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b/scraper/reports/paper_title_report.html index d964e3b1..efd73c17 100644 --- a/scraper/reports/paper_title_report.html +++ b/scraper/reports/paper_title_report.html @@ -1,3 +1,3 @@ -Paper Title Sanity Check

Paper Title Sanity Check

keynameour titlefound titleaddresss2 id
10k_US_adult_faces10K US Adult FacesThe intrinsic memorability of face imagesThe intrinsic memorability of face photographs.[pdf][s2]8b2dd5c61b23ead5ae5508bb8ce808b5ea266730
3d_rma3D-RMAAutomatic 3D Face AuthenticationAutomatic 3D face authentication[pdf][s2]2160788824c4c29ffe213b2cbeb3f52972d73f37
3dddb_unconstrained3D DynamicA 3D Dynamic Database for Unconstrained Face RecognitionA 3 D Dynamic Database for Unconstrained Face Recognition[pdf][s2]4d4bb462c9f1d4e4ab1e4aa6a75cc0bc71b38461
3dpes3DPeS3DPes: 3D People Dataset for Surveillance and Forensics3DPeS: 3D people dataset for surveillance and forensics[pdf][s2]2e8d0f1802e50cccfd3c0aabac0d0beab3a7846e
4dfab4DFAB4DFAB: A Large Scale 4D Facial Expression Database for Biometric Applications4DFAB: A Large Scale 4D Facial Expression Database for Biometric Applications[pdf][s2]9696ad8b164f5e10fcfe23aacf74bd6168aebb15
50_people_one_question50 People One QuestionMerging Pose Estimates Across Space and TimeMerging Pose Estimates Across Space and Time[pdf][s2]5753b2b5e442eaa3be066daa4a2ca8d8a0bb1725
a_pascal_yahoo#N/ADescribing Objects by their AttributesDescribing objects by their attributes[pdf][s2]University of Illinois, Urbana-Champaign2e384f057211426ac5922f1b33d2aa8df5d51f57
adienceAdienceAge and Gender Estimation of Unfiltered FacesAge and Gender Estimation of Unfiltered Faces[pdf][s2]1be498d4bbc30c3bfd0029114c784bc2114d67c0
afadAFADOrdinal Regression with a Multiple Output CNN for Age EstimationOrdinal Regression with Multiple Output CNN for Age Estimation[pdf][s2]6618cff7f2ed440a0d2fa9e74ad5469df5cdbe4c
afew_vaAFEW-VAAFEW-VA database for valence and arousal estimation in-the-wildAFEW-VA database for valence and arousal estimation in-the-wild[pdf][s2]2624d84503bc2f8e190e061c5480b6aa4d89277a
afew_vaAFEW-VACollecting Large, Richly Annotated Facial-Expression Databases from MoviesCollecting Large, Richly Annotated Facial-Expression Databases from Movies[pdf][s2]Australian National Universityb1f4423c227fa37b9680787be38857069247a307
affectnetAffectNetAffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the WildAffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild[pdf][s2]758d7e1be64cc668c59ef33ba8882c8597406e53
aflwAFLWAnnotated Facial Landmarks in the Wild: A Large-scale, Real-world Database for Facial Landmark LocalizationAnnotated Facial Landmarks in the Wild: A large-scale, real-world database for facial landmark localization[pdf][s2]a74251efa970b92925b89eeef50a5e37d9281ad0
afwAFWFace detection, pose estimation and landmark localization in the wildFace detection, pose estimation, and landmark localization in the wild[pdf][s2]0e986f51fe45b00633de9fd0c94d082d2be51406
agedbAgeDBAgeDB: the first manually collected, in-the-wild age databaseAgeDB: The First Manually Collected, In-the-Wild Age Database[pdf][s2]6dcf418c778f528b5792104760f1fbfe90c6dd6a
alert_airportALERT AirportA Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and DatasetsA Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets.[pdf][s2]6403117f9c005ae81f1e8e6d1302f4a045e3d99d
am_fedAM-FEDAffectiva MIT Facial Expression Dataset (AM-FED): Naturalistic and Spontaneous Facial Expressions Collected “In the Wild”Affectiva-MIT Facial Expression Dataset (AM-FED): Naturalistic and Spontaneous Facial Expressions Collected "In-the-Wild"[pdf][s2]47aeb3b82f54b5ae8142b4bdda7b614433e69b9a
apisAPiS1.0Pedestrian Attribute Classification in Surveillance: Database and EvaluationPedestrian Attribute Classification in Surveillance: Database and Evaluation[pdf][s2]488e475eeb3bb39a145f23ede197cd3620f1d98a
ar_facedbAR FaceThe AR Face DatabaseThe AR face database[pdf][s2]6d96f946aaabc734af7fe3fc4454cf8547fcd5ed
awe_earsAWE EarsEar Recognition: More Than a SurveyEar Recognition: More Than a Survey[pdf][s2]84fe5b4ac805af63206012d29523a1e033bc827e
b3d_acB3D(AC)A 3-D Audio-Visual Corpus of Affective CommunicationA 3-D Audio-Visual Corpus of Affective Communication[pdf][s2]d08cc366a4a0192a01e9a7495af1eb5d9f9e73ae
bbc_poseBBC PoseAutomatic and Efficient Human Pose Estimation for Sign Language VideosAutomatic and Efficient Human Pose Estimation for Sign Language Videos[pdf][s2]213a579af9e4f57f071b884aa872651372b661fd
bpadBPADDescribing People: A Poselet-Based Approach to Attribute ClassificationDescribing people: A poselet-based approach to attribute classification[pdf][s2]7808937b46acad36e43c30ae4e9f3fd57462853d
bfmBFMA 3D Face Model for Pose and Illumination Invariant Face RecognitionA 3D Face Model for Pose and Illumination Invariant Face Recognition[pdf][s2]639937b3a1b8bded3f7e9a40e85bd3770016cf3c
bio_idBioID FaceRobust Face Detection Using the Hausdorff DistanceRobust Face Detection Using the Hausdorff Distance[pdf][s2]4053e3423fb70ad9140ca89351df49675197196a
bosphorusThe BosphorusBosphorus Database for 3D Face AnalysisBosphorus Database for 3D Face Analysis[pdf][s2]2acf7e58f0a526b957be2099c10aab693f795973
bp4d_plusBP4D+Multimodal Spontaneous Emotion Corpus for Human Behavior AnalysisMultimodal Spontaneous Emotion Corpus for Human Behavior Analysis[pdf][s2]53ae38a6bb2b21b42bac4f0c4c8ed1f9fa02f9d4
bp4d_spontanousBP4D-SpontanousA high resolution spontaneous 3D dynamic facial expression databaseA high-resolution spontaneous 3D dynamic facial expression database[pdf][s2]SUNY Binghamtonb91f54e1581fbbf60392364323d00a0cd43e493c
bu_3dfeBU-3DFEA 3D Facial Expression Database For Facial Behavior ResearchA 3D facial expression database for facial behavior research[pdf][s2]cc589c499dcf323fe4a143bbef0074c3e31f9b60
buhmap_dbBUHMAP-DB Facial Feature Tracking and Expression Recognition for Sign LanguageFacial feature tracking and expression recognition for sign language[pdf][s2]014b8df0180f33b9fea98f34ae611c6447d761d2
cafeCAFEThe Child Affective Facial Expression (CAFE) Set: Validity and reliability from untrained adultsThe Child Affective Facial Expression (CAFE) set: validity and reliability from untrained adults[pdf][s2]20388099cc415c772926e47bcbbe554e133343d1
caltech_10k_web_facesCaltech 10K Web FacesPruning Training Sets for Learning of Object CategoriesPruning training sets for learning of object categories[pdf][s2]636b8ffc09b1b23ff714ac8350bb35635e49fa3c
caltech_pedestriansCaltech PedestriansPedestrian Detection: A BenchmarkPedestrian detection: A benchmark[pdf][s2]1dc35905a1deff8bc74688f2d7e2f48fd2273275
caltech_pedestriansCaltech PedestriansPedestrian Detection: An Evaluation of the State of the ArtPedestrian Detection: An Evaluation of the State of the Art[pdf][s2]California Institute of Technologyf72f6a45ee240cc99296a287ff725aaa7e7ebb35
cas_pealCAS-PEALThe CAS-PEAL Large-Scale Chinese Face Database and Baseline EvaluationsThe CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations[pdf][s2]2485c98aa44131d1a2f7d1355b1e372f2bb148ad
casablancaCasablancaContext-aware {CNNs} for person head detectionContext-Aware CNNs for Person Head Detection[pdf][s2]0ceda9dae8b9f322df65ca2ef02caca9758aec6f
casia_webfaceCASIA WebfaceLearning Face Representation from ScratchLearning Face Representation from Scratch[pdf][s2]853bd61bc48a431b9b1c7cab10c603830c488e39
celebaCelebADeep Learning Face Attributes in the WildDeep Learning Face Attributes in the Wild[pdf][s2]6424b69f3ff4d35249c0bb7ef912fbc2c86f4ff4
celeba_plusCelebFaces+Deep Learning Face Representation from Predicting 10,000 ClassesDeep Learning Face Representation from Predicting 10,000 Classes[pdf][s2]177bc509dd0c7b8d388bb47403f28d6228c14b5c
cfdCFDThe Chicago face database: A free stimulus set of faces and norming dataThe Chicago face database: A free stimulus set of faces and norming data.[pdf][s2]4df3143922bcdf7db78eb91e6b5359d6ada004d2
chalearnChaLearnChaLearn Looking at People: A Review of Events and ResourcesChaLearn looking at people: A review of events and resources[pdf][s2]8d5998cd984e7cce307da7d46f155f9db99c6590
chokepointChokePointPatch-based Probabilistic Image Quality Assessment for Face Selection and Improved Video-based Face RecognitionPatch-based probabilistic image quality assessment for face selection and improved video-based face recognition[pdf][s2]0486214fb58ee9a04edfe7d6a74c6d0f661a7668
cityscapesCityscapesThe Cityscapes Dataset for Semantic Urban Scene UnderstandingThe Cityscapes Dataset for Semantic Urban Scene Understanding[pdf][s2]32cde90437ab5a70cf003ea36f66f2de0e24b3ab
cityscapesCityscapesThe Cityscapes DatasetThe Cityscapes Dataset[pdf][s2]5ffd74d2873b7cba2cbc5fd295cc7fbdedca22a2
clothing_co_parsingCCPClothing Co-Parsing by Joint Image Segmentation and LabelingClothing Co-parsing by Joint Image Segmentation and Labeling[pdf][s2]2bf8541199728262f78d4dced6fb91479b39b738
cmdpCMDPDistance Estimation of an Unknown Person from a PortraitDistance Estimation of an Unknown Person from a Portrait[pdf][s2]56ae6d94fc6097ec4ca861f0daa87941d1c10b70
cmu_pieCMU PIEThe CMU Pose, Illumination, and Expression DatabaseThe CMU Pose, Illumination, and Expression (PIE) Database[pdf][s2]4d423acc78273b75134e2afd1777ba6d3a398973
cocoCOCOMicrosoft COCO: Common Objects in ContextMicrosoft COCO: Common Objects in Context[pdf][s2]5e0f8c355a37a5a89351c02f174e7a5ddcb98683
coco_actionCOCO-aDescribing Common Human Visual Actions in ImagesDescribing Common Human Visual Actions in Images[pdf][s2]4946ba10a4d5a7d0a38372f23e6622bd347ae273
coco_qaCOCO QAExploring Models and Data for Image Question AnsweringExploring Models and Data for Image Question Answering[pdf][s2]35b0331dfcd2897abd5749b49ff5e2b8ba0f7a62
cofwCOFWRobust face landmark estimation under occlusionRobust Face Landmark Estimation under Occlusion[pdf][s2]2724ba85ec4a66de18da33925e537f3902f21249
cohn_kanadeCKComprehensive Database for Facial Expression AnalysisComprehensive Database for Facial Expression Analysis[pdf][s2]23fc83c8cfff14a16df7ca497661264fc54ed746
cohn_kanade_plusCK+The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expressionThe Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression[pdf][s2]University of Pittsburgh4d9a02d080636e9666c4d1cc438b9893391ec6c7
columbia_gazeColumbia GazeGaze Locking: Passive Eye Contact Detection for Human–Object InteractionGaze locking: passive eye contact detection for human-object interaction[pdf][s2]Columbia University06f02199690961ba52997cde1527e714d2b3bf8f
complex_activitiesOngoing Complex ActivitiesRecognition of Ongoing Complex Activities by Sequence Prediction over a Hierarchical Label SpaceRecognition of ongoing complex activities by sequence prediction over a hierarchical label space[pdf][s2]65355cbb581a219bd7461d48b3afd115263ea760
cuhk01CUHK01Human Reidentification with Transferred Metric LearningHuman Reidentification with Transferred Metric Learning[pdf][s2]44484d2866f222bbb9b6b0870890f9eea1ffb2d0
cuhk02CUHK02Locally Aligned Feature Transforms across ViewsLocally Aligned Feature Transforms across Views[pdf][s2]38b55d95189c5e69cf4ab45098a48fba407609b4
cuhk03CUHK03DeepReID: Deep Filter Pairing Neural Network for Person Re-identificationDeepReID: Deep Filter Pairing Neural Network for Person Re-identification[pdf][s2]6bd36e9fd0ef20a3074e1430a6cc601e6d407fc3
cvc_01_barcelonaCVC-01Adaptive Image Sampling and Windows Classification for On-board Pedestrian DetectionAdaptive Image Sampling and Windows Classification for On – board Pedestrian Detection[pdf][s2]57fe081950f21ca03b5b375ae3e84b399c015861
czech_news_agencyUFIUnconstrained Facial Images: Database for Face Recognition under Real-world ConditionsUnconstrained Facial Images: Database for Face Recognition Under Real-World Conditions[pdf][s2]4b4106614c1d553365bad75d7866bff0de6056ed
d3dfacsD3DFACSA FACS Valid 3D Dynamic Action Unit database with Applications to 3D Dynamic Morphable Facial ModellingA FACS valid 3D dynamic action unit database with applications to 3D dynamic morphable facial modeling[pdf][s2]070de852bc6eb275d7ca3a9cdde8f6be8795d1a3
dartmouth_childrenDartmouth ChildrenThe Dartmouth Database of Children's Faces: Acquisition and validation of a new face stimulus setThe Dartmouth Database of Children’s Faces: Acquisition and Validation of a New Face Stimulus Set[pdf][s2]4e6ee936eb50dd032f7138702fa39b7c18ee8907
data_61Data61 PedestrianA Multi-Modal Graphical Model for Scene AnalysisA Multi-modal Graphical Model for Scene Analysis[pdf][s2]563c940054e4b456661762c1ab858e6f730c3159
deep_fashionDeepFashionDeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich AnnotationsDeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations[pdf][s2]18010284894ed0edcca74e5bf768ee2e15ef7841
deep_fashionDeepFashionFashion Landmark Detection in the WildFashion Landmark Detection in the Wild[pdf][s2]4fefd1bc8dc4e0ab37ee3324ddfa43ad9d6a04a7
disfaDISFADISFA: A Spontaneous Facial Action Intensity DatabaseDISFA: A Spontaneous Facial Action Intensity Database[pdf][s2]University of Denver5a5f0287484f0d480fed1ce585dbf729586f0edc
distance_nighttimeLong Distance Heterogeneous FaceNighttime Face Recognition at Long Distance: Cross-distance and Cross-spectral MatchingNighttime Face Recognition at Long Distance: Cross-Distance and Cross-Spectral Matching[pdf][s2]4156b7e88f2e0ab0a7c095b9bab199ae2b23bd06
duke_mtmcDuke MTMCPerformance Measures and a Data Set for Multi-Target, Multi-Camera TrackingPerformance Measures and a Data Set for Multi-Target, Multi-Camera Tracking[pdf][s2]27a2fad58dd8727e280f97036e0d2bc55ef5424c
emotio_netEmotioNet DatabaseEmotioNet: An Accurate, Real-Time Algorithm for the Automatic Annotation of a Million Facial Expressions in the WildEmotioNet: An Accurate, Real-Time Algorithm for the Automatic Annotation of a Million Facial Expressions in the Wild[pdf][s2]c900e0ad4c95948baaf0acd8449fde26f9b4952a
eth_andreas_essETHZ PedestrianDepth and Appearance for Mobile Scene AnalysisDepth and Appearance for Mobile Scene Analysis[pdf][s2]ETH Zurich13f06b08f371ba8b5d31c3e288b4deb61335b462
europersonsEuroCity PersonsThe EuroCity Persons Dataset: A Novel Benchmark for Object DetectionThe EuroCity Persons Dataset: A Novel Benchmark for Object Detection[pdf][s2]f0e17f27f029db4ad650ff278fe3c10ecb6cb0c4
expwExpWFrom Facial Expression Recognition to Interpersonal Relation PredictionFrom Facial Expression Recognition to Interpersonal Relation Prediction[pdf][s2]22f656d0f8426c84a33a267977f511f127bfd7f3
face_scrubFaceScrubA data-driven approach to cleaning large face datasetsA data-driven approach to cleaning large face datasets[pdf][s2]0d3bb75852098b25d90f31d2f48fd0cb4944702b
face_tracerFaceTracerFaceTracer: A Search Engine for Large Collections of Images with FacesFaceTracer: A Search Engine for Large Collections of Images with Faces[pdf][s2]4c170a0dcc8de75587dae21ca508dab2f9343974
face_tracerFaceTracerFace Swapping: Automatically Replacing Faces in PhotographsFace swapping: automatically replacing faces in photographs[pdf][s2]670637d0303a863c1548d5b19f705860a23e285c
facebook_100Facebook100Scaling Up Biologically-Inspired Computer Vision: A Case Study in Unconstrained Face Recognition on FacebookScaling up biologically-inspired computer vision: A case study in unconstrained face recognition on facebook[pdf][s2]Harvard University9c23859ec7313f2e756a3e85575735e0c52249f4
faceplaceFace PlaceRecognizing disguised facesRecognizing disguised faces[pdf][s2]25474c21613607f6bb7687a281d5f9d4ffa1f9f3
families_in_the_wildFIWVisual Kinship Recognition of Families in the WildVisual Kinship Recognition of Families in the Wild[pdf][s2]University of Massachusetts Dartmouthdd65f71dac86e36eecbd3ed225d016c3336b4a13
fddbFDDBFDDB: A Benchmark for Face Detection in Unconstrained SettingsA Benchmark for Face Detection in Unconstrained Settings[pdf][s2]75da1df4ed319926c544eefe17ec8d720feef8c0
feiFEICaptura e Alinhamento de Imagens: Um Banco de Faces BrasileiroA new ranking method for principal components analysis and its application to face image analysis[pdf][s2]8b56e33f33e582f3e473dba573a16b598ed9bcdc
feretFERETThe FERET Verification Testing Protocol for Face Recognition AlgorithmsThe FERET Verification Testing Protocol for Face Recognition Algorithms[pdf][s2]0c4a139bb87c6743c7905b29a3cfec27a5130652
feretFERETThe FERET Evaluation Methodology for Face-Recognition AlgorithmsThe FERET Evaluation Methodology for Face-Recognition Algorithms[pdf][s2]0f0fcf041559703998abf310e56f8a2f90ee6f21
feretFERETFERET ( Face Recognition Technology ) Recognition Algorithm Development and Test ResultsFERET ( Face Recognition Technology ) Recognition Algorithm Development and Test Results[pdf][s2]31de9b3dd6106ce6eec9a35991b2b9083395fd0b
feretFERETThe FERET database and evaluation procedure for face-recognition algorithmsThe FERET database and evaluation procedure for face-recognition algorithms[pdf][s2]dc8b25e35a3acb812beb499844734081722319b4
ferplusFER+Training Deep Networks for Facial Expression Recognition with Crowd-Sourced Label DistributionTraining deep networks for facial expression recognition with crowd-sourced label distribution[pdf][s2]298cbc3dfbbb3a20af4eed97906650a4ea1c29e0
fiaCMU FiAThe CMU Face In Action (FIA) DatabaseThe CMU Face In Action (FIA) Database[pdf][s2]47662d1a368daf70ba70ef2d59eb6209f98b675d
fiw_300300-WA semi-automatic methodology for facial landmark annotationA Semi-automatic Methodology for Facial Landmark Annotation[pdf][s2]013909077ad843eb6df7a3e8e290cfd5575999d2
fiw_300300-W300 Faces in-the-Wild Challenge: The first facial landmark localization Challenge300 Faces in-the-Wild Challenge: The First Facial Landmark Localization Challenge[pdf][s2]044d9a8c61383312cdafbcc44b9d00d650b21c70
fiw_300300-W300 faces In-the-wild challenge: Database and results300 Faces In-The-Wild Challenge: database and results[pdf][s2]e4754afaa15b1b53e70743880484b8d0736990ff
frav3dFRAV3DMULTIMODAL 2D, 2.5D & 3D FACE VERIFICATIONMultimodal 2D, 2.5D & 3D Face Verification[pdf][s2]Universidad Rey Juan Carlos, Spain2b926b3586399d028b46315d7d9fb9d879e4f79c
frgcFRGCOverview of the Face Recognition Grand ChallengeOverview of the face recognition grand challenge[pdf][s2]NIST18ae7c9a4bbc832b8b14bc4122070d7939f5e00e
gallagherGallagherClothing Cosegmentation for Recognizing PeopleClothing cosegmentation for recognizing people[pdf][s2]Carnegie Mellon University22ad2c8c0f4d6aa4328b38d894b814ec22579761
geofacesGeoFacesGeoFaceExplorer: Exploring the Geo-Dependence of Facial AttributesGeoFaceExplorer: exploring the geo-dependence of facial attributes[pdf][s2]17b46e2dad927836c689d6787ddb3387c6159ece
georgia_tech_face_databaseGeorgia Tech FaceMaximum likelihood training of the embedded HMM for face detection and recognitionMAXIMUM 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[pdf][s2]3dc3f0b64ef80f573e3a5f96e456e52ee980b877
gfwGrouping Face in the WildMerge or Not? Learning to Group Faces via Imitation LearningMerge or Not? Learning to Group Faces via Imitation Learning[pdf][s2]e58dd160a76349d46f881bd6ddbc2921f08d1050
grazGraz PedestrianWeak Hypotheses and Boosting for Generic Object Detection and RecognitionWeak Hypotheses and Boosting for Generic Object Detection and Recognition[pdf][s2]0c91808994a250d7be332400a534a9291ca3b60e
grazGraz PedestrianObject Recognition Using Segmentation for Feature DetectionObject recognition using segmentation for feature detection[pdf][s2]Inst. of Comput. Sci., Univ. of Leoben, Austria12ad3b5bbbf407f8e54ea692c07633d1a867c566
grazGraz PedestrianGeneric Object Recognition with BoostingGeneric object recognition with boosting[pdf][s2]TU Graz2eed184680edcdec8a3b605ad1a3ba8e8f7cc2e9
h3dH3DPoselets: Body Part Detectors Trained Using 3D Human Pose AnnotationsPoselets: Body part detectors trained using 3D human pose annotations[pdf][s2]2830fb5282de23d7784b4b4bc37065d27839a412
hda_plusHDA+The HDA+ data set for research on fully automated re-identification systemsThe HDA+ Data Set for Research on Fully Automated Re-identification Systems[pdf][s2]8f02ec0be21461fbcedf51d864f944cfc42c875f
hda_plusHDA+A Multi-camera video data set for research on High-Definition surveillanceHDA dataset-DRAFT 1 A Multi-camera video data set for research on High-Definition surveillance[pdf][s2]bd88bb2e4f351352d88ee7375af834360e223498
helenHelenInteractive Facial Feature LocalizationInteractive Facial Feature Localization[pdf][s2]95f12d27c3b4914e0668a268360948bce92f7db3
hi4d_adsipHi4D-ADSIPHi4D-ADSIP 3-D dynamic facial articulation databaseHi4D-ADSIP 3-D dynamic facial articulation database[pdf][s2]a8d0b149c2eadaa02204d3e4356fbc8eccf3b315
hipsterwarsHipsterwarsHipster Wars: Discovering Elements of Fashion StylesHipster Wars: Discovering Elements of Fashion Styles[pdf][s2]04c2cda00e5536f4b1508cbd80041e9552880e67
hollywood_headsetHollywoodHeadsContext-aware CNNs for person head detectionContext-Aware CNNs for Person Head Detection[pdf][s2]0ceda9dae8b9f322df65ca2ef02caca9758aec6f
hrt_transgenderHRT TransgenderIs the Eye Region More Reliable Than the Face? A Preliminary Study of Face-based Recognition on a Transgender DatasetIs the eye region more reliable than the face? A preliminary study of face-based recognition on a transgender dataset[pdf][s2]137aa2f891d474fce1e7a1d1e9b3aefe21e22b34
hrt_transgenderHRT TransgenderInvestigating the Periocular-Based Face Recognition Across Gender TransformationInvestigating the Periocular-Based Face Recognition Across Gender Transformation[pdf][s2]University of North Carolina at Wilmington2f43b614607163abf41dfe5d17ef6749a1b61304
ibm_difIBM Diversity in FacesDiversity in FacesDiversity in Faces[pdf][s2]2b89de1d81cee50552f10e26e865df3365e9bc88
ifadIFADIndian Face Age Database: A Database for Face Recognition with Age VariationIndian Face Age Database : A Database for Face Recognition with Age Variation[pdf][s2]55c40cbcf49a0225e72d911d762c27bb1c2d14aa
ifdbIFDBIranian Face Database and Evaluation with a New Detection AlgorithmIranian Face Database and Evaluation with a New Detection Algorithm[pdf][s2]066d71fcd997033dce4ca58df924397dfe0b5fd1
ifdbIFDBIranian Face Database with age, pose and expressionIranian Face Database with age, pose and expression[pdf][s2]Islamic Azad Universityb71d1aa90dcbe3638888725314c0d56640c1fef1
iit_dehli_earIIT Dehli EarAutomated human identification using ear imagingAutomated Human Identification Using Ear Imaging[pdf][s2]faf40ce28857aedf183e193486f5b4b0a8c478a2
ijb_cIJB-CIARPA Janus Benchmark-B Face DatasetIARPA Janus Benchmark-B Face Dataset[pdf][s2]0cb2dd5f178e3a297a0c33068961018659d0f443
ijb_cIJB-CPushing the Frontiers of Unconstrained Face Detection and Recognition: IARPA Janus Benchmark APushing the frontiers of unconstrained face detection and recognition: IARPA Janus Benchmark A[pdf][s2]140c95e53c619eac594d70f6369f518adfea12ef
ijb_cIJB-CIARPA Janus Benchmark CIARPA Janus Benchmark - C: Face Dataset and Protocol[pdf][s2]57178b36c21fd7f4529ac6748614bb3374714e91
ilids_mctsi-LIDS Multiple-CameraImagery Library for Intelligent Detection Systems: The i-LIDS User GuideImagery Library for Intelligent Detection Systems (i-LIDS); A Standard for Testing Video Based Detection Systems[pdf][s2]0297448f3ed948e136bb06ceff10eccb34e5bb77
ilids_vid_reidiLIDS-VIDPerson Re-Identi cation by Video RankingPerson Re-identification by Video Ranking[pdf][s2]98bb029afe2a1239c3fdab517323066f0957b81b
images_of_groupsImages of GroupsUnderstanding Groups of Images of PeopleUnderstanding images of groups of people[pdf][s2]Carnegie Mellon University21d9d0deed16f0ad62a4865e9acf0686f4f15492
imdb_faceIMDb FaceThe Devil of Face Recognition is in the NoiseThe Devil of Face Recognition is in the Noise[pdf][s2]9e31e77f9543ab42474ba4e9330676e18c242e72
imdb_wikiIMDBDeep expectation of real and apparent age from a single image without facial landmarksDeep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks[pdf][s2]10195a163ab6348eef37213a46f60a3d87f289c5
imdb_wikiIMDBDEX: Deep EXpectation of apparent age from a single imageDEX: Deep EXpectation of Apparent Age from a Single Image[pdf][s2]8355d095d3534ef511a9af68a3b2893339e3f96b
imfdbIMFDBIndian Movie Face Database: A Benchmark for Face Recognition Under Wide VariationsIndian Movie Face Database: A benchmark for face recognition under wide variations[pdf][s2]CVIT, IIITH, Indiaca3e88d87e1344d076c964ea89d91a75c417f5ee
immediacyImmediacyMulti-task Recurrent Neural Network for Immediacy PredictionMulti-task Recurrent Neural Network for Immediacy Prediction[pdf][s2]1e3df3ca8feab0b36fd293fe689f93bb2aaac591
imsituimSituSituation Recognition: Visual Semantic Role Labeling for Image UnderstandingSituation Recognition: Visual Semantic Role Labeling for Image Understanding[pdf][s2]51eba481dac6b229a7490f650dff7b17ce05df73
inria_personINRIA PedestrianHistograms of Oriented Gradients for Human DetectionHistograms of oriented gradients for human detection[pdf][s2]INRIA Rhone-Alps, Montbonnot, France10d6b12fa07c7c8d6c8c3f42c7f1c061c131d4c5
jaffeJAFFECoding Facial Expressions with Gabor WaveletsCoding Facial Expressions with Gabor Wavelets[pdf][s2]45c31cde87258414f33412b3b12fc5bec7cb3ba9
jiku_mobileJiku Mobile Video DatasetThe Jiku Mobile Video DatasetThe jiku mobile video dataset[pdf][s2]d178cde92ab3dc0dd2ebee5a76a33d556c39448b
jpl_poseJPL-Interaction datasetFirst-Person Activity Recognition: What Are They Doing to Me?First-Person Activity Recognition: What Are They Doing to Me?[pdf][s2]1aad2da473888cb7ebc1bfaa15bfa0f1502ce005
kin_faceUB KinFaceUnderstanding Kin Relationships in a PhotoUnderstanding Kin Relationships in a Photo[pdf][s2]08f6745bc6c1b0fb68953ea61054bdcdde6d2fc7
kin_faceUB KinFaceGenealogical Face Recognition based on UB KinFace DatabaseGenealogical face recognition based on UB KinFace database[pdf][s2]SUNY Buffalo2eb84aaba316b095d4bb51da1a3e4365bbf9ab1d
kin_faceUB KinFaceKinship Verification through Transfer LearningKinship verification through transfer learning[pdf][s2]4793f11fbca4a7dba898b9fff68f70d868e2497c
kinectfaceKinectFaceDBKinectFaceDB: A Kinect Database for Face RecognitionKinectFaceDB: A Kinect Database for Face Recognition[pdf][s2]University of North Carolina at Chapel Hill0b440695c822a8e35184fb2f60dcdaa8a6de84ae
kittiKITTIVision meets Robotics: The KITTI DatasetVision meets robotics: The KITTI dataset[pdf][s2]026e3363b7f76b51cc711886597a44d5f1fd1de2
lagLAGLarge Age-Gap Face Verification by Feature Injection in Deep NetworksLarge age-gap face verification by feature injection in deep networks[pdf][s2]0d2dd4fc016cb6a517d8fb43a7cc3ff62964832e
large_scale_person_searchLarge Scale Person SearchEnd-to-End Deep Learning for Person SearchEnd-to-End Deep Learning for Person Search[pdf][s2]2161f6b7ee3c0acc81603b01dc0df689683577b9
leeds_sports_poseLeeds Sports PoseClustered Pose and Nonlinear Appearance Models for Human Pose EstimationClustered Pose and Nonlinear Appearance Models for Human Pose Estimation[pdf][s2]4b1d23d17476fcf78f4cbadf69fb130b1aa627c0
leeds_sports_pose_extendedLeeds Sports Pose ExtendedLearning Effective Human Pose Estimation from Inaccurate AnnotationLearning effective human pose estimation from inaccurate annotation[pdf][s2]University of Leeds4e4746094bf60ee83e40d8597a6191e463b57f76
lfpwLFWPLocalizing Parts of Faces Using a Consensus of ExemplarsLocalizing Parts of Faces Using a Consensus of Exemplars[pdf][s2]140438a77a771a8fb656b39a78ff488066eb6b50
lfwLFWLabeled Faces in the Wild: Updates and New Reporting ProceduresLabeled Faces in the Wild : Updates and New Reporting Procedures[pdf][s2]2d3482dcff69c7417c7b933f22de606a0e8e42d4
lfwLFWLabeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained EnvironmentsLabeled Faces in the Wild : A Database for Studying Face Recognition in Unconstrained Environments[pdf][s2]370b5757a5379b15e30d619e4d3fb9e8e13f3256
lfwLFWLabeled Faces in the Wild: A SurveyLabeled Faces in the Wild : A Survey[pdf][s2]7de6e81d775e9cd7becbfd1bd685f4e2a5eebb22
lfw_a#N/AEffective Unconstrained Face Recognition by Combining Multiple Descriptors and Learned Background StatisticsEffective Unconstrained Face Recognition by Combining Multiple Descriptors and Learned Background Statistics[pdf][s2]133f01aec1534604d184d56de866a4bd531dac87
m2vtsm2vtsThe M2VTS Multimodal Face Database (Release 1.00)The M2VTS Multimodal Face Database (Release 1.00)[pdf][s2]Laboratoire de Télécommunications et Télédétection, UCL, Louvain-La-Neuve, Belgium9a9877791945c6fa4c1743ec6d3fb32570ef8481
m2vtsdb_extendedxm2vtsdbXM2VTSDB: The Extended M2VTS DatabaseXm2vtsdb: the Extended M2vts Database[pdf][s2]b62628ac06bbac998a3ab825324a41a11bc3a988
maflMAFLFacial Landmark Detection by Deep Multi-task LearningFacial Landmark Detection by Deep Multi-task Learning[pdf][s2]8a3c5507237957d013a0fe0f082cab7f757af6ee
maflMAFLLearning Deep Representation for Face Alignment with Auxiliary AttributesLearning Deep Representation for Face Alignment with Auxiliary Attributes[pdf][s2]a0fd85b3400c7b3e11122f44dc5870ae2de9009a
malfMALFFine-grained Evaluation on Face Detection in the Wild.Fine-grained evaluation on face detection in the wild[pdf][s2]45e616093a92e5f1e61a7c6037d5f637aa8964af
mapillaryMapillaryThe Mapillary Vistas Dataset for Semantic Understanding of Street ScenesThe Mapillary Vistas Dataset for Semantic Understanding of Street Scenes[pdf][s2]79828e6e9f137a583082b8b5a9dfce0c301989b8
market_1501Market 1501Scalable Person Re-identification: A BenchmarkScalable Person Re-identification: A Benchmark[pdf][s2]4308bd8c28e37e2ed9a3fcfe74d5436cce34b410
market1203Market 1203Orientation Driven Bag of Appearances for Person Re-identificationOrientation Driven Bag of Appearances for Person Re-identification[pdf][s2]a7fe834a0af614ce6b50dc093132b031dd9a856b
marsMARSMARS: A Video Benchmark for Large-Scale Person Re-identificationMARS: A Video Benchmark for Large-Scale Person Re-Identification[pdf][s2]c0387e788a52f10bf35d4d50659cfa515d89fbec
mcgillMcGill Real WorldHierarchical Temporal Graphical Model for Head Pose Estimation and Subsequent Attribute Classification in Real-World VideosHierarchical temporal graphical model for head pose estimation and subsequent attribute classification in real-world videos[pdf][s2]a5a3bc3e5e9753769163cb30b16dbd12e266b93e
mcgillMcGill Real WorldRobust Semi-automatic Head Pose Labeling for Real-World Face Video SequencesRobust semi-automatic head pose labeling for real-world face video sequences[pdf][s2]McGill Universityc570d1247e337f91e555c3be0e8c8a5aba539d9f
megaageMegaAgeQuantifying Facial Age by Posterior of Age ComparisonsQuantifying Facial Age by Posterior of Age Comparisons[pdf][s2]d80a3d1f3a438e02a6685e66ee908446766fefa9
megafaceMegaFaceLevel Playing Field for Million Scale Face RecognitionLevel Playing Field for Million Scale Face Recognition[pdf][s2]28d4e027c7e90b51b7d8908fce68128d1964668a
megafaceMegaFaceThe MegaFace Benchmark: 1 Million Faces for Recognition at ScaleThe MegaFace Benchmark: 1 Million Faces for Recognition at Scale[pdf][s2]96e0cfcd81cdeb8282e29ef9ec9962b125f379b0
mifsMIFSSpoofing Faces Using Makeup: An Investigative StudySpoofing faces using makeup: An investigative study[pdf][s2]INRIA Méditerranée23e824d1dfc33f3780dd18076284f07bd99f1c43
mit_cbclMIT CBCLComponent-based Face Recognition with 3D Morphable ModelsComponent-Based Face Recognition with 3D Morphable Models[pdf][s2]079a0a3bf5200994e1f972b1b9197bf2f90e87d4
miwMIWAutomatic Facial Makeup Detection with Application in Face RecognitionAutomatic facial makeup detection with application in face recognition[pdf][s2]West Virginia Universityfcc6fe6007c322641796cb8792718641856a22a7
mmi_facial_expressionMMI Facial Expression DatasetWEB-BASED DATABASE FOR FACIAL EXPRESSION ANALYSISWeb-based database for facial expression analysis[pdf][s2]2a75f34663a60ab1b04a0049ed1d14335129e908
moments_in_timeMoments in TimeMoments in Time Dataset: one million videos for event understandingMoments in Time Dataset: one million videos for event understanding[pdf][s2]a5a44a32a91474f00a3cda671a802e87c899fbb4
morphMORPH CommercialMORPH: A Longitudinal Image Database of Normal Adult Age-ProgressionMORPH: a longitudinal image database of normal adult age-progression[pdf][s2]9055b155cbabdce3b98e16e5ac9c0edf00f9552f
morph_ncMORPH Non-CommercialMORPH: A Longitudinal Image Database of Normal Adult Age-ProgressionMORPH: a longitudinal image database of normal adult age-progression[pdf][s2]9055b155cbabdce3b98e16e5ac9c0edf00f9552f
motMOTEvaluating Multiple Object Tracking Performance: The CLEAR MOT MetricsEvaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics[pdf][s2]2258e01865367018ed6f4262c880df85b94959f8
motMOTPerformance Measures and a Data Set for Multi-Target, Multi-Camera TrackingPerformance Measures and a Data Set for Multi-Target, Multi-Camera Tracking[pdf][s2]27a2fad58dd8727e280f97036e0d2bc55ef5424c
motMOTLearning to associate: HybridBoosted multi-target tracker for crowded sceneLearning to associate: HybridBoosted multi-target tracker for crowded scene[pdf][s2]University of Southern California5981e6479c3fd4e31644db35d236bfb84ae46514
mpi_largeLarge MPI Facial ExpressionThe MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial ExpressionsThe MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial Expressions[pdf][s2]ea050801199f98a1c7c1df6769f23f658299a3ae
mpi_smallSmall MPI Facial ExpressionThe MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial ExpressionsThe MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial Expressions[pdf][s2]ea050801199f98a1c7c1df6769f23f658299a3ae
mpii_gazeMPIIGazeAppearance-based Gaze Estimation in the WildAppearance-based gaze estimation in the wild[pdf][s2]0df0d1adea39a5bef318b74faa37de7f3e00b452
mpii_human_poseMPII Human Pose2D Human Pose Estimation: New Benchmark and State of the Art Analysis2D Human Pose Estimation: New Benchmark and State of the Art Analysis[pdf][s2]3325860c0c82a93b2eac654f5324dd6a776f609e
mr2MR2The MR2: A multi-racial mega-resolution database of facial stimuliThe MR2: A multi-racial, mega-resolution database of facial stimuli.[pdf][s2]578d4ad74818086bb64f182f72e2c8bd31e3d426
mrp_droneMRP DroneInvestigating Open-World Person Re-identification Using a DroneInvestigating Open-World Person Re-identification Using a Drone[pdf][s2]ad01687649d95cd5b56d7399a9603c4b8e2217d7
mscelebMsCelebMS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face RecognitionMS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition[pdf][s2]291265db88023e92bb8c8e6390438e5da148e8f5
msmt_17MSMT17Person Transfer GAN to Bridge Domain Gap for Person Re-IdentificationPerson Transfer GAN to Bridge Domain Gap for Person Re-Identification[pdf][s2]ec792ad2433b6579f2566c932ee414111e194537
mtflMTFLFacial Landmark Detection by Deep Multi-task LearningFacial Landmark Detection by Deep Multi-task Learning[pdf][s2]8a3c5507237957d013a0fe0f082cab7f757af6ee
mtflMTFLLearning Deep Representation for Face Alignment with Auxiliary AttributesLearning Deep Representation for Face Alignment with Auxiliary Attributes[pdf][s2]a0fd85b3400c7b3e11122f44dc5870ae2de9009a
mug_facesMUG FacesThe MUG Facial Expression DatabaseThe MUG facial expression database[pdf][s2]Aristotle University of Thessalonikif1af714b92372c8e606485a3982eab2f16772ad8
multi_pieMULTIPIEMulti-PIEThe CMU Pose, Illumination, and Expression (PIE) Database[pdf][s2]4d423acc78273b75134e2afd1777ba6d3a398973
names_and_facesNews DatasetNames and FacesNames and faces in the news[pdf][s2]2fda164863a06a92d3a910b96eef927269aeb730
nd_2006ND-2006Using a Multi-Instance Enrollment Representation to Improve 3D Face RecognitionUsing a Multi-Instance Enrollment Representation to Improve 3D Face Recognition[pdf][s2]University of Notre Damefd8168f1c50de85bac58a8d328df0a50248b16ae
nova_emotionsNovaemötions DatasetCompetitive affective gamming: Winning with a smileCompetitive affective gaming: winning with a smile[pdf][s2]Universidade NOVA de Lisboa, Caparica, Portugal7f4040b482d16354d5938c1d1b926b544652bf5b
nova_emotionsNovaemötions DatasetCrowdsourcing facial expressions for affective-interactionCrowdsourcing facial expressions for affective-interaction[pdf][s2]c06b13d0ec3f5c43e2782cd22542588e233733c3
nudedetectionNude DetectionA Bag-of-Features Approach based on Hue-SIFT Descriptor for Nude DetectionA bag-of-features approach based on Hue-SIFT descriptor for nude detection[pdf][s2]7ace44190729927e5cb0dd5d363fcae966fe13f7
orlORLParameterisation of a Stochastic Model for Human Face IdentificationParameterisation of a stochastic model for human face identification[pdf][s2]55206f0b5f57ce17358999145506cd01e570358c
penn_fudanPenn FudanObject Detection Combining Recognition and SegmentationObject Detection Combining Recognition and Segmentation[pdf][s2]3394168ff0719b03ff65bcea35336a76b21fe5e4
petaPETAPedestrian Attribute Recognition At Far DistancePedestrian Attribute Recognition At Far Distance[pdf][s2]2a4bbee0b4cf52d5aadbbc662164f7efba89566c
petsPETS 2017PETS 2017: Dataset and ChallengePETS 2017: Dataset and Challenge[pdf][s2]22909dd19a0ec3b6065334cb5be5392cb24d839d
pilot_parliamentPPBGender Shades: Intersectional Accuracy Disparities in Commercial Gender ClassificationGender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification[pdf][s2]18858cc936947fc96b5c06bbe3c6c2faa5614540
pipaPIPABeyond Frontal Faces: Improving Person Recognition Using Multiple CuesBeyond frontal faces: Improving Person Recognition using multiple cues[pdf][s2]0a85bdff552615643dd74646ac881862a7c7072d
pkuPKUSwiss-System Based Cascade Ranking for Gait-based Person Re-identificationSwiss-System Based Cascade Ranking for Gait-Based Person Re-Identification[pdf][s2]f6c8d5e35d7e4d60a0104f233ac1a3ab757da53f
pku_reidPKU-ReidOrientation driven bag of appearances for person re-identificationOrientation Driven Bag of Appearances for Person Re-identification[pdf][s2]a7fe834a0af614ce6b50dc093132b031dd9a856b
pornodb#N/APooling in Image Representation: the Visual Codeword Point of ViewPooling in image representation: The visual codeword point of view[pdf][s2]b92a1ed9622b8268ae3ac9090e25789fc41cc9b8
precariousPrecariousExpecting the Unexpected: Training Detectors for Unusual Pedestrians With Adversarial ImpostersExpecting the Unexpected: Training Detectors for Unusual Pedestrians with Adversarial Imposters[pdf][s2]9e5378e7b336c89735d3bb15cf67eff96f86d39a
pridPRIDPerson Re-Identification by Descriptive and Discriminative ClassificationPerson Re-identification by Descriptive and Discriminative Classification[pdf][s2]16c7c31a7553d99f1837fc6e88e77b5ccbb346b8
prwPRWPerson Re-identification in the WildPerson Re-identification in the Wild[pdf][s2]0b84f07af44f964817675ad961def8a51406dd2e
psuPSUVision-based Analysis of Small Groups in Pedestrian CrowdsVision-Based Analysis of Small Groups in Pedestrian Crowds[pdf][s2]066000d44d6691d27202896691f08b27117918b9
pubfigPubFigAttribute and Simile Classifiers for Face VerificationAttribute and simile classifiers for face verification[pdf][s2]759a3b3821d9f0e08e0b0a62c8b693230afc3f8d
pubfig_83pubfig83Scaling Up Biologically-Inspired Computer Vision: A Case Study in Unconstrained Face Recognition on FacebookScaling up biologically-inspired computer vision: A case study in unconstrained face recognition on facebook[pdf][s2]Harvard University9c23859ec7313f2e756a3e85575735e0c52249f4
put_facePut FaceThe PUT face databaseThe put face database[pdf][s2]ae0aee03d946efffdc7af2362a42d3750e7dd48a
qmul_gridGRIDTime-delayed correlation analysis for multi-camera activity understandingTime-Delayed Correlation Analysis for Multi-Camera Activity Understanding[pdf][s2]Queen Mary University of London2edb87494278ad11641b6cf7a3f8996de12b8e14
qmul_gridGRIDMulti-Camera Activity Correlation AnalysisMulti-camera activity correlation analysis[pdf][s2]Queen Mary University of London3b5b6d19d4733ab606c39c69a889f9e67967f151
qmul_surv_faceQMUL-SurvFaceSurveillance Face Recognition ChallengeSurveillance Face Recognition Challenge[pdf][s2]c866a2afc871910e3282fd9498dce4ab20f6a332
rafdRaFDPresentation and validation of the Radboud Faces DatabasePresentation and validation of the Radboud Faces Database[pdf][s2]3765df816dc5a061bc261e190acc8bdd9d47bec0
raidRAiDConsistent Re-identification in a Camera NetworkConsistent Re-identification in a Camera Network[pdf][s2]09d78009687bec46e70efcf39d4612822e61cb8c
rap_pedestrianRAPA Richly Annotated Dataset for Pedestrian Attribute RecognitionA Richly Annotated Dataset for Pedestrian Attribute Recognition[pdf][s2]221c18238b829c12b911706947ab38fd017acef7
reseedReSEEDReSEED: Social Event dEtection DatasetReSEED: social event dEtection dataset[pdf][s2]54983972aafc8e149259d913524581357b0f91c3
saivtSAIVT SoftBioA Database for Person Re-Identification in Multi-Camera Surveillance NetworksA Database for Person Re-Identification in Multi-Camera Surveillance Networks[pdf][s2]22646e00a7ba34d1b5fbe3b1efcd91a1e1be3c2b
sarc3dSarc3DSARC3D: a new 3D body model for People Tracking and Re-identificationSARC3D: A New 3D Body Model for People Tracking and Re-identification[pdf][s2]e27ef52c641c2b5100a1b34fd0b819e84a31b4df
scfaceSCfaceSCface – surveillance cameras face databaseSCface – surveillance cameras face database[pdf][s2]29a705a5fa76641e0d8963f1fdd67ee4c0d92d3d
scut_fbpSCUT-FBPSCUT-FBP: A Benchmark Dataset for Facial Beauty PerceptionSCUT-FBP: A Benchmark Dataset for Facial Beauty Perception[pdf][s2]bd26dabab576adb6af30484183c9c9c8379bf2e0
scut_headSCUT HEADDetecting Heads using Feature Refine Net and Cascaded Multi-scale ArchitectureDetecting Heads using Feature Refine Net and Cascaded Multi-scale Architecture[pdf][s2]d3200d49a19a4a4e4e9745ee39649b65d80c834b
sdu_vidSDU-VIDA Spatio-Temporal Appearance Representation for Video-Based Pedestrian Re-IdentificationA Spatio-Temporal Appearance Representation for Video-Based Pedestrian Re-Identification[pdf][s2]3b4ec8af470948a72a6ed37a9fd226719a874ebc
sdu_vidSDU-VIDLocal descriptors encoded by Fisher vectors for person re-identificationLocal Descriptors Encoded by Fisher Vectors for Person Re-identification[pdf][s2]46a01565e6afe7c074affb752e7069ee3bf2e4ef
sdu_vidSDU-VIDPerson reidentification by video rankingPerson Re-identification by Video Ranking[pdf][s2]98bb029afe2a1239c3fdab517323066f0957b81b
social_relationSocial RelationLearning Social Relation Traits from Face ImagesLearning Social Relation Traits from Face Images[pdf][s2]2a171f8d14b6b8735001a11c217af9587d095848
sotonSOTON HiDOn a Large Sequence-Based Human Gait DatabaseOn a large sequence-based human gait database[pdf][s2]4f93cd09785c6e77bf4bc5a788e079df524c8d21
sports_videos_in_the_wildSVWSports Videos in the Wild (SVW): A Video Dataset for Sports AnalysisSports Videos in the Wild (SVW): A video dataset for sports analysis[pdf][s2]1a40092b493c6b8840257ab7f96051d1a4dbfeb2
stair_actionsSTAIR ActionSTAIR Actions: A Video Dataset of Everyday Home ActionsSTAIR Actions: A Video Dataset of Everyday Home Actions[pdf][s2]d3f5a1848b0028d8ab51d0b0673732cad2e3c8c9
stanford_droneStanford DroneLearning Social Etiquette: Human Trajectory Prediction In Crowded ScenesSocial LSTM: Human Trajectory Prediction in Crowded Spaces[pdf][s2]570f37ed63142312e6ccdf00ecc376341ec72b9f
stickmen_buffyBuffy StickmenClustered Pose and Nonlinear Appearance Models for Human Pose EstimationClustered Pose and Nonlinear Appearance Models for Human Pose Estimation[pdf][s2]4b1d23d17476fcf78f4cbadf69fb130b1aa627c0
stickmen_buffyBuffy StickmenLearning to Parse Images of Articulated ObjectsLearning to parse images of articulated bodies[pdf][s2]6dd0597f8513dc100cd0bc1b493768cde45098a9
stickmen_familyWe Are Family StickmenWe Are Family: Joint Pose Estimation of Multiple PersonsWe Are Family: Joint Pose Estimation of Multiple Persons[pdf][s2]0dc11a37cadda92886c56a6fb5191ded62099c28
stickmen_pascalStickmen PASCALClustered Pose and Nonlinear Appearance Models for Human Pose EstimationLearning to parse images of articulated bodies[pdf][s2]6dd0597f8513dc100cd0bc1b493768cde45098a9
stickmen_pascalStickmen PASCALLearning to Parse Images of Articulated ObjectsLearning to parse images of articulated bodies[pdf][s2]6dd0597f8513dc100cd0bc1b493768cde45098a9
sun_attributesSUNThe SUN Attribute Database: Beyond Categories for Deeper Scene UnderstandingThe SUN Attribute Database: Beyond Categories for Deeper Scene Understanding[pdf][s2]66e6f08873325d37e0ec20a4769ce881e04e964e
sun_attributesSUNSUN Attribute Database: +Paper Title Sanity Check

