIPIE,Multi-PIE,109df0e8e5969ddf01e073143e83599228a1163f

+names_and_faces_news,News Dataset,Names and Faces,2fda164863a06a92d3a910b96eef927269aeb730

+nd_2006,ND-2006,Using a Multi-Instance Enrollment Representation to Improve 3D Face Recognition,fd8168f1c50de85bac58a8d328df0a50248b16ae

+nova_emotions,Novaemötions Dataset,Crowdsourcing facial expressions for affective-interaction,7f4040b482d16354d5938c1d1b926b544652bf5b

+nova_emotions,Novaemötions Dataset,Competitive affective gamming: Winning with a smile,7f4040b482d16354d5938c1d1b926b544652bf5b

+nudedetection,Nude Detection,A Bag-of-Features Approach based on Hue-SIFT Descriptor for Nude Detection,7ace44190729927e5cb0dd5d363fcae966fe13f7

+orl,ORL,Parameterisation of a Stochastic Model for Human Face Identification,55206f0b5f57ce17358999145506cd01e570358c

+penn_fudan,Penn Fudan,Object Detection Combining Recognition and Segmentation,3394168ff0719b03ff65bcea35336a76b21fe5e4

+peta,PETA,Pedestrian Attribute Recognition At Far Distance,2a4bbee0b4cf52d5aadbbc662164f7efba89566c

+pets,PETS 2017,PETS 2017: Dataset and Challenge,22909dd19a0ec3b6065334cb5be5392cb24d839d

+pilot_parliament,PPB,Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classication,fb82681ac5d3487bd8e52dbb3d1fa220eeac855e

+pipa,PIPA,Beyond Frontal Faces: Improving Person Recognition Using Multiple Cues,0a85bdff552615643dd74646ac881862a7c7072d

+pku,PKU,Swiss-System Based Cascade Ranking for Gait-based Person Re-identification,f6c8d5e35d7e4d60a0104f233ac1a3ab757da53f

+pku_reid,PKU-Reid,Orientation driven bag of appearances for person re-identification,a7fe834a0af614ce6b50dc093132b031dd9a856b

+pornodb,Pornography DB,Pooling in Image Representation: the Visual Codeword Point of View,b92a1ed9622b8268ae3ac9090e25789fc41cc9b8

+precarious,Precarious,Expecting the Unexpected: Training Detectors for Unusual Pedestrians With Adversarial Imposters,9e5378e7b336c89735d3bb15cf67eff96f86d39a

+prid,PRID,Person Re-Identification by Descriptive and Discriminative Classification,16c7c31a7553d99f1837fc6e88e77b5ccbb346b8

+prw,PRW,Person Re-identification in the Wild,0b84f07af44f964817675ad961def8a51406dd2e

+psu,PSU,Vision-based Analysis of Small Groups in Pedestrian Crowds,066000d44d6691d27202896691f08b27117918b9

+pubfig,PubFig,Attribute and Simile Classifiers for Face Verification,759a3b3821d9f0e08e0b0a62c8b693230afc3f8d

+pubfig_83,pubfig83,Scaling Up Biologically-Inspired Computer Vision: A Case Study in Unconstrained Face Recognition on Facebook,9c23859ec7313f2e756a3e85575735e0c52249f4

+put_face,Put Face,The PUT face database,370b5757a5379b15e30d619e4d3fb9e8e13f3256

+qmul_grid,GRID,Multi-Camera Activity Correlation Analysis,2edb87494278ad11641b6cf7a3f8996de12b8e14

+qmul_grid,GRID,Time-delayed correlation analysis for multi-camera activity understanding,2edb87494278ad11641b6cf7a3f8996de12b8e14

+qmul_surv_face,QMUL-SurvFace,Surveillance Face Recognition Challenge,c866a2afc871910e3282fd9498dce4ab20f6a332

+rafd,RaFD,Presentation and validation of the Radboud Faces Database,3765df816dc5a061bc261e190acc8bdd9d47bec0

+raid,RAiD,Consistent Re-identification in a Camera Network,09d78009687bec46e70efcf39d4612822e61cb8c