Paper Title Sanity Check

keynameour titlefound titleaddresss2 id
10k_US_adult_faces10K US Adult FacesThe intrinsic memorability of face imagesThe intrinsic memorability of face photographs.[pdf][s2]8b2dd5c61b23ead5ae5508bb8ce808b5ea266730
3d_rma3D-RMAAutomatic 3D Face AuthenticationAutomatic 3D face authentication[pdf][s2]2160788824c4c29ffe213b2cbeb3f52972d73f37
3dddb_unconstrained3D DynamicA 3D Dynamic Database for Unconstrained Face RecognitionA 3 D Dynamic Database for Unconstrained Face Recognition[pdf][s2]4d4bb462c9f1d4e4ab1e4aa6a75cc0bc71b38461
3dpes3DPeS3DPes: 3D People Dataset for Surveillance and Forensics3DPeS: 3D people dataset for surveillance and forensics[pdf][s2]2e8d0f1802e50cccfd3c0aabac0d0beab3a7846e
4dfab4DFAB4DFAB: A Large Scale 4D Facial Expression Database for Biometric Applications4DFAB: A Large Scale 4D Facial Expression Database for Biometric Applications[pdf][s2]9696ad8b164f5e10fcfe23aacf74bd6168aebb15
50_people_one_question50 People One QuestionMerging Pose Estimates Across Space and TimeMerging Pose Estimates Across Space and Time[pdf][s2]5753b2b5e442eaa3be066daa4a2ca8d8a0bb1725
a_pascal_yahoo#N/ADescribing Objects by their AttributesDescribing objects by their attributes[pdf][s2]University of Illinois, Urbana-Champaign2e384f057211426ac5922f1b33d2aa8df5d51f57
adienceAdienceAge and Gender Estimation of Unfiltered FacesAge and Gender Estimation of Unfiltered Faces[pdf][s2]1be498d4bbc30c3bfd0029114c784bc2114d67c0
afadAFADOrdinal Regression with a Multiple Output CNN for Age EstimationOrdinal Regression with Multiple Output CNN for Age Estimation[pdf][s2]6618cff7f2ed440a0d2fa9e74ad5469df5cdbe4c
afew_vaAFEW-VAAFEW-VA database for valence and arousal estimation in-the-wildAFEW-VA database for valence and arousal estimation in-the-wild[pdf][s2]2624d84503bc2f8e190e061c5480b6aa4d89277a
afew_vaAFEW-VACollecting Large, Richly Annotated Facial-Expression Databases from MoviesCollecting Large, Richly Annotated Facial-Expression Databases from Movies[pdf][s2]Australian National Universityb1f4423c227fa37b9680787be38857069247a307
affectnetAffectNetAffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the WildAffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild[pdf][s2]758d7e1be64cc668c59ef33ba8882c8597406e53
aflwAFLWAnnotated Facial Landmarks in the Wild: A Large-scale, Real-world Database for Facial Landmark LocalizationAnnotated Facial Landmarks in the Wild: A large-scale, real-world database for facial landmark localization[pdf][s2]a74251efa970b92925b89eeef50a5e37d9281ad0
afwAFWFace detection, pose estimation and landmark localization in the wildFace detection, pose estimation, and landmark localization in the wild[pdf][s2]0e986f51fe45b00633de9fd0c94d082d2be51406
agedbAgeDBAgeDB: the first manually collected, in-the-wild age databaseAgeDB: The First Manually Collected, In-the-Wild Age Database[pdf][s2]6dcf418c778f528b5792104760f1fbfe90c6dd6a
alert_airportALERT AirportA Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and DatasetsA Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets.[pdf][s2]6403117f9c005ae81f1e8e6d1302f4a045e3d99d
am_fedAM-FEDAffectiva MIT Facial Expression Dataset (AM-FED): Naturalistic and Spontaneous Facial Expressions Collected “In the Wild”Affectiva-MIT Facial Expression Dataset (AM-FED): Naturalistic and Spontaneous Facial Expressions Collected "In-the-Wild"[pdf][s2]47aeb3b82f54b5ae8142b4bdda7b614433e69b9a
apisAPiS1.0Pedestrian Attribute Classification in Surveillance: Database and EvaluationPedestrian Attribute Classification in Surveillance: Database and Evaluation[pdf][s2]488e475eeb3bb39a145f23ede197cd3620f1d98a
ar_facedbAR FaceThe AR Face DatabaseThe AR face database[pdf][s2]6d96f946aaabc734af7fe3fc4454cf8547fcd5ed
awe_earsAWE EarsEar Recognition: More Than a SurveyEar Recognition: More Than a Survey[pdf][s2]84fe5b4ac805af63206012d29523a1e033bc827e
b3d_acB3D(AC)A 3-D Audio-Visual Corpus of Affective CommunicationA 3-D Audio-Visual Corpus of Affective Communication[pdf][s2]d08cc366a4a0192a01e9a7495af1eb5d9f9e73ae
bbc_poseBBC PoseAutomatic and Efficient Human Pose Estimation for Sign Language VideosAutomatic and Efficient Human Pose Estimation for Sign Language Videos[pdf][s2]213a579af9e4f57f071b884aa872651372b661fd
bpadBPADDescribing People: A Poselet-Based Approach to Attribute ClassificationDescribing people: A poselet-based approach to attribute classification[pdf][s2]7808937b46acad36e43c30ae4e9f3fd57462853d
bfmBFMA 3D Face Model for Pose and Illumination Invariant Face RecognitionA 3D Face Model for Pose and Illumination Invariant Face Recognition[pdf][s2]639937b3a1b8bded3f7e9a40e85bd3770016cf3c
bio_idBioID FaceRobust Face Detection Using the Hausdorff DistanceRobust Face Detection Using the Hausdorff Distance[pdf][s2]4053e3423fb70ad9140ca89351df49675197196a
bosphorusThe BosphorusBosphorus Database for 3D Face AnalysisBosphorus Database for 3D Face Analysis[pdf][s2]2acf7e58f0a526b957be2099c10aab693f795973
bp4d_plusBP4D+Multimodal Spontaneous Emotion Corpus for Human Behavior AnalysisMultimodal Spontaneous Emotion Corpus for Human Behavior Analysis[pdf][s2]53ae38a6bb2b21b42bac4f0c4c8ed1f9fa02f9d4
bp4d_spontanousBP4D-SpontanousA high resolution spontaneous 3D dynamic facial expression databaseA high-resolution spontaneous 3D dynamic facial expression database[pdf][s2]SUNY Binghamtonb91f54e1581fbbf60392364323d00a0cd43e493c
brainwashBrainwashEnd-to-End People Detection in Crowded ScenesEnd-to-End People Detection in Crowded Scenes[pdf][s2]1bd1645a629f1b612960ab9bba276afd4cf7c666
bu_3dfeBU-3DFEA 3D Facial Expression Database For Facial Behavior ResearchA 3D facial expression database for facial behavior research[pdf][s2]cc589c499dcf323fe4a143bbef0074c3e31f9b60
buhmap_db#N/AFacial Feature Tracking and Expression Recognition for Sign LanguageFacial feature tracking and expression recognition for sign language[pdf][s2]014b8df0180f33b9fea98f34ae611c6447d761d2
cafe#N/AThe Child Affective Facial Expression (CAFE) Set: Validity and reliability from untrained adultsThe Child Affective Facial Expression (CAFE) set: validity and reliability from untrained adults[pdf][s2]20388099cc415c772926e47bcbbe554e133343d1
cacdCross-Age Reference Coding for Age-Invariant Face Recognition and RetrievalCross-Age Reference Coding for Age-Invariant Face Recognition and Retrieval[pdf][s2]c44c84540db1c38ace232ef34b03bda1c81ba039
caltech_10k_web_facesCaltech 10K Web FacesPruning Training Sets for Learning of Object CategoriesPruning training sets for learning of object categories[pdf][s2]636b8ffc09b1b23ff714ac8350bb35635e49fa3c
caltech_pedestriansCaltech PedestriansPedestrian Detection: A BenchmarkPedestrian detection: A benchmark[pdf][s2]1dc35905a1deff8bc74688f2d7e2f48fd2273275
caltech_pedestriansCaltech PedestriansPedestrian Detection: An Evaluation of the State of the ArtPedestrian Detection: An Evaluation of the State of the Art[pdf][s2]California Institute of Technologyf72f6a45ee240cc99296a287ff725aaa7e7ebb35
cas_pealCAS-PEALThe CAS-PEAL Large-Scale Chinese Face Database and Baseline EvaluationsThe CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations[pdf][s2]2485c98aa44131d1a2f7d1355b1e372f2bb148ad
casablancaCasablancaContext-aware {CNNs} for person head detectionContext-Aware CNNs for Person Head Detection[pdf][s2]0ceda9dae8b9f322df65ca2ef02caca9758aec6f
casia_webfaceCASIA WebfaceLearning Face Representation from ScratchLearning Face Representation from Scratch[pdf][s2]853bd61bc48a431b9b1c7cab10c603830c488e39
celebaCelebADeep Learning Face Attributes in the WildDeep Learning Face Attributes in the Wild[pdf][s2]6424b69f3ff4d35249c0bb7ef912fbc2c86f4ff4
cfdCFDThe Chicago face database: A free stimulus set of faces and norming dataThe Chicago face database: A free stimulus set of faces and norming data.[pdf][s2]4df3143922bcdf7db78eb91e6b5359d6ada004d2
chalearnChaLearnChaLearn Looking at People: A Review of Events and ResourcesChaLearn looking at people: A review of events and resources[pdf][s2]8d5998cd984e7cce307da7d46f155f9db99c6590
chokepointChokePointPatch-based Probabilistic Image Quality Assessment for Face Selection and Improved Video-based Face RecognitionPatch-based probabilistic image quality assessment for face selection and improved video-based face recognition[pdf][s2]0486214fb58ee9a04edfe7d6a74c6d0f661a7668
clothing_co_parsingCCPClothing Co-Parsing by Joint Image Segmentation and LabelingClothing Co-parsing by Joint Image Segmentation and Labeling[pdf][s2]2bf8541199728262f78d4dced6fb91479b39b738
cmdpCMDPDistance Estimation of an Unknown Person from a PortraitDistance Estimation of an Unknown Person from a Portrait[pdf][s2]56ae6d94fc6097ec4ca861f0daa87941d1c10b70
cmu_pieCMU PIEThe CMU Pose, Illumination, and Expression DatabaseThe CMU Pose, Illumination, and Expression (PIE) Database[pdf][s2]4d423acc78273b75134e2afd1777ba6d3a398973
cocoCOCOMicrosoft COCO: Common Objects in ContextMicrosoft COCO: Common Objects in Context[pdf][s2]5e0f8c355a37a5a89351c02f174e7a5ddcb98683
coco_actionCOCO-aDescribing Common Human Visual Actions in ImagesDescribing Common Human Visual Actions in Images[pdf][s2]4946ba10a4d5a7d0a38372f23e6622bd347ae273
coco_qaCOCO QAExploring Models and Data for Image Question AnsweringExploring Models and Data for Image Question Answering[pdf][s2]35b0331dfcd2897abd5749b49ff5e2b8ba0f7a62
cofwCOFWRobust face landmark estimation under occlusionRobust Face Landmark Estimation under Occlusion[pdf][s2]2724ba85ec4a66de18da33925e537f3902f21249
cohn_kanadeCKComprehensive Database for Facial Expression AnalysisComprehensive Database for Facial Expression Analysis[pdf][s2]23fc83c8cfff14a16df7ca497661264fc54ed746
cohn_kanade_plusCK+The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expressionThe Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression[pdf][s2]University of Pittsburgh4d9a02d080636e9666c4d1cc438b9893391ec6c7
columbia_gazeColumbia GazeGaze Locking: Passive Eye Contact Detection for Human–Object InteractionGaze locking: passive eye contact detection for human-object interaction[pdf][s2]Columbia University06f02199690961ba52997cde1527e714d2b3bf8f
complex_activitiesOngoing Complex ActivitiesRecognition of Ongoing Complex Activities by Sequence Prediction over a Hierarchical Label SpaceRecognition of ongoing complex activities by sequence prediction over a hierarchical label space[pdf][s2]65355cbb581a219bd7461d48b3afd115263ea760
cuhk01CUHK01Human Reidentification with Transferred Metric LearningHuman Reidentification with Transferred Metric Learning[pdf][s2]44484d2866f222bbb9b6b0870890f9eea1ffb2d0
cuhk02CUHK02Locally Aligned Feature Transforms across ViewsLocally Aligned Feature Transforms across Views[pdf][s2]38b55d95189c5e69cf4ab45098a48fba407609b4
cuhk03CUHK03DeepReID: Deep Filter Pairing Neural Network for Person Re-identificationDeepReID: Deep Filter Pairing Neural Network for Person Re-identification[pdf][s2]6bd36e9fd0ef20a3074e1430a6cc601e6d407fc3
cvc_01_barcelonaCVC-01Adaptive Image Sampling and Windows Classification for On-board Pedestrian DetectionAdaptive Image Sampling and Windows Classification for On – board Pedestrian Detection[pdf][s2]57fe081950f21ca03b5b375ae3e84b399c015861
czech_news_agencyUFIUnconstrained Facial Images: Database for Face Recognition under Real-world ConditionsUnconstrained Facial Images: Database for Face Recognition Under Real-World Conditions[pdf][s2]4b4106614c1d553365bad75d7866bff0de6056ed
d3dfacsD3DFACSA FACS Valid 3D Dynamic Action Unit database with Applications to 3D Dynamic Morphable Facial ModellingA FACS valid 3D dynamic action unit database with applications to 3D dynamic morphable facial modeling[pdf][s2]070de852bc6eb275d7ca3a9cdde8f6be8795d1a3
dartmouth_childrenDartmouth ChildrenThe Dartmouth Database of Children's Faces: Acquisition and validation of a new face stimulus setThe Dartmouth Database of Children’s Faces: Acquisition and Validation of a New Face Stimulus Set[pdf][s2]4e6ee936eb50dd032f7138702fa39b7c18ee8907
data_61Data61 PedestrianA Multi-Modal Graphical Model for Scene AnalysisA Multi-modal Graphical Model for Scene Analysis[pdf][s2]563c940054e4b456661762c1ab858e6f730c3159
deep_fashionDeepFashionDeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich AnnotationsDeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations[pdf][s2]18010284894ed0edcca74e5bf768ee2e15ef7841
deep_fashionDeepFashionFashion Landmark Detection in the WildFashion Landmark Detection in the Wild[pdf][s2]4fefd1bc8dc4e0ab37ee3324ddfa43ad9d6a04a7
disfaDISFADISFA: A Spontaneous Facial Action Intensity DatabaseDISFA: A Spontaneous Facial Action Intensity Database[pdf][s2]University of Denver5a5f0287484f0d480fed1ce585dbf729586f0edc
distance_nighttimeLong Distance Heterogeneous FaceNighttime Face Recognition at Long Distance: Cross-distance and Cross-spectral MatchingNighttime Face Recognition at Long Distance: Cross-Distance and Cross-Spectral Matching[pdf][s2]4156b7e88f2e0ab0a7c095b9bab199ae2b23bd06
duke_mtmcDuke MTMCPerformance Measures and a Data Set for Multi-Target, Multi-Camera TrackingPerformance Measures and a Data Set for Multi-Target, Multi-Camera Tracking[pdf][s2]27a2fad58dd8727e280f97036e0d2bc55ef5424c
emotio_netEmotioNet DatabaseEmotioNet: An Accurate, Real-Time Algorithm for the Automatic Annotation of a Million Facial Expressions in the WildEmotioNet: An Accurate, Real-Time Algorithm for the Automatic Annotation of a Million Facial Expressions in the Wild[pdf][s2]c900e0ad4c95948baaf0acd8449fde26f9b4952a
eth_andreas_essETHZ PedestrianDepth and Appearance for Mobile Scene AnalysisDepth and Appearance for Mobile Scene Analysis[pdf][s2]ETH Zurich13f06b08f371ba8b5d31c3e288b4deb61335b462
europersonsEuroCity PersonsThe EuroCity Persons Dataset: A Novel Benchmark for Object DetectionThe EuroCity Persons Dataset: A Novel Benchmark for Object Detection[pdf][s2]f0e17f27f029db4ad650ff278fe3c10ecb6cb0c4
expwExpWFrom Facial Expression Recognition to Interpersonal Relation PredictionFrom Facial Expression Recognition to Interpersonal Relation Prediction[pdf][s2]22f656d0f8426c84a33a267977f511f127bfd7f3
face_scrubFaceScrubA data-driven approach to cleaning large face datasetsA data-driven approach to cleaning large face datasets[pdf][s2]0d3bb75852098b25d90f31d2f48fd0cb4944702b
face_tracerFaceTracerFaceTracer: A Search Engine for Large Collections of Images with FacesFaceTracer: A Search Engine for Large Collections of Images with Faces[pdf][s2]4c170a0dcc8de75587dae21ca508dab2f9343974
face_tracerFaceTracerFace Swapping: Automatically Replacing Faces in PhotographsFace swapping: automatically replacing faces in photographs[pdf][s2]670637d0303a863c1548d5b19f705860a23e285c
facebook_100Facebook100Scaling Up Biologically-Inspired Computer Vision: A Case Study in Unconstrained Face Recognition on FacebookScaling up biologically-inspired computer vision: A case study in unconstrained face recognition on facebook[pdf][s2]Harvard University9c23859ec7313f2e756a3e85575735e0c52249f4
faceplaceFace PlaceRecognizing disguised facesRecognizing disguised faces[pdf][s2]25474c21613607f6bb7687a281d5f9d4ffa1f9f3
families_in_the_wildFIWVisual Kinship Recognition of Families in the WildVisual Kinship Recognition of Families in the Wild[pdf][s2]University of Massachusetts Dartmouthdd65f71dac86e36eecbd3ed225d016c3336b4a13
fddbFDDBFDDB: A Benchmark for Face Detection in Unconstrained SettingsA Benchmark for Face Detection in Unconstrained Settings[pdf][s2]75da1df4ed319926c544eefe17ec8d720feef8c0
feiFEICaptura e Alinhamento de Imagens: Um Banco de Faces BrasileiroA new ranking method for principal components analysis and its application to face image analysis[pdf][s2]8b56e33f33e582f3e473dba573a16b598ed9bcdc
feretFERETThe FERET Verification Testing Protocol for Face Recognition AlgorithmsThe FERET Verification Testing Protocol for Face Recognition Algorithms[pdf][s2]0c4a139bb87c6743c7905b29a3cfec27a5130652
feretFERETThe FERET Evaluation Methodology for Face-Recognition AlgorithmsThe FERET Evaluation Methodology for Face-Recognition Algorithms[pdf][s2]0f0fcf041559703998abf310e56f8a2f90ee6f21
feretFERETFERET ( Face Recognition Technology ) Recognition Algorithm Development and Test ResultsFERET ( Face Recognition Technology ) Recognition Algorithm Development and Test Results[pdf][s2]31de9b3dd6106ce6eec9a35991b2b9083395fd0b
feretFERETThe FERET database and evaluation procedure for face-recognition algorithmsThe FERET database and evaluation procedure for face-recognition algorithms[pdf][s2]dc8b25e35a3acb812beb499844734081722319b4
ferplusFER+Training Deep Networks for Facial Expression Recognition with Crowd-Sourced Label DistributionTraining deep networks for facial expression recognition with crowd-sourced label distribution[pdf][s2]298cbc3dfbbb3a20af4eed97906650a4ea1c29e0
fiaCMU FiAThe CMU Face In Action (FIA) DatabaseThe CMU Face In Action (FIA) Database[pdf][s2]47662d1a368daf70ba70ef2d59eb6209f98b675d
fiw_300300-WA semi-automatic methodology for facial landmark annotationA Semi-automatic Methodology for Facial Landmark Annotation[pdf][s2]013909077ad843eb6df7a3e8e290cfd5575999d2
fiw_300300-W300 Faces in-the-Wild Challenge: The first facial landmark localization Challenge300 Faces in-the-Wild Challenge: The First Facial Landmark Localization Challenge[pdf][s2]044d9a8c61383312cdafbcc44b9d00d650b21c70
fiw_300300-W300 faces In-the-wild challenge: Database and results300 Faces In-The-Wild Challenge: database and results[pdf][s2]e4754afaa15b1b53e70743880484b8d0736990ff
frav3dFRAV3DMULTIMODAL 2D, 2.5D & 3D FACE VERIFICATIONMultimodal 2D, 2.5D & 3D Face Verification[pdf][s2]Universidad Rey Juan Carlos, Spain2b926b3586399d028b46315d7d9fb9d879e4f79c
frgcFRGCOverview of the Face Recognition Grand ChallengeOverview of the face recognition grand challenge[pdf][s2]NIST18ae7c9a4bbc832b8b14bc4122070d7939f5e00e
gallagherGallagherClothing Cosegmentation for Recognizing PeopleClothing cosegmentation for recognizing people[pdf][s2]Carnegie Mellon University Silicon Valley22ad2c8c0f4d6aa4328b38d894b814ec22579761
geofacesGeoFacesGeoFaceExplorer: Exploring the Geo-Dependence of Facial AttributesGeoFaceExplorer: exploring the geo-dependence of facial attributes[pdf][s2]17b46e2dad927836c689d6787ddb3387c6159ece
georgia_tech_face_databaseGeorgia Tech FaceMaximum likelihood training of the embedded HMM for face detection and recognitionMAXIMUM 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[pdf][s2]3dc3f0b64ef80f573e3a5f96e456e52ee980b877
gfwGrouping Face in the WildMerge or Not? Learning to Group Faces via Imitation LearningMerge or Not? Learning to Group Faces via Imitation Learning[pdf][s2]e58dd160a76349d46f881bd6ddbc2921f08d1050
grazGraz PedestrianWeak Hypotheses and Boosting for Generic Object Detection and RecognitionWeak Hypotheses and Boosting for Generic Object Detection and Recognition[pdf][s2]0c91808994a250d7be332400a534a9291ca3b60e
grazGraz PedestrianObject Recognition Using Segmentation for Feature DetectionObject recognition using segmentation for feature detection[pdf][s2]Inst. of Comput. Sci., Univ. of Leoben, Austria12ad3b5bbbf407f8e54ea692c07633d1a867c566
grazGraz PedestrianGeneric Object Recognition with BoostingGeneric object recognition with boosting[pdf][s2]TU Graz2eed184680edcdec8a3b605ad1a3ba8e8f7cc2e9
h3dH3DPoselets: Body Part Detectors Trained Using 3D Human Pose AnnotationsPoselets: Body part detectors trained using 3D human pose annotations[pdf][s2]2830fb5282de23d7784b4b4bc37065d27839a412
hda_plusHDA+The HDA+ data set for research on fully automated re-identification systemsThe HDA+ Data Set for Research on Fully Automated Re-identification Systems[pdf][s2]8f02ec0be21461fbcedf51d864f944cfc42c875f
hda_plusHDA+A Multi-camera video data set for research on High-Definition surveillanceHDA dataset-DRAFT 1 A Multi-camera video data set for research on High-Definition surveillance[pdf][s2]bd88bb2e4f351352d88ee7375af834360e223498
helenHelenInteractive Facial Feature LocalizationInteractive Facial Feature Localization[pdf][s2]95f12d27c3b4914e0668a268360948bce92f7db3
hi4d_adsipHi4D-ADSIPHi4D-ADSIP 3-D dynamic facial articulation databaseHi4D-ADSIP 3-D dynamic facial articulation database[pdf][s2]a8d0b149c2eadaa02204d3e4356fbc8eccf3b315
hipsterwarsHipsterwarsHipster Wars: Discovering Elements of Fashion StylesHipster Wars: Discovering Elements of Fashion Styles[pdf][s2]04c2cda00e5536f4b1508cbd80041e9552880e67
hollywood_headsetHollywoodHeadsContext-aware CNNs for person head detectionContext-Aware CNNs for Person Head Detection[pdf][s2]0ceda9dae8b9f322df65ca2ef02caca9758aec6f
hrt_transgenderHRT TransgenderIs the Eye Region More Reliable Than the Face? A Preliminary Study of Face-based Recognition on a Transgender DatasetIs the eye region more reliable than the face? A preliminary study of face-based recognition on a transgender dataset[pdf][s2]137aa2f891d474fce1e7a1d1e9b3aefe21e22b34
hrt_transgenderHRT TransgenderInvestigating the Periocular-Based Face Recognition Across Gender TransformationInvestigating the Periocular-Based Face Recognition Across Gender Transformation[pdf][s2]University of North Carolina at Wilmington2f43b614607163abf41dfe5d17ef6749a1b61304
ibm_difIBM Diversity in FacesDiversity in FacesDiversity in Faces[pdf][s2]2b89de1d81cee50552f10e26e865df3365e9bc88
ifadIFADIndian Face Age Database: A Database for Face Recognition with Age VariationIndian Face Age Database : A Database for Face Recognition with Age Variation[pdf][s2]55c40cbcf49a0225e72d911d762c27bb1c2d14aa
ifdbIFDBIranian Face Database and Evaluation with a New Detection AlgorithmIranian Face Database and Evaluation with a New Detection Algorithm[pdf][s2]066d71fcd997033dce4ca58df924397dfe0b5fd1
ifdbIFDBIranian Face Database with age, pose and expressionIranian Face Database with age, pose and expression[pdf][s2]Islamic Azad Universityb71d1aa90dcbe3638888725314c0d56640c1fef1
iit_dehli_earIIT Dehli EarAutomated human identification using ear imagingAutomated Human Identification Using Ear Imaging[pdf][s2]faf40ce28857aedf183e193486f5b4b0a8c478a2
ijb_cIJB-CIARPA Janus Benchmark-B Face DatasetIARPA Janus Benchmark-B Face Dataset[pdf][s2]0cb2dd5f178e3a297a0c33068961018659d0f443
ijb_cIJB-CPushing the Frontiers of Unconstrained Face Detection and Recognition: IARPA Janus Benchmark APushing the frontiers of unconstrained face detection and recognition: IARPA Janus Benchmark A[pdf][s2]140c95e53c619eac594d70f6369f518adfea12ef
ijb_cIJB-CIARPA Janus Benchmark CIARPA Janus Benchmark - C: Face Dataset and Protocol[pdf][s2]57178b36c21fd7f4529ac6748614bb3374714e91
ilids_mctsi-LIDS Multiple-CameraImagery Library for Intelligent Detection Systems: The i-LIDS User GuideImagery Library for Intelligent Detection Systems (i-LIDS); A Standard for Testing Video Based Detection Systems[pdf][s2]0297448f3ed948e136bb06ceff10eccb34e5bb77
ilids_vid_reidiLIDS-VIDPerson Re-Identi cation by Video RankingPerson Re-identification by Video Ranking[pdf][s2]98bb029afe2a1239c3fdab517323066f0957b81b
images_of_groupsImages of GroupsUnderstanding Groups of Images of PeopleUnderstanding images of groups of people[pdf][s2]Carnegie Mellon University Silicon Valley21d9d0deed16f0ad62a4865e9acf0686f4f15492
imdb_faceIMDb FaceThe Devil of Face Recognition is in the NoiseThe Devil of Face Recognition is in the Noise[pdf][s2]9e31e77f9543ab42474ba4e9330676e18c242e72
imdb_wikiIMDBDeep expectation of real and apparent age from a single image without facial landmarksDeep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks[pdf][s2]10195a163ab6348eef37213a46f60a3d87f289c5
imdb_wikiIMDBDEX: Deep EXpectation of apparent age from a single imageDEX: Deep EXpectation of Apparent Age from a Single Image[pdf][s2]8355d095d3534ef511a9af68a3b2893339e3f96b
imfdbIMFDBIndian Movie Face Database: A Benchmark for Face Recognition Under Wide VariationsIndian Movie Face Database: A benchmark for face recognition under wide variations[pdf][s2]CVIT, IIITH, Indiaca3e88d87e1344d076c964ea89d91a75c417f5ee
immediacyImmediacyMulti-task Recurrent Neural Network for Immediacy PredictionMulti-task Recurrent Neural Network for Immediacy Prediction[pdf][s2]1e3df3ca8feab0b36fd293fe689f93bb2aaac591
imsituimSituSituation Recognition: Visual Semantic Role Labeling for Image UnderstandingSituation Recognition: Visual Semantic Role Labeling for Image Understanding[pdf][s2]51eba481dac6b229a7490f650dff7b17ce05df73
inria_personINRIA PedestrianHistograms of Oriented Gradients for Human DetectionHistograms of oriented gradients for human detection[pdf][s2]INRIA Rhone-Alps, Montbonnot, France10d6b12fa07c7c8d6c8c3f42c7f1c061c131d4c5
jaffeJAFFECoding Facial Expressions with Gabor WaveletsCoding Facial Expressions with Gabor Wavelets[pdf][s2]45c31cde87258414f33412b3b12fc5bec7cb3ba9
jiku_mobileJiku Mobile Video DatasetThe Jiku Mobile Video DatasetThe jiku mobile video dataset[pdf][s2]d178cde92ab3dc0dd2ebee5a76a33d556c39448b
jpl_poseJPL-Interaction datasetFirst-Person Activity Recognition: What Are They Doing to Me?First-Person Activity Recognition: What Are They Doing to Me?[pdf][s2]1aad2da473888cb7ebc1bfaa15bfa0f1502ce005
kin_faceUB KinFaceUnderstanding Kin Relationships in a PhotoUnderstanding Kin Relationships in a Photo[pdf][s2]08f6745bc6c1b0fb68953ea61054bdcdde6d2fc7
kin_faceUB KinFaceGenealogical Face Recognition based on UB KinFace DatabaseGenealogical face recognition based on UB KinFace database[pdf][s2]SUNY Buffalo2eb84aaba316b095d4bb51da1a3e4365bbf9ab1d
kin_faceUB KinFaceKinship Verification through Transfer LearningKinship verification through transfer learning[pdf][s2]4793f11fbca4a7dba898b9fff68f70d868e2497c
kinectfaceKinectFaceDBKinectFaceDB: A Kinect Database for Face RecognitionKinectFaceDB: A Kinect Database for Face Recognition[pdf][s2]University of North Carolina at Chapel Hill0b440695c822a8e35184fb2f60dcdaa8a6de84ae
kittiKITTIVision meets Robotics: The KITTI DatasetVision meets robotics: The KITTI dataset[pdf][s2]026e3363b7f76b51cc711886597a44d5f1fd1de2
lagLAGLarge Age-Gap Face Verification by Feature Injection in Deep NetworksLarge age-gap face verification by feature injection in deep networks[pdf][s2]0d2dd4fc016cb6a517d8fb43a7cc3ff62964832e
large_scale_person_searchLarge Scale Person SearchEnd-to-End Deep Learning for Person SearchEnd-to-End Deep Learning for Person Search[pdf][s2]2161f6b7ee3c0acc81603b01dc0df689683577b9
leeds_sports_poseLeeds Sports PoseClustered Pose and Nonlinear Appearance Models for Human Pose EstimationClustered Pose and Nonlinear Appearance Models for Human Pose Estimation[pdf][s2]4b1d23d17476fcf78f4cbadf69fb130b1aa627c0
leeds_sports_pose_extendedLeeds Sports Pose ExtendedLearning Effective Human Pose Estimation from Inaccurate AnnotationLearning effective human pose estimation from inaccurate annotation[pdf][s2]University of Leeds4e4746094bf60ee83e40d8597a6191e463b57f76
lfpwLFWPLocalizing Parts of Faces Using a Consensus of ExemplarsLocalizing Parts of Faces Using a Consensus of Exemplars[pdf][s2]140438a77a771a8fb656b39a78ff488066eb6b50
lfwLFWLabeled Faces in the Wild: Updates and New Reporting ProceduresLabeled Faces in the Wild : Updates and New Reporting Procedures[pdf][s2]2d3482dcff69c7417c7b933f22de606a0e8e42d4
lfwLFWLabeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained EnvironmentsLabeled Faces in the Wild : A Database for Studying Face Recognition in Unconstrained Environments[pdf][s2]370b5757a5379b15e30d619e4d3fb9e8e13f3256
lfwLFWLabeled Faces in the Wild: A SurveyLabeled Faces in the Wild : A Survey[pdf][s2]7de6e81d775e9cd7becbfd1bd685f4e2a5eebb22
lfwLFWEffective Unconstrained Face Recognition by Combining Multiple Descriptors and Learned Background StatisticsEffective Unconstrained Face Recognition by Combining Multiple Descriptors and Learned Background Statistics[pdf][s2]133f01aec1534604d184d56de866a4bd531dac87
m2vtsm2vtsThe M2VTS Multimodal Face Database (Release 1.00)The M2VTS Multimodal Face Database (Release 1.00)[pdf][s2]Laboratoire de Télécommunications et Télédétection, UCL, Louvain-La-Neuve, Belgium9a9877791945c6fa4c1743ec6d3fb32570ef8481
m2vtsdb_extendedxm2vtsdbXM2VTSDB: The Extended M2VTS DatabaseXm2vtsdb: the Extended M2vts Database[pdf][s2]b62628ac06bbac998a3ab825324a41a11bc3a988
maflMAFLFacial Landmark Detection by Deep Multi-task LearningFacial Landmark Detection by Deep Multi-task Learning[pdf][s2]8a3c5507237957d013a0fe0f082cab7f757af6ee
maflMAFLLearning Deep Representation for Face Alignment with Auxiliary AttributesLearning Deep Representation for Face Alignment with Auxiliary Attributes[pdf][s2]a0fd85b3400c7b3e11122f44dc5870ae2de9009a
malfMALFFine-grained Evaluation on Face Detection in the Wild.Fine-grained evaluation on face detection in the wild[pdf][s2]45e616093a92e5f1e61a7c6037d5f637aa8964af
mapillaryMapillaryThe Mapillary Vistas Dataset for Semantic Understanding of Street ScenesThe Mapillary Vistas Dataset for Semantic Understanding of Street Scenes[pdf][s2]79828e6e9f137a583082b8b5a9dfce0c301989b8
market_1501Market 1501Scalable Person Re-identification: A BenchmarkScalable Person Re-identification: A Benchmark[pdf][s2]4308bd8c28e37e2ed9a3fcfe74d5436cce34b410
market1203Market 1203Orientation Driven Bag of Appearances for Person Re-identificationOrientation Driven Bag of Appearances for Person Re-identification[pdf][s2]a7fe834a0af614ce6b50dc093132b031dd9a856b
marsMARSMARS: A Video Benchmark for Large-Scale Person Re-identificationMARS: A Video Benchmark for Large-Scale Person Re-Identification[pdf][s2]c0387e788a52f10bf35d4d50659cfa515d89fbec
mcgillMcGill Real WorldHierarchical Temporal Graphical Model for Head Pose Estimation and Subsequent Attribute Classification in Real-World VideosHierarchical temporal graphical model for head pose estimation and subsequent attribute classification in real-world videos[pdf][s2]a5a3bc3e5e9753769163cb30b16dbd12e266b93e
mcgillMcGill Real WorldRobust Semi-automatic Head Pose Labeling for Real-World Face Video SequencesRobust semi-automatic head pose labeling for real-world face video sequences[pdf][s2]McGill Universityc570d1247e337f91e555c3be0e8c8a5aba539d9f
megaageMegaAgeQuantifying Facial Age by Posterior of Age ComparisonsQuantifying Facial Age by Posterior of Age Comparisons[pdf][s2]d80a3d1f3a438e02a6685e66ee908446766fefa9
megafaceMegaFaceLevel Playing Field for Million Scale Face RecognitionLevel Playing Field for Million Scale Face Recognition[pdf][s2]28d4e027c7e90b51b7d8908fce68128d1964668a
megafaceMegaFaceThe MegaFace Benchmark: 1 Million Faces for Recognition at ScaleThe MegaFace Benchmark: 1 Million Faces for Recognition at Scale[pdf][s2]96e0cfcd81cdeb8282e29ef9ec9962b125f379b0
mifsMIFSSpoofing Faces Using Makeup: An Investigative StudySpoofing faces using makeup: An investigative study[pdf][s2]INRIA Méditerranée23e824d1dfc33f3780dd18076284f07bd99f1c43
mit_cbclMIT CBCLComponent-based Face Recognition with 3D Morphable ModelsComponent-Based Face Recognition with 3D Morphable Models[pdf][s2]079a0a3bf5200994e1f972b1b9197bf2f90e87d4
miwMIWAutomatic Facial Makeup Detection with Application in Face RecognitionAutomatic facial makeup detection with application in face recognition[pdf][s2]West Virginia Universityfcc6fe6007c322641796cb8792718641856a22a7
mmi_facial_expressionMMI Facial Expression DatasetWEB-BASED DATABASE FOR FACIAL EXPRESSION ANALYSISWeb-based database for facial expression analysis[pdf][s2]2a75f34663a60ab1b04a0049ed1d14335129e908
moments_in_timeMoments in TimeMoments in Time Dataset: one million videos for event understandingMoments in Time Dataset: one million videos for event understanding[pdf][s2]a5a44a32a91474f00a3cda671a802e87c899fbb4
morphMORPH CommercialMORPH: A Longitudinal Image Database of Normal Adult Age-ProgressionMORPH: a longitudinal image database of normal adult age-progression[pdf][s2]9055b155cbabdce3b98e16e5ac9c0edf00f9552f
morph_ncMORPH Non-CommercialMORPH: A Longitudinal Image Database of Normal Adult Age-ProgressionMORPH: a longitudinal image database of normal adult age-progression[pdf][s2]9055b155cbabdce3b98e16e5ac9c0edf00f9552f
motMOTEvaluating Multiple Object Tracking Performance: The CLEAR MOT MetricsEvaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics[pdf][s2]2258e01865367018ed6f4262c880df85b94959f8
motMOTPerformance Measures and a Data Set for Multi-Target, Multi-Camera TrackingPerformance Measures and a Data Set for Multi-Target, Multi-Camera Tracking[pdf][s2]27a2fad58dd8727e280f97036e0d2bc55ef5424c
motMOTLearning to associate: HybridBoosted multi-target tracker for crowded sceneLearning to associate: HybridBoosted multi-target tracker for crowded scene[pdf][s2]University of Southern California5981e6479c3fd4e31644db35d236bfb84ae46514
mpi_largeLarge MPI Facial ExpressionThe MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial ExpressionsThe MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial Expressions[pdf][s2]ea050801199f98a1c7c1df6769f23f658299a3ae
mpi_smallSmall MPI Facial ExpressionThe MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial ExpressionsThe MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial Expressions[pdf][s2]ea050801199f98a1c7c1df6769f23f658299a3ae
mpii_gazeMPIIGazeAppearance-based Gaze Estimation in the WildAppearance-based gaze estimation in the wild[pdf][s2]0df0d1adea39a5bef318b74faa37de7f3e00b452
mpii_human_poseMPII Human Pose2D Human Pose Estimation: New Benchmark and State of the Art Analysis2D Human Pose Estimation: New Benchmark and State of the Art Analysis[pdf][s2]3325860c0c82a93b2eac654f5324dd6a776f609e
mr2MR2The MR2: A multi-racial mega-resolution database of facial stimuliThe MR2: A multi-racial, mega-resolution database of facial stimuli.[pdf][s2]578d4ad74818086bb64f182f72e2c8bd31e3d426
mrp_droneMRP DroneInvestigating Open-World Person Re-identification Using a DroneInvestigating Open-World Person Re-identification Using a Drone[pdf][s2]ad01687649d95cd5b56d7399a9603c4b8e2217d7
mscelebMsCelebMS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face RecognitionMS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition[pdf][s2]291265db88023e92bb8c8e6390438e5da148e8f5
msmt_17MSMT17Person Transfer GAN to Bridge Domain Gap for Person Re-IdentificationPerson Transfer GAN to Bridge Domain Gap for Person Re-Identification[pdf][s2]ec792ad2433b6579f2566c932ee414111e194537
mtflMTFLFacial Landmark Detection by Deep Multi-task LearningFacial Landmark Detection by Deep Multi-task Learning[pdf][s2]8a3c5507237957d013a0fe0f082cab7f757af6ee
mtflMTFLLearning Deep Representation for Face Alignment with Auxiliary AttributesLearning Deep Representation for Face Alignment with Auxiliary Attributes[pdf][s2]a0fd85b3400c7b3e11122f44dc5870ae2de9009a
mug_facesMUG FacesThe MUG Facial Expression DatabaseThe MUG facial expression database[pdf][s2]Aristotle University of Thessalonikif1af714b92372c8e606485a3982eab2f16772ad8
multi_pieMULTIPIEMulti-PIEThe CMU Pose, Illumination, and Expression (PIE) Database[pdf][s2]4d423acc78273b75134e2afd1777ba6d3a398973
names_and_facesNews DatasetNames and FacesNames and faces in the news[pdf][s2]2fda164863a06a92d3a910b96eef927269aeb730
nd_2006ND-2006Using a Multi-Instance Enrollment Representation to Improve 3D Face RecognitionUsing a Multi-Instance Enrollment Representation to Improve 3D Face Recognition[pdf][s2]University of Notre Damefd8168f1c50de85bac58a8d328df0a50248b16ae
nova_emotionsNovaemötions DatasetCompetitive affective gamming: Winning with a smileCompetitive affective gaming: winning with a smile[pdf][s2]Universidade NOVA de Lisboa, Caparica, Portugal7f4040b482d16354d5938c1d1b926b544652bf5b
nova_emotionsNovaemötions DatasetCrowdsourcing facial expressions for affective-interactionCrowdsourcing facial expressions for affective-interaction[pdf][s2]c06b13d0ec3f5c43e2782cd22542588e233733c3
nudedetection#N/AA Bag-of-Features Approach based on Hue-SIFT Descriptor for Nude DetectionA bag-of-features approach based on Hue-SIFT descriptor for nude detection[pdf][s2]7ace44190729927e5cb0dd5d363fcae966fe13f7
orlORLParameterisation of a Stochastic Model for Human Face IdentificationParameterisation of a stochastic model for human face identification[pdf][s2]55206f0b5f57ce17358999145506cd01e570358c
penn_fudanPenn FudanObject Detection Combining Recognition and SegmentationObject Detection Combining Recognition and Segmentation[pdf][s2]3394168ff0719b03ff65bcea35336a76b21fe5e4
petaPETAPedestrian Attribute Recognition At Far DistancePedestrian Attribute Recognition At Far Distance[pdf][s2]2a4bbee0b4cf52d5aadbbc662164f7efba89566c
petsPETS 2017PETS 2017: Dataset and ChallengePETS 2017: Dataset and Challenge[pdf][s2]22909dd19a0ec3b6065334cb5be5392cb24d839d
pilot_parliamentPPBGender Shades: Intersectional Accuracy Disparities in Commercial Gender ClassificationGender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification[pdf][s2]18858cc936947fc96b5c06bbe3c6c2faa5614540
pipaPIPABeyond Frontal Faces: Improving Person Recognition Using Multiple CuesBeyond frontal faces: Improving Person Recognition using multiple cues[pdf][s2]0a85bdff552615643dd74646ac881862a7c7072d
pkuPKUSwiss-System Based Cascade Ranking for Gait-based Person Re-identificationSwiss-System Based Cascade Ranking for Gait-Based Person Re-Identification[pdf][s2]f6c8d5e35d7e4d60a0104f233ac1a3ab757da53f
pku_reidPKU-ReidOrientation driven bag of appearances for person re-identificationOrientation Driven Bag of Appearances for Person Re-identification[pdf][s2]a7fe834a0af614ce6b50dc093132b031dd9a856b
pornodb#N/APooling in Image Representation: the Visual Codeword Point of ViewPooling in image representation: The visual codeword point of view[pdf][s2]b92a1ed9622b8268ae3ac9090e25789fc41cc9b8
precariousPrecariousExpecting the Unexpected: Training Detectors for Unusual Pedestrians With Adversarial ImpostersExpecting the Unexpected: Training Detectors for Unusual Pedestrians with Adversarial Imposters[pdf][s2]9e5378e7b336c89735d3bb15cf67eff96f86d39a
pridPRIDPerson Re-Identification by Descriptive and Discriminative ClassificationPerson Re-identification by Descriptive and Discriminative Classification[pdf][s2]16c7c31a7553d99f1837fc6e88e77b5ccbb346b8
prwPRWPerson Re-identification in the WildPerson Re-identification in the Wild[pdf][s2]0b84f07af44f964817675ad961def8a51406dd2e
psuPSUVision-based Analysis of Small Groups in Pedestrian CrowdsVision-Based Analysis of Small Groups in Pedestrian Crowds[pdf][s2]066000d44d6691d27202896691f08b27117918b9
pubfigPubFigAttribute and Simile Classifiers for Face VerificationAttribute and simile classifiers for face verification[pdf][s2]759a3b3821d9f0e08e0b0a62c8b693230afc3f8d
pubfig_83pubfig83Scaling Up Biologically-Inspired Computer Vision: A Case Study in Unconstrained Face Recognition on FacebookScaling up biologically-inspired computer vision: A case study in unconstrained face recognition on facebook[pdf][s2]Harvard University9c23859ec7313f2e756a3e85575735e0c52249f4
put_facePut FaceThe PUT face databaseThe put face database[pdf][s2]ae0aee03d946efffdc7af2362a42d3750e7dd48a
qmul_gridGRIDTime-delayed correlation analysis for multi-camera activity understandingTime-Delayed Correlation Analysis for Multi-Camera Activity Understanding[pdf][s2]Queen Mary University of London2edb87494278ad11641b6cf7a3f8996de12b8e14
qmul_gridGRIDMulti-Camera Activity Correlation AnalysisMulti-camera activity correlation analysis[pdf][s2]Queen Mary University of London3b5b6d19d4733ab606c39c69a889f9e67967f151
qmul_surv_faceQMUL-SurvFaceSurveillance Face Recognition ChallengeSurveillance Face Recognition Challenge[pdf][s2]c866a2afc871910e3282fd9498dce4ab20f6a332
rafdRaFDPresentation and validation of the Radboud Faces DatabasePresentation and validation of the Radboud Faces Database[pdf][s2]3765df816dc5a061bc261e190acc8bdd9d47bec0
raidRAiDConsistent Re-identification in a Camera NetworkConsistent Re-identification in a Camera Network[pdf][s2]09d78009687bec46e70efcf39d4612822e61cb8c
rap_pedestrianRAPA Richly Annotated Dataset for Pedestrian Attribute RecognitionA Richly Annotated Dataset for Pedestrian Attribute Recognition[pdf][s2]221c18238b829c12b911706947ab38fd017acef7
reseedReSEEDReSEED: Social Event dEtection DatasetReSEED: social event dEtection dataset[pdf][s2]54983972aafc8e149259d913524581357b0f91c3
saivtSAIVT SoftBioA Database for Person Re-Identification in Multi-Camera Surveillance NetworksA Database for Person Re-Identification in Multi-Camera Surveillance Networks[pdf][s2]22646e00a7ba34d1b5fbe3b1efcd91a1e1be3c2b
sarc3dSarc3DSARC3D: a new 3D body model for People Tracking and Re-identificationSARC3D: A New 3D Body Model for People Tracking and Re-identification[pdf][s2]e27ef52c641c2b5100a1b34fd0b819e84a31b4df
scfaceSCfaceSCface – surveillance cameras face databaseSCface – surveillance cameras face database[pdf][s2]29a705a5fa76641e0d8963f1fdd67ee4c0d92d3d
scut_fbpSCUT-FBPSCUT-FBP: A Benchmark Dataset for Facial Beauty PerceptionSCUT-FBP: A Benchmark Dataset for Facial Beauty Perception[pdf][s2]bd26dabab576adb6af30484183c9c9c8379bf2e0
scut_headSCUT HEADDetecting Heads using Feature Refine Net and Cascaded Multi-scale ArchitectureDetecting Heads using Feature Refine Net and Cascaded Multi-scale Architecture[pdf][s2]d3200d49a19a4a4e4e9745ee39649b65d80c834b
sdu_vidSDU-VIDA Spatio-Temporal Appearance Representation for Video-Based Pedestrian Re-IdentificationA Spatio-Temporal Appearance Representation for Video-Based Pedestrian Re-Identification[pdf][s2]3b4ec8af470948a72a6ed37a9fd226719a874ebc
sdu_vidSDU-VIDLocal descriptors encoded by Fisher vectors for person re-identificationLocal Descriptors Encoded by Fisher Vectors for Person Re-identification[pdf][s2]46a01565e6afe7c074affb752e7069ee3bf2e4ef
sdu_vidSDU-VIDPerson reidentification by video rankingPerson Re-identification by Video Ranking[pdf][s2]98bb029afe2a1239c3fdab517323066f0957b81b
social_relationSocial RelationLearning Social Relation Traits from Face ImagesLearning Social Relation Traits from Face Images[pdf][s2]2a171f8d14b6b8735001a11c217af9587d095848
sotonSOTON HiDOn a Large Sequence-Based Human Gait DatabaseOn a large sequence-based human gait database[pdf][s2]4f93cd09785c6e77bf4bc5a788e079df524c8d21
sports_videos_in_the_wildSVWSports Videos in the Wild (SVW): A Video Dataset for Sports AnalysisSports Videos in the Wild (SVW): A video dataset for sports analysis[pdf][s2]1a40092b493c6b8840257ab7f96051d1a4dbfeb2
stair_actionsSTAIR ActionSTAIR Actions: A Video Dataset of Everyday Home ActionsSTAIR Actions: A Video Dataset of Everyday Home Actions[pdf][s2]d3f5a1848b0028d8ab51d0b0673732cad2e3c8c9
stanford_droneStanford DroneLearning Social Etiquette: Human Trajectory Prediction In Crowded ScenesSocial LSTM: Human Trajectory Prediction in Crowded Spaces[pdf][s2]570f37ed63142312e6ccdf00ecc376341ec72b9f
stickmen_buffyBuffy StickmenClustered Pose and Nonlinear Appearance Models for Human Pose EstimationClustered Pose and Nonlinear Appearance Models for Human Pose Estimation[pdf][s2]4b1d23d17476fcf78f4cbadf69fb130b1aa627c0
stickmen_buffyBuffy StickmenLearning to Parse Images of Articulated ObjectsLearning to parse images of articulated bodies[pdf][s2]6dd0597f8513dc100cd0bc1b493768cde45098a9
stickmen_familyWe Are Family StickmenWe Are Family: Joint Pose Estimation of Multiple PersonsWe Are Family: Joint Pose Estimation of Multiple Persons[pdf][s2]0dc11a37cadda92886c56a6fb5191ded62099c28
stickmen_pascalStickmen PASCALClustered Pose and Nonlinear Appearance Models for Human Pose EstimationLearning to parse images of articulated bodies[pdf][s2]6dd0597f8513dc100cd0bc1b493768cde45098a9
stickmen_pascalStickmen PASCALLearning to Parse Images of Articulated ObjectsLearning to parse images of articulated bodies[pdf][s2]6dd0597f8513dc100cd0bc1b493768cde45098a9
sun_attributesSUNThe SUN Attribute Database: Beyond Categories for Deeper Scene UnderstandingThe SUN Attribute Database: Beyond Categories for Deeper Scene Understanding[pdf][s2]66e6f08873325d37e0ec20a4769ce881e04e964e
sun_attributesSUNSUN Attribute Database: Discovering, Annotating, and Recognizing Scene AttributesSUN attribute database: Discovering, annotating, and recognizing scene attributes[pdf][s2]Brown University833fa04463d90aab4a9fe2870d480f0b40df446e
svsSVSPedestrian Attribute Classification in Surveillance: Database and EvaluationPedestrian Attribute Classification in Surveillance: Database and Evaluation[pdf][s2]488e475eeb3bb39a145f23ede197cd3620f1d98a
texas_3dfrdTexas 3DFRDAnthropometric 3D Face RecognitionAnthropometric 3D Face Recognition[pdf][s2]2ce2560cf59db59ce313bbeb004e8ce55c5ce928
texas_3dfrdTexas 3DFRDTexas 3D Face Recognition DatabaseTexas 3D Face Recognition Database[pdf][s2]4d58f886f5150b2d5e48fd1b5a49e09799bf895d
tiny_facesTinyFaceLow-Resolution Face RecognitionLow-Resolution Face Recognition[pdf][s2]8990cdce3f917dad622e43e033db686b354d057c
tiny_imagesTiny Images80 million tiny images: a large dataset for non-parametric object and scene recognition80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition[pdf][s2]31b58ced31f22eab10bd3ee2d9174e7c14c27c01
towncenterTownCenterStable Multi-Target Tracking in Real-Time Surveillance VideoStable multi-target tracking in real-time surveillance video[pdf][s2]9361b784e73e9238d5cefbea5ac40d35d1e3103f
tud_brusselsTUD-BrusselsMulti-Cue Onboard Pedestrian DetectionMulti-cue onboard pedestrian detection[pdf][s2]6ad5a38df8dd4cdddd74f31996ce096d41219f72
tud_campusTUD-CampusPeople-Tracking-by-Detection and People-Detection-by-TrackingPeople-tracking-by-detection and people-detection-by-tracking[pdf][s2]3316521a5527c7700af8ae6aef32a79a8b83672c
tud_crossingTUD-CrossingPeople-Tracking-by-Detection and People-Detection-by-TrackingPeople-tracking-by-detection and people-detection-by-tracking[pdf][s2]3316521a5527c7700af8ae6aef32a79a8b83672c
tud_motionpairsTUD-MotionparisMulti-Cue Onboard Pedestrian DetectionMulti-cue onboard pedestrian detection[pdf][s2]6ad5a38df8dd4cdddd74f31996ce096d41219f72
tud_multiviewTUD-MultiviewMonocular 3D Pose Estimation and Tracking by DetectionMonocular 3D pose estimation and tracking by detection[pdf][s2]TU Darmstadt436f798d1a4e54e5947c1e7d7375c31b2bdb4064
tud_pedestrianTUD-PedestrianPeople-Tracking-by-Detection and People-Detection-by-TrackingPeople-tracking-by-detection and people-detection-by-tracking[pdf][s2]3316521a5527c7700af8ae6aef32a79a8b83672c
tud_stadtmitteTUD-StadtmitteMonocular 3D Pose Estimation and Tracking by DetectionMonocular 3D pose estimation and tracking by detection[pdf][s2]TU Darmstadt436f798d1a4e54e5947c1e7d7375c31b2bdb4064
tvhiTVHIHigh Five: Recognising human interactions in TV showsHigh Five: Recognising human interactions in TV shows[pdf][s2]3cd40bfa1ff193a96bde0207e5140a399476466c
uccsUCCSLarge scale unconstrained open set face databaseLarge scale unconstrained open set face database[pdf][s2]07fcbae86f7a3ad3ea1cf95178459ee9eaf77cb1
uccsUCCSUnconstrained Face Detection and Open-Set Face Recognition ChallengeUnconstrained Face Detection and Open-Set Face Recognition Challenge[pdf][s2]d4f1eb008eb80595bcfdac368e23ae9754e1e745
ucf_101UCF101UCF101: A Dataset of 101 Human Actions Classes From Videos in The WildUCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild[pdf][s2]b5f2846a506fc417e7da43f6a7679146d99c5e96
ucf_crowdUCF-CC-50Multi-Source Multi-Scale Counting in Extremely Dense Crowd ImagesMulti-source Multi-scale Counting in Extremely Dense Crowd Images[pdf][s2]32c801cb7fbeb742edfd94cccfca4934baec71da
ucf_selfieUCF SelfieHow to Take a Good Selfie?How to Take a Good Selfie?[pdf][s2]041d3eedf5e45ce5c5229f0181c5c576ed1fafd6
ufddUFDDPushing the Limits of Unconstrained Face Detection: a Challenge Dataset and Baseline ResultsPushing the Limits of Unconstrained Face Detection: a Challenge Dataset and Baseline Results[pdf][s2]377f2b65e6a9300448bdccf678cde59449ecd337
umbUMBUMB-DB: A Database of Partially Occluded 3D FacesUMB-DB: A database of partially occluded 3D faces[pdf][s2]16e8b0a1e8451d5f697b94c0c2b32a00abee1d52
umd_facesUMDUMDFaces: An Annotated Face Dataset for Training Deep NetworksUMDFaces: An annotated face dataset for training deep networks[pdf][s2]31b05f65405534a696a847dd19c621b7b8588263
umd_facesUMDThe Do's and Don'ts for CNN-based Face VerificationThe Do’s and Don’ts for CNN-Based Face Verification[pdf][s2]71b7fc715e2f1bb24c0030af8d7e7b6e7cd128a6
unbc_shoulder_painUNBC-McMaster PainPAINFUL DATA: The UNBC-McMaster Shoulder Pain Expression Archive DatabasePainful data: The UNBC-McMaster shoulder pain expression archive database[pdf][s2]Carnegie Mellon University56ffa7d906b08d02d6d5a12c7377a57e24ef3391
urban_tribesUrban TribesFrom Bikers to Surfers: Visual Recognition of Urban TribesFrom Bikers to Surfers: Visual Recognition of Urban Tribes[pdf][s2]774cbb45968607a027ae4729077734db000a1ec5
usedUSED Social Event DatasetUSED: A Large-scale Social Event Detection DatasetUSED: a large-scale social event detection dataset[pdf][s2]University of Trento8627f019882b024aef92e4eb9355c499c733e5b7
v47V47Re-identification of Pedestrians with Variable Occlusion and ScaleRe-identification of pedestrians with variable occlusion and scale[pdf][s2]Kingston University922e0a51a3b8c67c4c6ac09a577ff674cbd28b34
vadanaVADANAVADANA: A dense dataset for facial image analysisVADANA: A dense dataset for facial image analysis[pdf][s2]University of Delaware4563b46d42079242f06567b3f2e2f7a80cb3befe
vgg_celebs_in_placesCIPFaces in Places: Compound Query RetrievalFaces in Places: compound query retrieval[pdf][s2]7ebb153704706e457ab57b432793d2b6e5d12592
vgg_facesVGG FaceDeep Face RecognitionDeep Face Recognition[pdf][s2]162ea969d1929ed180cc6de9f0bf116993ff6e06
vgg_faces2VGG Face2VGGFace2: A dataset for recognising faces across pose and ageVGGFace2: A Dataset for Recognising Faces across Pose and Age[pdf][s2]eb027969f9310e0ae941e2adee2d42cdf07d938c
violent_flowsViolent FlowsViolent Flows: Real-Time Detection of Violent Crowd BehaviorViolent flows: Real-time detection of violent crowd behavior[pdf][s2]Open University of Israel5194cbd51f9769ab25260446b4fa17204752e799
viperVIPeREvaluating Appearance Models for Recognition, Reacquisition, and TrackingEvaluating Appearance Models for Recognition , Reacquisition , and Tracking[pdf][s2]6273b3491e94ea4dd1ce42b791d77bdc96ee73a8
visual_phrasesPhrasal RecognitionRecognition using Visual PhrasesRecognition using visual phrases[pdf][s2]University of Illinois, Urbana-Champaigne8de844fefd54541b71c9823416daa238be65546
vmuVMUCan Facial Cosmetics Affect the Matching Accuracy of Face Recognition Systems?Can facial cosmetics affect the matching accuracy of face recognition systems?[pdf][s2]West Virginia University37d6f0eb074d207b53885bd2eb78ccc8a04be597
vocVOCThe PASCAL Visual Object Classes (VOC) ChallengeThe Pascal Visual Object Classes (VOC) Challenge[pdf][s2]0ee1916a0cb2dc7d3add086b5f1092c3d4beb38a
vqaVQAVQA: Visual Question AnsweringVQA: Visual Question Answering[pdf][s2]01959ef569f74c286956024866c1d107099199f7
wardWARDRe-identify people in wide area camera networkRe-identify people in wide area camera network[pdf][s2]6f3c76b7c0bd8e1d122c6ea808a271fd4749c951
who_goes_thereWGTWho Goes There? Approaches to Mapping Facial Appearance DiversityWho goes there?: approaches to mapping facial appearance diversity[pdf][s2]University of Kentucky9b9bf5e623cb8af7407d2d2d857bc3f1b531c182
widerWIDERRecognize Complex Events from Static Images by Fusing Deep ChannelsRecognize complex events from static images by fusing deep channels[pdf][s2]356b431d4f7a2a0a38cf971c84568207dcdbf189
wider_attributeWIDER AttributeHuman Attribute Recognition by Deep Hierarchical ContextsHuman Attribute Recognition by Deep Hierarchical Contexts[pdf][s2]44d23df380af207f5ac5b41459c722c87283e1eb
wider_faceWIDER FACEWIDER FACE: A Face Detection BenchmarkWIDER FACE: A Face Detection Benchmark[pdf][s2]52d7eb0fbc3522434c13cc247549f74bb9609c5d
wildtrackWildTrackWILDTRACK: A Multi-camera HD Dataset for Dense Unscripted Pedestrian DetectionWILDTRACK : A Multi-camera HD Dataset for Dense Unscripted Pedestrian Detection[pdf][s2]77c81c13a110a341c140995bedb98101b9e84f7f
wlfdbWLFDBWLFDB: Weakly Labeled Face DatabasesWLFDB: Weakly Labeled Face Databases[pdf][s2]5ad4e9f947c1653c247d418f05dad758a3f9277b
yale_facesYaleFacesFrom Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and PoseFrom Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose[pdf][s2]18c72175ddbb7d5956d180b65a96005c100f6014
yale_facesYaleFacesAcquiring Linear Subspaces for Face Recognition under Variable LightingAcquiring linear subspaces for face recognition under variable lighting[pdf][s2]2ad0ee93d029e790ebb50574f403a09854b65b7e
yawddYawDDYawDD: A Yawning Detection DatasetYawDD: a yawning detection dataset[pdf][s2]a94cae786d515d3450d48267e12ca954aab791c4
yfcc_100mYFCC100MYFCC100M: The New Data in Multimedia ResearchYFCC100M: the new data in multimedia research[pdf][s2]010f0f4929e6a6644fb01f0e43820f91d0fad292
york_3dUOY 3D Face DatabaseThree-Dimensional Face Recognition: An Eigensurface ApproachThree-dimensional face recognition: an eigensurface approach[pdf][s2]19d1b811df60f86cbd5e04a094b07f32fff7a32a
youtube_facesYouTubeFacesFace Recognition in Unconstrained Videos with Matched Background SimilarityFace recognition in unconstrained videos with matched background similarity[pdf][s2]560e0e58d0059259ddf86fcec1fa7975dee6a868
youtube_makeupYMUCan Facial Cosmetics Affect the Matching Accuracy of Face Recognition Systems?Can facial cosmetics affect the matching accuracy of face recognition systems?[pdf][s2]West Virginia University37d6f0eb074d207b53885bd2eb78ccc8a04be597
youtube_makeupYMUAutomatic Facial Makeup Detection with Application in Face RecognitionAutomatic facial makeup detection with application in face recognition[pdf][s2]West Virginia Universityfcc6fe6007c322641796cb8792718641856a22a7
youtube_posesYouTube PosePersonalizing Human Video Pose EstimationPersonalizing Human Video Pose Estimation[pdf][s2]1c2802c2199b6d15ecefe7ba0c39bfe44363de38
youtube_celebritiesYouTube CelebritiesFace Tracking and Recognition with Visual Constraints in Real-World VideosFace tracking and recognition with visual constraints in real-world videos[pdf][s2]6204776d31359d129a582057c2d788a14f8aadeb
\ No newline at end of file +discovering, annotating, and recognizing scene attributes&sort=relevance" target="_blank">[s2]		Brown University833fa04463d90aab4a9fe2870d480f0b40df446e
svsSVSPedestrian Attribute Classification in Surveillance: Database and EvaluationPedestrian Attribute Classification in Surveillance: Database and Evaluation[pdf][s2]488e475eeb3bb39a145f23ede197cd3620f1d98a
texas_3dfrdTexas 3DFRDAnthropometric 3D Face RecognitionAnthropometric 3D Face Recognition[pdf][s2]2ce2560cf59db59ce313bbeb004e8ce55c5ce928
texas_3dfrdTexas 3DFRDTexas 3D Face Recognition DatabaseTexas 3D Face Recognition Database[pdf][s2]4d58f886f5150b2d5e48fd1b5a49e09799bf895d
tiny_facesTinyFaceLow-Resolution Face RecognitionLow-Resolution Face Recognition[pdf][s2]8990cdce3f917dad622e43e033db686b354d057c
tiny_imagesTiny Images80 million tiny images: a large dataset for non-parametric object and scene recognition80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition[pdf][s2]31b58ced31f22eab10bd3ee2d9174e7c14c27c01
towncenterTownCenterStable Multi-Target Tracking in Real-Time Surveillance VideoStable multi-target tracking in real-time surveillance video[pdf][s2]9361b784e73e9238d5cefbea5ac40d35d1e3103f
tud_brusselsTUD-BrusselsMulti-Cue Onboard Pedestrian DetectionMulti-cue onboard pedestrian detection[pdf][s2]6ad5a38df8dd4cdddd74f31996ce096d41219f72
tud_campusTUD-CampusPeople-Tracking-by-Detection and People-Detection-by-TrackingPeople-tracking-by-detection and people-detection-by-tracking[pdf][s2]3316521a5527c7700af8ae6aef32a79a8b83672c
tud_crossingTUD-CrossingPeople-Tracking-by-Detection and People-Detection-by-TrackingPeople-tracking-by-detection and people-detection-by-tracking[pdf][s2]3316521a5527c7700af8ae6aef32a79a8b83672c
tud_motionpairsTUD-MotionparisMulti-Cue Onboard Pedestrian DetectionMulti-cue onboard pedestrian detection[pdf][s2]6ad5a38df8dd4cdddd74f31996ce096d41219f72
tud_multiviewTUD-MultiviewMonocular 3D Pose Estimation and Tracking by DetectionMonocular 3D pose estimation and tracking by detection[pdf][s2]TU Darmstadt436f798d1a4e54e5947c1e7d7375c31b2bdb4064
tud_pedestrianTUD-PedestrianPeople-Tracking-by-Detection and People-Detection-by-TrackingPeople-tracking-by-detection and people-detection-by-tracking[pdf][s2]3316521a5527c7700af8ae6aef32a79a8b83672c
tud_stadtmitteTUD-StadtmitteMonocular 3D Pose Estimation and Tracking by DetectionMonocular 3D pose estimation and tracking by detection[pdf][s2]TU Darmstadt436f798d1a4e54e5947c1e7d7375c31b2bdb4064
tvhiTVHIHigh Five: Recognising human interactions in TV showsHigh Five: Recognising human interactions in TV shows[pdf][s2]3cd40bfa1ff193a96bde0207e5140a399476466c
uccsUCCSLarge scale unconstrained open set face databaseLarge scale unconstrained open set face database[pdf][s2]07fcbae86f7a3ad3ea1cf95178459ee9eaf77cb1
uccsUCCSUnconstrained Face Detection and Open-Set Face Recognition ChallengeUnconstrained Face Detection and Open-Set Face Recognition Challenge[pdf][s2]d4f1eb008eb80595bcfdac368e23ae9754e1e745
ucf_101UCF101UCF101: A Dataset of 101 Human Actions Classes From Videos in The WildUCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild[pdf][s2]b5f2846a506fc417e7da43f6a7679146d99c5e96
ucf_crowdUCF-CC-50Multi-Source Multi-Scale Counting in Extremely Dense Crowd ImagesMulti-source Multi-scale Counting in Extremely Dense Crowd Images[pdf][s2]32c801cb7fbeb742edfd94cccfca4934baec71da
ucf_selfieUCF SelfieHow to Take a Good Selfie?How to Take a Good Selfie?[pdf][s2]041d3eedf5e45ce5c5229f0181c5c576ed1fafd6
ufddUFDDPushing the Limits of Unconstrained Face Detection: a Challenge Dataset and Baseline ResultsPushing the Limits of Unconstrained Face Detection: a Challenge Dataset and Baseline Results[pdf][s2]377f2b65e6a9300448bdccf678cde59449ecd337
umbUMBUMB-DB: A Database of Partially Occluded 3D FacesUMB-DB: A database of partially occluded 3D faces[pdf][s2]16e8b0a1e8451d5f697b94c0c2b32a00abee1d52
umd_facesUMDUMDFaces: An Annotated Face Dataset for Training Deep NetworksUMDFaces: An annotated face dataset for training deep networks[pdf][s2]31b05f65405534a696a847dd19c621b7b8588263
umd_facesUMDThe Do's and Don'ts for CNN-based Face VerificationThe Do’s and Don’ts for CNN-Based Face Verification[pdf][s2]71b7fc715e2f1bb24c0030af8d7e7b6e7cd128a6
unbc_shoulder_painUNBC-McMaster PainPAINFUL DATA: The UNBC-McMaster Shoulder Pain Expression Archive DatabasePainful data: The UNBC-McMaster shoulder pain expression archive database[pdf][s2]Carnegie Mellon University Silicon Valley56ffa7d906b08d02d6d5a12c7377a57e24ef3391
urban_tribesUrban TribesFrom Bikers to Surfers: Visual Recognition of Urban TribesFrom Bikers to Surfers: Visual Recognition of Urban Tribes[pdf][s2]774cbb45968607a027ae4729077734db000a1ec5
usedUSED Social Event DatasetUSED: A Large-scale Social Event Detection DatasetUSED: a large-scale social event detection dataset[pdf][s2]University of Trento8627f019882b024aef92e4eb9355c499c733e5b7
v47V47Re-identification of Pedestrians with Variable Occlusion and ScaleRe-identification of pedestrians with variable occlusion and scale[pdf][s2]Kingston University922e0a51a3b8c67c4c6ac09a577ff674cbd28b34
vadanaVADANAVADANA: A dense dataset for facial image analysisVADANA: A dense dataset for facial image analysis[pdf][s2]University of Delaware4563b46d42079242f06567b3f2e2f7a80cb3befe
vgg_celebs_in_placesCIPFaces in Places: Compound Query RetrievalFaces in Places: compound query retrieval[pdf][s2]7ebb153704706e457ab57b432793d2b6e5d12592
vgg_facesVGG FaceDeep Face RecognitionDeep Face Recognition[pdf][s2]162ea969d1929ed180cc6de9f0bf116993ff6e06
vgg_faces2VGG Face2VGGFace2: A dataset for recognising faces across pose and ageVGGFace2: A Dataset for Recognising Faces across Pose and Age[pdf][s2]eb027969f9310e0ae941e2adee2d42cdf07d938c
violent_flowsViolent FlowsViolent Flows: Real-Time Detection of Violent Crowd BehaviorViolent flows: Real-time detection of violent crowd behavior[pdf][s2]Open University of Israel5194cbd51f9769ab25260446b4fa17204752e799
viperVIPeREvaluating Appearance Models for Recognition, Reacquisition, and TrackingEvaluating Appearance Models for Recognition , Reacquisition , and Tracking[pdf][s2]6273b3491e94ea4dd1ce42b791d77bdc96ee73a8
visual_phrasesPhrasal RecognitionRecognition using Visual PhrasesRecognition using visual phrases[pdf][s2]University of Illinois, Urbana-Champaigne8de844fefd54541b71c9823416daa238be65546
vmuVMUCan Facial Cosmetics Affect the Matching Accuracy of Face Recognition Systems?Can facial cosmetics affect the matching accuracy of face recognition systems?[pdf][s2]West Virginia University37d6f0eb074d207b53885bd2eb78ccc8a04be597
vocVOCThe PASCAL Visual Object Classes (VOC) ChallengeThe Pascal Visual Object Classes (VOC) Challenge[pdf][s2]0ee1916a0cb2dc7d3add086b5f1092c3d4beb38a
vqaVQAVQA: Visual Question AnsweringVQA: Visual Question Answering[pdf][s2]01959ef569f74c286956024866c1d107099199f7
wardWARDRe-identify people in wide area camera networkRe-identify people in wide area camera network[pdf][s2]6f3c76b7c0bd8e1d122c6ea808a271fd4749c951
who_goes_thereWGTWho Goes There? Approaches to Mapping Facial Appearance DiversityWho goes there?: approaches to mapping facial appearance diversity[pdf][s2]University of Kentucky9b9bf5e623cb8af7407d2d2d857bc3f1b531c182
widerWIDERRecognize Complex Events from Static Images by Fusing Deep ChannelsRecognize complex events from static images by fusing deep channels[pdf][s2]356b431d4f7a2a0a38cf971c84568207dcdbf189
wider_attributeWIDER AttributeHuman Attribute Recognition by Deep Hierarchical ContextsHuman Attribute Recognition by Deep Hierarchical Contexts[pdf][s2]44d23df380af207f5ac5b41459c722c87283e1eb
wider_faceWIDER FACEWIDER FACE: A Face Detection BenchmarkWIDER FACE: A Face Detection Benchmark[pdf][s2]52d7eb0fbc3522434c13cc247549f74bb9609c5d
wildtrackWildTrackWILDTRACK: A Multi-camera HD Dataset for Dense Unscripted Pedestrian DetectionWILDTRACK : A Multi-camera HD Dataset for Dense Unscripted Pedestrian Detection[pdf][s2]77c81c13a110a341c140995bedb98101b9e84f7f
wlfdbWLFDBWLFDB: Weakly Labeled Face DatabasesWLFDB: Weakly Labeled Face Databases[pdf][s2]5ad4e9f947c1653c247d418f05dad758a3f9277b
yale_facesYaleFacesFrom Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and PoseFrom Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose[pdf][s2]18c72175ddbb7d5956d180b65a96005c100f6014
yale_facesYaleFacesAcquiring Linear Subspaces for Face Recognition under Variable LightingAcquiring linear subspaces for face recognition under variable lighting[pdf][s2]2ad0ee93d029e790ebb50574f403a09854b65b7e
yawddYawDDYawDD: A Yawning Detection DatasetYawDD: a yawning detection dataset[pdf][s2]a94cae786d515d3450d48267e12ca954aab791c4
yfcc_100mYFCC100MYFCC100M: The New Data in Multimedia ResearchYFCC100M: the new data in multimedia research[pdf][s2]010f0f4929e6a6644fb01f0e43820f91d0fad292
york_3dUOY 3D Face DatabaseThree-Dimensional Face Recognition: An Eigensurface ApproachThree-dimensional face recognition: an eigensurface approach[pdf][s2]19d1b811df60f86cbd5e04a094b07f32fff7a32a
youtube_facesYouTubeFacesFace Recognition in Unconstrained Videos with Matched Background SimilarityFace recognition in unconstrained videos with matched background similarity[pdf][s2]560e0e58d0059259ddf86fcec1fa7975dee6a868
youtube_makeupYMUCan Facial Cosmetics Affect the Matching Accuracy of Face Recognition Systems?Can facial cosmetics affect the matching accuracy of face recognition systems?[pdf][s2]West Virginia University37d6f0eb074d207b53885bd2eb78ccc8a04be597
youtube_makeupYMUAutomatic Facial Makeup Detection with Application in Face RecognitionAutomatic facial makeup detection with application in face recognition[pdf][s2]West Virginia Universityfcc6fe6007c322641796cb8792718641856a22a7
youtube_posesYouTube PosePersonalizing Human Video Pose EstimationPersonalizing Human Video Pose Estimation[pdf][s2]1c2802c2199b6d15ecefe7ba0c39bfe44363de38
youtube_celebritiesYouTube CelebritiesFace Tracking and Recognition with Visual Constraints in Real-World VideosFace tracking and recognition with visual constraints in real-world videos[pdf][s2]6204776d31359d129a582057c2d788a14f8aadeb
erceERCeVideo Synopsis by Heterogeneous Multi-source CorrelationVideo Synopsis by Heterogeneous Multi-source Correlation[pdf][s2]b6c293f0420f7e945b5916ae44269fb53e139275
erceERCeLearning from Multiple Sources for Video SummarisationLearning from Multiple Sources for Video Summarisation[pdf][s2]287ddcb3db5562235d83aee318f318b8d5e43fb1
tisiTimes Square IntersectionVideo Synopsis by Heterogeneous Multi-source CorrelationVideo Synopsis by Heterogeneous Multi-source Correlation[pdf][s2]b6c293f0420f7e945b5916ae44269fb53e139275
tisiTimes Square IntersectionLearning from Multiple Sources for Video SummarisationLearning from Multiple Sources for Video Summarisation[pdf][s2]287ddcb3db5562235d83aee318f318b8d5e43fb1
laofiwLAOFIWTurning a Blind Eye: Explicit Removal of Biases and Variation from Deep Neural Network EmbeddingsTurning a Blind Eye: Explicit Removal of Biases and Variation from Deep Neural Network Embeddings[pdf][s2]4eab317b5ac436a949849ed286baa3de2a541eef
appa_realAPPA-REALApparent and Real Age Estimation in Still Images with Deep Residual Regressors on Appa-Real DatabaseApparent and Real Age Estimation in Still Images with Deep Residual Regressors on Appa-Real Database[pdf][s2]633c851ebf625ad7abdda2324e9de093cf623141
appa_realAPPA-REALFrom Apparent to Real Age: Gender, Age, Ethnic, Makeup, and Expression Bias Analysis in Real Age EstimationFrom Apparent to Real Age: Gender, Age, Ethnic, Makeup, and Expression Bias Analysis in Real Age Estimation[pdf][s2]7b92d1e53cc87f7a4256695de590098a2f30261e
mafaMAsked FAcesDetecting Masked Faces in the Wild with LLE-CNNsDetecting Masked Faces in the Wild with LLE-CNNs[pdf][s2]9cc8cf0c7d7fa7607659921b6ff657e17e135ecc
\ No newline at end of file diff --git a/scraper/reports/paper_title_report_no_location.html b/scraper/reports/paper_title_report_no_location.html index 59820fa8..923c98a6 100644 --- a/scraper/reports/paper_title_report_no_location.html +++ b/scraper/reports/paper_title_report_no_location.html @@ -1 +1 @@ -Papers with no location