+rap_pedestrian,RAP,A Richly Annotated Dataset for Pedestrian Attribute Recognition,221c18238b829c12b911706947ab38fd017acef7

+reseed,ReSEED,ReSEED: Social Event dEtection Dataset,54983972aafc8e149259d913524581357b0f91c3

+saivt,SAIVT SoftBio,A Database for Person Re-Identification in Multi-Camera Surveillance Networks,22646e00a7ba34d1b5fbe3b1efcd91a1e1be3c2b

+sarc3d,Sarc3D,SARC3D: a new 3D body model for People Tracking and Re-identification,e27ef52c641c2b5100a1b34fd0b819e84a31b4df

+scface,SCface,SCface – surveillance cameras face database,f3b84a03985de3890b400b68e2a92c0a00afd9d0

+scut_fbp,SCUT-FBP,SCUT-FBP: A Benchmark Dataset for Facial Beauty Perception,bd26dabab576adb6af30484183c9c9c8379bf2e0

+scut_head,SCUT HEAD,Detecting Heads using Feature Refine Net and Cascaded Multi-scale Architecture,dfdcd8c7c91813ba1624c9a21d2d01ef06a49afd

+sdu_vid,SDU-VID,A Spatio-Temporal Appearance Representation for Video-Based Pedestrian Re-Identification,98bb029afe2a1239c3fdab517323066f0957b81b

+sdu_vid,SDU-VID,Local descriptors encoded by Fisher vectors for person re-identification,98bb029afe2a1239c3fdab517323066f0957b81b

+sdu_vid,SDU-VID,Person reidentification by video ranking,98bb029afe2a1239c3fdab517323066f0957b81b

+sheffield,Sheffield Face,Face Recognition: From Theory to Applications,3607afdb204de9a5a9300ae98aa4635d9effcda2

+social_relation,Social Relation,From Facial Expression Recognition to Interpersonal Relation Prediction,2a171f8d14b6b8735001a11c217af9587d095848

+social_relation,Social Relation,Learning Social Relation Traits from Face Images,2a171f8d14b6b8735001a11c217af9587d095848

+soton,SOTON HiD,On a Large Sequence-Based Human Gait Database,4f93cd09785c6e77bf4bc5a788e079df524c8d21

+sports_videos_in_the_wild,SVW,Sports Videos in the Wild (SVW): A Video Dataset for Sports Analysis,1a40092b493c6b8840257ab7f96051d1a4dbfeb2

+stair_actions,STAIR Action,STAIR Actions: A Video Dataset of Everyday Home Actions,d3f5a1848b0028d8ab51d0b0673732cad2e3c8c9

+stanford_drone,Stanford Drone,Learning Social Etiquette: Human Trajectory Prediction In Crowded Scenes,c9bda86e23cab9e4f15ea0c4cbb6cc02b9dfb709

+stickmen_buffy,Buffy Stickmen,Learning to Parse Images of Articulated Objects,4b1d23d17476fcf78f4cbadf69fb130b1aa627c0

+stickmen_buffy,Buffy Stickmen,Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation,4b1d23d17476fcf78f4cbadf69fb130b1aa627c0

+stickmen_family,We Are Family Stickmen,We Are Family: Joint Pose Estimation of Multiple Persons,0dc11a37cadda92886c56a6fb5191ded62099c28

+stickmen_pascal,Stickmen PASCAL,Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation,6dd0597f8513dc100cd0bc1b493768cde45098a9

+stickmen_pascal,Stickmen PASCAL,Learning to Parse Images of Articulated Objects,6dd0597f8513dc100cd0bc1b493768cde45098a9

+sun_attributes,SUN,The SUN Attribute Database: Beyond Categories for Deeper Scene Understanding,833fa04463d90aab4a9fe2870d480f0b40df446e

+sun_attributes,SUN,"SUN Attribute Database: 
+Discovering, Annotating, and Recognizing Scene Attributes",833fa04463d90aab4a9fe2870d480f0b40df446e

+svs,SVS,Pedestrian Attribute Classification in Surveillance: Database and Evaluation,488e475eeb3bb39a145f23ede197cd3620f1d98a