Papers with no location

keynameour titlefound titleaddresss2 id
10k_US_adult_faces10K US Adult FacesThe intrinsic memorability of face imagesThe intrinsic memorability of face photographs.[pdf][s2]8b2dd5c61b23ead5ae5508bb8ce808b5ea266730
3d_rma3D-RMAAutomatic 3D Face AuthenticationAutomatic 3D face authentication[pdf][s2]2160788824c4c29ffe213b2cbeb3f52972d73f37
3dddb_unconstrained3D DynamicA 3D Dynamic Database for Unconstrained Face RecognitionA 3 D Dynamic Database for Unconstrained Face Recognition[pdf][s2]4d4bb462c9f1d4e4ab1e4aa6a75cc0bc71b38461
3dpes3DPeS3DPes: 3D People Dataset for Surveillance and Forensics3DPeS: 3D people dataset for surveillance and forensics[pdf][s2]2e8d0f1802e50cccfd3c0aabac0d0beab3a7846e
4dfab4DFAB4DFAB: A Large Scale 4D Facial Expression Database for Biometric Applications4DFAB: A Large Scale 4D Facial Expression Database for Biometric Applications[pdf][s2]9696ad8b164f5e10fcfe23aacf74bd6168aebb15
50_people_one_question50 People One QuestionMerging Pose Estimates Across Space and TimeMerging Pose Estimates Across Space and Time[pdf][s2]5753b2b5e442eaa3be066daa4a2ca8d8a0bb1725
adienceAdienceAge and Gender Estimation of Unfiltered FacesAge and Gender Estimation of Unfiltered Faces[pdf][s2]1be498d4bbc30c3bfd0029114c784bc2114d67c0
afadAFADOrdinal Regression with a Multiple Output CNN for Age EstimationOrdinal Regression with Multiple Output CNN for Age Estimation[pdf][s2]6618cff7f2ed440a0d2fa9e74ad5469df5cdbe4c
afew_vaAFEW-VAAFEW-VA database for valence and arousal estimation in-the-wildAFEW-VA database for valence and arousal estimation in-the-wild[pdf][s2]2624d84503bc2f8e190e061c5480b6aa4d89277a
affectnetAffectNetAffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the WildAffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild[pdf][s2]758d7e1be64cc668c59ef33ba8882c8597406e53
aflwAFLWAnnotated Facial Landmarks in the Wild: A Large-scale, Real-world Database for Facial Landmark LocalizationAnnotated Facial Landmarks in the Wild: A large-scale, real-world database for facial landmark localization[pdf][s2]a74251efa970b92925b89eeef50a5e37d9281ad0
afwAFWFace detection, pose estimation and landmark localization in the wildFace detection, pose estimation, and landmark localization in the wild[pdf][s2]0e986f51fe45b00633de9fd0c94d082d2be51406
agedbAgeDBAgeDB: the first manually collected, in-the-wild age databaseAgeDB: The First Manually Collected, In-the-Wild Age Database[pdf][s2]6dcf418c778f528b5792104760f1fbfe90c6dd6a
alert_airportALERT AirportA Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and DatasetsA Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets.[pdf][s2]6403117f9c005ae81f1e8e6d1302f4a045e3d99d
am_fedAM-FEDAffectiva MIT Facial Expression Dataset (AM-FED): Naturalistic and Spontaneous Facial Expressions Collected “In the Wild”Affectiva-MIT Facial Expression Dataset (AM-FED): Naturalistic and Spontaneous Facial Expressions Collected "In-the-Wild"[pdf][s2]47aeb3b82f54b5ae8142b4bdda7b614433e69b9a
apisAPiS1.0Pedestrian Attribute Classification in Surveillance: Database and EvaluationPedestrian Attribute Classification in Surveillance: Database and Evaluation[pdf][s2]488e475eeb3bb39a145f23ede197cd3620f1d98a
ar_facedbAR FaceThe AR Face DatabaseThe AR face database[pdf][s2]6d96f946aaabc734af7fe3fc4454cf8547fcd5ed
awe_earsAWE EarsEar Recognition: More Than a SurveyEar Recognition: More Than a Survey[pdf][s2]84fe5b4ac805af63206012d29523a1e033bc827e
b3d_acB3D(AC)A 3-D Audio-Visual Corpus of Affective CommunicationA 3-D Audio-Visual Corpus of Affective Communication[pdf][s2]d08cc366a4a0192a01e9a7495af1eb5d9f9e73ae
bbc_poseBBC PoseAutomatic and Efficient Human Pose Estimation for Sign Language VideosAutomatic and Efficient Human Pose Estimation for Sign Language Videos[pdf][s2]213a579af9e4f57f071b884aa872651372b661fd
bpadBPADDescribing People: A Poselet-Based Approach to Attribute ClassificationDescribing people: A poselet-based approach to attribute classification[pdf][s2]7808937b46acad36e43c30ae4e9f3fd57462853d
bfmBFMA 3D Face Model for Pose and Illumination Invariant Face RecognitionA 3D Face Model for Pose and Illumination Invariant Face Recognition[pdf][s2]639937b3a1b8bded3f7e9a40e85bd3770016cf3c
bio_idBioID FaceRobust Face Detection Using the Hausdorff DistanceRobust Face Detection Using the Hausdorff Distance[pdf][s2]4053e3423fb70ad9140ca89351df49675197196a
bosphorusThe BosphorusBosphorus Database for 3D Face AnalysisBosphorus Database for 3D Face Analysis[pdf][s2]2acf7e58f0a526b957be2099c10aab693f795973
bp4d_plusBP4D+Multimodal Spontaneous Emotion Corpus for Human Behavior AnalysisMultimodal Spontaneous Emotion Corpus for Human Behavior Analysis[pdf][s2]53ae38a6bb2b21b42bac4f0c4c8ed1f9fa02f9d4
bu_3dfeBU-3DFEA 3D Facial Expression Database For Facial Behavior ResearchA 3D facial expression database for facial behavior research[pdf][s2]cc589c499dcf323fe4a143bbef0074c3e31f9b60
buhmap_dbBUHMAP-DB Facial Feature Tracking and Expression Recognition for Sign LanguageFacial feature tracking and expression recognition for sign language[pdf][s2]014b8df0180f33b9fea98f34ae611c6447d761d2
cafeCAFEThe Child Affective Facial Expression (CAFE) Set: Validity and reliability from untrained adultsThe Child Affective Facial Expression (CAFE) set: validity and reliability from untrained adults[pdf][s2]20388099cc415c772926e47bcbbe554e133343d1
caltech_10k_web_facesCaltech 10K Web FacesPruning Training Sets for Learning of Object CategoriesPruning training sets for learning of object categories[pdf][s2]636b8ffc09b1b23ff714ac8350bb35635e49fa3c
caltech_pedestriansCaltech PedestriansPedestrian Detection: A BenchmarkPedestrian detection: A benchmark[pdf][s2]1dc35905a1deff8bc74688f2d7e2f48fd2273275
cas_pealCAS-PEALThe CAS-PEAL Large-Scale Chinese Face Database and Baseline EvaluationsThe CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations[pdf][s2]2485c98aa44131d1a2f7d1355b1e372f2bb148ad
casablancaCasablancaContext-aware {CNNs} for person head detectionContext-Aware CNNs for Person Head Detection[pdf][s2]0ceda9dae8b9f322df65ca2ef02caca9758aec6f
casia_webfaceCASIA WebfaceLearning Face Representation from ScratchLearning Face Representation from Scratch[pdf][s2]853bd61bc48a431b9b1c7cab10c603830c488e39
celebaCelebADeep Learning Face Attributes in the WildDeep Learning Face Attributes in the Wild[pdf][s2]6424b69f3ff4d35249c0bb7ef912fbc2c86f4ff4
celeba_plusCelebFaces+Deep Learning Face Representation from Predicting 10,000 ClassesDeep Learning Face Representation from Predicting 10,000 Classes[pdf][s2]177bc509dd0c7b8d388bb47403f28d6228c14b5c
cfdCFDThe Chicago face database: A free stimulus set of faces and norming dataThe Chicago face database: A free stimulus set of faces and norming data.[pdf][s2]4df3143922bcdf7db78eb91e6b5359d6ada004d2
chalearnChaLearnChaLearn Looking at People: A Review of Events and ResourcesChaLearn looking at people: A review of events and resources[pdf][s2]8d5998cd984e7cce307da7d46f155f9db99c6590
chokepointChokePointPatch-based Probabilistic Image Quality Assessment for Face Selection and Improved Video-based Face RecognitionPatch-based probabilistic image quality assessment for face selection and improved video-based face recognition[pdf][s2]0486214fb58ee9a04edfe7d6a74c6d0f661a7668
cityscapesCityscapesThe Cityscapes Dataset for Semantic Urban Scene UnderstandingThe Cityscapes Dataset for Semantic Urban Scene Understanding[pdf][s2]32cde90437ab5a70cf003ea36f66f2de0e24b3ab
cityscapesCityscapesThe Cityscapes DatasetThe Cityscapes Dataset[pdf][s2]5ffd74d2873b7cba2cbc5fd295cc7fbdedca22a2
clothing_co_parsingCCPClothing Co-Parsing by Joint Image Segmentation and LabelingClothing Co-parsing by Joint Image Segmentation and Labeling[pdf][s2]2bf8541199728262f78d4dced6fb91479b39b738
cmdpCMDPDistance Estimation of an Unknown Person from a PortraitDistance Estimation of an Unknown Person from a Portrait[pdf][s2]56ae6d94fc6097ec4ca861f0daa87941d1c10b70
cmu_pieCMU PIEThe CMU Pose, Illumination, and Expression DatabaseThe CMU Pose, Illumination, and Expression (PIE) Database[pdf][s2]4d423acc78273b75134e2afd1777ba6d3a398973
cocoCOCOMicrosoft COCO: Common Objects in ContextMicrosoft COCO: Common Objects in Context[pdf][s2]5e0f8c355a37a5a89351c02f174e7a5ddcb98683
coco_actionCOCO-aDescribing Common Human Visual Actions in ImagesDescribing Common Human Visual Actions in Images[pdf][s2]4946ba10a4d5a7d0a38372f23e6622bd347ae273
coco_qaCOCO QAExploring Models and Data for Image Question AnsweringExploring Models and Data for Image Question Answering[pdf][s2]35b0331dfcd2897abd5749b49ff5e2b8ba0f7a62
cofwCOFWRobust face landmark estimation under occlusionRobust Face Landmark Estimation under Occlusion[pdf][s2]2724ba85ec4a66de18da33925e537f3902f21249
cohn_kanadeCKComprehensive Database for Facial Expression AnalysisComprehensive Database for Facial Expression Analysis[pdf][s2]23fc83c8cfff14a16df7ca497661264fc54ed746
complex_activitiesOngoing Complex ActivitiesRecognition of Ongoing Complex Activities by Sequence Prediction over a Hierarchical Label SpaceRecognition of ongoing complex activities by sequence prediction over a hierarchical label space[pdf][s2]65355cbb581a219bd7461d48b3afd115263ea760
cuhk01CUHK01Human Reidentification with Transferred Metric LearningHuman Reidentification with Transferred Metric Learning[pdf][s2]44484d2866f222bbb9b6b0870890f9eea1ffb2d0
cuhk02CUHK02Locally Aligned Feature Transforms across ViewsLocally Aligned Feature Transforms across Views[pdf][s2]38b55d95189c5e69cf4ab45098a48fba407609b4
cuhk03CUHK03DeepReID: Deep Filter Pairing Neural Network for Person Re-identificationDeepReID: Deep Filter Pairing Neural Network for Person Re-identification[pdf][s2]6bd36e9fd0ef20a3074e1430a6cc601e6d407fc3
cvc_01_barcelonaCVC-01Adaptive Image Sampling and Windows Classification for On-board Pedestrian DetectionAdaptive Image Sampling and Windows Classification for On – board Pedestrian Detection[pdf][s2]57fe081950f21ca03b5b375ae3e84b399c015861
czech_news_agencyUFIUnconstrained Facial Images: Database for Face Recognition under Real-world ConditionsUnconstrained Facial Images: Database for Face Recognition Under Real-World Conditions[pdf][s2]4b4106614c1d553365bad75d7866bff0de6056ed
d3dfacsD3DFACSA FACS Valid 3D Dynamic Action Unit database with Applications to 3D Dynamic Morphable Facial ModellingA FACS valid 3D dynamic action unit database with applications to 3D dynamic morphable facial modeling[pdf][s2]070de852bc6eb275d7ca3a9cdde8f6be8795d1a3
dartmouth_childrenDartmouth ChildrenThe Dartmouth Database of Children's Faces: Acquisition and validation of a new face stimulus setThe Dartmouth Database of Children’s Faces: Acquisition and Validation of a New Face Stimulus Set[pdf][s2]4e6ee936eb50dd032f7138702fa39b7c18ee8907
data_61Data61 PedestrianA Multi-Modal Graphical Model for Scene AnalysisA Multi-modal Graphical Model for Scene Analysis[pdf][s2]563c940054e4b456661762c1ab858e6f730c3159
deep_fashionDeepFashionDeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich AnnotationsDeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations[pdf][s2]18010284894ed0edcca74e5bf768ee2e15ef7841
deep_fashionDeepFashionFashion Landmark Detection in the WildFashion Landmark Detection in the Wild[pdf][s2]4fefd1bc8dc4e0ab37ee3324ddfa43ad9d6a04a7
distance_nighttimeLong Distance Heterogeneous FaceNighttime Face Recognition at Long Distance: Cross-distance and Cross-spectral MatchingNighttime Face Recognition at Long Distance: Cross-Distance and Cross-Spectral Matching[pdf][s2]4156b7e88f2e0ab0a7c095b9bab199ae2b23bd06
duke_mtmcDuke MTMCPerformance Measures and a Data Set for Multi-Target, Multi-Camera TrackingPerformance Measures and a Data Set for Multi-Target, Multi-Camera Tracking[pdf][s2]27a2fad58dd8727e280f97036e0d2bc55ef5424c
emotio_netEmotioNet DatabaseEmotioNet: An Accurate, Real-Time Algorithm for the Automatic Annotation of a Million Facial Expressions in the WildEmotioNet: An Accurate, Real-Time Algorithm for the Automatic Annotation of a Million Facial Expressions in the Wild[pdf][s2]c900e0ad4c95948baaf0acd8449fde26f9b4952a
europersonsEuroCity PersonsThe EuroCity Persons Dataset: A Novel Benchmark for Object DetectionThe EuroCity Persons Dataset: A Novel Benchmark for Object Detection[pdf][s2]f0e17f27f029db4ad650ff278fe3c10ecb6cb0c4
expwExpWFrom Facial Expression Recognition to Interpersonal Relation PredictionFrom Facial Expression Recognition to Interpersonal Relation Prediction[pdf][s2]22f656d0f8426c84a33a267977f511f127bfd7f3
face_scrubFaceScrubA data-driven approach to cleaning large face datasetsA data-driven approach to cleaning large face datasets[pdf][s2]0d3bb75852098b25d90f31d2f48fd0cb4944702b
face_tracerFaceTracerFaceTracer: A Search Engine for Large Collections of Images with FacesFaceTracer: A Search Engine for Large Collections of Images with Faces[pdf][s2]4c170a0dcc8de75587dae21ca508dab2f9343974
face_tracerFaceTracerFace Swapping: Automatically Replacing Faces in PhotographsFace swapping: automatically replacing faces in photographs[pdf][s2]670637d0303a863c1548d5b19f705860a23e285c
faceplaceFace PlaceRecognizing disguised facesRecognizing disguised faces[pdf][s2]25474c21613607f6bb7687a281d5f9d4ffa1f9f3
fddbFDDBFDDB: A Benchmark for Face Detection in Unconstrained SettingsA Benchmark for Face Detection in Unconstrained Settings[pdf][s2]75da1df4ed319926c544eefe17ec8d720feef8c0
feiFEICaptura e Alinhamento de Imagens: Um Banco de Faces BrasileiroA new ranking method for principal components analysis and its application to face image analysis[pdf][s2]8b56e33f33e582f3e473dba573a16b598ed9bcdc
feretFERETThe FERET Verification Testing Protocol for Face Recognition AlgorithmsThe FERET Verification Testing Protocol for Face Recognition Algorithms[pdf][s2]0c4a139bb87c6743c7905b29a3cfec27a5130652
feretFERETThe FERET Evaluation Methodology for Face-Recognition AlgorithmsThe FERET Evaluation Methodology for Face-Recognition Algorithms[pdf][s2]0f0fcf041559703998abf310e56f8a2f90ee6f21
feretFERETFERET ( Face Recognition Technology ) Recognition Algorithm Development and Test ResultsFERET ( Face Recognition Technology ) Recognition Algorithm Development and Test Results[pdf][s2]31de9b3dd6106ce6eec9a35991b2b9083395fd0b
feretFERETThe FERET database and evaluation procedure for face-recognition algorithmsThe FERET database and evaluation procedure for face-recognition algorithms[pdf][s2]dc8b25e35a3acb812beb499844734081722319b4
ferplusFER+Training Deep Networks for Facial Expression Recognition with Crowd-Sourced Label DistributionTraining deep networks for facial expression recognition with crowd-sourced label distribution[pdf][s2]298cbc3dfbbb3a20af4eed97906650a4ea1c29e0
fiaCMU FiAThe CMU Face In Action (FIA) DatabaseThe CMU Face In Action (FIA) Database[pdf][s2]47662d1a368daf70ba70ef2d59eb6209f98b675d
fiw_300300-WA semi-automatic methodology for facial landmark annotationA Semi-automatic Methodology for Facial Landmark Annotation[pdf][s2]013909077ad843eb6df7a3e8e290cfd5575999d2
fiw_300300-W300 Faces in-the-Wild Challenge: The first facial landmark localization Challenge300 Faces in-the-Wild Challenge: The First Facial Landmark Localization Challenge[pdf][s2]044d9a8c61383312cdafbcc44b9d00d650b21c70
fiw_300300-W300 faces In-the-wild challenge: Database and results300 Faces In-The-Wild Challenge: database and results[pdf][s2]e4754afaa15b1b53e70743880484b8d0736990ff
geofacesGeoFacesGeoFaceExplorer: Exploring the Geo-Dependence of Facial AttributesGeoFaceExplorer: exploring the geo-dependence of facial attributes[pdf][s2]17b46e2dad927836c689d6787ddb3387c6159ece
georgia_tech_face_databaseGeorgia Tech FaceMaximum likelihood training of the embedded HMM for face detection and recognitionMAXIMUM 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[pdf][s2]3dc3f0b64ef80f573e3a5f96e456e52ee980b877
gfwGrouping Face in the WildMerge or Not? Learning to Group Faces via Imitation LearningMerge or Not? Learning to Group Faces via Imitation Learning[pdf][s2]e58dd160a76349d46f881bd6ddbc2921f08d1050
grazGraz PedestrianWeak Hypotheses and Boosting for Generic Object Detection and RecognitionWeak Hypotheses and Boosting for Generic Object Detection and Recognition[pdf][s2]0c91808994a250d7be332400a534a9291ca3b60e
h3dH3DPoselets: Body Part Detectors Trained Using 3D Human Pose AnnotationsPoselets: Body part detectors trained using 3D human pose annotations[pdf][s2]2830fb5282de23d7784b4b4bc37065d27839a412
hda_plusHDA+The HDA+ data set for research on fully automated re-identification systemsThe HDA+ Data Set for Research on Fully Automated Re-identification Systems[pdf][s2]8f02ec0be21461fbcedf51d864f944cfc42c875f
hda_plusHDA+A Multi-camera video data set for research on High-Definition surveillanceHDA dataset-DRAFT 1 A Multi-camera video data set for research on High-Definition surveillance[pdf][s2]bd88bb2e4f351352d88ee7375af834360e223498
helenHelenInteractive Facial Feature LocalizationInteractive Facial Feature Localization[pdf][s2]95f12d27c3b4914e0668a268360948bce92f7db3
hi4d_adsipHi4D-ADSIPHi4D-ADSIP 3-D dynamic facial articulation databaseHi4D-ADSIP 3-D dynamic facial articulation database[pdf][s2]a8d0b149c2eadaa02204d3e4356fbc8eccf3b315
hipsterwarsHipsterwarsHipster Wars: Discovering Elements of Fashion StylesHipster Wars: Discovering Elements of Fashion Styles[pdf][s2]04c2cda00e5536f4b1508cbd80041e9552880e67
hollywood_headsetHollywoodHeadsContext-aware CNNs for person head detectionContext-Aware CNNs for Person Head Detection[pdf][s2]0ceda9dae8b9f322df65ca2ef02caca9758aec6f
hrt_transgenderHRT TransgenderIs the Eye Region More Reliable Than the Face? A Preliminary Study of Face-based Recognition on a Transgender DatasetIs the eye region more reliable than the face? A preliminary study of face-based recognition on a transgender dataset[pdf][s2]137aa2f891d474fce1e7a1d1e9b3aefe21e22b34
ibm_difIBM Diversity in FacesDiversity in FacesDiversity in Faces[pdf][s2]2b89de1d81cee50552f10e26e865df3365e9bc88
ifadIFADIndian Face Age Database: A Database for Face Recognition with Age VariationIndian Face Age Database : A Database for Face Recognition with Age Variation[pdf][s2]55c40cbcf49a0225e72d911d762c27bb1c2d14aa
ifdbIFDBIranian Face Database and Evaluation with a New Detection AlgorithmIranian Face Database and Evaluation with a New Detection Algorithm[pdf][s2]066d71fcd997033dce4ca58df924397dfe0b5fd1
iit_dehli_earIIT Dehli EarAutomated human identification using ear imagingAutomated Human Identification Using Ear Imaging[pdf][s2]faf40ce28857aedf183e193486f5b4b0a8c478a2
ijb_cIJB-CIARPA Janus Benchmark-B Face DatasetIARPA Janus Benchmark-B Face Dataset[pdf][s2]0cb2dd5f178e3a297a0c33068961018659d0f443
ijb_cIJB-CPushing the Frontiers of Unconstrained Face Detection and Recognition: IARPA Janus Benchmark APushing the frontiers of unconstrained face detection and recognition: IARPA Janus Benchmark A[pdf][s2]140c95e53c619eac594d70f6369f518adfea12ef
ijb_cIJB-CIARPA Janus Benchmark CIARPA Janus Benchmark - C: Face Dataset and Protocol[pdf][s2]57178b36c21fd7f4529ac6748614bb3374714e91
ilids_mctsi-LIDS Multiple-CameraImagery Library for Intelligent Detection Systems: The i-LIDS User GuideImagery Library for Intelligent Detection Systems (i-LIDS); A Standard for Testing Video Based Detection Systems[pdf][s2]0297448f3ed948e136bb06ceff10eccb34e5bb77
ilids_vid_reidiLIDS-VIDPerson Re-Identi cation by Video RankingPerson Re-identification by Video Ranking[pdf][s2]98bb029afe2a1239c3fdab517323066f0957b81b
imdb_faceIMDb FaceThe Devil of Face Recognition is in the NoiseThe Devil of Face Recognition is in the Noise[pdf][s2]9e31e77f9543ab42474ba4e9330676e18c242e72
imdb_wikiIMDBDeep expectation of real and apparent age from a single image without facial landmarksDeep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks[pdf][s2]10195a163ab6348eef37213a46f60a3d87f289c5
imdb_wikiIMDBDEX: Deep EXpectation of apparent age from a single imageDEX: Deep EXpectation of Apparent Age from a Single Image[pdf][s2]8355d095d3534ef511a9af68a3b2893339e3f96b
immediacyImmediacyMulti-task Recurrent Neural Network for Immediacy PredictionMulti-task Recurrent Neural Network for Immediacy Prediction[pdf][s2]1e3df3ca8feab0b36fd293fe689f93bb2aaac591
imsituimSituSituation Recognition: Visual Semantic Role Labeling for Image UnderstandingSituation Recognition: Visual Semantic Role Labeling for Image Understanding[pdf][s2]51eba481dac6b229a7490f650dff7b17ce05df73
jaffeJAFFECoding Facial Expressions with Gabor WaveletsCoding Facial Expressions with Gabor Wavelets[pdf][s2]45c31cde87258414f33412b3b12fc5bec7cb3ba9
jiku_mobileJiku Mobile Video DatasetThe Jiku Mobile Video DatasetThe jiku mobile video dataset[pdf][s2]d178cde92ab3dc0dd2ebee5a76a33d556c39448b
jpl_poseJPL-Interaction datasetFirst-Person Activity Recognition: What Are They Doing to Me?First-Person Activity Recognition: What Are They Doing to Me?[pdf][s2]1aad2da473888cb7ebc1bfaa15bfa0f1502ce005
kin_faceUB KinFaceUnderstanding Kin Relationships in a PhotoUnderstanding Kin Relationships in a Photo[pdf][s2]08f6745bc6c1b0fb68953ea61054bdcdde6d2fc7
kin_faceUB KinFaceKinship Verification through Transfer LearningKinship verification through transfer learning[pdf][s2]4793f11fbca4a7dba898b9fff68f70d868e2497c
kittiKITTIVision meets Robotics: The KITTI DatasetVision meets robotics: The KITTI dataset[pdf][s2]026e3363b7f76b51cc711886597a44d5f1fd1de2
lagLAGLarge Age-Gap Face Verification by Feature Injection in Deep NetworksLarge age-gap face verification by feature injection in deep networks[pdf][s2]0d2dd4fc016cb6a517d8fb43a7cc3ff62964832e
large_scale_person_searchLarge Scale Person SearchEnd-to-End Deep Learning for Person SearchEnd-to-End Deep Learning for Person Search[pdf][s2]2161f6b7ee3c0acc81603b01dc0df689683577b9
leeds_sports_poseLeeds Sports PoseClustered Pose and Nonlinear Appearance Models for Human Pose EstimationClustered Pose and Nonlinear Appearance Models for Human Pose Estimation[pdf][s2]4b1d23d17476fcf78f4cbadf69fb130b1aa627c0
lfpwLFWPLocalizing Parts of Faces Using a Consensus of ExemplarsLocalizing Parts of Faces Using a Consensus of Exemplars[pdf][s2]140438a77a771a8fb656b39a78ff488066eb6b50
lfwLFWLabeled Faces in the Wild: Updates and New Reporting ProceduresLabeled Faces in the Wild : Updates and New Reporting Procedures[pdf][s2]2d3482dcff69c7417c7b933f22de606a0e8e42d4
lfwLFWLabeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained EnvironmentsLabeled Faces in the Wild : A Database for Studying Face Recognition in Unconstrained Environments[pdf][s2]370b5757a5379b15e30d619e4d3fb9e8e13f3256
lfwLFWLabeled Faces in the Wild: A SurveyLabeled Faces in the Wild : A Survey[pdf][s2]7de6e81d775e9cd7becbfd1bd685f4e2a5eebb22
lfw_a#N/AEffective Unconstrained Face Recognition by Combining Multiple Descriptors and Learned Background StatisticsEffective Unconstrained Face Recognition by Combining Multiple Descriptors and Learned Background Statistics[pdf][s2]133f01aec1534604d184d56de866a4bd531dac87
m2vtsdb_extendedxm2vtsdbXM2VTSDB: The Extended M2VTS DatabaseXm2vtsdb: the Extended M2vts Database[pdf][s2]b62628ac06bbac998a3ab825324a41a11bc3a988
maflMAFLFacial Landmark Detection by Deep Multi-task LearningFacial Landmark Detection by Deep Multi-task Learning[pdf][s2]8a3c5507237957d013a0fe0f082cab7f757af6ee
maflMAFLLearning Deep Representation for Face Alignment with Auxiliary AttributesLearning Deep Representation for Face Alignment with Auxiliary Attributes[pdf][s2]a0fd85b3400c7b3e11122f44dc5870ae2de9009a
malfMALFFine-grained Evaluation on Face Detection in the Wild.Fine-grained evaluation on face detection in the wild[pdf][s2]45e616093a92e5f1e61a7c6037d5f637aa8964af
mapillaryMapillaryThe Mapillary Vistas Dataset for Semantic Understanding of Street ScenesThe Mapillary Vistas Dataset for Semantic Understanding of Street Scenes[pdf][s2]79828e6e9f137a583082b8b5a9dfce0c301989b8
market_1501Market 1501Scalable Person Re-identification: A BenchmarkScalable Person Re-identification: A Benchmark[pdf][s2]4308bd8c28e37e2ed9a3fcfe74d5436cce34b410
market1203Market 1203Orientation Driven Bag of Appearances for Person Re-identificationOrientation Driven Bag of Appearances for Person Re-identification[pdf][s2]a7fe834a0af614ce6b50dc093132b031dd9a856b
marsMARSMARS: A Video Benchmark for Large-Scale Person Re-identificationMARS: A Video Benchmark for Large-Scale Person Re-Identification[pdf][s2]c0387e788a52f10bf35d4d50659cfa515d89fbec
mcgillMcGill Real WorldHierarchical Temporal Graphical Model for Head Pose Estimation and Subsequent Attribute Classification in Real-World VideosHierarchical temporal graphical model for head pose estimation and subsequent attribute classification in real-world videos[pdf][s2]a5a3bc3e5e9753769163cb30b16dbd12e266b93e
megaageMegaAgeQuantifying Facial Age by Posterior of Age ComparisonsQuantifying Facial Age by Posterior of Age Comparisons[pdf][s2]d80a3d1f3a438e02a6685e66ee908446766fefa9
megafaceMegaFaceLevel Playing Field for Million Scale Face RecognitionLevel Playing Field for Million Scale Face Recognition[pdf][s2]28d4e027c7e90b51b7d8908fce68128d1964668a
megafaceMegaFaceThe MegaFace Benchmark: 1 Million Faces for Recognition at ScaleThe MegaFace Benchmark: 1 Million Faces for Recognition at Scale[pdf][s2]96e0cfcd81cdeb8282e29ef9ec9962b125f379b0
mit_cbclMIT CBCLComponent-based Face Recognition with 3D Morphable ModelsComponent-Based Face Recognition with 3D Morphable Models[pdf][s2]079a0a3bf5200994e1f972b1b9197bf2f90e87d4
mmi_facial_expressionMMI Facial Expression DatasetWEB-BASED DATABASE FOR FACIAL EXPRESSION ANALYSISWeb-based database for facial expression analysis[pdf][s2]2a75f34663a60ab1b04a0049ed1d14335129e908
moments_in_timeMoments in TimeMoments in Time Dataset: one million videos for event understandingMoments in Time Dataset: one million videos for event understanding[pdf][s2]a5a44a32a91474f00a3cda671a802e87c899fbb4
morphMORPH CommercialMORPH: A Longitudinal Image Database of Normal Adult Age-ProgressionMORPH: a longitudinal image database of normal adult age-progression[pdf][s2]9055b155cbabdce3b98e16e5ac9c0edf00f9552f
morph_ncMORPH Non-CommercialMORPH: A Longitudinal Image Database of Normal Adult Age-ProgressionMORPH: a longitudinal image database of normal adult age-progression[pdf][s2]9055b155cbabdce3b98e16e5ac9c0edf00f9552f
motMOTEvaluating Multiple Object Tracking Performance: The CLEAR MOT MetricsEvaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics[pdf][s2]2258e01865367018ed6f4262c880df85b94959f8
motMOTPerformance Measures and a Data Set for Multi-Target, Multi-Camera TrackingPerformance Measures and a Data Set for Multi-Target, Multi-Camera Tracking[pdf][s2]27a2fad58dd8727e280f97036e0d2bc55ef5424c
mpi_largeLarge MPI Facial ExpressionThe MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial ExpressionsThe MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial Expressions[pdf][s2]ea050801199f98a1c7c1df6769f23f658299a3ae
mpi_smallSmall MPI Facial ExpressionThe MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial ExpressionsThe MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial Expressions[pdf][s2]ea050801199f98a1c7c1df6769f23f658299a3ae
mpii_gazeMPIIGazeAppearance-based Gaze Estimation in the WildAppearance-based gaze estimation in the wild[pdf][s2]0df0d1adea39a5bef318b74faa37de7f3e00b452
mpii_human_poseMPII Human Pose2D Human Pose Estimation: New Benchmark and State of the Art Analysis2D Human Pose Estimation: New Benchmark and State of the Art Analysis[pdf][s2]3325860c0c82a93b2eac654f5324dd6a776f609e
mr2MR2The MR2: A multi-racial mega-resolution database of facial stimuliThe MR2: A multi-racial, mega-resolution database of facial stimuli.[pdf][s2]578d4ad74818086bb64f182f72e2c8bd31e3d426
mrp_droneMRP DroneInvestigating Open-World Person Re-identification Using a DroneInvestigating Open-World Person Re-identification Using a Drone[pdf][s2]ad01687649d95cd5b56d7399a9603c4b8e2217d7
mscelebMsCelebMS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face RecognitionMS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition[pdf][s2]291265db88023e92bb8c8e6390438e5da148e8f5
msmt_17MSMT17Person Transfer GAN to Bridge Domain Gap for Person Re-IdentificationPerson Transfer GAN to Bridge Domain Gap for Person Re-Identification[pdf][s2]ec792ad2433b6579f2566c932ee414111e194537
mtflMTFLFacial Landmark Detection by Deep Multi-task LearningFacial Landmark Detection by Deep Multi-task Learning[pdf][s2]8a3c5507237957d013a0fe0f082cab7f757af6ee
mtflMTFLLearning Deep Representation for Face Alignment with Auxiliary AttributesLearning Deep Representation for Face Alignment with Auxiliary Attributes[pdf][s2]a0fd85b3400c7b3e11122f44dc5870ae2de9009a
multi_pieMULTIPIEMulti-PIEThe CMU Pose, Illumination, and Expression (PIE) Database[pdf][s2]4d423acc78273b75134e2afd1777ba6d3a398973
names_and_facesNews DatasetNames and FacesNames and faces in the news[pdf][s2]2fda164863a06a92d3a910b96eef927269aeb730
nova_emotionsNovaemötions DatasetCrowdsourcing facial expressions for affective-interactionCrowdsourcing facial expressions for affective-interaction[pdf][s2]c06b13d0ec3f5c43e2782cd22542588e233733c3
nudedetectionNude DetectionA Bag-of-Features Approach based on Hue-SIFT Descriptor for Nude DetectionA bag-of-features approach based on Hue-SIFT descriptor for nude detection[pdf][s2]7ace44190729927e5cb0dd5d363fcae966fe13f7
orlORLParameterisation of a Stochastic Model for Human Face IdentificationParameterisation of a stochastic model for human face identification[pdf][s2]55206f0b5f57ce17358999145506cd01e570358c
penn_fudanPenn FudanObject Detection Combining Recognition and SegmentationObject Detection Combining Recognition and Segmentation[pdf][s2]3394168ff0719b03ff65bcea35336a76b21fe5e4
petaPETAPedestrian Attribute Recognition At Far DistancePedestrian Attribute Recognition At Far Distance[pdf][s2]2a4bbee0b4cf52d5aadbbc662164f7efba89566c
petsPETS 2017PETS 2017: Dataset and ChallengePETS 2017: Dataset and Challenge[pdf][s2]22909dd19a0ec3b6065334cb5be5392cb24d839d
pilot_parliamentPPBGender Shades: Intersectional Accuracy Disparities in Commercial Gender ClassificationGender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification[pdf][s2]18858cc936947fc96b5c06bbe3c6c2faa5614540
pipaPIPABeyond Frontal Faces: Improving Person Recognition Using Multiple CuesBeyond frontal faces: Improving Person Recognition using multiple cues[pdf][s2]0a85bdff552615643dd74646ac881862a7c7072d
pkuPKUSwiss-System Based Cascade Ranking for Gait-based Person Re-identificationSwiss-System Based Cascade Ranking for Gait-Based Person Re-Identification[pdf][s2]f6c8d5e35d7e4d60a0104f233ac1a3ab757da53f
pku_reidPKU-ReidOrientation driven bag of appearances for person re-identificationOrientation Driven Bag of Appearances for Person Re-identification[pdf][s2]a7fe834a0af614ce6b50dc093132b031dd9a856b
pornodb#N/APooling in Image Representation: the Visual Codeword Point of ViewPooling in image representation: The visual codeword point of view[pdf][s2]b92a1ed9622b8268ae3ac9090e25789fc41cc9b8
precariousPrecariousExpecting the Unexpected: Training Detectors for Unusual Pedestrians With Adversarial ImpostersExpecting the Unexpected: Training Detectors for Unusual Pedestrians with Adversarial Imposters[pdf][s2]9e5378e7b336c89735d3bb15cf67eff96f86d39a
pridPRIDPerson Re-Identification by Descriptive and Discriminative ClassificationPerson Re-identification by Descriptive and Discriminative Classification[pdf][s2]16c7c31a7553d99f1837fc6e88e77b5ccbb346b8
prwPRWPerson Re-identification in the WildPerson Re-identification in the Wild[pdf][s2]0b84f07af44f964817675ad961def8a51406dd2e
psuPSUVision-based Analysis of Small Groups in Pedestrian CrowdsVision-Based Analysis of Small Groups in Pedestrian Crowds[pdf][s2]066000d44d6691d27202896691f08b27117918b9
pubfigPubFigAttribute and Simile Classifiers for Face VerificationAttribute and simile classifiers for face verification[pdf][s2]759a3b3821d9f0e08e0b0a62c8b693230afc3f8d
put_facePut FaceThe PUT face databaseThe put face database[pdf][s2]ae0aee03d946efffdc7af2362a42d3750e7dd48a
qmul_surv_faceQMUL-SurvFaceSurveillance Face Recognition ChallengeSurveillance Face Recognition Challenge[pdf][s2]c866a2afc871910e3282fd9498dce4ab20f6a332
rafdRaFDPresentation and validation of the Radboud Faces DatabasePresentation and validation of the Radboud Faces Database[pdf][s2]3765df816dc5a061bc261e190acc8bdd9d47bec0
raidRAiDConsistent Re-identification in a Camera NetworkConsistent Re-identification in a Camera Network[pdf][s2]09d78009687bec46e70efcf39d4612822e61cb8c
rap_pedestrianRAPA Richly Annotated Dataset for Pedestrian Attribute RecognitionA Richly Annotated Dataset for Pedestrian Attribute Recognition[pdf][s2]221c18238b829c12b911706947ab38fd017acef7
reseedReSEEDReSEED: Social Event dEtection DatasetReSEED: social event dEtection dataset[pdf][s2]54983972aafc8e149259d913524581357b0f91c3
saivtSAIVT SoftBioA Database for Person Re-Identification in Multi-Camera Surveillance NetworksA Database for Person Re-Identification in Multi-Camera Surveillance Networks[pdf][s2]22646e00a7ba34d1b5fbe3b1efcd91a1e1be3c2b
sarc3dSarc3DSARC3D: a new 3D body model for People Tracking and Re-identificationSARC3D: A New 3D Body Model for People Tracking and Re-identification[pdf][s2]e27ef52c641c2b5100a1b34fd0b819e84a31b4df
scfaceSCfaceSCface – surveillance cameras face databaseSCface – surveillance cameras face database[pdf][s2]29a705a5fa76641e0d8963f1fdd67ee4c0d92d3d
scut_fbpSCUT-FBPSCUT-FBP: A Benchmark Dataset for Facial Beauty PerceptionSCUT-FBP: A Benchmark Dataset for Facial Beauty Perception[pdf][s2]bd26dabab576adb6af30484183c9c9c8379bf2e0
scut_headSCUT HEADDetecting Heads using Feature Refine Net and Cascaded Multi-scale ArchitectureDetecting Heads using Feature Refine Net and Cascaded Multi-scale Architecture[pdf][s2]d3200d49a19a4a4e4e9745ee39649b65d80c834b
sdu_vidSDU-VIDA Spatio-Temporal Appearance Representation for Video-Based Pedestrian Re-IdentificationA Spatio-Temporal Appearance Representation for Video-Based Pedestrian Re-Identification[pdf][s2]3b4ec8af470948a72a6ed37a9fd226719a874ebc
sdu_vidSDU-VIDLocal descriptors encoded by Fisher vectors for person re-identificationLocal Descriptors Encoded by Fisher Vectors for Person Re-identification[pdf][s2]46a01565e6afe7c074affb752e7069ee3bf2e4ef
sdu_vidSDU-VIDPerson reidentification by video rankingPerson Re-identification by Video Ranking[pdf][s2]98bb029afe2a1239c3fdab517323066f0957b81b
social_relationSocial RelationLearning Social Relation Traits from Face ImagesLearning Social Relation Traits from Face Images[pdf][s2]2a171f8d14b6b8735001a11c217af9587d095848
sotonSOTON HiDOn a Large Sequence-Based Human Gait DatabaseOn a large sequence-based human gait database[pdf][s2]4f93cd09785c6e77bf4bc5a788e079df524c8d21
sports_videos_in_the_wildSVWSports Videos in the Wild (SVW): A Video Dataset for Sports AnalysisSports Videos in the Wild (SVW): A video dataset for sports analysis[pdf][s2]1a40092b493c6b8840257ab7f96051d1a4dbfeb2
stair_actionsSTAIR ActionSTAIR Actions: A Video Dataset of Everyday Home ActionsSTAIR Actions: A Video Dataset of Everyday Home Actions[pdf][s2]d3f5a1848b0028d8ab51d0b0673732cad2e3c8c9
stanford_droneStanford DroneLearning Social Etiquette: Human Trajectory Prediction In Crowded ScenesSocial LSTM: Human Trajectory Prediction in Crowded Spaces[pdf][s2]570f37ed63142312e6ccdf00ecc376341ec72b9f
stickmen_buffyBuffy StickmenClustered Pose and Nonlinear Appearance Models for Human Pose EstimationClustered Pose and Nonlinear Appearance Models for Human Pose Estimation[pdf][s2]4b1d23d17476fcf78f4cbadf69fb130b1aa627c0
stickmen_buffyBuffy StickmenLearning to Parse Images of Articulated ObjectsLearning to parse images of articulated bodies[pdf][s2]6dd0597f8513dc100cd0bc1b493768cde45098a9
stickmen_familyWe Are Family StickmenWe Are Family: Joint Pose Estimation of Multiple PersonsWe Are Family: Joint Pose Estimation of Multiple Persons[pdf][s2]0dc11a37cadda92886c56a6fb5191ded62099c28
stickmen_pascalStickmen PASCALClustered Pose and Nonlinear Appearance Models for Human Pose EstimationLearning to parse images of articulated bodies[pdf][s2]6dd0597f8513dc100cd0bc1b493768cde45098a9
stickmen_pascalStickmen PASCALLearning to Parse Images of Articulated ObjectsLearning to parse images of articulated bodies[pdf][s2]6dd0597f8513dc100cd0bc1b493768cde45098a9
sun_attributesSUNThe SUN Attribute Database: Beyond Categories for Deeper Scene UnderstandingThe SUN Attribute Database: Beyond Categories for Deeper Scene Understanding[pdf][s2]66e6f08873325d37e0ec20a4769ce881e04e964e
svsSVSPedestrian Attribute Classification in Surveillance: Database and EvaluationPedestrian Attribute Classification in Surveillance: Database and Evaluation[pdf][s2]488e475eeb3bb39a145f23ede197cd3620f1d98a
texas_3dfrdTexas 3DFRDAnthropometric 3D Face RecognitionAnthropometric 3D Face Recognition[pdf][s2]2ce2560cf59db59ce313bbeb004e8ce55c5ce928
texas_3dfrdTexas 3DFRDTexas 3D Face Recognition DatabaseTexas 3D Face Recognition Database[pdf][s2]4d58f886f5150b2d5e48fd1b5a49e09799bf895d
tiny_facesTinyFaceLow-Resolution Face RecognitionLow-Resolution Face Recognition[pdf][s2]8990cdce3f917dad622e43e033db686b354d057c
tiny_imagesTiny Images80 million tiny images: a large dataset for non-parametric object and scene recognition80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition[pdf][s2]31b58ced31f22eab10bd3ee2d9174e7c14c27c01
towncenterTownCenterStable Multi-Target Tracking in Real-Time Surveillance VideoStable multi-target tracking in real-time surveillance video[pdf][s2]9361b784e73e9238d5cefbea5ac40d35d1e3103f
tud_brusselsTUD-BrusselsMulti-Cue Onboard Pedestrian DetectionMulti-cue onboard pedestrian detection[pdf][s2]6ad5a38df8dd4cdddd74f31996ce096d41219f72
tud_campusTUD-CampusPeople-Tracking-by-Detection and People-Detection-by-TrackingPeople-tracking-by-detection and people-detection-by-tracking[pdf][s2]3316521a5527c7700af8ae6aef32a79a8b83672c
tud_crossingTUD-CrossingPeople-Tracking-by-Detection and People-Detection-by-TrackingPeople-tracking-by-detection and people-detection-by-tracking[pdf][s2]3316521a5527c7700af8ae6aef32a79a8b83672c
tud_motionpairsTUD-MotionparisMulti-Cue Onboard Pedestrian DetectionMulti-cue onboard pedestrian detection[pdf][s2]6ad5a38df8dd4cdddd74f31996ce096d41219f72
tud_pedestrianTUD-PedestrianPeople-Tracking-by-Detection and People-Detection-by-TrackingPeople-tracking-by-detection and people-detection-by-tracking[pdf][s2]3316521a5527c7700af8ae6aef32a79a8b83672c
tvhiTVHIHigh Five: Recognising human interactions in TV showsHigh Five: Recognising human interactions in TV shows[pdf][s2]3cd40bfa1ff193a96bde0207e5140a399476466c
uccsUCCSLarge scale unconstrained open set face databaseLarge scale unconstrained open set face database[pdf][s2]07fcbae86f7a3ad3ea1cf95178459ee9eaf77cb1
uccsUCCSUnconstrained Face Detection and Open-Set Face Recognition ChallengeUnconstrained Face Detection and Open-Set Face Recognition Challenge[pdf][s2]d4f1eb008eb80595bcfdac368e23ae9754e1e745
ucf_101UCF101UCF101: A Dataset of 101 Human Actions Classes From Videos in The WildUCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild[pdf][s2]b5f2846a506fc417e7da43f6a7679146d99c5e96
ucf_crowdUCF-CC-50Multi-Source Multi-Scale Counting in Extremely Dense Crowd ImagesMulti-source Multi-scale Counting in Extremely Dense Crowd Images[pdf][s2]32c801cb7fbeb742edfd94cccfca4934baec71da
ucf_selfieUCF SelfieHow to Take a Good Selfie?How to Take a Good Selfie?[pdf][s2]041d3eedf5e45ce5c5229f0181c5c576ed1fafd6
ufddUFDDPushing the Limits of Unconstrained Face Detection: a Challenge Dataset and Baseline ResultsPushing the Limits of Unconstrained Face Detection: a Challenge Dataset and Baseline Results[pdf][s2]377f2b65e6a9300448bdccf678cde59449ecd337
umbUMBUMB-DB: A Database of Partially Occluded 3D FacesUMB-DB: A database of partially occluded 3D faces[pdf][s2]16e8b0a1e8451d5f697b94c0c2b32a00abee1d52
umd_facesUMDUMDFaces: An Annotated Face Dataset for Training Deep NetworksUMDFaces: An annotated face dataset for training deep networks[pdf][s2]31b05f65405534a696a847dd19c621b7b8588263
umd_facesUMDThe Do's and Don'ts for CNN-based Face VerificationThe Do’s and Don’ts for CNN-Based Face Verification[pdf][s2]71b7fc715e2f1bb24c0030af8d7e7b6e7cd128a6
urban_tribesUrban TribesFrom Bikers to Surfers: Visual Recognition of Urban TribesFrom Bikers to Surfers: Visual Recognition of Urban Tribes[pdf][s2]774cbb45968607a027ae4729077734db000a1ec5
vgg_celebs_in_placesCIPFaces in Places: Compound Query RetrievalFaces in Places: compound query retrieval[pdf][s2]7ebb153704706e457ab57b432793d2b6e5d12592
vgg_facesVGG FaceDeep Face RecognitionDeep Face Recognition[pdf][s2]162ea969d1929ed180cc6de9f0bf116993ff6e06
vgg_faces2VGG Face2VGGFace2: A dataset for recognising faces across pose and ageVGGFace2: A Dataset for Recognising Faces across Pose and Age[pdf][s2]eb027969f9310e0ae941e2adee2d42cdf07d938c
viperVIPeREvaluating Appearance Models for Recognition, Reacquisition, and TrackingEvaluating Appearance Models for Recognition , Reacquisition , and Tracking[pdf][s2]6273b3491e94ea4dd1ce42b791d77bdc96ee73a8
vocVOCThe PASCAL Visual Object Classes (VOC) ChallengeThe Pascal Visual Object Classes (VOC) Challenge[pdf][s2]0ee1916a0cb2dc7d3add086b5f1092c3d4beb38a
vqaVQAVQA: Visual Question AnsweringVQA: Visual Question Answering[pdf][s2]01959ef569f74c286956024866c1d107099199f7
wardWARDRe-identify people in wide area camera networkRe-identify people in wide area camera network[pdf][s2]6f3c76b7c0bd8e1d122c6ea808a271fd4749c951
widerWIDERRecognize Complex Events from Static Images by Fusing Deep ChannelsRecognize complex events from static images by fusing deep channels[pdf][s2]356b431d4f7a2a0a38cf971c84568207dcdbf189
wider_attributeWIDER AttributeHuman Attribute Recognition by Deep Hierarchical ContextsHuman Attribute Recognition by Deep Hierarchical Contexts[pdf][s2]44d23df380af207f5ac5b41459c722c87283e1eb
wider_faceWIDER FACEWIDER FACE: A Face Detection BenchmarkWIDER FACE: A Face Detection Benchmark[pdf][s2]52d7eb0fbc3522434c13cc247549f74bb9609c5d
wildtrackWildTrackWILDTRACK: A Multi-camera HD Dataset for Dense Unscripted Pedestrian DetectionWILDTRACK : A Multi-camera HD Dataset for Dense Unscripted Pedestrian Detection[pdf][s2]77c81c13a110a341c140995bedb98101b9e84f7f
wlfdbWLFDBWLFDB: Weakly Labeled Face DatabasesWLFDB: Weakly Labeled Face Databases[pdf][s2]5ad4e9f947c1653c247d418f05dad758a3f9277b
yale_facesYaleFacesFrom Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and PoseFrom Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose[pdf][s2]18c72175ddbb7d5956d180b65a96005c100f6014
yale_facesYaleFacesAcquiring Linear Subspaces for Face Recognition under Variable LightingAcquiring linear subspaces for face recognition under variable lighting[pdf][s2]2ad0ee93d029e790ebb50574f403a09854b65b7e
yawddYawDDYawDD: A Yawning Detection DatasetYawDD: a yawning detection dataset[pdf][s2]a94cae786d515d3450d48267e12ca954aab791c4
yfcc_100mYFCC100MYFCC100M: The New Data in Multimedia ResearchYFCC100M: the new data in multimedia research[pdf][s2]010f0f4929e6a6644fb01f0e43820f91d0fad292
york_3dUOY 3D Face DatabaseThree-Dimensional Face Recognition: An Eigensurface ApproachThree-dimensional face recognition: an eigensurface approach[pdf][s2]19d1b811df60f86cbd5e04a094b07f32fff7a32a
youtube_facesYouTubeFacesFace Recognition in Unconstrained Videos with Matched Background SimilarityFace recognition in unconstrained videos with matched background similarity[pdf][s2]560e0e58d0059259ddf86fcec1fa7975dee6a868
youtube_posesYouTube PosePersonalizing Human Video Pose EstimationPersonalizing Human Video Pose Estimation[pdf][s2]1c2802c2199b6d15ecefe7ba0c39bfe44363de38
youtube_celebritiesYouTube CelebritiesFace Tracking and Recognition with Visual Constraints in Real-World VideosFace tracking and recognition with visual constraints in real-world videos[pdf][s2]6204776d31359d129a582057c2d788a14f8aadeb
\ No newline at end of file +Papers with no location

Papers with no location

keynameour titlefound titleaddresss2 id
10k_US_adult_faces10K US Adult FacesThe intrinsic memorability of face imagesThe intrinsic memorability of face photographs.[pdf][s2]8b2dd5c61b23ead5ae5508bb8ce808b5ea266730
3d_rma3D-RMAAutomatic 3D Face AuthenticationAutomatic 3D face authentication[pdf][s2]2160788824c4c29ffe213b2cbeb3f52972d73f37
3dddb_unconstrained3D DynamicA 3D Dynamic Database for Unconstrained Face RecognitionA 3 D Dynamic Database for Unconstrained Face Recognition[pdf][s2]4d4bb462c9f1d4e4ab1e4aa6a75cc0bc71b38461
3dpes3DPeS3DPes: 3D People Dataset for Surveillance and Forensics3DPeS: 3D people dataset for surveillance and forensics[pdf][s2]2e8d0f1802e50cccfd3c0aabac0d0beab3a7846e
4dfab4DFAB4DFAB: A Large Scale 4D Facial Expression Database for Biometric Applications4DFAB: A Large Scale 4D Facial Expression Database for Biometric Applications[pdf][s2]9696ad8b164f5e10fcfe23aacf74bd6168aebb15
50_people_one_question50 People One QuestionMerging Pose Estimates Across Space and TimeMerging Pose Estimates Across Space and Time[pdf][s2]5753b2b5e442eaa3be066daa4a2ca8d8a0bb1725
adienceAdienceAge and Gender Estimation of Unfiltered FacesAge and Gender Estimation of Unfiltered Faces[pdf][s2]1be498d4bbc30c3bfd0029114c784bc2114d67c0
afadAFADOrdinal Regression with a Multiple Output CNN for Age EstimationOrdinal Regression with Multiple Output CNN for Age Estimation[pdf][s2]6618cff7f2ed440a0d2fa9e74ad5469df5cdbe4c
afew_vaAFEW-VAAFEW-VA database for valence and arousal estimation in-the-wildAFEW-VA database for valence and arousal estimation in-the-wild[pdf][s2]2624d84503bc2f8e190e061c5480b6aa4d89277a
affectnetAffectNetAffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the WildAffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild[pdf][s2]758d7e1be64cc668c59ef33ba8882c8597406e53
aflwAFLWAnnotated Facial Landmarks in the Wild: A Large-scale, Real-world Database for Facial Landmark LocalizationAnnotated Facial Landmarks in the Wild: A large-scale, real-world database for facial landmark localization[pdf][s2]a74251efa970b92925b89eeef50a5e37d9281ad0
afwAFWFace detection, pose estimation and landmark localization in the wildFace detection, pose estimation, and landmark localization in the wild[pdf][s2]0e986f51fe45b00633de9fd0c94d082d2be51406
agedbAgeDBAgeDB: the first manually collected, in-the-wild age databaseAgeDB: The First Manually Collected, In-the-Wild Age Database[pdf][s2]6dcf418c778f528b5792104760f1fbfe90c6dd6a
alert_airportALERT AirportA Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and DatasetsA Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets.[pdf][s2]6403117f9c005ae81f1e8e6d1302f4a045e3d99d
am_fedAM-FEDAffectiva MIT Facial Expression Dataset (AM-FED): Naturalistic and Spontaneous Facial Expressions Collected “In the Wild”Affectiva-MIT Facial Expression Dataset (AM-FED): Naturalistic and Spontaneous Facial Expressions Collected "In-the-Wild"[pdf][s2]47aeb3b82f54b5ae8142b4bdda7b614433e69b9a
apisAPiS1.0Pedestrian Attribute Classification in Surveillance: Database and EvaluationPedestrian Attribute Classification in Surveillance: Database and Evaluation[pdf][s2]488e475eeb3bb39a145f23ede197cd3620f1d98a
ar_facedbAR FaceThe AR Face DatabaseThe AR face database[pdf][s2]6d96f946aaabc734af7fe3fc4454cf8547fcd5ed
awe_earsAWE EarsEar Recognition: More Than a SurveyEar Recognition: More Than a Survey[pdf][s2]84fe5b4ac805af63206012d29523a1e033bc827e
b3d_acB3D(AC)A 3-D Audio-Visual Corpus of Affective CommunicationA 3-D Audio-Visual Corpus of Affective Communication[pdf][s2]d08cc366a4a0192a01e9a7495af1eb5d9f9e73ae
bbc_poseBBC PoseAutomatic and Efficient Human Pose Estimation for Sign Language VideosAutomatic and Efficient Human Pose Estimation for Sign Language Videos[pdf][s2]213a579af9e4f57f071b884aa872651372b661fd
bpadBPADDescribing People: A Poselet-Based Approach to Attribute ClassificationDescribing people: A poselet-based approach to attribute classification[pdf][s2]7808937b46acad36e43c30ae4e9f3fd57462853d
bfmBFMA 3D Face Model for Pose and Illumination Invariant Face RecognitionA 3D Face Model for Pose and Illumination Invariant Face Recognition[pdf][s2]639937b3a1b8bded3f7e9a40e85bd3770016cf3c
bio_idBioID FaceRobust Face Detection Using the Hausdorff DistanceRobust Face Detection Using the Hausdorff Distance[pdf][s2]4053e3423fb70ad9140ca89351df49675197196a
bosphorusThe BosphorusBosphorus Database for 3D Face AnalysisBosphorus Database for 3D Face Analysis[pdf][s2]2acf7e58f0a526b957be2099c10aab693f795973
bp4d_plusBP4D+Multimodal Spontaneous Emotion Corpus for Human Behavior AnalysisMultimodal Spontaneous Emotion Corpus for Human Behavior Analysis[pdf][s2]53ae38a6bb2b21b42bac4f0c4c8ed1f9fa02f9d4
brainwashBrainwashEnd-to-End People Detection in Crowded ScenesEnd-to-End People Detection in Crowded Scenes[pdf][s2]1bd1645a629f1b612960ab9bba276afd4cf7c666
bu_3dfeBU-3DFEA 3D Facial Expression Database For Facial Behavior ResearchA 3D facial expression database for facial behavior research[pdf][s2]cc589c499dcf323fe4a143bbef0074c3e31f9b60
buhmap_db#N/AFacial Feature Tracking and Expression Recognition for Sign LanguageFacial feature tracking and expression recognition for sign language[pdf][s2]014b8df0180f33b9fea98f34ae611c6447d761d2
cafe#N/AThe Child Affective Facial Expression (CAFE) Set: Validity and reliability from untrained adultsThe Child Affective Facial Expression (CAFE) set: validity and reliability from untrained adults[pdf][s2]20388099cc415c772926e47bcbbe554e133343d1
cacdCross-Age Reference Coding for Age-Invariant Face Recognition and RetrievalCross-Age Reference Coding for Age-Invariant Face Recognition and Retrieval[pdf][s2]c44c84540db1c38ace232ef34b03bda1c81ba039
caltech_10k_web_facesCaltech 10K Web FacesPruning Training Sets for Learning of Object CategoriesPruning training sets for learning of object categories[pdf][s2]636b8ffc09b1b23ff714ac8350bb35635e49fa3c
caltech_pedestriansCaltech PedestriansPedestrian Detection: A BenchmarkPedestrian detection: A benchmark[pdf][s2]1dc35905a1deff8bc74688f2d7e2f48fd2273275
cas_pealCAS-PEALThe CAS-PEAL Large-Scale Chinese Face Database and Baseline EvaluationsThe CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations[pdf][s2]2485c98aa44131d1a2f7d1355b1e372f2bb148ad
casablancaCasablancaContext-aware {CNNs} for person head detectionContext-Aware CNNs for Person Head Detection[pdf][s2]0ceda9dae8b9f322df65ca2ef02caca9758aec6f
casia_webfaceCASIA WebfaceLearning Face Representation from ScratchLearning Face Representation from Scratch[pdf][s2]853bd61bc48a431b9b1c7cab10c603830c488e39
celebaCelebADeep Learning Face Attributes in the WildDeep Learning Face Attributes in the Wild[pdf][s2]6424b69f3ff4d35249c0bb7ef912fbc2c86f4ff4
cfdCFDThe Chicago face database: A free stimulus set of faces and norming dataThe Chicago face database: A free stimulus set of faces and norming data.[pdf][s2]4df3143922bcdf7db78eb91e6b5359d6ada004d2
chalearnChaLearnChaLearn Looking at People: A Review of Events and ResourcesChaLearn looking at people: A review of events and resources[pdf][s2]8d5998cd984e7cce307da7d46f155f9db99c6590
chokepointChokePointPatch-based Probabilistic Image Quality Assessment for Face Selection and Improved Video-based Face RecognitionPatch-based probabilistic image quality assessment for face selection and improved video-based face recognition[pdf][s2]0486214fb58ee9a04edfe7d6a74c6d0f661a7668
clothing_co_parsingCCPClothing Co-Parsing by Joint Image Segmentation and LabelingClothing Co-parsing by Joint Image Segmentation and Labeling[pdf][s2]2bf8541199728262f78d4dced6fb91479b39b738
cmdpCMDPDistance Estimation of an Unknown Person from a PortraitDistance Estimation of an Unknown Person from a Portrait[pdf][s2]56ae6d94fc6097ec4ca861f0daa87941d1c10b70
cmu_pieCMU PIEThe CMU Pose, Illumination, and Expression DatabaseThe CMU Pose, Illumination, and Expression (PIE) Database[pdf][s2]4d423acc78273b75134e2afd1777ba6d3a398973
cocoCOCOMicrosoft COCO: Common Objects in ContextMicrosoft COCO: Common Objects in Context[pdf][s2]5e0f8c355a37a5a89351c02f174e7a5ddcb98683
coco_actionCOCO-aDescribing Common Human Visual Actions in ImagesDescribing Common Human Visual Actions in Images[pdf][s2]4946ba10a4d5a7d0a38372f23e6622bd347ae273
coco_qaCOCO QAExploring Models and Data for Image Question AnsweringExploring Models and Data for Image Question Answering[pdf][s2]35b0331dfcd2897abd5749b49ff5e2b8ba0f7a62
cofwCOFWRobust face landmark estimation under occlusionRobust Face Landmark Estimation under Occlusion[pdf][s2]2724ba85ec4a66de18da33925e537f3902f21249
cohn_kanadeCKComprehensive Database for Facial Expression AnalysisComprehensive Database for Facial Expression Analysis[pdf][s2]23fc83c8cfff14a16df7ca497661264fc54ed746
complex_activitiesOngoing Complex ActivitiesRecognition of Ongoing Complex Activities by Sequence Prediction over a Hierarchical Label SpaceRecognition of ongoing complex activities by sequence prediction over a hierarchical label space[pdf][s2]65355cbb581a219bd7461d48b3afd115263ea760
cuhk01CUHK01Human Reidentification with Transferred Metric LearningHuman Reidentification with Transferred Metric Learning[pdf][s2]44484d2866f222bbb9b6b0870890f9eea1ffb2d0
cuhk02CUHK02Locally Aligned Feature Transforms across ViewsLocally Aligned Feature Transforms across Views[pdf][s2]38b55d95189c5e69cf4ab45098a48fba407609b4
cuhk03CUHK03DeepReID: Deep Filter Pairing Neural Network for Person Re-identificationDeepReID: Deep Filter Pairing Neural Network for Person Re-identification[pdf][s2]6bd36e9fd0ef20a3074e1430a6cc601e6d407fc3
cvc_01_barcelonaCVC-01Adaptive Image Sampling and Windows Classification for On-board Pedestrian DetectionAdaptive Image Sampling and Windows Classification for On – board Pedestrian Detection[pdf][s2]57fe081950f21ca03b5b375ae3e84b399c015861
czech_news_agencyUFIUnconstrained Facial Images: Database for Face Recognition under Real-world ConditionsUnconstrained Facial Images: Database for Face Recognition Under Real-World Conditions[pdf][s2]4b4106614c1d553365bad75d7866bff0de6056ed
d3dfacsD3DFACSA FACS Valid 3D Dynamic Action Unit database with Applications to 3D Dynamic Morphable Facial ModellingA FACS valid 3D dynamic action unit database with applications to 3D dynamic morphable facial modeling[pdf][s2]070de852bc6eb275d7ca3a9cdde8f6be8795d1a3
dartmouth_childrenDartmouth ChildrenThe Dartmouth Database of Children's Faces: Acquisition and validation of a new face stimulus setThe Dartmouth Database of Children’s Faces: Acquisition and Validation of a New Face Stimulus Set[pdf][s2]4e6ee936eb50dd032f7138702fa39b7c18ee8907
data_61Data61 PedestrianA Multi-Modal Graphical Model for Scene AnalysisA Multi-modal Graphical Model for Scene Analysis[pdf][s2]563c940054e4b456661762c1ab858e6f730c3159
deep_fashionDeepFashionDeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich AnnotationsDeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations[pdf][s2]18010284894ed0edcca74e5bf768ee2e15ef7841
deep_fashionDeepFashionFashion Landmark Detection in the WildFashion Landmark Detection in the Wild[pdf][s2]4fefd1bc8dc4e0ab37ee3324ddfa43ad9d6a04a7
distance_nighttimeLong Distance Heterogeneous FaceNighttime Face Recognition at Long Distance: Cross-distance and Cross-spectral MatchingNighttime Face Recognition at Long Distance: Cross-Distance and Cross-Spectral Matching[pdf][s2]4156b7e88f2e0ab0a7c095b9bab199ae2b23bd06
duke_mtmcDuke MTMCPerformance Measures and a Data Set for Multi-Target, Multi-Camera TrackingPerformance Measures and a Data Set for Multi-Target, Multi-Camera Tracking[pdf][s2]27a2fad58dd8727e280f97036e0d2bc55ef5424c
emotio_netEmotioNet DatabaseEmotioNet: An Accurate, Real-Time Algorithm for the Automatic Annotation of a Million Facial Expressions in the WildEmotioNet: An Accurate, Real-Time Algorithm for the Automatic Annotation of a Million Facial Expressions in the Wild[pdf][s2]c900e0ad4c95948baaf0acd8449fde26f9b4952a
europersonsEuroCity PersonsThe EuroCity Persons Dataset: A Novel Benchmark for Object DetectionThe EuroCity Persons Dataset: A Novel Benchmark for Object Detection[pdf][s2]f0e17f27f029db4ad650ff278fe3c10ecb6cb0c4
expwExpWFrom Facial Expression Recognition to Interpersonal Relation PredictionFrom Facial Expression Recognition to Interpersonal Relation Prediction[pdf][s2]22f656d0f8426c84a33a267977f511f127bfd7f3
face_scrubFaceScrubA data-driven approach to cleaning large face datasetsA data-driven approach to cleaning large face datasets[pdf][s2]0d3bb75852098b25d90f31d2f48fd0cb4944702b
face_tracerFaceTracerFaceTracer: A Search Engine for Large Collections of Images with FacesFaceTracer: A Search Engine for Large Collections of Images with Faces[pdf][s2]4c170a0dcc8de75587dae21ca508dab2f9343974
face_tracerFaceTracerFace Swapping: Automatically Replacing Faces in PhotographsFace swapping: automatically replacing faces in photographs[pdf][s2]670637d0303a863c1548d5b19f705860a23e285c
faceplaceFace PlaceRecognizing disguised facesRecognizing disguised faces[pdf][s2]25474c21613607f6bb7687a281d5f9d4ffa1f9f3
fddbFDDBFDDB: A Benchmark for Face Detection in Unconstrained SettingsA Benchmark for Face Detection in Unconstrained Settings[pdf][s2]75da1df4ed319926c544eefe17ec8d720feef8c0
feiFEICaptura e Alinhamento de Imagens: Um Banco de Faces BrasileiroA new ranking method for principal components analysis and its application to face image analysis[pdf][s2]8b56e33f33e582f3e473dba573a16b598ed9bcdc
feretFERETThe FERET Verification Testing Protocol for Face Recognition AlgorithmsThe FERET Verification Testing Protocol for Face Recognition Algorithms[pdf][s2]0c4a139bb87c6743c7905b29a3cfec27a5130652
feretFERETThe FERET Evaluation Methodology for Face-Recognition AlgorithmsThe FERET Evaluation Methodology for Face-Recognition Algorithms[pdf][s2]0f0fcf041559703998abf310e56f8a2f90ee6f21
feretFERETFERET ( Face Recognition Technology ) Recognition Algorithm Development and Test ResultsFERET ( Face Recognition Technology ) Recognition Algorithm Development and Test Results[pdf][s2]31de9b3dd6106ce6eec9a35991b2b9083395fd0b
feretFERETThe FERET database and evaluation procedure for face-recognition algorithmsThe FERET database and evaluation procedure for face-recognition algorithms[pdf][s2]dc8b25e35a3acb812beb499844734081722319b4
ferplusFER+Training Deep Networks for Facial Expression Recognition with Crowd-Sourced Label DistributionTraining deep networks for facial expression recognition with crowd-sourced label distribution[pdf][s2]298cbc3dfbbb3a20af4eed97906650a4ea1c29e0
fiaCMU FiAThe CMU Face In Action (FIA) DatabaseThe CMU Face In Action (FIA) Database[pdf][s2]47662d1a368daf70ba70ef2d59eb6209f98b675d
fiw_300300-WA semi-automatic methodology for facial landmark annotationA Semi-automatic Methodology for Facial Landmark Annotation[pdf][s2]013909077ad843eb6df7a3e8e290cfd5575999d2
fiw_300300-W300 Faces in-the-Wild Challenge: The first facial landmark localization Challenge300 Faces in-the-Wild Challenge: The First Facial Landmark Localization Challenge[pdf][s2]044d9a8c61383312cdafbcc44b9d00d650b21c70
fiw_300300-W300 faces In-the-wild challenge: Database and results300 Faces In-The-Wild Challenge: database and results[pdf][s2]e4754afaa15b1b53e70743880484b8d0736990ff
geofacesGeoFacesGeoFaceExplorer: Exploring the Geo-Dependence of Facial AttributesGeoFaceExplorer: exploring the geo-dependence of facial attributes[pdf][s2]17b46e2dad927836c689d6787ddb3387c6159ece
georgia_tech_face_databaseGeorgia Tech FaceMaximum likelihood training of the embedded HMM for face detection and recognitionMAXIMUM 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[pdf][s2]3dc3f0b64ef80f573e3a5f96e456e52ee980b877
gfwGrouping Face in the WildMerge or Not? Learning to Group Faces via Imitation LearningMerge or Not? Learning to Group Faces via Imitation Learning[pdf][s2]e58dd160a76349d46f881bd6ddbc2921f08d1050
grazGraz PedestrianWeak Hypotheses and Boosting for Generic Object Detection and RecognitionWeak Hypotheses and Boosting for Generic Object Detection and Recognition[pdf][s2]0c91808994a250d7be332400a534a9291ca3b60e
h3dH3DPoselets: Body Part Detectors Trained Using 3D Human Pose AnnotationsPoselets: Body part detectors trained using 3D human pose annotations[pdf][s2]2830fb5282de23d7784b4b4bc37065d27839a412
hda_plusHDA+The HDA+ data set for research on fully automated re-identification systemsThe HDA+ Data Set for Research on Fully Automated Re-identification Systems[pdf][s2]8f02ec0be21461fbcedf51d864f944cfc42c875f
hda_plusHDA+A Multi-camera video data set for research on High-Definition surveillanceHDA dataset-DRAFT 1 A Multi-camera video data set for research on High-Definition surveillance[pdf][s2]bd88bb2e4f351352d88ee7375af834360e223498
helenHelenInteractive Facial Feature LocalizationInteractive Facial Feature Localization[pdf][s2]95f12d27c3b4914e0668a268360948bce92f7db3
hi4d_adsipHi4D-ADSIPHi4D-ADSIP 3-D dynamic facial articulation databaseHi4D-ADSIP 3-D dynamic facial articulation database[pdf][s2]a8d0b149c2eadaa02204d3e4356fbc8eccf3b315
hipsterwarsHipsterwarsHipster Wars: Discovering Elements of Fashion StylesHipster Wars: Discovering Elements of Fashion Styles[pdf][s2]04c2cda00e5536f4b1508cbd80041e9552880e67
hollywood_headsetHollywoodHeadsContext-aware CNNs for person head detectionContext-Aware CNNs for Person Head Detection[pdf][s2]0ceda9dae8b9f322df65ca2ef02caca9758aec6f
hrt_transgenderHRT TransgenderIs the Eye Region More Reliable Than the Face? A Preliminary Study of Face-based Recognition on a Transgender DatasetIs the eye region more reliable than the face? A preliminary study of face-based recognition on a transgender dataset[pdf][s2]137aa2f891d474fce1e7a1d1e9b3aefe21e22b34
ibm_difIBM Diversity in FacesDiversity in FacesDiversity in Faces[pdf][s2]2b89de1d81cee50552f10e26e865df3365e9bc88
ifadIFADIndian Face Age Database: A Database for Face Recognition with Age VariationIndian Face Age Database : A Database for Face Recognition with Age Variation[pdf][s2]55c40cbcf49a0225e72d911d762c27bb1c2d14aa
ifdbIFDBIranian Face Database and Evaluation with a New Detection AlgorithmIranian Face Database and Evaluation with a New Detection Algorithm[pdf][s2]066d71fcd997033dce4ca58df924397dfe0b5fd1
iit_dehli_earIIT Dehli EarAutomated human identification using ear imagingAutomated Human Identification Using Ear Imaging[pdf][s2]faf40ce28857aedf183e193486f5b4b0a8c478a2
ijb_cIJB-CIARPA Janus Benchmark-B Face DatasetIARPA Janus Benchmark-B Face Dataset[pdf][s2]0cb2dd5f178e3a297a0c33068961018659d0f443
ijb_cIJB-CPushing the Frontiers of Unconstrained Face Detection and Recognition: IARPA Janus Benchmark APushing the frontiers of unconstrained face detection and recognition: IARPA Janus Benchmark A[pdf][s2]140c95e53c619eac594d70f6369f518adfea12ef
ijb_cIJB-CIARPA Janus Benchmark CIARPA Janus Benchmark - C: Face Dataset and Protocol[pdf][s2]57178b36c21fd7f4529ac6748614bb3374714e91
ilids_mctsi-LIDS Multiple-CameraImagery Library for Intelligent Detection Systems: The i-LIDS User GuideImagery Library for Intelligent Detection Systems (i-LIDS); A Standard for Testing Video Based Detection Systems[pdf][s2]0297448f3ed948e136bb06ceff10eccb34e5bb77
ilids_vid_reidiLIDS-VIDPerson Re-Identi cation by Video RankingPerson Re-identification by Video Ranking[pdf][s2]98bb029afe2a1239c3fdab517323066f0957b81b
imdb_faceIMDb FaceThe Devil of Face Recognition is in the NoiseThe Devil of Face Recognition is in the Noise[pdf][s2]9e31e77f9543ab42474ba4e9330676e18c242e72
imdb_wikiIMDBDeep expectation of real and apparent age from a single image without facial landmarksDeep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks[pdf][s2]10195a163ab6348eef37213a46f60a3d87f289c5
imdb_wikiIMDBDEX: Deep EXpectation of apparent age from a single imageDEX: Deep EXpectation of Apparent Age from a Single Image[pdf][s2]8355d095d3534ef511a9af68a3b2893339e3f96b
immediacyImmediacyMulti-task Recurrent Neural Network for Immediacy PredictionMulti-task Recurrent Neural Network for Immediacy Prediction[pdf][s2]1e3df3ca8feab0b36fd293fe689f93bb2aaac591
imsituimSituSituation Recognition: Visual Semantic Role Labeling for Image UnderstandingSituation Recognition: Visual Semantic Role Labeling for Image Understanding[pdf][s2]51eba481dac6b229a7490f650dff7b17ce05df73
jaffeJAFFECoding Facial Expressions with Gabor WaveletsCoding Facial Expressions with Gabor Wavelets[pdf][s2]45c31cde87258414f33412b3b12fc5bec7cb3ba9
jiku_mobileJiku Mobile Video DatasetThe Jiku Mobile Video DatasetThe jiku mobile video dataset[pdf][s2]d178cde92ab3dc0dd2ebee5a76a33d556c39448b
jpl_poseJPL-Interaction datasetFirst-Person Activity Recognition: What Are They Doing to Me?First-Person Activity Recognition: What Are They Doing to Me?[pdf][s2]1aad2da473888cb7ebc1bfaa15bfa0f1502ce005
kin_faceUB KinFaceUnderstanding Kin Relationships in a PhotoUnderstanding Kin Relationships in a Photo[pdf][s2]08f6745bc6c1b0fb68953ea61054bdcdde6d2fc7
kin_faceUB KinFaceKinship Verification through Transfer LearningKinship verification through transfer learning[pdf][s2]4793f11fbca4a7dba898b9fff68f70d868e2497c
kittiKITTIVision meets Robotics: The KITTI DatasetVision meets robotics: The KITTI dataset[pdf][s2]026e3363b7f76b51cc711886597a44d5f1fd1de2
lagLAGLarge Age-Gap Face Verification by Feature Injection in Deep NetworksLarge age-gap face verification by feature injection in deep networks[pdf][s2]0d2dd4fc016cb6a517d8fb43a7cc3ff62964832e
large_scale_person_searchLarge Scale Person SearchEnd-to-End Deep Learning for Person SearchEnd-to-End Deep Learning for Person Search[pdf][s2]2161f6b7ee3c0acc81603b01dc0df689683577b9
leeds_sports_poseLeeds Sports PoseClustered Pose and Nonlinear Appearance Models for Human Pose EstimationClustered Pose and Nonlinear Appearance Models for Human Pose Estimation[pdf][s2]4b1d23d17476fcf78f4cbadf69fb130b1aa627c0
lfpwLFWPLocalizing Parts of Faces Using a Consensus of ExemplarsLocalizing Parts of Faces Using a Consensus of Exemplars[pdf][s2]140438a77a771a8fb656b39a78ff488066eb6b50
lfwLFWLabeled Faces in the Wild: Updates and New Reporting ProceduresLabeled Faces in the Wild : Updates and New Reporting Procedures[pdf][s2]2d3482dcff69c7417c7b933f22de606a0e8e42d4
lfwLFWLabeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained EnvironmentsLabeled Faces in the Wild : A Database for Studying Face Recognition in Unconstrained Environments[pdf][s2]370b5757a5379b15e30d619e4d3fb9e8e13f3256
lfwLFWLabeled Faces in the Wild: A SurveyLabeled Faces in the Wild : A Survey[pdf][s2]7de6e81d775e9cd7becbfd1bd685f4e2a5eebb22
lfwLFWEffective Unconstrained Face Recognition by Combining Multiple Descriptors and Learned Background StatisticsEffective Unconstrained Face Recognition by Combining Multiple Descriptors and Learned Background Statistics[pdf][s2]133f01aec1534604d184d56de866a4bd531dac87
m2vtsdb_extendedxm2vtsdbXM2VTSDB: The Extended M2VTS DatabaseXm2vtsdb: the Extended M2vts Database[pdf][s2]b62628ac06bbac998a3ab825324a41a11bc3a988
maflMAFLFacial Landmark Detection by Deep Multi-task LearningFacial Landmark Detection by Deep Multi-task Learning[pdf][s2]8a3c5507237957d013a0fe0f082cab7f757af6ee
maflMAFLLearning Deep Representation for Face Alignment with Auxiliary AttributesLearning Deep Representation for Face Alignment with Auxiliary Attributes[pdf][s2]a0fd85b3400c7b3e11122f44dc5870ae2de9009a
malfMALFFine-grained Evaluation on Face Detection in the Wild.Fine-grained evaluation on face detection in the wild[pdf][s2]45e616093a92e5f1e61a7c6037d5f637aa8964af
mapillaryMapillaryThe Mapillary Vistas Dataset for Semantic Understanding of Street ScenesThe Mapillary Vistas Dataset for Semantic Understanding of Street Scenes[pdf][s2]79828e6e9f137a583082b8b5a9dfce0c301989b8
market_1501Market 1501Scalable Person Re-identification: A BenchmarkScalable Person Re-identification: A Benchmark[pdf][s2]4308bd8c28e37e2ed9a3fcfe74d5436cce34b410
market1203Market 1203Orientation Driven Bag of Appearances for Person Re-identificationOrientation Driven Bag of Appearances for Person Re-identification[pdf][s2]a7fe834a0af614ce6b50dc093132b031dd9a856b
marsMARSMARS: A Video Benchmark for Large-Scale Person Re-identificationMARS: A Video Benchmark for Large-Scale Person Re-Identification[pdf][s2]c0387e788a52f10bf35d4d50659cfa515d89fbec
mcgillMcGill Real WorldHierarchical Temporal Graphical Model for Head Pose Estimation and Subsequent Attribute Classification in Real-World VideosHierarchical temporal graphical model for head pose estimation and subsequent attribute classification in real-world videos[pdf][s2]a5a3bc3e5e9753769163cb30b16dbd12e266b93e
megaageMegaAgeQuantifying Facial Age by Posterior of Age ComparisonsQuantifying Facial Age by Posterior of Age Comparisons[pdf][s2]d80a3d1f3a438e02a6685e66ee908446766fefa9
megafaceMegaFaceLevel Playing Field for Million Scale Face RecognitionLevel Playing Field for Million Scale Face Recognition[pdf][s2]28d4e027c7e90b51b7d8908fce68128d1964668a
megafaceMegaFaceThe MegaFace Benchmark: 1 Million Faces for Recognition at ScaleThe MegaFace Benchmark: 1 Million Faces for Recognition at Scale[pdf][s2]96e0cfcd81cdeb8282e29ef9ec9962b125f379b0
mit_cbclMIT CBCLComponent-based Face Recognition with 3D Morphable ModelsComponent-Based Face Recognition with 3D Morphable Models[pdf][s2]079a0a3bf5200994e1f972b1b9197bf2f90e87d4
mmi_facial_expressionMMI Facial Expression DatasetWEB-BASED DATABASE FOR FACIAL EXPRESSION ANALYSISWeb-based database for facial expression analysis[pdf][s2]2a75f34663a60ab1b04a0049ed1d14335129e908
moments_in_timeMoments in TimeMoments in Time Dataset: one million videos for event understandingMoments in Time Dataset: one million videos for event understanding[pdf][s2]a5a44a32a91474f00a3cda671a802e87c899fbb4
morphMORPH CommercialMORPH: A Longitudinal Image Database of Normal Adult Age-ProgressionMORPH: a longitudinal image database of normal adult age-progression[pdf][s2]9055b155cbabdce3b98e16e5ac9c0edf00f9552f
morph_ncMORPH Non-CommercialMORPH: A Longitudinal Image Database of Normal Adult Age-ProgressionMORPH: a longitudinal image database of normal adult age-progression[pdf][s2]9055b155cbabdce3b98e16e5ac9c0edf00f9552f
motMOTEvaluating Multiple Object Tracking Performance: The CLEAR MOT MetricsEvaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics[pdf][s2]2258e01865367018ed6f4262c880df85b94959f8
motMOTPerformance Measures and a Data Set for Multi-Target, Multi-Camera TrackingPerformance Measures and a Data Set for Multi-Target, Multi-Camera Tracking[pdf][s2]27a2fad58dd8727e280f97036e0d2bc55ef5424c
mpi_largeLarge MPI Facial ExpressionThe MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial ExpressionsThe MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial Expressions[pdf][s2]ea050801199f98a1c7c1df6769f23f658299a3ae
mpi_smallSmall MPI Facial ExpressionThe MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial ExpressionsThe MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial Expressions[pdf][s2]ea050801199f98a1c7c1df6769f23f658299a3ae
mpii_gazeMPIIGazeAppearance-based Gaze Estimation in the WildAppearance-based gaze estimation in the wild[pdf][s2]0df0d1adea39a5bef318b74faa37de7f3e00b452
mpii_human_poseMPII Human Pose2D Human Pose Estimation: New Benchmark and State of the Art Analysis2D Human Pose Estimation: New Benchmark and State of the Art Analysis[pdf][s2]3325860c0c82a93b2eac654f5324dd6a776f609e
mr2MR2The MR2: A multi-racial mega-resolution database of facial stimuliThe MR2: A multi-racial, mega-resolution database of facial stimuli.[pdf][s2]578d4ad74818086bb64f182f72e2c8bd31e3d426
mrp_droneMRP DroneInvestigating Open-World Person Re-identification Using a DroneInvestigating Open-World Person Re-identification Using a Drone[pdf][s2]ad01687649d95cd5b56d7399a9603c4b8e2217d7
mscelebMsCelebMS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face RecognitionMS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition[pdf][s2]291265db88023e92bb8c8e6390438e5da148e8f5
msmt_17MSMT17Person Transfer GAN to Bridge Domain Gap for Person Re-IdentificationPerson Transfer GAN to Bridge Domain Gap for Person Re-Identification[pdf][s2]ec792ad2433b6579f2566c932ee414111e194537
mtflMTFLFacial Landmark Detection by Deep Multi-task LearningFacial Landmark Detection by Deep Multi-task Learning[pdf][s2]8a3c5507237957d013a0fe0f082cab7f757af6ee
mtflMTFLLearning Deep Representation for Face Alignment with Auxiliary AttributesLearning Deep Representation for Face Alignment with Auxiliary Attributes[pdf][s2]a0fd85b3400c7b3e11122f44dc5870ae2de9009a
multi_pieMULTIPIEMulti-PIEThe CMU Pose, Illumination, and Expression (PIE) Database[pdf][s2]4d423acc78273b75134e2afd1777ba6d3a398973
names_and_facesNews DatasetNames and FacesNames and faces in the news[pdf][s2]2fda164863a06a92d3a910b96eef927269aeb730
nova_emotionsNovaemötions DatasetCrowdsourcing facial expressions for affective-interactionCrowdsourcing facial expressions for affective-interaction[pdf][s2]c06b13d0ec3f5c43e2782cd22542588e233733c3
nudedetection#N/AA Bag-of-Features Approach based on Hue-SIFT Descriptor for Nude DetectionA bag-of-features approach based on Hue-SIFT descriptor for nude detection[pdf][s2]7ace44190729927e5cb0dd5d363fcae966fe13f7
orlORLParameterisation of a Stochastic Model for Human Face IdentificationParameterisation of a stochastic model for human face identification[pdf][s2]55206f0b5f57ce17358999145506cd01e570358c
penn_fudanPenn FudanObject Detection Combining Recognition and SegmentationObject Detection Combining Recognition and Segmentation[pdf][s2]3394168ff0719b03ff65bcea35336a76b21fe5e4
petaPETAPedestrian Attribute Recognition At Far DistancePedestrian Attribute Recognition At Far Distance[pdf][s2]2a4bbee0b4cf52d5aadbbc662164f7efba89566c
petsPETS 2017PETS 2017: Dataset and ChallengePETS 2017: Dataset and Challenge[pdf][s2]22909dd19a0ec3b6065334cb5be5392cb24d839d
pilot_parliamentPPBGender Shades: Intersectional Accuracy Disparities in Commercial Gender ClassificationGender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification[pdf][s2]18858cc936947fc96b5c06bbe3c6c2faa5614540
pipaPIPABeyond Frontal Faces: Improving Person Recognition Using Multiple CuesBeyond frontal faces: Improving Person Recognition using multiple cues[pdf][s2]0a85bdff552615643dd74646ac881862a7c7072d
pkuPKUSwiss-System Based Cascade Ranking for Gait-based Person Re-identificationSwiss-System Based Cascade Ranking for Gait-Based Person Re-Identification[pdf][s2]f6c8d5e35d7e4d60a0104f233ac1a3ab757da53f
pku_reidPKU-ReidOrientation driven bag of appearances for person re-identificationOrientation Driven Bag of Appearances for Person Re-identification[pdf][s2]a7fe834a0af614ce6b50dc093132b031dd9a856b
pornodb#N/APooling in Image Representation: the Visual Codeword Point of ViewPooling in image representation: The visual codeword point of view[pdf][s2]b92a1ed9622b8268ae3ac9090e25789fc41cc9b8
precariousPrecariousExpecting the Unexpected: Training Detectors for Unusual Pedestrians With Adversarial ImpostersExpecting the Unexpected: Training Detectors for Unusual Pedestrians with Adversarial Imposters[pdf][s2]9e5378e7b336c89735d3bb15cf67eff96f86d39a
pridPRIDPerson Re-Identification by Descriptive and Discriminative ClassificationPerson Re-identification by Descriptive and Discriminative Classification[pdf][s2]16c7c31a7553d99f1837fc6e88e77b5ccbb346b8
prwPRWPerson Re-identification in the WildPerson Re-identification in the Wild[pdf][s2]0b84f07af44f964817675ad961def8a51406dd2e
psuPSUVision-based Analysis of Small Groups in Pedestrian CrowdsVision-Based Analysis of Small Groups in Pedestrian Crowds[pdf][s2]066000d44d6691d27202896691f08b27117918b9
pubfigPubFigAttribute and Simile Classifiers for Face VerificationAttribute and simile classifiers for face verification[pdf][s2]759a3b3821d9f0e08e0b0a62c8b693230afc3f8d
put_facePut FaceThe PUT face databaseThe put face database[pdf][s2]ae0aee03d946efffdc7af2362a42d3750e7dd48a
qmul_surv_faceQMUL-SurvFaceSurveillance Face Recognition ChallengeSurveillance Face Recognition Challenge[pdf][s2]c866a2afc871910e3282fd9498dce4ab20f6a332
rafdRaFDPresentation and validation of the Radboud Faces DatabasePresentation and validation of the Radboud Faces Database[pdf][s2]3765df816dc5a061bc261e190acc8bdd9d47bec0
raidRAiDConsistent Re-identification in a Camera NetworkConsistent Re-identification in a Camera Network[pdf][s2]09d78009687bec46e70efcf39d4612822e61cb8c
rap_pedestrianRAPA Richly Annotated Dataset for Pedestrian Attribute RecognitionA Richly Annotated Dataset for Pedestrian Attribute Recognition[pdf][s2]221c18238b829c12b911706947ab38fd017acef7
reseedReSEEDReSEED: Social Event dEtection DatasetReSEED: social event dEtection dataset[pdf][s2]54983972aafc8e149259d913524581357b0f91c3
saivtSAIVT SoftBioA Database for Person Re-Identification in Multi-Camera Surveillance NetworksA Database for Person Re-Identification in Multi-Camera Surveillance Networks[pdf][s2]22646e00a7ba34d1b5fbe3b1efcd91a1e1be3c2b
sarc3dSarc3DSARC3D: a new 3D body model for People Tracking and Re-identificationSARC3D: A New 3D Body Model for People Tracking and Re-identification[pdf][s2]e27ef52c641c2b5100a1b34fd0b819e84a31b4df
scfaceSCfaceSCface – surveillance cameras face databaseSCface – surveillance cameras face database[pdf][s2]29a705a5fa76641e0d8963f1fdd67ee4c0d92d3d
scut_fbpSCUT-FBPSCUT-FBP: A Benchmark Dataset for Facial Beauty PerceptionSCUT-FBP: A Benchmark Dataset for Facial Beauty Perception[pdf][s2]bd26dabab576adb6af30484183c9c9c8379bf2e0
scut_headSCUT HEADDetecting Heads using Feature Refine Net and Cascaded Multi-scale ArchitectureDetecting Heads using Feature Refine Net and Cascaded Multi-scale Architecture[pdf][s2]d3200d49a19a4a4e4e9745ee39649b65d80c834b
sdu_vidSDU-VIDA Spatio-Temporal Appearance Representation for Video-Based Pedestrian Re-IdentificationA Spatio-Temporal Appearance Representation for Video-Based Pedestrian Re-Identification[pdf][s2]3b4ec8af470948a72a6ed37a9fd226719a874ebc
sdu_vidSDU-VIDLocal descriptors encoded by Fisher vectors for person re-identificationLocal Descriptors Encoded by Fisher Vectors for Person Re-identification[pdf][s2]46a01565e6afe7c074affb752e7069ee3bf2e4ef
sdu_vidSDU-VIDPerson reidentification by video rankingPerson Re-identification by Video Ranking[pdf][s2]98bb029afe2a1239c3fdab517323066f0957b81b
social_relationSocial RelationLearning Social Relation Traits from Face ImagesLearning Social Relation Traits from Face Images[pdf][s2]2a171f8d14b6b8735001a11c217af9587d095848
sotonSOTON HiDOn a Large Sequence-Based Human Gait DatabaseOn a large sequence-based human gait database[pdf][s2]4f93cd09785c6e77bf4bc5a788e079df524c8d21
sports_videos_in_the_wildSVWSports Videos in the Wild (SVW): A Video Dataset for Sports AnalysisSports Videos in the Wild (SVW): A video dataset for sports analysis[pdf][s2]1a40092b493c6b8840257ab7f96051d1a4dbfeb2
stair_actionsSTAIR ActionSTAIR Actions: A Video Dataset of Everyday Home ActionsSTAIR Actions: A Video Dataset of Everyday Home Actions[pdf][s2]d3f5a1848b0028d8ab51d0b0673732cad2e3c8c9
stanford_droneStanford DroneLearning Social Etiquette: Human Trajectory Prediction In Crowded ScenesSocial LSTM: Human Trajectory Prediction in Crowded Spaces[pdf][s2]570f37ed63142312e6ccdf00ecc376341ec72b9f
stickmen_buffyBuffy StickmenClustered Pose and Nonlinear Appearance Models for Human Pose EstimationClustered Pose and Nonlinear Appearance Models for Human Pose Estimation[pdf][s2]4b1d23d17476fcf78f4cbadf69fb130b1aa627c0
stickmen_buffyBuffy StickmenLearning to Parse Images of Articulated ObjectsLearning to parse images of articulated bodies[pdf][s2]6dd0597f8513dc100cd0bc1b493768cde45098a9
stickmen_familyWe Are Family StickmenWe Are Family: Joint Pose Estimation of Multiple PersonsWe Are Family: Joint Pose Estimation of Multiple Persons[pdf][s2]0dc11a37cadda92886c56a6fb5191ded62099c28
stickmen_pascalStickmen PASCALClustered Pose and Nonlinear Appearance Models for Human Pose EstimationLearning to parse images of articulated bodies[pdf][s2]6dd0597f8513dc100cd0bc1b493768cde45098a9
stickmen_pascalStickmen PASCALLearning to Parse Images of Articulated ObjectsLearning to parse images of articulated bodies[pdf][s2]6dd0597f8513dc100cd0bc1b493768cde45098a9
sun_attributesSUNThe SUN Attribute Database: Beyond Categories for Deeper Scene UnderstandingThe SUN Attribute Database: Beyond Categories for Deeper Scene Understanding[pdf][s2]66e6f08873325d37e0ec20a4769ce881e04e964e
svsSVSPedestrian Attribute Classification in Surveillance: Database and EvaluationPedestrian Attribute Classification in Surveillance: Database and Evaluation[pdf][s2]488e475eeb3bb39a145f23ede197cd3620f1d98a
texas_3dfrdTexas 3DFRDAnthropometric 3D Face RecognitionAnthropometric 3D Face Recognition[pdf][s2]2ce2560cf59db59ce313bbeb004e8ce55c5ce928
texas_3dfrdTexas 3DFRDTexas 3D Face Recognition DatabaseTexas 3D Face Recognition Database[pdf][s2]4d58f886f5150b2d5e48fd1b5a49e09799bf895d
tiny_facesTinyFaceLow-Resolution Face RecognitionLow-Resolution Face Recognition[pdf][s2]8990cdce3f917dad622e43e033db686b354d057c
tiny_imagesTiny Images80 million tiny images: a large dataset for non-parametric object and scene recognition80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition[pdf][s2]31b58ced31f22eab10bd3ee2d9174e7c14c27c01
towncenterTownCenterStable Multi-Target Tracking in Real-Time Surveillance VideoStable multi-target tracking in real-time surveillance video[pdf][s2]9361b784e73e9238d5cefbea5ac40d35d1e3103f
tud_brusselsTUD-BrusselsMulti-Cue Onboard Pedestrian DetectionMulti-cue onboard pedestrian detection[pdf][s2]6ad5a38df8dd4cdddd74f31996ce096d41219f72
tud_campusTUD-CampusPeople-Tracking-by-Detection and People-Detection-by-TrackingPeople-tracking-by-detection and people-detection-by-tracking[pdf][s2]3316521a5527c7700af8ae6aef32a79a8b83672c
tud_crossingTUD-CrossingPeople-Tracking-by-Detection and People-Detection-by-TrackingPeople-tracking-by-detection and people-detection-by-tracking[pdf][s2]3316521a5527c7700af8ae6aef32a79a8b83672c
tud_motionpairsTUD-MotionparisMulti-Cue Onboard Pedestrian DetectionMulti-cue onboard pedestrian detection[pdf][s2]6ad5a38df8dd4cdddd74f31996ce096d41219f72
tud_pedestrianTUD-PedestrianPeople-Tracking-by-Detection and People-Detection-by-TrackingPeople-tracking-by-detection and people-detection-by-tracking[pdf][s2]3316521a5527c7700af8ae6aef32a79a8b83672c
tvhiTVHIHigh Five: Recognising human interactions in TV showsHigh Five: Recognising human interactions in TV shows[pdf][s2]3cd40bfa1ff193a96bde0207e5140a399476466c
uccsUCCSLarge scale unconstrained open set face databaseLarge scale unconstrained open set face database[pdf][s2]07fcbae86f7a3ad3ea1cf95178459ee9eaf77cb1
uccsUCCSUnconstrained Face Detection and Open-Set Face Recognition ChallengeUnconstrained Face Detection and Open-Set Face Recognition Challenge[pdf][s2]d4f1eb008eb80595bcfdac368e23ae9754e1e745
ucf_101UCF101UCF101: A Dataset of 101 Human Actions Classes From Videos in The WildUCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild[pdf][s2]b5f2846a506fc417e7da43f6a7679146d99c5e96
ucf_crowdUCF-CC-50Multi-Source Multi-Scale Counting in Extremely Dense Crowd ImagesMulti-source Multi-scale Counting in Extremely Dense Crowd Images[pdf][s2]32c801cb7fbeb742edfd94cccfca4934baec71da
ucf_selfieUCF SelfieHow to Take a Good Selfie?How to Take a Good Selfie?[pdf][s2]041d3eedf5e45ce5c5229f0181c5c576ed1fafd6
ufddUFDDPushing the Limits of Unconstrained Face Detection: a Challenge Dataset and Baseline ResultsPushing the Limits of Unconstrained Face Detection: a Challenge Dataset and Baseline Results[pdf][s2]377f2b65e6a9300448bdccf678cde59449ecd337
umbUMBUMB-DB: A Database of Partially Occluded 3D FacesUMB-DB: A database of partially occluded 3D faces[pdf][s2]16e8b0a1e8451d5f697b94c0c2b32a00abee1d52
umd_facesUMDUMDFaces: An Annotated Face Dataset for Training Deep NetworksUMDFaces: An annotated face dataset for training deep networks[pdf][s2]31b05f65405534a696a847dd19c621b7b8588263
umd_facesUMDThe Do's and Don'ts for CNN-based Face VerificationThe Do’s and Don’ts for CNN-Based Face Verification[pdf][s2]71b7fc715e2f1bb24c0030af8d7e7b6e7cd128a6
urban_tribesUrban TribesFrom Bikers to Surfers: Visual Recognition of Urban TribesFrom Bikers to Surfers: Visual Recognition of Urban Tribes[pdf][s2]774cbb45968607a027ae4729077734db000a1ec5
vgg_celebs_in_placesCIPFaces in Places: Compound Query RetrievalFaces in Places: compound query retrieval[pdf][s2]7ebb153704706e457ab57b432793d2b6e5d12592
vgg_facesVGG FaceDeep Face RecognitionDeep Face Recognition[pdf][s2]162ea969d1929ed180cc6de9f0bf116993ff6e06
vgg_faces2VGG Face2VGGFace2: A dataset for recognising faces across pose and ageVGGFace2: A Dataset for Recognising Faces across Pose and Age[pdf][s2]eb027969f9310e0ae941e2adee2d42cdf07d938c
viperVIPeREvaluating Appearance Models for Recognition, Reacquisition, and TrackingEvaluating Appearance Models for Recognition , Reacquisition , and Tracking[pdf][s2]6273b3491e94ea4dd1ce42b791d77bdc96ee73a8
vocVOCThe PASCAL Visual Object Classes (VOC) ChallengeThe Pascal Visual Object Classes (VOC) Challenge[pdf][s2]0ee1916a0cb2dc7d3add086b5f1092c3d4beb38a
vqaVQAVQA: Visual Question AnsweringVQA: Visual Question Answering[pdf][s2]01959ef569f74c286956024866c1d107099199f7
wardWARDRe-identify people in wide area camera networkRe-identify people in wide area camera network[pdf][s2]6f3c76b7c0bd8e1d122c6ea808a271fd4749c951
widerWIDERRecognize Complex Events from Static Images by Fusing Deep ChannelsRecognize complex events from static images by fusing deep channels[pdf][s2]356b431d4f7a2a0a38cf971c84568207dcdbf189
wider_attributeWIDER AttributeHuman Attribute Recognition by Deep Hierarchical ContextsHuman Attribute Recognition by Deep Hierarchical Contexts[pdf][s2]44d23df380af207f5ac5b41459c722c87283e1eb
wider_faceWIDER FACEWIDER FACE: A Face Detection BenchmarkWIDER FACE: A Face Detection Benchmark[pdf][s2]52d7eb0fbc3522434c13cc247549f74bb9609c5d
wildtrackWildTrackWILDTRACK: A Multi-camera HD Dataset for Dense Unscripted Pedestrian DetectionWILDTRACK : A Multi-camera HD Dataset for Dense Unscripted Pedestrian Detection[pdf][s2]77c81c13a110a341c140995bedb98101b9e84f7f
wlfdbWLFDBWLFDB: Weakly Labeled Face DatabasesWLFDB: Weakly Labeled Face Databases[pdf][s2]5ad4e9f947c1653c247d418f05dad758a3f9277b
yale_facesYaleFacesFrom Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and PoseFrom Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose[pdf][s2]18c72175ddbb7d5956d180b65a96005c100f6014
yale_facesYaleFacesAcquiring Linear Subspaces for Face Recognition under Variable LightingAcquiring linear subspaces for face recognition under variable lighting[pdf][s2]2ad0ee93d029e790ebb50574f403a09854b65b7e
yawddYawDDYawDD: A Yawning Detection DatasetYawDD: a yawning detection dataset[pdf][s2]a94cae786d515d3450d48267e12ca954aab791c4
yfcc_100mYFCC100MYFCC100M: The New Data in Multimedia ResearchYFCC100M: the new data in multimedia research[pdf][s2]010f0f4929e6a6644fb01f0e43820f91d0fad292
york_3dUOY 3D Face DatabaseThree-Dimensional Face Recognition: An Eigensurface ApproachThree-dimensional face recognition: an eigensurface approach[pdf][s2]19d1b811df60f86cbd5e04a094b07f32fff7a32a
youtube_facesYouTubeFacesFace Recognition in Unconstrained Videos with Matched Background SimilarityFace recognition in unconstrained videos with matched background similarity[pdf][s2]560e0e58d0059259ddf86fcec1fa7975dee6a868
youtube_posesYouTube PosePersonalizing Human Video Pose EstimationPersonalizing Human Video Pose Estimation[pdf][s2]1c2802c2199b6d15ecefe7ba0c39bfe44363de38
youtube_celebritiesYouTube CelebritiesFace Tracking and Recognition with Visual Constraints in Real-World VideosFace tracking and recognition with visual constraints in real-world videos[pdf][s2]6204776d31359d129a582057c2d788a14f8aadeb
erceERCeVideo Synopsis by Heterogeneous Multi-source CorrelationVideo Synopsis by Heterogeneous Multi-source Correlation[pdf][s2]b6c293f0420f7e945b5916ae44269fb53e139275
erceERCeLearning from Multiple Sources for Video SummarisationLearning from Multiple Sources for Video Summarisation[pdf][s2]287ddcb3db5562235d83aee318f318b8d5e43fb1
tisiTimes Square IntersectionVideo Synopsis by Heterogeneous Multi-source CorrelationVideo Synopsis by Heterogeneous Multi-source Correlation[pdf][s2]b6c293f0420f7e945b5916ae44269fb53e139275
tisiTimes Square IntersectionLearning from Multiple Sources for Video SummarisationLearning from Multiple Sources for Video Summarisation[pdf][s2]287ddcb3db5562235d83aee318f318b8d5e43fb1
laofiwLAOFIWTurning a Blind Eye: Explicit Removal of Biases and Variation from Deep Neural Network EmbeddingsTurning a Blind Eye: Explicit Removal of Biases and Variation from Deep Neural Network Embeddings[pdf][s2]4eab317b5ac436a949849ed286baa3de2a541eef
appa_realAPPA-REALApparent and Real Age Estimation in Still Images with Deep Residual Regressors on Appa-Real DatabaseApparent and Real Age Estimation in Still Images with Deep Residual Regressors on Appa-Real Database[pdf][s2]633c851ebf625ad7abdda2324e9de093cf623141
appa_realAPPA-REALFrom Apparent to Real Age: Gender, Age, Ethnic, Makeup, and Expression Bias Analysis in Real Age EstimationFrom Apparent to Real Age: Gender, Age, Ethnic, Makeup, and Expression Bias Analysis in Real Age Estimation[pdf][s2]7b92d1e53cc87f7a4256695de590098a2f30261e
mafaMAsked FAcesDetecting Masked Faces in the Wild with LLE-CNNsDetecting Masked Faces in the Wild with LLE-CNNs[pdf][s2]9cc8cf0c7d7fa7607659921b6ff657e17e135ecc
\ No newline at end of file diff --git a/scraper/reports/paper_title_report_nonmatching.html b/scraper/reports/paper_title_report_nonmatching.html index 448d90e2..412ea9ae 100644 --- a/scraper/reports/paper_title_report_nonmatching.html +++ b/scraper/reports/paper_title_report_nonmatching.html @@ -1,3 +1,3 @@ -Paper Titles that do not match