+texas_3dfrd,Texas 3DFRD,Texas 3D Face Recognition Database,2ce2560cf59db59ce313bbeb004e8ce55c5ce928

+texas_3dfrd,Texas 3DFRD,Anthropometric 3D Face Recognition,2ce2560cf59db59ce313bbeb004e8ce55c5ce928

+tiny_faces,TinyFace,Low-Resolution Face Recognition,8990cdce3f917dad622e43e033db686b354d057c

+tiny_images,Tiny Images,80 million tiny images: a large dataset for non-parametric object and scene recognition,31b58ced31f22eab10bd3ee2d9174e7c14c27c01

+towncenter,TownCenter,Stable Multi-Target Tracking in Real-Time Surveillance Video,9361b784e73e9238d5cefbea5ac40d35d1e3103f

+tud_brussels,TUD-Brussels,Multi-Cue Onboard Pedestrian Detection,6ad5a38df8dd4cdddd74f31996ce096d41219f72

+tud_campus,TUD-Campus,People-Tracking-by-Detection and People-Detection-by-Tracking,3316521a5527c7700af8ae6aef32a79a8b83672c

+tud_crossing,TUD-Crossing,People-Tracking-by-Detection and People-Detection-by-Tracking,3316521a5527c7700af8ae6aef32a79a8b83672c

+tud_motionpairs,TUD-Motionparis,Multi-Cue Onboard Pedestrian Detection,6ad5a38df8dd4cdddd74f31996ce096d41219f72

+tud_multiview,TUD-Multiview,Monocular 3D Pose Estimation and Tracking by Detection,436f798d1a4e54e5947c1e7d7375c31b2bdb4064

+tud_pedestrian,TUD-Pedestrian,People-Tracking-by-Detection and People-Detection-by-Tracking,3316521a5527c7700af8ae6aef32a79a8b83672c

+tud_stadtmitte,TUD-Stadtmitte,Monocular 3D Pose Estimation and Tracking by Detection,436f798d1a4e54e5947c1e7d7375c31b2bdb4064

+tvhi,TVHI,High Five: Recognising human interactions in TV shows,3cd40bfa1ff193a96bde0207e5140a399476466c

+uccs,UCCS,Large scale unconstrained open set face database,07fcbae86f7a3ad3ea1cf95178459ee9eaf77cb1

+ucf_101,UCF101,UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild,b5f2846a506fc417e7da43f6a7679146d99c5e96

+ucf_crowd,UCF-CC-50,Multi-Source Multi-Scale Counting in Extremely Dense Crowd Images,32c801cb7fbeb742edfd94cccfca4934baec71da

+ucf_selfie,UCF Selfie,How to Take a Good Selfie?,041d3eedf5e45ce5c5229f0181c5c576ed1fafd6

+ufdd,UFDD,Pushing the Limits of Unconstrained Face Detection: a Challenge Dataset and Baseline Results,377f2b65e6a9300448bdccf678cde59449ecd337

+umb,UMB,UMB-DB: A Database of Partially Occluded 3D Faces,16e8b0a1e8451d5f697b94c0c2b32a00abee1d52

+umd_faces,UMD,UMDFaces: An Annotated Face Dataset for Training Deep Networks,31b05f65405534a696a847dd19c621b7b8588263

+umd_faces,UMD,The Do's and Don'ts for CNN-based Face Verification,71b7fc715e2f1bb24c0030af8d7e7b6e7cd128a6

+unbc_shoulder_pain,UNBC-McMaster Pain,PAINFUL DATA: The UNBC-McMaster Shoulder Pain Expression Archive Database,56ffa7d906b08d02d6d5a12c7377a57e24ef3391

+urban_tribes,Urban Tribes,From Bikers to Surfers: Visual Recognition of Urban Tribes,774cbb45968607a027ae4729077734db000a1ec5

+used,USED Social Event Dataset,USED: A Large-scale Social Event Detection Dataset,8627f019882b024aef92e4eb9355c499c733e5b7

+v47,V47,Re-identification of Pedestrians with Variable Occlusion and Scale,922e0a51a3b8c67c4c6ac09a577ff674cbd28b34