Paper Titles that do not match

keynameour titlefound titleaddresss2 id
10k_US_adult_faces10K US Adult FacesThe intrinsic memorability of face imagesThe intrinsic memorability of face photographs.[pdf][s2]8b2dd5c61b23ead5ae5508bb8ce808b5ea266730
3dddb_unconstrained3D DynamicA 3D Dynamic Database for Unconstrained Face RecognitionA 3 D Dynamic Database for Unconstrained Face Recognition[pdf][s2]4d4bb462c9f1d4e4ab1e4aa6a75cc0bc71b38461
afadAFADOrdinal Regression with a Multiple Output CNN for Age EstimationOrdinal Regression with Multiple Output CNN for Age Estimation[pdf][s2]6618cff7f2ed440a0d2fa9e74ad5469df5cdbe4c
afwAFWFace detection, pose estimation and landmark localization in the wildFace detection, pose estimation, and landmark localization in the wild[pdf][s2]0e986f51fe45b00633de9fd0c94d082d2be51406
alert_airportALERT AirportA Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and DatasetsA Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets.[pdf][s2]6403117f9c005ae81f1e8e6d1302f4a045e3d99d
am_fedAM-FEDAffectiva MIT Facial Expression Dataset (AM-FED): Naturalistic and Spontaneous Facial Expressions Collected “In the Wild”Affectiva-MIT Facial Expression Dataset (AM-FED): Naturalistic and Spontaneous Facial Expressions Collected "In-the-Wild"[pdf][s2]47aeb3b82f54b5ae8142b4bdda7b614433e69b9a
bp4d_spontanousBP4D-SpontanousA high resolution spontaneous 3D dynamic facial expression databaseA high-resolution spontaneous 3D dynamic facial expression database[pdf][s2]SUNY Binghamtonb91f54e1581fbbf60392364323d00a0cd43e493c
casablancaCasablancaContext-aware {CNNs} for person head detectionContext-Aware CNNs for Person Head Detection[pdf][s2]0ceda9dae8b9f322df65ca2ef02caca9758aec6f
cfdCFDThe Chicago face database: A free stimulus set of faces and norming dataThe Chicago face database: A free stimulus set of faces and norming data.[pdf][s2]4df3143922bcdf7db78eb91e6b5359d6ada004d2
cmu_pieCMU PIEThe CMU Pose, Illumination, and Expression DatabaseThe CMU Pose, Illumination, and Expression (PIE) Database[pdf][s2]4d423acc78273b75134e2afd1777ba6d3a398973
columbia_gazeColumbia GazeGaze Locking: Passive Eye Contact Detection for Human–Object InteractionGaze locking: passive eye contact detection for human-object interaction[pdf][s2]Columbia University06f02199690961ba52997cde1527e714d2b3bf8f
cvc_01_barcelonaCVC-01Adaptive Image Sampling and Windows Classification for On-board Pedestrian DetectionAdaptive Image Sampling and Windows Classification for On – board Pedestrian Detection[pdf][s2]57fe081950f21ca03b5b375ae3e84b399c015861
d3dfacsD3DFACSA FACS Valid 3D Dynamic Action Unit database with Applications to 3D Dynamic Morphable Facial ModellingA FACS valid 3D dynamic action unit database with applications to 3D dynamic morphable facial modeling[pdf][s2]070de852bc6eb275d7ca3a9cdde8f6be8795d1a3
dartmouth_childrenDartmouth ChildrenThe Dartmouth Database of Children's Faces: Acquisition and validation of a new face stimulus setThe Dartmouth Database of Children’s Faces: Acquisition and Validation of a New Face Stimulus Set[pdf][s2]4e6ee936eb50dd032f7138702fa39b7c18ee8907
fddbFDDBFDDB: A Benchmark for Face Detection in Unconstrained SettingsA Benchmark for Face Detection in Unconstrained Settings[pdf][s2]75da1df4ed319926c544eefe17ec8d720feef8c0
feiFEICaptura e Alinhamento de Imagens: Um Banco de Faces BrasileiroA new ranking method for principal components analysis and its application to face image analysis[pdf][s2]8b56e33f33e582f3e473dba573a16b598ed9bcdc
frgcFRGCOverview of the Face Recognition Grand ChallengeOverview of the face recognition grand challenge[pdf][s2]NIST18ae7c9a4bbc832b8b14bc4122070d7939f5e00e
georgia_tech_face_databaseGeorgia Tech FaceMaximum likelihood training of the embedded HMM for face detection and recognitionMAXIMUM 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[pdf][s2]3dc3f0b64ef80f573e3a5f96e456e52ee980b877
hda_plusHDA+A Multi-camera video data set for research on High-Definition surveillanceHDA dataset-DRAFT 1 A Multi-camera video data set for research on High-Definition surveillance[pdf][s2]bd88bb2e4f351352d88ee7375af834360e223498
ifadIFADIndian Face Age Database: A Database for Face Recognition with Age VariationIndian Face Age Database : A Database for Face Recognition with Age Variation[pdf][s2]55c40cbcf49a0225e72d911d762c27bb1c2d14aa
ijb_cIJB-CIARPA Janus Benchmark CIARPA Janus Benchmark - C: Face Dataset and Protocol[pdf][s2]57178b36c21fd7f4529ac6748614bb3374714e91
ilids_mctsi-LIDS Multiple-CameraImagery Library for Intelligent Detection Systems: The i-LIDS User GuideImagery Library for Intelligent Detection Systems (i-LIDS); A Standard for Testing Video Based Detection Systems[pdf][s2]0297448f3ed948e136bb06ceff10eccb34e5bb77
ilids_vid_reidiLIDS-VIDPerson Re-Identi cation by Video RankingPerson Re-identification by Video Ranking[pdf][s2]98bb029afe2a1239c3fdab517323066f0957b81b
images_of_groupsImages of GroupsUnderstanding Groups of Images of PeopleUnderstanding images of groups of people[pdf][s2]Carnegie Mellon University21d9d0deed16f0ad62a4865e9acf0686f4f15492
lfwLFWLabeled Faces in the Wild: Updates and New Reporting ProceduresLabeled Faces in the Wild : Updates and New Reporting Procedures[pdf][s2]2d3482dcff69c7417c7b933f22de606a0e8e42d4
lfwLFWLabeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained EnvironmentsLabeled Faces in the Wild : A Database for Studying Face Recognition in Unconstrained Environments[pdf][s2]370b5757a5379b15e30d619e4d3fb9e8e13f3256
lfwLFWLabeled Faces in the Wild: A SurveyLabeled Faces in the Wild : A Survey[pdf][s2]7de6e81d775e9cd7becbfd1bd685f4e2a5eebb22
malfMALFFine-grained Evaluation on Face Detection in the Wild.Fine-grained evaluation on face detection in the wild[pdf][s2]45e616093a92e5f1e61a7c6037d5f637aa8964af
mr2MR2The MR2: A multi-racial mega-resolution database of facial stimuliThe MR2: A multi-racial, mega-resolution database of facial stimuli.[pdf][s2]578d4ad74818086bb64f182f72e2c8bd31e3d426
multi_pieMULTIPIEMulti-PIEThe CMU Pose, Illumination, and Expression (PIE) Database[pdf][s2]4d423acc78273b75134e2afd1777ba6d3a398973
names_and_facesNews DatasetNames and FacesNames and faces in the news[pdf][s2]2fda164863a06a92d3a910b96eef927269aeb730
nova_emotionsNovaemötions DatasetCompetitive affective gamming: Winning with a smileCompetitive affective gaming: winning with a smile[pdf][s2]Universidade NOVA de Lisboa, Caparica, Portugal7f4040b482d16354d5938c1d1b926b544652bf5b
sdu_vidSDU-VIDPerson reidentification by video rankingPerson Re-identification by Video Ranking[pdf][s2]98bb029afe2a1239c3fdab517323066f0957b81b
stanford_droneStanford DroneLearning Social Etiquette: Human Trajectory Prediction In Crowded ScenesSocial LSTM: Human Trajectory Prediction in Crowded Spaces[pdf][s2]570f37ed63142312e6ccdf00ecc376341ec72b9f
stickmen_buffyBuffy StickmenLearning to Parse Images of Articulated ObjectsLearning to parse images of articulated bodies[pdf][s2]6dd0597f8513dc100cd0bc1b493768cde45098a9
stickmen_pascalStickmen PASCALClustered Pose and Nonlinear Appearance Models for Human Pose EstimationLearning to parse images of articulated bodies[pdf][s2]6dd0597f8513dc100cd0bc1b493768cde45098a9
stickmen_pascalStickmen PASCALLearning to Parse Images of Articulated ObjectsLearning to parse images of articulated bodies[pdf][s2]6dd0597f8513dc100cd0bc1b493768cde45098a9
sun_attributesSUNSUN Attribute Database: +Paper Titles that do not match

Paper Titles that do not match

keynameour titlefound titleaddresss2 id
10k_US_adult_faces10K US Adult FacesThe intrinsic memorability of face imagesThe intrinsic memorability of face photographs.[pdf][s2]8b2dd5c61b23ead5ae5508bb8ce808b5ea266730
3dddb_unconstrained3D DynamicA 3D Dynamic Database for Unconstrained Face RecognitionA 3 D Dynamic Database for Unconstrained Face Recognition[pdf][s2]4d4bb462c9f1d4e4ab1e4aa6a75cc0bc71b38461
afadAFADOrdinal Regression with a Multiple Output CNN for Age EstimationOrdinal Regression with Multiple Output CNN for Age Estimation[pdf][s2]6618cff7f2ed440a0d2fa9e74ad5469df5cdbe4c
afwAFWFace detection, pose estimation and landmark localization in the wildFace detection, pose estimation, and landmark localization in the wild[pdf][s2]0e986f51fe45b00633de9fd0c94d082d2be51406
alert_airportALERT AirportA Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and DatasetsA Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets.[pdf][s2]6403117f9c005ae81f1e8e6d1302f4a045e3d99d
am_fedAM-FEDAffectiva MIT Facial Expression Dataset (AM-FED): Naturalistic and Spontaneous Facial Expressions Collected “In the Wild”Affectiva-MIT Facial Expression Dataset (AM-FED): Naturalistic and Spontaneous Facial Expressions Collected "In-the-Wild"[pdf][s2]47aeb3b82f54b5ae8142b4bdda7b614433e69b9a
bp4d_spontanousBP4D-SpontanousA high resolution spontaneous 3D dynamic facial expression databaseA high-resolution spontaneous 3D dynamic facial expression database[pdf][s2]SUNY Binghamtonb91f54e1581fbbf60392364323d00a0cd43e493c
casablancaCasablancaContext-aware {CNNs} for person head detectionContext-Aware CNNs for Person Head Detection[pdf][s2]0ceda9dae8b9f322df65ca2ef02caca9758aec6f
cfdCFDThe Chicago face database: A free stimulus set of faces and norming dataThe Chicago face database: A free stimulus set of faces and norming data.[pdf][s2]4df3143922bcdf7db78eb91e6b5359d6ada004d2
cmu_pieCMU PIEThe CMU Pose, Illumination, and Expression DatabaseThe CMU Pose, Illumination, and Expression (PIE) Database[pdf][s2]4d423acc78273b75134e2afd1777ba6d3a398973
columbia_gazeColumbia GazeGaze Locking: Passive Eye Contact Detection for Human–Object InteractionGaze locking: passive eye contact detection for human-object interaction[pdf][s2]Columbia University06f02199690961ba52997cde1527e714d2b3bf8f
cvc_01_barcelonaCVC-01Adaptive Image Sampling and Windows Classification for On-board Pedestrian DetectionAdaptive Image Sampling and Windows Classification for On – board Pedestrian Detection[pdf][s2]57fe081950f21ca03b5b375ae3e84b399c015861
d3dfacsD3DFACSA FACS Valid 3D Dynamic Action Unit database with Applications to 3D Dynamic Morphable Facial ModellingA FACS valid 3D dynamic action unit database with applications to 3D dynamic morphable facial modeling[pdf][s2]070de852bc6eb275d7ca3a9cdde8f6be8795d1a3
dartmouth_childrenDartmouth ChildrenThe Dartmouth Database of Children's Faces: Acquisition and validation of a new face stimulus setThe Dartmouth Database of Children’s Faces: Acquisition and Validation of a New Face Stimulus Set[pdf][s2]4e6ee936eb50dd032f7138702fa39b7c18ee8907
fddbFDDBFDDB: A Benchmark for Face Detection in Unconstrained SettingsA Benchmark for Face Detection in Unconstrained Settings[pdf][s2]75da1df4ed319926c544eefe17ec8d720feef8c0
feiFEICaptura e Alinhamento de Imagens: Um Banco de Faces BrasileiroA new ranking method for principal components analysis and its application to face image analysis[pdf][s2]8b56e33f33e582f3e473dba573a16b598ed9bcdc
frgcFRGCOverview of the Face Recognition Grand ChallengeOverview of the face recognition grand challenge[pdf][s2]NIST18ae7c9a4bbc832b8b14bc4122070d7939f5e00e
georgia_tech_face_databaseGeorgia Tech FaceMaximum likelihood training of the embedded HMM for face detection and recognitionMAXIMUM 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[pdf][s2]3dc3f0b64ef80f573e3a5f96e456e52ee980b877
hda_plusHDA+A Multi-camera video data set for research on High-Definition surveillanceHDA dataset-DRAFT 1 A Multi-camera video data set for research on High-Definition surveillance[pdf][s2]bd88bb2e4f351352d88ee7375af834360e223498
ifadIFADIndian Face Age Database: A Database for Face Recognition with Age VariationIndian Face Age Database : A Database for Face Recognition with Age Variation[pdf][s2]55c40cbcf49a0225e72d911d762c27bb1c2d14aa
ijb_cIJB-CIARPA Janus Benchmark CIARPA Janus Benchmark - C: Face Dataset and Protocol[pdf][s2]57178b36c21fd7f4529ac6748614bb3374714e91
ilids_mctsi-LIDS Multiple-CameraImagery Library for Intelligent Detection Systems: The i-LIDS User GuideImagery Library for Intelligent Detection Systems (i-LIDS); A Standard for Testing Video Based Detection Systems[pdf][s2]0297448f3ed948e136bb06ceff10eccb34e5bb77
ilids_vid_reidiLIDS-VIDPerson Re-Identi cation by Video RankingPerson Re-identification by Video Ranking[pdf][s2]98bb029afe2a1239c3fdab517323066f0957b81b
images_of_groupsImages of GroupsUnderstanding Groups of Images of PeopleUnderstanding images of groups of people[pdf][s2]Carnegie Mellon University Silicon Valley21d9d0deed16f0ad62a4865e9acf0686f4f15492
lfwLFWLabeled Faces in the Wild: Updates and New Reporting ProceduresLabeled Faces in the Wild : Updates and New Reporting Procedures[pdf][s2]2d3482dcff69c7417c7b933f22de606a0e8e42d4
lfwLFWLabeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained EnvironmentsLabeled Faces in the Wild : A Database for Studying Face Recognition in Unconstrained Environments[pdf][s2]370b5757a5379b15e30d619e4d3fb9e8e13f3256
lfwLFWLabeled Faces in the Wild: A SurveyLabeled Faces in the Wild : A Survey[pdf][s2]7de6e81d775e9cd7becbfd1bd685f4e2a5eebb22
malfMALFFine-grained Evaluation on Face Detection in the Wild.Fine-grained evaluation on face detection in the wild[pdf][s2]45e616093a92e5f1e61a7c6037d5f637aa8964af
mr2MR2The MR2: A multi-racial mega-resolution database of facial stimuliThe MR2: A multi-racial, mega-resolution database of facial stimuli.[pdf][s2]578d4ad74818086bb64f182f72e2c8bd31e3d426
multi_pieMULTIPIEMulti-PIEThe CMU Pose, Illumination, and Expression (PIE) Database[pdf][s2]4d423acc78273b75134e2afd1777ba6d3a398973
names_and_facesNews DatasetNames and FacesNames and faces in the news[pdf][s2]2fda164863a06a92d3a910b96eef927269aeb730
nova_emotionsNovaemötions DatasetCompetitive affective gamming: Winning with a smileCompetitive affective gaming: winning with a smile[pdf][s2]Universidade NOVA de Lisboa, Caparica, Portugal7f4040b482d16354d5938c1d1b926b544652bf5b
sdu_vidSDU-VIDPerson reidentification by video rankingPerson Re-identification by Video Ranking[pdf][s2]98bb029afe2a1239c3fdab517323066f0957b81b
stanford_droneStanford DroneLearning Social Etiquette: Human Trajectory Prediction In Crowded ScenesSocial LSTM: Human Trajectory Prediction in Crowded Spaces[pdf][s2]570f37ed63142312e6ccdf00ecc376341ec72b9f
stickmen_buffyBuffy StickmenLearning to Parse Images of Articulated ObjectsLearning to parse images of articulated bodies[pdf][s2]6dd0597f8513dc100cd0bc1b493768cde45098a9
stickmen_pascalStickmen PASCALClustered Pose and Nonlinear Appearance Models for Human Pose EstimationLearning to parse images of articulated bodies[pdf][s2]6dd0597f8513dc100cd0bc1b493768cde45098a9
stickmen_pascalStickmen PASCALLearning to Parse Images of Articulated ObjectsLearning to parse images of articulated bodies[pdf][s2]6dd0597f8513dc100cd0bc1b493768cde45098a9
sun_attributesSUNSUN Attribute Database: Discovering, Annotating, and Recognizing Scene AttributesSUN attribute database: Discovering, annotating, and recognizing scene attributes[pdf][s2]Brown University833fa04463d90aab4a9fe2870d480f0b40df446e
tiny_imagesTiny Images80 million tiny images: a large dataset for non-parametric object and scene recognition80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition[pdf][s2]31b58ced31f22eab10bd3ee2d9174e7c14c27c01
umd_facesUMDThe Do's and Don'ts for CNN-based Face VerificationThe Do’s and Don’ts for CNN-Based Face Verification[pdf][s2]71b7fc715e2f1bb24c0030af8d7e7b6e7cd128a6
viperVIPeREvaluating Appearance Models for Recognition, Reacquisition, and TrackingEvaluating Appearance Models for Recognition , Reacquisition , and Tracking[pdf][s2]6273b3491e94ea4dd1ce42b791d77bdc96ee73a8
who_goes_thereWGTWho Goes There? Approaches to Mapping Facial Appearance DiversityWho goes there?: approaches to mapping facial appearance diversity[pdf][s2]University of Kentucky9b9bf5e623cb8af7407d2d2d857bc3f1b531c182
wildtrackWildTrackWILDTRACK: A Multi-camera HD Dataset for Dense Unscripted Pedestrian DetectionWILDTRACK : A Multi-camera HD Dataset for Dense Unscripted Pedestrian Detection[pdf][s2]77c81c13a110a341c140995bedb98101b9e84f7f
\ No newline at end of file diff --git a/scraper/reports/report_coverage.html b/scraper/reports/report_coverage.html index f384cf95..84aec8fa 100644 --- a/scraper/reports/report_coverage.html +++ b/scraper/reports/report_coverage.html @@ -1 +1 @@ -Coverage

Coverage

Paper IDMegapixels KeyMegapixels NameReport LinkPDF LinkJournalTypeAddressCountryLatLngCoverageTotal CitationsGeocoded CitationsUnknown CitationsEmpty CitationsWith PDFWith DOI
b5f2846a506fc417e7da43f6a7679146d99c5e96ucf_101UCF101UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild[pdf]CoRR61%99961138873716283
0e986f51fe45b00633de9fd0c94d082d2be51406afwAFWFace detection, pose estimation, and landmark localization in the wild[pdf]2012 IEEE Conference on Computer Vision and Pattern Recognition61%99960839159622387
370b5757a5379b15e30d619e4d3fb9e8e13f3256lfwLFWLabeled Faces in the Wild : A Database for Studying Face Recognition in Unconstrained Environments[pdf]Unknown58%99957542271639371
0ee1916a0cb2dc7d3add086b5f1092c3d4beb38avocVOCThe Pascal Visual Object Classes (VOC) Challenge[pdf]International Journal of Computer VisioneduOxford UniversityUnited Kingdom51.75208490-1.2516646058%99957542435613414
5e0f8c355a37a5a89351c02f174e7a5ddcb98683cocoCOCOMicrosoft COCO: Common Objects in Context[pdf]Unknown57%99956943029799193
2e384f057211426ac5922f1b33d2aa8df5d51f57a_pascal_yahoo#N/ADescribing objects by their attributes[pdf]2009 IEEE Conference on Computer Vision and Pattern RecognitioneduUniversity of Illinois, Urbana-ChampaignUnited States40.11116745-88.2258766557%99956543374738264
759a3b3821d9f0e08e0b0a62c8b693230afc3f8dpubfigPubFigAttribute and simile classifiers for face verification[pdf]2009 IEEE 12th International Conference on Computer Vision61%89454435056604300
162ea969d1929ed180cc6de9f0bf116993ff6e06vgg_facesVGG FaceDeep Face Recognition[pdf]Unknown54%99954345670635370
10d6b12fa07c7c8d6c8c3f42c7f1c061c131d4c5inria_personINRIA PedestrianHistograms of oriented gradients for human detection[pdf]2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)eduINRIA Rhone-Alps, Montbonnot, FranceFrance45.217886005.8073690054%99953946067537477
4d9a02d080636e9666c4d1cc438b9893391ec6c7cohn_kanade_plusCK+The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression[pdf]2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - WorkshopseduUniversity of PittsburghUnited States40.44415295-79.9624399355%97553543967475510
31b58ced31f22eab10bd3ee2d9174e7c14c27c01tiny_imagesTiny Images80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition[pdf]IEEE Transactions on Pattern Analysis and Machine Intelligence54%99953546394685327
026e3363b7f76b51cc711886597a44d5f1fd1de2kittiKITTIVision meets robotics: The KITTI dataset[pdf]I. J. Robotics Res.53%99953246737571448
6d96f946aaabc734af7fe3fc4454cf8547fcd5edar_facedbAR FaceThe AR face database[pdf]53%99952647351459573
18c72175ddbb7d5956d180b65a96005c100f6014yale_facesYaleFacesFrom Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose[pdf]IEEE Trans. Pattern Anal. Mach. Intell.51%99951448577551459
23fc83c8cfff14a16df7ca497661264fc54ed746cohn_kanadeCKComprehensive Database for Facial Expression Analysis[pdf]Unknown50%99950349675572439
18ae7c9a4bbc832b8b14bc4122070d7939f5e00efrgcFRGCOverview of the face recognition grand challenge[pdf]2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)eduNISTUnited States39.14004000-77.2185060050%999497501114594424
f72f6a45ee240cc99296a287ff725aaa7e7ebb35caltech_pedestriansCaltech PedestriansPedestrian Detection: An Evaluation of the State of the Art[pdf]IEEE Transactions on Pattern Analysis and Machine IntelligenceeduCalifornia Institute of TechnologyUnited States34.13710185-118.1252748749%99948551471541464
2ad0ee93d029e790ebb50574f403a09854b65b7eyale_facesYaleFacesAcquiring linear subspaces for face recognition under variable lighting[pdf]IEEE Transactions on Pattern Analysis and Machine Intelligence48%999484515110525485
0f0fcf041559703998abf310e56f8a2f90ee6f21feretFERETThe FERET Evaluation Methodology for Face-Recognition Algorithms[pdf]Unknown48%999482517103560454
b62628ac06bbac998a3ab825324a41a11bc3a988m2vtsdb_extendedxm2vtsdbXm2vtsdb: the Extended M2vts Database[pdf]Unknown53%90648142544542408
01959ef569f74c286956024866c1d107099199f7vqaVQAVQA: Visual Question Answering[pdf]2015 IEEE International Conference on Computer Vision (ICCV)61%7314442874762996
dc8b25e35a3acb812beb499844734081722319b4feretFERETThe FERET database and evaluation procedure for face-recognition algorithms[pdf]Image Vision Comput.44%999443556106606413
55206f0b5f57ce17358999145506cd01e570358corlORLParameterisation of a stochastic model for human face identification[pdf]Unknown44%99944255797569445
45c31cde87258414f33412b3b12fc5bec7cb3ba9jaffeJAFFECoding Facial Expressions with Gabor Wavelets[pdf]Unknown50%84842242655420433
6424b69f3ff4d35249c0bb7ef912fbc2c86f4ff4celebaCelebADeep Learning Face Attributes in the Wild[pdf]2015 IEEE International Conference on Computer Vision (ICCV)eduChinese University of Hong KongChina22.41626320114.2109318052%80842138668670118
32cde90437ab5a70cf003ea36f66f2de0e24b3abcityscapesCityscapesThe Cityscapes Dataset for Semantic Urban Scene Understanding[pdf]2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)52%77140336854624138
177bc509dd0c7b8d388bb47403f28d6228c14b5cceleba_plusCelebFaces+Deep Learning Face Representation from Predicting 10,000 Classes[pdf]2014 IEEE Conference on Computer Vision and Pattern Recognition60%66039726325340330
4d423acc78273b75134e2afd1777ba6d3a398973cmu_pieCMU PIEThe CMU Pose, Illumination, and Expression (PIE) Database of Human Faces[pdf]Unknown53%74239634459416329
4d423acc78273b75134e2afd1777ba6d3a398973multi_pieMULTIPIEThe CMU Pose, Illumination, and Expression (PIE) Database of Human Faces[pdf]Unknown53%74239634459416329
2830fb5282de23d7784b4b4bc37065d27839a412h3dH3DPoselets: Body part detectors trained using 3D human pose annotations[pdf]2009 IEEE 12th International Conference on Computer Vision52%70736833967509215
6273b3491e94ea4dd1ce42b791d77bdc96ee73a8viperVIPeREvaluating Appearance Models for Recognition, Reacquisition, and Tracking[pdf]Unknown56%58432925538338245
140438a77a771a8fb656b39a78ff488066eb6b50lfpwLFWPLocalizing Parts of Faces Using a Consensus of Exemplars[pdf]IEEE Transactions on Pattern Analysis and Machine Intelligence60%52131520642337195
6bd36e9fd0ef20a3074e1430a6cc601e6d407fc3cuhk03CUHK03DeepReID: Deep Filter Pairing Neural Network for Person Re-identification[pdf]2014 IEEE Conference on Computer Vision and Pattern Recognition59%51230420829324180
560e0e58d0059259ddf86fcec1fa7975dee6a868youtube_facesYouTubeFacesFace recognition in unconstrained videos with matched background similarity[pdf]CVPR 2011eduTel Aviv UniversityIsrael32.1119889034.8045970260%48529219230298193
2258e01865367018ed6f4262c880df85b94959f8motMOTEvaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics[pdf]EURASIP J. Image and Video Processing49%58629029448345244
3316521a5527c7700af8ae6aef32a79a8b83672ctud_campusTUD-CampusPeople-tracking-by-detection and people-detection-by-tracking[pdf]2008 IEEE Conference on Computer Vision and Pattern Recognition53%52928024840324213
3316521a5527c7700af8ae6aef32a79a8b83672ctud_crossingTUD-CrossingPeople-tracking-by-detection and people-detection-by-tracking[pdf]2008 IEEE Conference on Computer Vision and Pattern Recognition53%52928024840324213
3316521a5527c7700af8ae6aef32a79a8b83672ctud_pedestrianTUD-PedestrianPeople-tracking-by-detection and people-detection-by-tracking[pdf]2008 IEEE Conference on Computer Vision and Pattern Recognition53%52928024840324213
cc589c499dcf323fe4a143bbef0074c3e31f9b60bu_3dfeBU-3DFEA 3D facial expression database for facial behavior research[pdf]7th International Conference on Automatic Face and Gesture Recognition (FGR06)47%55526329147299270
1dc35905a1deff8bc74688f2d7e2f48fd2273275caltech_pedestriansCaltech PedestriansPedestrian detection: A benchmark[pdf]2009 IEEE Conference on Computer Vision and Pattern Recognition50%51926125827289233
853bd61bc48a431b9b1c7cab10c603830c488e39casia_webfaceCASIA WebfaceLearning Face Representation from Scratch[pdf]CoRReduChinese Academy of SciencesChina40.00447950116.3702380060%43626017630288150
4308bd8c28e37e2ed9a3fcfe74d5436cce34b410market_1501Market 1501Scalable Person Re-identification: A Benchmark[pdf]2015 IEEE International Conference on Computer Vision (ICCV)60%39423815618272116
4053e3423fb70ad9140ca89351df49675197196abio_idBioID FaceRobust Face Detection Using the Hausdorff Distance[pdf]Unknown48%49823726156330179
8a3c5507237957d013a0fe0f082cab7f757af6eemaflMAFLFacial Landmark Detection by Deep Multi-task Learning[pdf]Unknown60%38323115225265121
8a3c5507237957d013a0fe0f082cab7f757af6eemtflMTFLFacial Landmark Detection by Deep Multi-task Learning[pdf]Unknown60%38323115225265121
9055b155cbabdce3b98e16e5ac9c0edf00f9552fmorphMORPH CommercialMORPH: a longitudinal image database of normal adult age-progression[pdf]7th International Conference on Automatic Face and Gesture Recognition (FGR06)53%42422519826239190
9055b155cbabdce3b98e16e5ac9c0edf00f9552fmorph_ncMORPH Non-CommercialMORPH: a longitudinal image database of normal adult age-progression[pdf]7th International Conference on Automatic Face and Gesture Recognition (FGR06)53%42422519826239190
3325860c0c82a93b2eac654f5324dd6a776f609empii_human_poseMPII Human Pose2D Human Pose Estimation: New Benchmark and State of the Art Analysis[pdf]2014 IEEE Conference on Computer Vision and Pattern Recognition62%3562211352130453
6dd0597f8513dc100cd0bc1b493768cde45098a9stickmen_buffyBuffy StickmenLearning to parse images of articulated bodies[pdf]Unknown58%37321515735251129
6dd0597f8513dc100cd0bc1b493768cde45098a9stickmen_pascalStickmen PASCALLearning to parse images of articulated bodies[pdf]Unknown58%37321515735251129
6dd0597f8513dc100cd0bc1b493768cde45098a9stickmen_pascalStickmen PASCALLearning to parse images of articulated bodies[pdf]Unknown58%37321515735251129
2a75f34663a60ab1b04a0049ed1d14335129e908mmi_facial_expressionMMI Facial Expression DatasetWeb-based database for facial expression analysis[pdf]2005 IEEE International Conference on Multimedia and Expo48%44021222844267181
2485c98aa44131d1a2f7d1355b1e372f2bb148adcas_pealCAS-PEALThe CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations[pdf]IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans50%41520920639189232
16c7c31a7553d99f1837fc6e88e77b5ccbb346b8pridPRIDPerson Re-identification by Descriptive and Discriminative Classification[pdf]Unknown58%35220414827196157
95f12d27c3b4914e0668a268360948bce92f7db3helenHelenInteractive Facial Feature Localization[pdf]UnknowncompanyFacebookUnited States37.39367170-122.0807262059%33920113829219129
044d9a8c61383312cdafbcc44b9d00d650b21c70fiw_300300-W300 Faces in-the-Wild Challenge: The First Facial Landmark Localization Challenge[pdf]2013 IEEE International Conference on Computer Vision Workshops61%32419912529211118
2724ba85ec4a66de18da33925e537f3902f21249cofwCOFWRobust Face Landmark Estimation under Occlusion[pdf]2013 IEEE International Conference on Computer VisioneduCalifornia Institute of TechnologyUnited States34.13710185-118.1252748761%30518611916192116
4b1d23d17476fcf78f4cbadf69fb130b1aa627c0leeds_sports_poseLeeds Sports PoseClustered Pose and Nonlinear Appearance Models for Human Pose Estimation[pdf]Unknown65%278180981320878
4b1d23d17476fcf78f4cbadf69fb130b1aa627c0stickmen_buffyBuffy StickmenClustered Pose and Nonlinear Appearance Models for Human Pose Estimation[pdf]Unknown65%278180981320878
639937b3a1b8bded3f7e9a40e85bd3770016cf3cbfmBFMA 3D Face Model for Pose and Illumination Invariant Face Recognition[pdf]2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance54%3231761472922698
13f06b08f371ba8b5d31c3e288b4deb61335b462eth_andreas_essETHZ PedestrianDepth and Appearance for Mobile Scene Analysis[pdf]2007 IEEE 11th International Conference on Computer VisioneduETH ZurichSwitzerland47.376313008.5476699055%31917614327195127
a74251efa970b92925b89eeef50a5e37d9281ad0aflwAFLWAnnotated Facial Landmarks in the Wild: A large-scale, real-world database for facial landmark localization[pdf]2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)60%2921751173721284
3765df816dc5a061bc261e190acc8bdd9d47bec0rafdRaFDPresentation and validation of the Radboud Faces Database[pdf]Unknown39%44617527143322136
5981e6479c3fd4e31644db35d236bfb84ae46514motMOTLearning to associate: HybridBoosted multi-target tracker for crowded scene[pdf]2009 IEEE Conference on Computer Vision and Pattern RecognitioneduUniversity of Southern CaliforniaUnited States34.02241490-118.2863440752%33017215727196139
436f798d1a4e54e5947c1e7d7375c31b2bdb4064tud_multiviewTUD-MultiviewMonocular 3D pose estimation and tracking by detection[pdf]2010 IEEE Computer Society Conference on Computer Vision and Pattern RecognitioneduTU DarmstadtGermany49.874827708.6563281054%30216413834207100
436f798d1a4e54e5947c1e7d7375c31b2bdb4064tud_stadtmitteTUD-StadtmitteMonocular 3D pose estimation and tracking by detection[pdf]2010 IEEE Computer Society Conference on Computer Vision and Pattern RecognitioneduTU DarmstadtGermany49.874827708.6563281054%30216413834207100
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2acf7e58f0a526b957be2099c10aab693f795973bosphorusThe BosphorusBosphorus Database for 3D Face Analysis[pdf]Unknown48%32815817019149183
833fa04463d90aab4a9fe2870d480f0b40df446esun_attributesSUNSUN attribute database: Discovering, annotating, and recognizing scene attributes[pdf]2012 IEEE Conference on Computer Vision and Pattern RecognitioneduBrown UniversityUnited States41.82686820-71.4012314658%2691561132921557
010f0f4929e6a6644fb01f0e43820f91d0fad292yfcc_100mYFCC100MYFCC100M: the new data in multimedia research[pdf]Commun. ACMeduCarnegie Mellon UniversityUnited States37.41021930-122.0596548756%2761551212317599
2fda164863a06a92d3a910b96eef927269aeb730names_and_facesNews DatasetNames and faces in the news[pdf]Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004.51%2941501432921582
9361b784e73e9238d5cefbea5ac40d35d1e3103ftowncenterTownCenterStable multi-target tracking in real-time surveillance video[pdf]CVPR 201144%31013717324180131
2eed184680edcdec8a3b605ad1a3ba8e8f7cc2e9grazGraz PedestrianGeneric object recognition with boosting[pdf]IEEE Transactions on Pattern Analysis and Machine IntelligenceeduTU GrazAustria47.0707140015.4395040048%2861361501619397
e8de844fefd54541b71c9823416daa238be65546visual_phrasesPhrasal RecognitionRecognition using visual phrases[pdf]CVPR 2011eduUniversity of Illinois, Urbana-ChampaignUnited States40.11116745-88.2258766558%233135981817758
38b55d95189c5e69cf4ab45098a48fba407609b4cuhk02CUHK02Locally Aligned Feature Transforms across Views[pdf]2013 IEEE Conference on Computer Vision and Pattern Recognition53%24212911317139102
7808937b46acad36e43c30ae4e9f3fd57462853dbpadBPADDescribing people: A poselet-based approach to attribute classification[pdf]2011 International Conference on Computer Vision57%221125961416559
4c170a0dcc8de75587dae21ca508dab2f9343974face_tracerFaceTracerFaceTracer: A Search Engine for Large Collections of Images with Faces[pdf]Unknown57%218125921715271
0c91808994a250d7be332400a534a9291ca3b60egrazGraz PedestrianWeak Hypotheses and Boosting for Generic Object Detection and Recognition[pdf]Unknown51%2471251221817778
140c95e53c619eac594d70f6369f518adfea12efijb_cIJB-CPushing the frontiers of unconstrained face detection and recognition: IARPA Janus Benchmark A[pdf]2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)55%222123991916162
6ad5a38df8dd4cdddd74f31996ce096d41219f72tud_brusselsTUD-BrusselsMulti-cue onboard pedestrian detection[pdf]2009 IEEE Conference on Computer Vision and Pattern Recognition56%217121961413386
6ad5a38df8dd4cdddd74f31996ce096d41219f72tud_motionpairsTUD-MotionparisMulti-cue onboard pedestrian detection[pdf]2009 IEEE Conference on Computer Vision and Pattern Recognition56%217121961413386
98bb029afe2a1239c3fdab517323066f0957b81bilids_vid_reidiLIDS-VIDPerson Re-identification by Video Ranking[pdf]Unknown57%210120901011594
98bb029afe2a1239c3fdab517323066f0957b81bsdu_vidSDU-VIDPerson Re-identification by Video Ranking[pdf]Unknown57%210120901011594
35b0331dfcd2897abd5749b49ff5e2b8ba0f7a62coco_qaCOCO QAExploring Models and Data for Image Question Answering[pdf]Unknown60%191115761216527
46a01565e6afe7c074affb752e7069ee3bf2e4efsdu_vidSDU-VIDLocal Descriptors Encoded by Fisher Vectors for Person Re-identification[pdf]Unknown58%198114841611188
013909077ad843eb6df7a3e8e290cfd5575999d2fiw_300300-WA Semi-automatic Methodology for Facial Landmark Annotation[pdf]2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops60%185111741512464
4e4746094bf60ee83e40d8597a6191e463b57f76leeds_sports_pose_extendedLeeds Sports Pose ExtendedLearning effective human pose estimation from inaccurate annotation[pdf]CVPR 2011eduUniversity of LeedsUnited Kingdom53.80387185-1.5524571264%173111621012256
21d9d0deed16f0ad62a4865e9acf0686f4f15492images_of_groupsImages of GroupsUnderstanding images of groups of people[pdf]2009 IEEE Conference on Computer Vision and Pattern RecognitioneduCarnegie Mellon UniversityUnited States37.41021930-122.0596548754%202110921213275
b1f4423c227fa37b9680787be38857069247a307afew_vaAFEW-VACollecting Large, Richly Annotated Facial-Expression Databases from Movies[pdf]IEEE MultiMediaeduAustralian National UniversityAustralia-35.27769990149.1185270060%1821097388699
570f37ed63142312e6ccdf00ecc376341ec72b9fstanford_droneStanford DroneSocial LSTM: Human Trajectory Prediction in Crowded Spaces[pdf]2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)46%229106123515079
22ad2c8c0f4d6aa4328b38d894b814ec22579761gallagherGallagherClothing cosegmentation for recognizing people[pdf]2008 IEEE Conference on Computer Vision and Pattern RecognitioneduCarnegie Mellon UniversityUnited States37.41021930-122.0596548758%17710374710184
1aad2da473888cb7ebc1bfaa15bfa0f1502ce005jpl_poseJPL-Interaction datasetFirst-Person Activity Recognition: What Are They Doing to Me?[pdf]2013 IEEE Conference on Computer Vision and Pattern Recognition70%14810345811138
133f01aec1534604d184d56de866a4bd531dac87lfw_a#N/AEffective Unconstrained Face Recognition by Combining Multiple Descriptors and Learned Background Statistics[pdf]IEEE Transactions on Pattern Analysis and Machine Intelligence55%17798791510475
18010284894ed0edcca74e5bf768ee2e15ef7841deep_fashionDeepFashionDeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations[pdf]2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)65%1509753411138
6204776d31359d129a582057c2d788a14f8aadebyoutube_celebritiesYouTube CelebritiesFace tracking and recognition with visual constraints in real-world videos[pdf]2008 IEEE Conference on Computer Vision and Pattern Recognition32%3019720218144133
56ffa7d906b08d02d6d5a12c7377a57e24ef3391unbc_shoulder_painUNBC-McMaster PainPainful data: The UNBC-McMaster shoulder pain expression archive database[pdf]Face and Gesture 2011eduCarnegie Mellon UniversityUnited States37.41021930-122.0596548752%18495892311271
5a5f0287484f0d480fed1ce585dbf729586f0edcdisfaDISFADISFA: A Spontaneous Facial Action Intensity Database[pdf]IEEE Transactions on Affective ComputingeduUniversity of DenverUnited States39.67665410-104.9622030049%19094961910091
1be498d4bbc30c3bfd0029114c784bc2114d67c0adienceAdienceAge and Gender Estimation of Unfiltered Faces[pdf]IEEE Transactions on Information Forensics and SecurityeduOpen University of IsraelIsrael32.7782416534.9956567355%168927659478
291265db88023e92bb8c8e6390438e5da148e8f5mscelebMsCelebMS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition[pdf]Unknown54%16791761413136
0df0d1adea39a5bef318b74faa37de7f3e00b452mpii_gazeMPIIGazeAppearance-based gaze estimation in the wild[pdf]2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)65%138904839742
29a705a5fa76641e0d8963f1fdd67ee4c0d92d3dscfaceSCfaceSCface – surveillance cameras face database[pdf]Multimedia Tools and Applications51%1789088159089
32c801cb7fbeb742edfd94cccfca4934baec71daucf_crowdUCF-CC-50Multi-source Multi-scale Counting in Extremely Dense Crowd Images[pdf]2013 IEEE Conference on Computer Vision and Pattern Recognition70%125883767352
3b5b6d19d4733ab606c39c69a889f9e67967f151qmul_gridGRIDMulti-camera activity correlation analysis[pdf]2009 IEEE Conference on Computer Vision and Pattern RecognitioneduQueen Mary University of LondonUnited Kingdom51.52472720-0.0393103562%138865287961
4f93cd09785c6e77bf4bc5a788e079df524c8d21sotonSOTON HiDOn a Large Sequence-Based Human Gait Database[pdf]Unknown58%14886621710449
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52d7eb0fbc3522434c13cc247549f74bb9609c5dwider_faceWIDER FACEWIDER FACE: A Face Detection Benchmark[pdf]2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)eduChinese University of Hong KongChina22.41626320114.2109318057%14885631510841
27a2fad58dd8727e280f97036e0d2bc55ef5424cduke_mtmcDuke MTMCPerformance Measures and a Data Set for Multi-Target, Multi-Camera Tracking[pdf]Unknown58%1367957710827
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b91f54e1581fbbf60392364323d00a0cd43e493cbp4d_spontanousBP4D-SpontanousA high-resolution spontaneous 3D dynamic facial expression database[pdf]2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG)eduSUNY BinghamtonUnited States42.08779975-75.9706606651%151777478765
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2d3482dcff69c7417c7b933f22de606a0e8e42d4lfwLFWLabeled Faces in the Wild : Updates and New Reporting Procedures[pdf]UnknowneduUniversity of MassachusettsUnited States42.38897850-72.5286987058%123715237250
96e0cfcd81cdeb8282e29ef9ec9962b125f379b0megafaceMegaFaceThe MegaFace Benchmark: 1 Million Faces for Recognition at Scale[pdf]2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)59%121715099822
66e6f08873325d37e0ec20a4769ce881e04e964esun_attributesSUNThe SUN Attribute Database: Beyond Categories for Deeper Scene Understanding[pdf]International Journal of Computer Vision62%1127042148429
0486214fb58ee9a04edfe7d6a74c6d0f661a7668chokepointChokePointPatch-based probabilistic image quality assessment for face selection and improved video-based face recognition[pdf]CVPR 2011 WORKSHOPS53%128686067360
8355d095d3534ef511a9af68a3b2893339e3f96bimdb_wikiIMDBDEX: Deep EXpectation of Apparent Age from a Single Image[pdf]2015 IEEE International Conference on Computer Vision Workshop (ICCVW)56%120675357447
0d3bb75852098b25d90f31d2f48fd0cb4944702bface_scrubFaceScrubA data-driven approach to cleaning large face datasets[pdf]2014 IEEE International Conference on Image Processing (ICIP)54%123665749627
e4754afaa15b1b53e70743880484b8d0736990fffiw_300300-W300 Faces In-The-Wild Challenge: database and results[pdf]Image Vision Comput.54%1146153107143
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a0fd85b3400c7b3e11122f44dc5870ae2de9009amtflMTFLLearning Deep Representation for Face Alignment with Auxiliary Attributes[pdf]IEEE Transactions on Pattern Analysis and Machine Intelligence55%1106050126943
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2a4bbee0b4cf52d5aadbbc662164f7efba89566cpetaPETAPedestrian Attribute Recognition At Far Distance[pdf]Unknown68%80542625128
3b4ec8af470948a72a6ed37a9fd226719a874ebcsdu_vidSDU-VIDA Spatio-Temporal Appearance Representation for Video-Based Pedestrian Re-Identification[pdf]2015 IEEE International Conference on Computer Vision (ICCV)62%85533295134
06f02199690961ba52997cde1527e714d2b3bf8fcolumbia_gazeColumbia GazeGaze locking: passive eye contact detection for human-object interaction[pdf]UnknowneduColumbia UniversityUnited States40.84198360-73.9436897164%80512904935
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3394168ff0719b03ff65bcea35336a76b21fe5e4penn_fudanPenn FudanObject Detection Combining Recognition and Segmentation[pdf]Unknown50%1015051115842
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3cd40bfa1ff193a96bde0207e5140a399476466ctvhiTVHIHigh Five: Recognising human interactions in TV shows[pdf]Unknown52%914744116427
0b440695c822a8e35184fb2f60dcdaa8a6de84aekinectfaceKinectFaceDBKinectFaceDB: A Kinect Database for Face Recognition[pdf]IEEE Transactions on Systems, Man, and Cybernetics: SystemseduUniversity of North Carolina at Chapel HillUnited States35.91139710-79.0504529061%75462962650
5194cbd51f9769ab25260446b4fa17204752e799violent_flowsViolent FlowsViolent flows: Real-time detection of violent crowd behavior[pdf]2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition WorkshopseduOpen University of IsraelIsrael32.7782416534.9956567355%83463764441
7de6e81d775e9cd7becbfd1bd685f4e2a5eebb22lfwLFWLabeled Faces in the Wild : A Survey[pdf]UnknowneduStevens Institute of TechnologyUnited States40.74225200-74.0270949045%99455486336
2ce2560cf59db59ce313bbeb004e8ce55c5ce928texas_3dfrdTexas 3DFRDAnthropometric 3D Face Recognition[pdf]International Journal of Computer Vision50%90454556031
2160788824c4c29ffe213b2cbeb3f52972d73f373d_rma3D-RMAAutomatic 3D face authentication[pdf]Image Vision Comput.46%95445186135
2bf8541199728262f78d4dced6fb91479b39b738clothing_co_parsingCCPClothing Co-parsing by Joint Image Segmentation and Labeling[pdf]2014 IEEE Conference on Computer Vision and Pattern Recognition70%60421803824
ae0aee03d946efffdc7af2362a42d3750e7dd48aput_facePut FaceThe put face database[pdf]Unknown42%100425875648
2edb87494278ad11641b6cf7a3f8996de12b8e14qmul_gridGRIDTime-Delayed Correlation Analysis for Multi-Camera Activity Understanding[pdf]International Journal of Computer VisioneduQueen Mary University of LondonUnited Kingdom51.52472720-0.0393103549%83414265133
4df3143922bcdf7db78eb91e6b5359d6ada004d2cfdCFDThe Chicago face database: A free stimulus set of faces and norming data.[pdf]Behavior research methods48%83404326319
636b8ffc09b1b23ff714ac8350bb35635e49fa3ccaltech_10k_web_facesCaltech 10K Web FacesPruning training sets for learning of object categories[pdf]2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)65%60392154317
c900e0ad4c95948baaf0acd8449fde26f9b4952aemotio_netEmotioNet DatabaseEmotioNet: An Accurate, Real-Time Algorithm for the Automatic Annotation of a Million Facial Expressions in the Wild[pdf]2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)54%72393375417
4793f11fbca4a7dba898b9fff68f70d868e2497ckin_faceUB KinFaceKinship verification through transfer learning[pdf]Unknown55%71393222943
31de9b3dd6106ce6eec9a35991b2b9083395fd0bferetFERETFERET ( Face Recognition Technology ) Recognition Algorithm Development and Test Results[pdf]Unknown46%82384456220
6618cff7f2ed440a0d2fa9e74ad5469df5cdbe4cafadAFADOrdinal Regression with Multiple Output CNN for Age Estimation[pdf]2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)53%68363284917
0a85bdff552615643dd74646ac881862a7c7072dpipaPIPABeyond frontal faces: Improving Person Recognition using multiple cues[pdf]2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)companyFacebookUnited States37.39367170-122.0807262072%5036132409
47aeb3b82f54b5ae8142b4bdda7b614433e69b9aam_fedAM-FEDAffectiva-MIT Facial Expression Dataset (AM-FED): Naturalistic and Spontaneous Facial Expressions Collected "In-the-Wild"[pdf]2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops47%73343964134
fcc6fe6007c322641796cb8792718641856a22a7miwMIWAutomatic facial makeup detection with application in face recognition[pdf]2013 International Conference on Biometrics (ICB)eduWest Virginia UniversityUnited States39.65404635-79.9647535574%46341211828
fcc6fe6007c322641796cb8792718641856a22a7youtube_makeupYMUAutomatic facial makeup detection with application in face recognition[pdf]2013 International Conference on Biometrics (ICB)eduWest Virginia UniversityUnited States39.65404635-79.9647535574%46341211828
0b84f07af44f964817675ad961def8a51406dd2eprwPRWPerson Re-identification in the Wild[pdf]2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)51%65333214617
5ffd74d2873b7cba2cbc5fd295cc7fbdedca22a2cityscapesCityscapesThe Cityscapes Dataset[pdf]Unknown59%54322234014
7ace44190729927e5cb0dd5d363fcae966fe13f7nudedetectionNude DetectionA bag-of-features approach based on Hue-SIFT descriptor for nude detection[pdf]2009 17th European Signal Processing Conference61%51312011833
070de852bc6eb275d7ca3a9cdde8f6be8795d1a3d3dfacsD3DFACSA FACS valid 3D dynamic action unit database with applications to 3D dynamic morphable facial modeling[pdf]2011 International Conference on Computer Vision58%52302253715
51eba481dac6b229a7490f650dff7b17ce05df73imsituimSituSituation Recognition: Visual Semantic Role Labeling for Image Understanding[pdf]2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)62%4830182462
37d6f0eb074d207b53885bd2eb78ccc8a04be597vmuVMUCan facial cosmetics affect the matching accuracy of face recognition systems?[pdf]2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS)eduWest Virginia UniversityUnited States39.65404635-79.9647535561%49301901831
37d6f0eb074d207b53885bd2eb78ccc8a04be597youtube_makeupYMUCan facial cosmetics affect the matching accuracy of face recognition systems?[pdf]2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS)eduWest Virginia UniversityUnited States39.65404635-79.9647535561%49301901831
9c23859ec7313f2e756a3e85575735e0c52249f4facebook_100Facebook100Scaling up biologically-inspired computer vision: A case study in unconstrained face recognition on facebook[pdf]CVPR 2011 WORKSHOPSeduHarvard UniversityUnited States42.36782045-71.1266665358%50292133911
f1af714b92372c8e606485a3982eab2f16772ad8mug_facesMUG FacesThe MUG facial expression database[pdf]11th International Workshop on Image Analysis for Multimedia Interactive Services WIAMIS 10eduAristotle University of ThessalonikiGreece40.6298414522.9588935043%68293952840
18858cc936947fc96b5c06bbe3c6c2faa5614540pilot_parliamentPPBGender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification[pdf]Unknown53%5529260477
9c23859ec7313f2e756a3e85575735e0c52249f4pubfig_83pubfig83Scaling up biologically-inspired computer vision: A case study in unconstrained face recognition on facebook[pdf]CVPR 2011 WORKSHOPSeduHarvard UniversityUnited States42.36782045-71.1266665358%50292133911
356b431d4f7a2a0a38cf971c84568207dcdbf189widerWIDERRecognize complex events from static images by fusing deep channels[pdf]2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)64%45291613015
16e8b0a1e8451d5f697b94c0c2b32a00abee1d52umbUMBUMB-DB: A database of partially occluded 3D faces[pdf]2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)60%45271822024
faf40ce28857aedf183e193486f5b4b0a8c478a2iit_dehli_earIIT Dehli EarAutomated Human Identification Using Ear Imaging[pdf]Unknown37%70264462842
22646e00a7ba34d1b5fbe3b1efcd91a1e1be3c2bsaivtSAIVT SoftBioA Database for Person Re-Identification in Multi-Camera Surveillance Networks[pdf]2012 International Conference on Digital Image Computing Techniques and Applications (DICTA)45%58263274118
b92a1ed9622b8268ae3ac9090e25789fc41cc9b8pornodb#N/APooling in image representation: The visual codeword point of view[pdf]Computer Vision and Image Understanding32%77255274634
eb027969f9310e0ae941e2adee2d42cdf07d938cvgg_faces2VGG Face2VGGFace2: A Dataset for Recognising Faces across Pose and Age[pdf]2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018)eduOxford UniversityUnited Kingdom51.75208490-1.2516646045%5625316506
8b2dd5c61b23ead5ae5508bb8ce808b5ea26673010k_US_adult_faces10K US Adult FacesThe intrinsic memorability of face photographs.[pdf]Journal of experimental psychology. General51%47242333413
47662d1a368daf70ba70ef2d59eb6209f98b675dfiaCMU FiAThe CMU Face In Action (FIA) Database[pdf]Unknown44%55243154117
4d58f886f5150b2d5e48fd1b5a49e09799bf895dtexas_3dfrdTexas 3DFRDTexas 3D Face Recognition Database[pdf]2010 IEEE Southwest Symposium on Image Analysis & Interpretation (SSIAI)39%61243733725
79828e6e9f137a583082b8b5a9dfce0c301989b8mapillaryMapillaryThe Mapillary Vistas Dataset for Semantic Understanding of Street Scenes[pdf]2017 IEEE International Conference on Computer Vision (ICCV)52%4423210367
09d78009687bec46e70efcf39d4612822e61cb8craidRAiDConsistent Re-identification in a Camera Network[pdf]Unknown51%45232273411
2161f6b7ee3c0acc81603b01dc0df689683577b9large_scale_person_searchLarge Scale Person SearchEnd-to-End Deep Learning for Person Search[pdf]CoRR54%41221922712
6f3c76b7c0bd8e1d122c6ea808a271fd4749c951wardWARDRe-identify people in wide area camera network[pdf]2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops38%55213423519
1c2802c2199b6d15ecefe7ba0c39bfe44363de38youtube_posesYouTube PosePersonalizing Human Video Pose Estimation[pdf]2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)eduOxford UniversityUnited Kingdom51.75208490-1.2516646066%3221112295
758d7e1be64cc668c59ef33ba8882c8597406e53affectnetAffectNetAffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild[pdf]CoRR53%38201812611
53ae38a6bb2b21b42bac4f0c4c8ed1f9fa02f9d4bp4d_plusBP4D+Multimodal Spontaneous Emotion Corpus for Human Behavior Analysis[pdf]2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)50%40202002120
31b05f65405534a696a847dd19c621b7b8588263umd_facesUMDUMDFaces: An annotated face dataset for training deep networks[pdf]2017 IEEE International Joint Conference on Biometrics (IJCB)eduUniversity of MarylandUnited States39.28996850-76.6219610357%3520154287
d08cc366a4a0192a01e9a7495af1eb5d9f9e73aeb3d_acB3D(AC)A 3-D Audio-Visual Corpus of Affective Communication[pdf]IEEE Transactions on Multimedia49%39192022712
213a579af9e4f57f071b884aa872651372b661fdbbc_poseBBC PoseAutomatic and Efficient Human Pose Estimation for Sign Language Videos[pdf]International Journal of Computer Vision76%251961197
488e475eeb3bb39a145f23ede197cd3620f1d98aapisAPiS1.0Pedestrian Attribute Classification in Surveillance: Database and Evaluation[pdf]2013 IEEE International Conference on Computer Vision Workshops69%2618811313
298cbc3dfbbb3a20af4eed97906650a4ea1c29e0ferplusFER+Training deep networks for facial expression recognition with crowd-sourced label distribution[pdf]Unknown62%29181101514
fd8168f1c50de85bac58a8d328df0a50248b16aend_2006ND-2006Using a Multi-Instance Enrollment Representation to Improve 3D Face Recognition[pdf]2007 First IEEE International Conference on Biometrics: Theory, Applications, and SystemseduUniversity of Notre DameUnited States41.70456775-86.2382202656%32181431715
488e475eeb3bb39a145f23ede197cd3620f1d98asvsSVSPedestrian Attribute Classification in Surveillance: Database and Evaluation[pdf]2013 IEEE International Conference on Computer Vision Workshops69%2618811313
57fe081950f21ca03b5b375ae3e84b399c015861cvc_01_barcelonaCVC-01Adaptive Image Sampling and Windows Classification for On – board Pedestrian Detection[pdf]Unknown39%44172712123
4fefd1bc8dc4e0ab37ee3324ddfa43ad9d6a04a7deep_fashionDeepFashionFashion Landmark Detection in the Wild[pdf]Unknown65%261791179
84fe5b4ac805af63206012d29523a1e033bc827eawe_earsAWE EarsEar Recognition: More Than a Survey[pdf]Neurocomputing67%2416801113
20388099cc415c772926e47bcbbe554e133343d1cafeCAFEThe Child Affective Facial Expression (CAFE) set: validity and reliability from untrained adults[pdf]48%3316173285
1e3df3ca8feab0b36fd293fe689f93bb2aaac591immediacyImmediacyMulti-task Recurrent Neural Network for Immediacy Prediction[pdf]2015 IEEE International Conference on Computer Vision (ICCV)64%251692215
a5a44a32a91474f00a3cda671a802e87c899fbb4moments_in_timeMoments in TimeMoments in Time Dataset: one million videos for event understanding[pdf]CoRR64%251692250
0ceda9dae8b9f322df65ca2ef02caca9758aec6fcasablancaCasablancaContext-Aware CNNs for Person Head Detection[pdf]2015 IEEE International Conference on Computer Vision (ICCV)56%2715121235
4946ba10a4d5a7d0a38372f23e6622bd347ae273coco_actionCOCO-aDescribing Common Human Visual Actions in Images[pdf]Unknown58%2615110251
0ceda9dae8b9f322df65ca2ef02caca9758aec6fhollywood_headsetHollywoodHeadsContext-Aware CNNs for Person Head Detection[pdf]2015 IEEE International Conference on Computer Vision (ICCV)56%2715121235
28d4e027c7e90b51b7d8908fce68128d1964668amegafaceMegaFaceLevel Playing Field for Million Scale Face Recognition[pdf]2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)eduUniversity of WashingtonUnited States47.65432380-122.3080089439%3815232298
2eb84aaba316b095d4bb51da1a3e4365bbf9ab1dkin_faceUB KinFaceGenealogical face recognition based on UB KinFace database[pdf]CVPR 2011 WORKSHOPSeduSUNY BuffaloUnited States42.93362780-78.8839447947%30141611021
221c18238b829c12b911706947ab38fd017acef7rap_pedestrianRAPA Richly Annotated Dataset for Pedestrian Attribute Recognition[pdf]CoRR67%211470183
44d23df380af207f5ac5b41459c722c87283e1ebwider_attributeWIDER AttributeHuman Attribute Recognition by Deep Hierarchical Contexts[pdf]Unknown78%181440162
5753b2b5e442eaa3be066daa4a2ca8d8a0bb172550_people_one_question50 People One QuestionMerging Pose Estimates Across Space and Time[pdf]Unknown87%151320124
ea050801199f98a1c7c1df6769f23f658299a3aempi_largeLarge MPI Facial ExpressionThe MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial Expressions[pdf]46%2813154244
ea050801199f98a1c7c1df6769f23f658299a3aempi_smallSmall MPI Facial ExpressionThe MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial Expressions[pdf]46%2813154244
014b8df0180f33b9fea98f34ae611c6447d761d2buhmap_dbBUHMAP-DB Facial feature tracking and expression recognition for sign language[pdf]2009 IEEE 17th Signal Processing and Communications Applications Conference48%25121311115
12ad3b5bbbf407f8e54ea692c07633d1a867c566grazGraz PedestrianObject recognition using segmentation for feature detection[pdf]Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004.eduInst. of Comput. Sci., Univ. of Leoben, AustriaAustria47.3847372015.0930201041%2912171218
0297448f3ed948e136bb06ceff10eccb34e5bb77ilids_mctsi-LIDS Multiple-CameraImagery Library for Intelligent Detection Systems (i-LIDS); A Standard for Testing Video Based Detection Systems[pdf]Proceedings 40th Annual 2006 International Carnahan Conference on Security Technology38%32122021815
45e616093a92e5f1e61a7c6037d5f637aa8964afmalfMALFFine-grained evaluation on face detection in the wild[pdf]2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG)71%171250134
e27ef52c641c2b5100a1b34fd0b819e84a31b4dfsarc3dSarc3DSARC3D: A New 3D Body Model for People Tracking and Re-identification[pdf]Unknown41%29121731712
19d1b811df60f86cbd5e04a094b07f32fff7a32ayork_3dUOY 3D Face DatabaseThree-dimensional face recognition: an eigensurface approach[pdf]2004 International Conference on Image Processing, 2004. ICIP '04.33%36122442511
a8d0b149c2eadaa02204d3e4356fbc8eccf3b315hi4d_adsipHi4D-ADSIPHi4D-ADSIP 3-D dynamic facial articulation database[pdf]Image Vision Comput.61%181171711
774cbb45968607a027ae4729077734db000a1ec5urban_tribesUrban TribesFrom Bikers to Surfers: Visual Recognition of Urban Tribes[pdf]Unknown65%171161125
6dcf418c778f528b5792104760f1fbfe90c6dd6aagedbAgeDBAgeDB: The First Manually Collected, In-the-Wild Age Database[pdf]2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)91%111010101
ec792ad2433b6579f2566c932ee414111e194537msmt_17MSMT17Person Transfer GAN to Bridge Domain Gap for Person Re-Identification[pdf]CoRR71%141041113
4e6ee936eb50dd032f7138702fa39b7c18ee8907dartmouth_childrenDartmouth ChildrenThe Dartmouth Database of Children’s Faces: Acquisition and Validation of a New Face Stimulus Set[pdf]45%209112174
4156b7e88f2e0ab0a7c095b9bab199ae2b23bd06distance_nighttimeLong Distance Heterogeneous FaceNighttime Face Recognition at Long Distance: Cross-Distance and Cross-Spectral Matching[pdf]Unknown43%2191231110
2f43b614607163abf41dfe5d17ef6749a1b61304hrt_transgenderHRT TransgenderInvestigating the Periocular-Based Face Recognition Across Gender Transformation[pdf]IEEE Transactions on Information Forensics and SecurityeduUniversity of North Carolina at WilmingtonUnited States34.22498270-77.8690774469%1394068
ca3e88d87e1344d076c964ea89d91a75c417f5eeimfdbIMFDBIndian Movie Face Database: A benchmark for face recognition under wide variations[pdf]2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG)eduCVIT, IIITH, IndiaIndia17.4459581078.3495994060%15960105
71b7fc715e2f1bb24c0030af8d7e7b6e7cd128a6umd_facesUMDThe Do’s and Don’ts for CNN-Based Face Verification[pdf]2017 IEEE International Conference on Computer Vision Workshops (ICCVW)36%259163176
25474c21613607f6bb7687a281d5f9d4ffa1f9f3faceplaceFace PlaceRecognizing disguised faces[pdf]Unknown33%248160186
c570d1247e337f91e555c3be0e8c8a5aba539d9fmcgillMcGill Real WorldRobust semi-automatic head pose labeling for real-world face video sequences[pdf]Multimedia Tools and ApplicationseduMcGill UniversityCanada45.50397610-73.5749687044%188100137
2a171f8d14b6b8735001a11c217af9587d095848social_relationSocial RelationLearning Social Relation Traits from Face Images[pdf]2015 IEEE International Conference on Computer Vision (ICCV)40%208125155
a94cae786d515d3450d48267e12ca954aab791c4yawddYawDDYawDD: a yawning detection dataset[pdf]Unknown57%14861212
2624d84503bc2f8e190e061c5480b6aa4d89277aafew_vaAFEW-VAAFEW-VA database for valence and arousal estimation in-the-wild[pdf]Image Vision Comput.47%15781104
6403117f9c005ae81f1e8e6d1302f4a045e3d99dalert_airportALERT AirportA Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets.[pdf]IEEE transactions on pattern analysis and machine intelligence47%15781105
22f656d0f8426c84a33a267977f511f127bfd7f3expwExpWFrom Facial Expression Recognition to Interpersonal Relation Prediction[pdf]International Journal of Computer Vision78%972054
2b926b3586399d028b46315d7d9fb9d879e4f79cfrav3dFRAV3DMultimodal 2D, 2.5D & 3D Face Verification[pdf]2006 International Conference on Image ProcessingeduUniversidad Rey Juan Carlos, Spain40.33586610-3.8769432050%14770212
b71d1aa90dcbe3638888725314c0d56640c1fef1ifdbIFDBIranian Face Database with age, pose and expression[pdf]2007 International Conference on Machine VisioneduIslamic Azad UniversityIran34.8452999048.5596212035%207132129
0cb2dd5f178e3a297a0c33068961018659d0f443ijb_cIJB-CIARPA Janus Benchmark-B Face Dataset[pdf]2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)eduMichigan State UniversityUnited States42.71856800-84.4779157128%257186214
4563b46d42079242f06567b3f2e2f7a80cb3befevadanaVADANAVADANA: A dense dataset for facial image analysis[pdf]2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)eduUniversity of DelawareUnited States39.68103280-75.7540184044%16790610
8f02ec0be21461fbcedf51d864f944cfc42c875fhda_plusHDA+The HDA+ Data Set for Research on Fully Automated Re-identification Systems[pdf]Unknown35%176112116
7f4040b482d16354d5938c1d1b926b544652bf5bnova_emotionsNovaemötions DatasetCompetitive affective gaming: winning with a smile[pdf]UnknowneduUniversidade NOVA de Lisboa, Caparica, PortugalPortugal38.66096400-9.2058130075%862044
bd26dabab576adb6af30484183c9c9c8379bf2e0scut_fbpSCUT-FBPSCUT-FBP: A Benchmark Dataset for Facial Beauty Perception[pdf]2015 IEEE International Conference on Systems, Man, and Cybernetics43%1468359
041d3eedf5e45ce5c5229f0181c5c576ed1fafd6ucf_selfieUCF SelfieHow to Take a Good Selfie?[pdf]Unknown67%963064
8d5998cd984e7cce307da7d46f155f9db99c6590chalearnChaLearnChaLearn looking at people: A review of events and resources[pdf]2017 International Joint Conference on Neural Networks (IJCNN)50%1055164
1a40092b493c6b8840257ab7f96051d1a4dbfeb2sports_videos_in_the_wildSVWSports Videos in the Wild (SVW): A video dataset for sports analysis[pdf]2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG)83%651151
8627f019882b024aef92e4eb9355c499c733e5b7usedUSED Social Event DatasetUSED: a large-scale social event detection dataset[pdf]UnknowneduUniversity of TrentoItaly46.0658836011.1159894071%752034
56ae6d94fc6097ec4ca861f0daa87941d1c10b70cmdpCMDPDistance Estimation of an Unknown Person from a Portrait[pdf]Unknown44%945063
4b4106614c1d553365bad75d7866bff0de6056edczech_news_agencyUFIUnconstrained Facial Images: Database for Face Recognition Under Real-World Conditions[pdf]Unknown40%1046046
563c940054e4b456661762c1ab858e6f730c3159data_61Data61 PedestrianA Multi-modal Graphical Model for Scene Analysis[pdf]2015 IEEE Winter Conference on Applications of Computer Vision50%844053
137aa2f891d474fce1e7a1d1e9b3aefe21e22b34hrt_transgenderHRT TransgenderIs the eye region more reliable than the face? A preliminary study of face-based recognition on a transgender dataset[pdf]2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS)57%743135
a5a3bc3e5e9753769163cb30b16dbd12e266b93emcgillMcGill Real WorldHierarchical temporal graphical model for head pose estimation and subsequent attribute classification in real-world videos[pdf]Computer Vision and Image Understanding50%844153
079a0a3bf5200994e1f972b1b9197bf2f90e87d4mit_cbclMIT CBCLComponent-Based Face Recognition with 3D Morphable Models[pdf]2004 Conference on Computer Vision and Pattern Recognition Workshop33%1248084
54983972aafc8e149259d913524581357b0f91c3reseedReSEEDReSEED: social event dEtection dataset[pdf]Unknown67%642115
07fcbae86f7a3ad3ea1cf95178459ee9eaf77cb1uccsUCCSLarge scale unconstrained open set face database[pdf]2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS)eduUniversity of Colorado at Colorado SpringsUnited States38.89646790-104.8050594080%541032
7ebb153704706e457ab57b432793d2b6e5d12592vgg_celebs_in_placesCIPFaces in Places: compound query retrieval[pdf]Unknown80%541041
dd65f71dac86e36eecbd3ed225d016c3336b4a13families_in_the_wildFIWVisual Kinship Recognition of Families in the Wild[pdf]IEEE Transactions on Pattern Analysis and Machine IntelligenceeduUniversity of Massachusetts DartmouthUnited States41.62772475-71.00724501100%330021
57178b36c21fd7f4529ac6748614bb3374714e91ijb_cIJB-CIARPA Janus Benchmark - C: Face Dataset and Protocol[pdf]2018 International Conference on Biometrics (ICB)33%936290
0d2dd4fc016cb6a517d8fb43a7cc3ff62964832elagLAGLarge age-gap face verification by feature injection in deep networks[pdf]Pattern Recognition Letters43%734034
a7fe834a0af614ce6b50dc093132b031dd9a856bmarket1203Market 1203Orientation Driven Bag of Appearances for Person Re-identification[pdf]CoRR43%734044
23e824d1dfc33f3780dd18076284f07bd99f1c43mifsMIFSSpoofing faces using makeup: An investigative study[pdf]2017 IEEE International Conference on Identity, Security and Behavior Analysis (ISBA)eduINRIA MéditerranéeFrance43.615813107.0683800060%532014
a7fe834a0af614ce6b50dc093132b031dd9a856bpku_reidPKU-ReidOrientation Driven Bag of Appearances for Person Re-identification[pdf]CoRR43%734044
17b46e2dad927836c689d6787ddb3387c6159ecegeofacesGeoFacesGeoFaceExplorer: exploring the geo-dependence of facial attributes[pdf]Unknown100%220011
ad01687649d95cd5b56d7399a9603c4b8e2217d7mrp_droneMRP DroneInvestigating Open-World Person Re-identification Using a Drone[pdf]Unknown40%523032
f6c8d5e35d7e4d60a0104f233ac1a3ab757da53fpkuPKUSwiss-System Based Cascade Ranking for Gait-Based Person Re-Identification[pdf]Unknown67%321012
9e5378e7b336c89735d3bb15cf67eff96f86d39aprecariousPrecariousExpecting the Unexpected: Training Detectors for Unusual Pedestrians with Adversarial Imposters[pdf]2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)17%122101111
d4f1eb008eb80595bcfdac368e23ae9754e1e745uccsUCCSUnconstrained Face Detection and Open-Set Face Recognition Challenge[pdf]2017 IEEE International Joint Conference on Biometrics (IJCB)40%523041
4d4bb462c9f1d4e4ab1e4aa6a75cc0bc71b384613dddb_unconstrained3D DynamicA 3 D Dynamic Database for Unconstrained Face Recognition[pdf]Unknown50%211011
65355cbb581a219bd7461d48b3afd115263ea760complex_activitiesOngoing Complex ActivitiesRecognition of ongoing complex activities by sequence prediction over a hierarchical label space[pdf]2016 IEEE Winter Conference on Applications of Computer Vision (WACV)50%211020
3dc3f0b64ef80f573e3a5f96e456e52ee980b877georgia_tech_face_databaseGeorgia Tech FaceMAXIMUM 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[pdf]Unknown33%312021
e58dd160a76349d46f881bd6ddbc2921f08d1050gfwGrouping Face in the WildMerge or Not? Learning to Group Faces via Imitation Learning[pdf]Unknown50%211020
55c40cbcf49a0225e72d911d762c27bb1c2d14aaifadIFADIndian Face Age Database : A Database for Face Recognition with Age Variation[pdf]Unknown50%211020
9e31e77f9543ab42474ba4e9330676e18c242e72imdb_faceIMDb FaceThe Devil of Face Recognition is in the Noise[pdf]UnknowneduNanyang Technological UniversitySingapore1.34841040103.6829796520%514031
d80a3d1f3a438e02a6685e66ee908446766fefa9megaageMegaAgeQuantifying Facial Age by Posterior of Age Comparisons[pdf]CoRR25%413140
578d4ad74818086bb64f182f72e2c8bd31e3d426mr2MR2The MR2: A multi-racial, mega-resolution database of facial stimuli.[pdf]Behavior research methods14%716070
c06b13d0ec3f5c43e2782cd22542588e233733c3nova_emotionsNovaemötions DatasetCrowdsourcing facial expressions for affective-interaction[pdf]Computer Vision and Image Understanding100%110010
922e0a51a3b8c67c4c6ac09a577ff674cbd28b34v47V47Re-identification of pedestrians with variable occlusion and scale[pdf]2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)eduKingston UniversityUnited Kingdom51.42930860-0.2684044010%1019264
5ad4e9f947c1653c247d418f05dad758a3f9277bwlfdbWLFDBWLFDB: Weakly Labeled Face Databases[pdf]Unknown100%110001
9696ad8b164f5e10fcfe23aacf74bd6168aebb154dfab4DFAB4DFAB: A Large Scale 4D Facial Expression Database for Biometric Applications[pdf]CoRR0%404022
f0e17f27f029db4ad650ff278fe3c10ecb6cb0c4europersonsEuroCity PersonsThe EuroCity Persons Dataset: A Novel Benchmark for Object Detection[pdf]CoRR0%101010
670637d0303a863c1548d5b19f705860a23e285cface_tracerFaceTracerFace swapping: automatically replacing faces in photographs[pdf]Unknown100%000000
75da1df4ed319926c544eefe17ec8d720feef8c0fddbFDDBFDDB: A benchmark for face detection in unconstrained settings[pdf]Unknown0%101001
bd88bb2e4f351352d88ee7375af834360e223498hda_plusHDA+HDA dataset-DRAFT 1 A Multi-camera video data set for research on High-Definition surveillance[pdf]Unknown0%202012
2b89de1d81cee50552f10e26e865df3365e9bc88ibm_difIBM Diversity in FacesDiversity in Faces[pdf]CoRR100%000000
066d71fcd997033dce4ca58df924397dfe0b5fd1ifdbIFDBIranian Face Database and Evaluation with a New Detection Algorithm[pdf]Unknown100%000000
22909dd19a0ec3b6065334cb5be5392cb24d839dpetsPETS 2017PETS 2017: Dataset and Challenge[pdf]2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)0%808026
c866a2afc871910e3282fd9498dce4ab20f6a332qmul_surv_faceQMUL-SurvFaceSurveillance Face Recognition Challenge[pdf]CoRR100%000000
d3200d49a19a4a4e4e9745ee39649b65d80c834bscut_headSCUT HEADDetecting Heads using Feature Refine Net and Cascaded Multi-scale Architecture[pdf]2018 24th International Conference on Pattern Recognition (ICPR)100%000000
d3f5a1848b0028d8ab51d0b0673732cad2e3c8c9stair_actionsSTAIR ActionSTAIR Actions: A Video Dataset of Everyday Home Actions[pdf]CoRR100%000000
8990cdce3f917dad622e43e033db686b354d057ctiny_facesTinyFaceLow-Resolution Face Recognition[pdf]CoRR100%000000
377f2b65e6a9300448bdccf678cde59449ecd337ufddUFDDPushing the Limits of Unconstrained Face Detection: a Challenge Dataset and Baseline Results[pdf]CoRR0%202020
9b9bf5e623cb8af7407d2d2d857bc3f1b531c182who_goes_thereWGTWho goes there?: approaches to mapping facial appearance diversity[pdf]UnknowneduUniversity of KentuckyUnited States38.03337420-84.50177580100%000000
77c81c13a110a341c140995bedb98101b9e84f7fwildtrackWildTrackWILDTRACK : A Multi-camera HD Dataset for Dense Unscripted Pedestrian Detection[pdf]Unknown100%000000
\ No newline at end of file +Coverage