+vadana,VADANA,VADANA: A dense dataset for facial image analysis,4563b46d42079242f06567b3f2e2f7a80cb3befe

+vgg_celebs_in_places,CIP,Faces in Places: Compound Query Retrieval,7ebb153704706e457ab57b432793d2b6e5d12592

+vgg_faces,VGG Face,Deep Face Recognition,162ea969d1929ed180cc6de9f0bf116993ff6e06

+vgg_faces2,VGG Face2,VGGFace2: A dataset for recognising faces across pose and age,eb027969f9310e0ae941e2adee2d42cdf07d938c

+violent_flows,Violent Flows,Violent Flows: Real-Time Detection of Violent Crowd Behavior,5194cbd51f9769ab25260446b4fa17204752e799

+viper,VIPeR,"Evaluating Appearance Models for Recognition, Reacquisition, and Tracking",6273b3491e94ea4dd1ce42b791d77bdc96ee73a8

+visual_phrases,Phrasal Recognition,Recognition using Visual Phrases,e8de844fefd54541b71c9823416daa238be65546

+vmu,VMU,Can Facial Cosmetics Affect the Matching Accuracy of Face Recognition Systems?,37d6f0eb074d207b53885bd2eb78ccc8a04be597

+voc,VOC,The PASCAL Visual Object Classes (VOC) Challenge,abe9f3b91fd26fa1b50cd685c0d20debfb372f73

+vqa,VQA,VQA: Visual Question Answering,01959ef569f74c286956024866c1d107099199f7

+ward,WARD,Re-identify people in wide area camera network,6f3c76b7c0bd8e1d122c6ea808a271fd4749c951

+who_goes_there,WGT,Who Goes There? Approaches to Mapping Facial Appearance Diversity,9b9bf5e623cb8af7407d2d2d857bc3f1b531c182

+wider,WIDER,Recognize Complex Events from Static Images by Fusing Deep Channels,356b431d4f7a2a0a38cf971c84568207dcdbf189

+wider_attribute,WIDER Attribute,Human Attribute Recognition by Deep Hierarchical Contexts,44d23df380af207f5ac5b41459c722c87283e1eb

+wider_face,WIDER FACE,WIDER FACE: A Face Detection Benchmark,52d7eb0fbc3522434c13cc247549f74bb9609c5d

+wildtrack,WildTrack,WILDTRACK: A Multi-camera HD Dataset for Dense Unscripted Pedestrian Detection,77c81c13a110a341c140995bedb98101b9e84f7f

+wlfdb,,WLFDB: Weakly Labeled Face Databases,5ad4e9f947c1653c247d418f05dad758a3f9277b

+yale_faces,YaleFaces,Acquiring Linear Subspaces for Face Recognition under Variable Lighting,18c72175ddbb7d5956d180b65a96005c100f6014

+yale_faces,YaleFaces,From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose,18c72175ddbb7d5956d180b65a96005c100f6014

+yawdd,YawDD,YawDD: A Yawning Detection Dataset,a94cae786d515d3450d48267e12ca954aab791c4

+yfcc_100m,YFCC100M,YFCC100M: The New Data in Multimedia Research,a6e695ddd07aad719001c0fc1129328452385949

+york_3d,UOY 3D Face Database,Three-Dimensional Face Recognition: An Eigensurface Approach,19d1b811df60f86cbd5e04a094b07f32fff7a32a

+youtube_faces,YouTubeFaces,Face Recognition in Unconstrained Videos with Matched Background Similarity,560e0e58d0059259ddf86fcec1fa7975dee6a868

+youtube_makeup,YMU,Can Facial Cosmetics Affect the Matching Accuracy of Face Recognition Systems?,fcc6fe6007c322641796cb8792718641856a22a7

+youtube_makeup,YMU,Automatic Facial Makeup Detection with Application in Face Recognition,fcc6fe6007c322641796cb8792718641856a22a7

+youtube_poses,YouTube Pose,Personalizing Human Video Pose Estimation,1c2802c2199b6d15ecefe7ba0c39bfe44363de38