Coverage

Paper IDMegapixels KeyMegapixels NameReport LinkPDF LinkJournalTypeAddressCountryLatLngCoverageTotal CitationsGeocoded CitationsUnknown CitationsEmpty CitationsWith PDFWith DOI
b5f2846a506fc417e7da43f6a7679146d99c5e96ucf_101UCF101UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild[pdf]CoRR61%99961138873716283
0e986f51fe45b00633de9fd0c94d082d2be51406afwAFWFace detection, pose estimation, and landmark localization in the wild[pdf]2012 IEEE Conference on Computer Vision and Pattern Recognition61%99960839159622387
370b5757a5379b15e30d619e4d3fb9e8e13f3256lfwLFWLabeled Faces in the Wild : A Database for Studying Face Recognition in Unconstrained Environments[pdf]Unknown58%99957542271639371
0ee1916a0cb2dc7d3add086b5f1092c3d4beb38avocVOCThe Pascal Visual Object Classes (VOC) Challenge[pdf]International Journal of Computer VisioneduOxford UniversityUnited Kingdom51.75208490-1.2516646058%99957542435613414
5e0f8c355a37a5a89351c02f174e7a5ddcb98683cocoCOCOMicrosoft COCO: Common Objects in Context[pdf]Unknown57%99956943029799193
2e384f057211426ac5922f1b33d2aa8df5d51f57a_pascal_yahoo#N/ADescribing objects by their attributes[pdf]2009 IEEE Conference on Computer Vision and Pattern RecognitioneduUniversity of Illinois, Urbana-ChampaignUnited States40.11116745-88.2258766557%99956543374738264
759a3b3821d9f0e08e0b0a62c8b693230afc3f8dpubfigPubFigAttribute and simile classifiers for face verification[pdf]2009 IEEE 12th International Conference on Computer Vision61%89454435056604300
162ea969d1929ed180cc6de9f0bf116993ff6e06vgg_facesVGG FaceDeep Face Recognition[pdf]Unknown54%99954345670635370
10d6b12fa07c7c8d6c8c3f42c7f1c061c131d4c5inria_personINRIA PedestrianHistograms of oriented gradients for human detection[pdf]2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)eduINRIA Rhone-Alps, Montbonnot, FranceFrance45.217886005.8073690054%99953946067537477
4d9a02d080636e9666c4d1cc438b9893391ec6c7cohn_kanade_plusCK+The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression[pdf]2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - WorkshopseduUniversity of PittsburghUnited States40.44415295-79.9624399355%97553543967475510
31b58ced31f22eab10bd3ee2d9174e7c14c27c01tiny_imagesTiny Images80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition[pdf]IEEE Transactions on Pattern Analysis and Machine Intelligence54%99953546394685327
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6d96f946aaabc734af7fe3fc4454cf8547fcd5edar_facedbAR FaceThe AR face database[pdf]53%99952647351459573
18c72175ddbb7d5956d180b65a96005c100f6014yale_facesYaleFacesFrom Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose[pdf]IEEE Trans. Pattern Anal. Mach. Intell.51%99951448577551459
23fc83c8cfff14a16df7ca497661264fc54ed746cohn_kanadeCKComprehensive Database for Facial Expression Analysis[pdf]Unknown50%99950349675572439
18ae7c9a4bbc832b8b14bc4122070d7939f5e00efrgcFRGCOverview of the face recognition grand challenge[pdf]2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)eduNISTUnited States39.14004000-77.2185060050%999497501114594424
f72f6a45ee240cc99296a287ff725aaa7e7ebb35caltech_pedestriansCaltech PedestriansPedestrian Detection: An Evaluation of the State of the Art[pdf]IEEE Transactions on Pattern Analysis and Machine IntelligenceeduCalifornia Institute of TechnologyUnited States34.13710185-118.1252748749%99948551471541464
2ad0ee93d029e790ebb50574f403a09854b65b7eyale_facesYaleFacesAcquiring linear subspaces for face recognition under variable lighting[pdf]IEEE Transactions on Pattern Analysis and Machine Intelligence48%999484515110525485
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b62628ac06bbac998a3ab825324a41a11bc3a988m2vtsdb_extendedxm2vtsdbXm2vtsdb: the Extended M2vts Database[pdf]Unknown53%90648142544542408
01959ef569f74c286956024866c1d107099199f7vqaVQAVQA: Visual Question Answering[pdf]2015 IEEE International Conference on Computer Vision (ICCV)61%7314442874762996
dc8b25e35a3acb812beb499844734081722319b4feretFERETThe FERET database and evaluation procedure for face-recognition algorithms[pdf]Image Vision Comput.44%999443556106606413
55206f0b5f57ce17358999145506cd01e570358corlORLParameterisation of a stochastic model for human face identification[pdf]Unknown44%99944255797569445
45c31cde87258414f33412b3b12fc5bec7cb3ba9jaffeJAFFECoding Facial Expressions with Gabor Wavelets[pdf]Unknown50%84842242655420433
6424b69f3ff4d35249c0bb7ef912fbc2c86f4ff4celebaCelebADeep Learning Face Attributes in the Wild[pdf]2015 IEEE International Conference on Computer Vision (ICCV)eduChinese University of Hong KongChina22.41626320114.2109318052%80842138668670118
4d423acc78273b75134e2afd1777ba6d3a398973cmu_pieCMU PIEThe CMU Pose, Illumination, and Expression (PIE) Database of Human Faces[pdf]Unknown53%74239634459416329
4d423acc78273b75134e2afd1777ba6d3a398973multi_pieMULTIPIEThe CMU Pose, Illumination, and Expression (PIE) Database of Human Faces[pdf]Unknown53%74239634459416329
2830fb5282de23d7784b4b4bc37065d27839a412h3dH3DPoselets: Body part detectors trained using 3D human pose annotations[pdf]2009 IEEE 12th International Conference on Computer Vision52%70736833967509215
6273b3491e94ea4dd1ce42b791d77bdc96ee73a8viperVIPeREvaluating Appearance Models for Recognition, Reacquisition, and Tracking[pdf]Unknown56%58432925538338245
140438a77a771a8fb656b39a78ff488066eb6b50lfpwLFWPLocalizing Parts of Faces Using a Consensus of Exemplars[pdf]IEEE Transactions on Pattern Analysis and Machine Intelligence60%52131520642337195
6bd36e9fd0ef20a3074e1430a6cc601e6d407fc3cuhk03CUHK03DeepReID: Deep Filter Pairing Neural Network for Person Re-identification[pdf]2014 IEEE Conference on Computer Vision and Pattern Recognition59%51230420829324180
560e0e58d0059259ddf86fcec1fa7975dee6a868youtube_facesYouTubeFacesFace recognition in unconstrained videos with matched background similarity[pdf]CVPR 2011eduTel Aviv UniversityIsrael32.1119889034.8045970260%48529219230298193
2258e01865367018ed6f4262c880df85b94959f8motMOTEvaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics[pdf]EURASIP J. Image and Video Processing49%58629029448345244
3316521a5527c7700af8ae6aef32a79a8b83672ctud_campusTUD-CampusPeople-tracking-by-detection and people-detection-by-tracking[pdf]2008 IEEE Conference on Computer Vision and Pattern Recognition53%52928024840324213
3316521a5527c7700af8ae6aef32a79a8b83672ctud_crossingTUD-CrossingPeople-tracking-by-detection and people-detection-by-tracking[pdf]2008 IEEE Conference on Computer Vision and Pattern Recognition53%52928024840324213
3316521a5527c7700af8ae6aef32a79a8b83672ctud_pedestrianTUD-PedestrianPeople-tracking-by-detection and people-detection-by-tracking[pdf]2008 IEEE Conference on Computer Vision and Pattern Recognition53%52928024840324213
cc589c499dcf323fe4a143bbef0074c3e31f9b60bu_3dfeBU-3DFEA 3D facial expression database for facial behavior research[pdf]7th International Conference on Automatic Face and Gesture Recognition (FGR06)47%55526329147299270
1dc35905a1deff8bc74688f2d7e2f48fd2273275caltech_pedestriansCaltech PedestriansPedestrian detection: A benchmark[pdf]2009 IEEE Conference on Computer Vision and Pattern Recognition50%51926125827289233
853bd61bc48a431b9b1c7cab10c603830c488e39casia_webfaceCASIA WebfaceLearning Face Representation from Scratch[pdf]CoRReduChinese Academy of SciencesChina40.00447950116.3702380060%43626017630288150
4308bd8c28e37e2ed9a3fcfe74d5436cce34b410market_1501Market 1501Scalable Person Re-identification: A Benchmark[pdf]2015 IEEE International Conference on Computer Vision (ICCV)60%39423815618272116
4053e3423fb70ad9140ca89351df49675197196abio_idBioID FaceRobust Face Detection Using the Hausdorff Distance[pdf]Unknown48%49823726156330179
8a3c5507237957d013a0fe0f082cab7f757af6eemaflMAFLFacial Landmark Detection by Deep Multi-task Learning[pdf]Unknown60%38323115225265121
8a3c5507237957d013a0fe0f082cab7f757af6eemtflMTFLFacial Landmark Detection by Deep Multi-task Learning[pdf]Unknown60%38323115225265121
9055b155cbabdce3b98e16e5ac9c0edf00f9552fmorphMORPH CommercialMORPH: a longitudinal image database of normal adult age-progression[pdf]7th International Conference on Automatic Face and Gesture Recognition (FGR06)53%42422519826239190
9055b155cbabdce3b98e16e5ac9c0edf00f9552fmorph_ncMORPH Non-CommercialMORPH: a longitudinal image database of normal adult age-progression[pdf]7th International Conference on Automatic Face and Gesture Recognition (FGR06)53%42422519826239190
3325860c0c82a93b2eac654f5324dd6a776f609empii_human_poseMPII Human Pose2D Human Pose Estimation: New Benchmark and State of the Art Analysis[pdf]2014 IEEE Conference on Computer Vision and Pattern Recognition62%3562211352130453
6dd0597f8513dc100cd0bc1b493768cde45098a9stickmen_buffyBuffy StickmenLearning to parse images of articulated bodies[pdf]Unknown58%37321515735251129
6dd0597f8513dc100cd0bc1b493768cde45098a9stickmen_pascalStickmen PASCALLearning to parse images of articulated bodies[pdf]Unknown58%37321515735251129
6dd0597f8513dc100cd0bc1b493768cde45098a9stickmen_pascalStickmen PASCALLearning to parse images of articulated bodies[pdf]Unknown58%37321515735251129
2a75f34663a60ab1b04a0049ed1d14335129e908mmi_facial_expressionMMI Facial Expression DatasetWeb-based database for facial expression analysis[pdf]2005 IEEE International Conference on Multimedia and Expo48%44021222844267181
2485c98aa44131d1a2f7d1355b1e372f2bb148adcas_pealCAS-PEALThe CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations[pdf]IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans50%41520920639189232
16c7c31a7553d99f1837fc6e88e77b5ccbb346b8pridPRIDPerson Re-identification by Descriptive and Discriminative Classification[pdf]Unknown58%35220414827196157
95f12d27c3b4914e0668a268360948bce92f7db3helenHelenInteractive Facial Feature Localization[pdf]UnknowncompanyFacebookUnited States37.39367170-122.0807262059%33920113829219129
044d9a8c61383312cdafbcc44b9d00d650b21c70fiw_300300-W300 Faces in-the-Wild Challenge: The First Facial Landmark Localization Challenge[pdf]2013 IEEE International Conference on Computer Vision Workshops61%32419912529211118
2724ba85ec4a66de18da33925e537f3902f21249cofwCOFWRobust Face Landmark Estimation under Occlusion[pdf]2013 IEEE International Conference on Computer VisioneduCalifornia Institute of TechnologyUnited States34.13710185-118.1252748761%30518611916192116
4b1d23d17476fcf78f4cbadf69fb130b1aa627c0leeds_sports_poseLeeds Sports PoseClustered Pose and Nonlinear Appearance Models for Human Pose Estimation[pdf]Unknown65%278180981320878
4b1d23d17476fcf78f4cbadf69fb130b1aa627c0stickmen_buffyBuffy StickmenClustered Pose and Nonlinear Appearance Models for Human Pose Estimation[pdf]Unknown65%278180981320878
639937b3a1b8bded3f7e9a40e85bd3770016cf3cbfmBFMA 3D Face Model for Pose and Illumination Invariant Face Recognition[pdf]2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance54%3231761472922698
13f06b08f371ba8b5d31c3e288b4deb61335b462eth_andreas_essETHZ PedestrianDepth and Appearance for Mobile Scene Analysis[pdf]2007 IEEE 11th International Conference on Computer VisioneduETH ZurichSwitzerland47.376313008.5476699055%31917614327195127
a74251efa970b92925b89eeef50a5e37d9281ad0aflwAFLWAnnotated Facial Landmarks in the Wild: A large-scale, real-world database for facial landmark localization[pdf]2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)60%2921751173721284
3765df816dc5a061bc261e190acc8bdd9d47bec0rafdRaFDPresentation and validation of the Radboud Faces Database[pdf]Unknown39%44617527143322136
5981e6479c3fd4e31644db35d236bfb84ae46514motMOTLearning to associate: HybridBoosted multi-target tracker for crowded scene[pdf]2009 IEEE Conference on Computer Vision and Pattern RecognitioneduUniversity of Southern CaliforniaUnited States34.02241490-118.2863440752%33017215727196139
436f798d1a4e54e5947c1e7d7375c31b2bdb4064tud_multiviewTUD-MultiviewMonocular 3D pose estimation and tracking by detection[pdf]2010 IEEE Computer Society Conference on Computer Vision and Pattern RecognitioneduTU DarmstadtGermany49.874827708.6563281054%30216413834207100
436f798d1a4e54e5947c1e7d7375c31b2bdb4064tud_stadtmitteTUD-StadtmitteMonocular 3D pose estimation and tracking by detection[pdf]2010 IEEE Computer Society Conference on Computer Vision and Pattern RecognitioneduTU DarmstadtGermany49.874827708.6563281054%30216413834207100
44484d2866f222bbb9b6b0870890f9eea1ffb2d0cuhk01CUHK01Human Reidentification with Transferred Metric Learning[pdf]Unknown62%2581609812142115
2acf7e58f0a526b957be2099c10aab693f795973bosphorusThe BosphorusBosphorus Database for 3D Face Analysis[pdf]Unknown48%32815817019149183
833fa04463d90aab4a9fe2870d480f0b40df446esun_attributesSUNSUN attribute database: Discovering, annotating, and recognizing scene attributes[pdf]2012 IEEE Conference on Computer Vision and Pattern RecognitioneduBrown UniversityUnited States41.82686820-71.4012314658%2691561132921557
010f0f4929e6a6644fb01f0e43820f91d0fad292yfcc_100mYFCC100MYFCC100M: the new data in multimedia research[pdf]Commun. ACMeduCarnegie Mellon University Silicon ValleyUnited States37.41021930-122.0596548756%2761551212317599
2fda164863a06a92d3a910b96eef927269aeb730names_and_facesNews DatasetNames and faces in the news[pdf]Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004.51%2941501432921582
9361b784e73e9238d5cefbea5ac40d35d1e3103ftowncenterTownCenterStable multi-target tracking in real-time surveillance video[pdf]CVPR 201144%31013717324180131
2eed184680edcdec8a3b605ad1a3ba8e8f7cc2e9grazGraz PedestrianGeneric object recognition with boosting[pdf]IEEE Transactions on Pattern Analysis and Machine IntelligenceeduTU GrazAustria47.0707140015.4395040048%2861361501619397
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38b55d95189c5e69cf4ab45098a48fba407609b4cuhk02CUHK02Locally Aligned Feature Transforms across Views[pdf]2013 IEEE Conference on Computer Vision and Pattern Recognition53%24212911317139102
7808937b46acad36e43c30ae4e9f3fd57462853dbpadBPADDescribing people: A poselet-based approach to attribute classification[pdf]2011 International Conference on Computer Vision57%221125961416559
4c170a0dcc8de75587dae21ca508dab2f9343974face_tracerFaceTracerFaceTracer: A Search Engine for Large Collections of Images with Faces[pdf]Unknown57%218125921715271
0c91808994a250d7be332400a534a9291ca3b60egrazGraz PedestrianWeak Hypotheses and Boosting for Generic Object Detection and Recognition[pdf]Unknown51%2471251221817778
140c95e53c619eac594d70f6369f518adfea12efijb_cIJB-CPushing the frontiers of unconstrained face detection and recognition: IARPA Janus Benchmark A[pdf]2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)55%222123991916162
6ad5a38df8dd4cdddd74f31996ce096d41219f72tud_brusselsTUD-BrusselsMulti-cue onboard pedestrian detection[pdf]2009 IEEE Conference on Computer Vision and Pattern Recognition56%217121961413386
6ad5a38df8dd4cdddd74f31996ce096d41219f72tud_motionpairsTUD-MotionparisMulti-cue onboard pedestrian detection[pdf]2009 IEEE Conference on Computer Vision and Pattern Recognition56%217121961413386
98bb029afe2a1239c3fdab517323066f0957b81bilids_vid_reidiLIDS-VIDPerson Re-identification by Video Ranking[pdf]Unknown57%210120901011594
98bb029afe2a1239c3fdab517323066f0957b81bsdu_vidSDU-VIDPerson Re-identification by Video Ranking[pdf]Unknown57%210120901011594
35b0331dfcd2897abd5749b49ff5e2b8ba0f7a62coco_qaCOCO QAExploring Models and Data for Image Question Answering[pdf]Unknown60%191115761216527
46a01565e6afe7c074affb752e7069ee3bf2e4efsdu_vidSDU-VIDLocal Descriptors Encoded by Fisher Vectors for Person Re-identification[pdf]Unknown58%198114841611188
013909077ad843eb6df7a3e8e290cfd5575999d2fiw_300300-WA Semi-automatic Methodology for Facial Landmark Annotation[pdf]2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops60%185111741512464
4e4746094bf60ee83e40d8597a6191e463b57f76leeds_sports_pose_extendedLeeds Sports Pose ExtendedLearning effective human pose estimation from inaccurate annotation[pdf]CVPR 2011eduUniversity of LeedsUnited Kingdom53.80387185-1.5524571264%173111621012256
21d9d0deed16f0ad62a4865e9acf0686f4f15492images_of_groupsImages of GroupsUnderstanding images of groups of people[pdf]2009 IEEE Conference on Computer Vision and Pattern RecognitioneduCarnegie Mellon University Silicon ValleyUnited States37.41021930-122.0596548754%202110921213275
b1f4423c227fa37b9680787be38857069247a307afew_vaAFEW-VACollecting Large, Richly Annotated Facial-Expression Databases from Movies[pdf]IEEE MultiMediaeduAustralian National UniversityAustralia-35.27769990149.1185270060%1821097388699
570f37ed63142312e6ccdf00ecc376341ec72b9fstanford_droneStanford DroneSocial LSTM: Human Trajectory Prediction in Crowded Spaces[pdf]2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)46%229106123515079
22ad2c8c0f4d6aa4328b38d894b814ec22579761gallagherGallagherClothing cosegmentation for recognizing people[pdf]2008 IEEE Conference on Computer Vision and Pattern RecognitioneduCarnegie Mellon University Silicon ValleyUnited States37.41021930-122.0596548758%17710374710184
1aad2da473888cb7ebc1bfaa15bfa0f1502ce005jpl_poseJPL-Interaction datasetFirst-Person Activity Recognition: What Are They Doing to Me?[pdf]2013 IEEE Conference on Computer Vision and Pattern Recognition70%14810345811138
133f01aec1534604d184d56de866a4bd531dac87lfwLFWEffective Unconstrained Face Recognition by Combining Multiple Descriptors and Learned Background Statistics[pdf]IEEE Transactions on Pattern Analysis and Machine Intelligence55%17798791510475
18010284894ed0edcca74e5bf768ee2e15ef7841deep_fashionDeepFashionDeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations[pdf]2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)65%1509753411138
6204776d31359d129a582057c2d788a14f8aadebyoutube_celebritiesYouTube CelebritiesFace tracking and recognition with visual constraints in real-world videos[pdf]2008 IEEE Conference on Computer Vision and Pattern Recognition32%3019720218144133
56ffa7d906b08d02d6d5a12c7377a57e24ef3391unbc_shoulder_painUNBC-McMaster PainPainful data: The UNBC-McMaster shoulder pain expression archive database[pdf]Face and Gesture 2011eduCarnegie Mellon University Silicon ValleyUnited States37.41021930-122.0596548752%18495892311271
5a5f0287484f0d480fed1ce585dbf729586f0edcdisfaDISFADISFA: A Spontaneous Facial Action Intensity Database[pdf]IEEE Transactions on Affective ComputingeduUniversity of DenverUnited States39.67665410-104.9622030049%19094961910091
1be498d4bbc30c3bfd0029114c784bc2114d67c0adienceAdienceAge and Gender Estimation of Unfiltered Faces[pdf]IEEE Transactions on Information Forensics and SecurityeduOpen University of IsraelIsrael32.7782416534.9956567355%168927659478
291265db88023e92bb8c8e6390438e5da148e8f5mscelebMsCelebMS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition[pdf]Unknown54%16791761413136
0df0d1adea39a5bef318b74faa37de7f3e00b452mpii_gazeMPIIGazeAppearance-based gaze estimation in the wild[pdf]2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)65%138904839742
29a705a5fa76641e0d8963f1fdd67ee4c0d92d3dscfaceSCfaceSCface – surveillance cameras face database[pdf]Multimedia Tools and Applications51%1789088159089
32c801cb7fbeb742edfd94cccfca4934baec71daucf_crowdUCF-CC-50Multi-source Multi-scale Counting in Extremely Dense Crowd Images[pdf]2013 IEEE Conference on Computer Vision and Pattern Recognition70%125883767352
3b5b6d19d4733ab606c39c69a889f9e67967f151qmul_gridGRIDMulti-camera activity correlation analysis[pdf]2009 IEEE Conference on Computer Vision and Pattern RecognitioneduQueen Mary University of LondonUnited Kingdom51.52472720-0.0393103562%138865287961
4f93cd09785c6e77bf4bc5a788e079df524c8d21sotonSOTON HiDOn a Large Sequence-Based Human Gait Database[pdf]Unknown58%14886621710449
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52d7eb0fbc3522434c13cc247549f74bb9609c5dwider_faceWIDER FACEWIDER FACE: A Face Detection Benchmark[pdf]2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)eduChinese University of Hong KongChina22.41626320114.2109318057%14885631510841
27a2fad58dd8727e280f97036e0d2bc55ef5424cduke_mtmcDuke MTMCPerformance Measures and a Data Set for Multi-Target, Multi-Camera Tracking[pdf]Unknown58%1367957710827
27a2fad58dd8727e280f97036e0d2bc55ef5424cmotMOTPerformance Measures and a Data Set for Multi-Target, Multi-Camera Tracking[pdf]Unknown58%1367957710827
8b56e33f33e582f3e473dba573a16b598ed9bcdcfeiFEIA new ranking method for principal components analysis and its application to face image analysis[pdf]Image Vision Comput.46%1697891672101
b91f54e1581fbbf60392364323d00a0cd43e493cbp4d_spontanousBP4D-SpontanousA high-resolution spontaneous 3D dynamic facial expression database[pdf]2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG)eduSUNY BinghamtonUnited States42.08779975-75.9706606651%151777478765
066000d44d6691d27202896691f08b27117918b9psuPSUVision-Based Analysis of Small Groups in Pedestrian Crowds[pdf]IEEE Transactions on Pattern Analysis and Machine Intelligence49%151747797973
10195a163ab6348eef37213a46f60a3d87f289c5imdb_wikiIMDBDeep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks[pdf]International Journal of Computer VisioneduETH ZurichSwitzerland47.376313008.5476699053%1337162139441
2d3482dcff69c7417c7b933f22de606a0e8e42d4lfwLFWLabeled Faces in the Wild : Updates and New Reporting Procedures[pdf]UnknowneduUniversity of MassachusettsUnited States42.38897850-72.5286987058%123715237250
96e0cfcd81cdeb8282e29ef9ec9962b125f379b0megafaceMegaFaceThe MegaFace Benchmark: 1 Million Faces for Recognition at Scale[pdf]2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)59%121715099822
66e6f08873325d37e0ec20a4769ce881e04e964esun_attributesSUNThe SUN Attribute Database: Beyond Categories for Deeper Scene Understanding[pdf]International Journal of Computer Vision62%1127042148429
0486214fb58ee9a04edfe7d6a74c6d0f661a7668chokepointChokePointPatch-based probabilistic image quality assessment for face selection and improved video-based face recognition[pdf]CVPR 2011 WORKSHOPS53%128686067360
8355d095d3534ef511a9af68a3b2893339e3f96bimdb_wikiIMDBDEX: Deep EXpectation of Apparent Age from a Single Image[pdf]2015 IEEE International Conference on Computer Vision Workshop (ICCVW)56%120675357447
0d3bb75852098b25d90f31d2f48fd0cb4944702bface_scrubFaceScrubA data-driven approach to cleaning large face datasets[pdf]2014 IEEE International Conference on Image Processing (ICIP)54%123665749627
e4754afaa15b1b53e70743880484b8d0736990fffiw_300300-W300 Faces In-The-Wild Challenge: database and results[pdf]Image Vision Comput.54%1146153107143
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0a85bdff552615643dd74646ac881862a7c7072dpipaPIPABeyond frontal faces: Improving Person Recognition using multiple cues[pdf]2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)companyFacebookUnited States37.39367170-122.0807262072%5036132409
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7ace44190729927e5cb0dd5d363fcae966fe13f7nudedetection#N/AA bag-of-features approach based on Hue-SIFT descriptor for nude detection[pdf]2009 17th European Signal Processing Conference61%51312011833
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37d6f0eb074d207b53885bd2eb78ccc8a04be597vmuVMUCan facial cosmetics affect the matching accuracy of face recognition systems?[pdf]2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS)eduWest Virginia UniversityUnited States39.65404635-79.9647535561%49301901831
37d6f0eb074d207b53885bd2eb78ccc8a04be597youtube_makeupYMUCan facial cosmetics affect the matching accuracy of face recognition systems?[pdf]2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS)eduWest Virginia UniversityUnited States39.65404635-79.9647535561%49301901831
9c23859ec7313f2e756a3e85575735e0c52249f4facebook_100Facebook100Scaling up biologically-inspired computer vision: A case study in unconstrained face recognition on facebook[pdf]CVPR 2011 WORKSHOPSeduHarvard UniversityUnited States42.36782045-71.1266665358%50292133911
f1af714b92372c8e606485a3982eab2f16772ad8mug_facesMUG FacesThe MUG facial expression database[pdf]11th International Workshop on Image Analysis for Multimedia Interactive Services WIAMIS 10eduAristotle University of ThessalonikiGreece40.6298414522.9588935043%68293952840
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1c2802c2199b6d15ecefe7ba0c39bfe44363de38youtube_posesYouTube PosePersonalizing Human Video Pose Estimation[pdf]2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)eduOxford UniversityUnited Kingdom51.75208490-1.2516646066%3221112295
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31b05f65405534a696a847dd19c621b7b8588263umd_facesUMDUMDFaces: An annotated face dataset for training deep networks[pdf]2017 IEEE International Joint Conference on Biometrics (IJCB)eduUniversity of MarylandUnited States39.28996850-76.6219610357%3520154287
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1bd1645a629f1b612960ab9bba276afd4cf7c666brainwashBrainwashEnd-to-End People Detection in Crowded Scenes[pdf]2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)eduMax Planck Institute for InformaticsGermany49.257956607.0457741737%49183112321
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6dcf418c778f528b5792104760f1fbfe90c6dd6aagedbAgeDBAgeDB: The First Manually Collected, In-the-Wild Age Database[pdf]2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)91%111010101
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4156b7e88f2e0ab0a7c095b9bab199ae2b23bd06distance_nighttimeLong Distance Heterogeneous FaceNighttime Face Recognition at Long Distance: Cross-Distance and Cross-Spectral Matching[pdf]Unknown43%2191231110
2f43b614607163abf41dfe5d17ef6749a1b61304hrt_transgenderHRT TransgenderInvestigating the Periocular-Based Face Recognition Across Gender Transformation[pdf]IEEE Transactions on Information Forensics and SecurityeduUniversity of North Carolina at WilmingtonUnited States34.22498270-77.8690774469%1394068
ca3e88d87e1344d076c964ea89d91a75c417f5eeimfdbIMFDBIndian Movie Face Database: A benchmark for face recognition under wide variations[pdf]2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG)eduCVIT, IIITH, IndiaIndia17.4459581078.3495994060%15960105
71b7fc715e2f1bb24c0030af8d7e7b6e7cd128a6umd_facesUMDThe Do’s and Don’ts for CNN-Based Face Verification[pdf]2017 IEEE International Conference on Computer Vision Workshops (ICCVW)36%259163176
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b6c293f0420f7e945b5916ae44269fb53e139275tisiTimes Square IntersectionVideo Synopsis by Heterogeneous Multi-source Correlation[pdf]2013 IEEE International Conference on Computer Vision29%3192221513
25474c21613607f6bb7687a281d5f9d4ffa1f9f3faceplaceFace PlaceRecognizing disguised faces[pdf]Unknown33%248160186
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2a171f8d14b6b8735001a11c217af9587d095848social_relationSocial RelationLearning Social Relation Traits from Face Images[pdf]2015 IEEE International Conference on Computer Vision (ICCV)40%208125155
a94cae786d515d3450d48267e12ca954aab791c4yawddYawDDYawDD: a yawning detection dataset[pdf]Unknown57%14861212
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6403117f9c005ae81f1e8e6d1302f4a045e3d99dalert_airportALERT AirportA Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets.[pdf]IEEE transactions on pattern analysis and machine intelligence47%15781105
22f656d0f8426c84a33a267977f511f127bfd7f3expwExpWFrom Facial Expression Recognition to Interpersonal Relation Prediction[pdf]International Journal of Computer Vision78%972054
2b926b3586399d028b46315d7d9fb9d879e4f79cfrav3dFRAV3DMultimodal 2D, 2.5D & 3D Face Verification[pdf]2006 International Conference on Image ProcessingeduUniversidad Rey Juan Carlos, Spain40.33586610-3.8769432050%14770212
b71d1aa90dcbe3638888725314c0d56640c1fef1ifdbIFDBIranian Face Database with age, pose and expression[pdf]2007 International Conference on Machine VisioneduIslamic Azad UniversityIran34.8452999048.5596212035%207132129
0cb2dd5f178e3a297a0c33068961018659d0f443ijb_cIJB-CIARPA Janus Benchmark-B Face Dataset[pdf]2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)eduMichigan State UniversityUnited States42.71856800-84.4779157128%257186214
4563b46d42079242f06567b3f2e2f7a80cb3befevadanaVADANAVADANA: A dense dataset for facial image analysis[pdf]2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)eduUniversity of DelawareUnited States39.68103280-75.7540184044%16790610
8f02ec0be21461fbcedf51d864f944cfc42c875fhda_plusHDA+The HDA+ Data Set for Research on Fully Automated Re-identification Systems[pdf]Unknown35%176112116
7f4040b482d16354d5938c1d1b926b544652bf5bnova_emotionsNovaemötions DatasetCompetitive affective gaming: winning with a smile[pdf]UnknowneduUniversidade NOVA de Lisboa, Caparica, PortugalPortugal38.66096400-9.2058130075%862044
bd26dabab576adb6af30484183c9c9c8379bf2e0scut_fbpSCUT-FBPSCUT-FBP: A Benchmark Dataset for Facial Beauty Perception[pdf]2015 IEEE International Conference on Systems, Man, and Cybernetics43%1468359
041d3eedf5e45ce5c5229f0181c5c576ed1fafd6ucf_selfieUCF SelfieHow to Take a Good Selfie?[pdf]Unknown67%963064
633c851ebf625ad7abdda2324e9de093cf623141appa_realAPPA-REALApparent and Real Age Estimation in Still Images with Deep Residual Regressors on Appa-Real Database[pdf]2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017)46%13670113
8d5998cd984e7cce307da7d46f155f9db99c6590chalearnChaLearnChaLearn looking at people: A review of events and resources[pdf]2017 International Joint Conference on Neural Networks (IJCNN)50%1055164
1a40092b493c6b8840257ab7f96051d1a4dbfeb2sports_videos_in_the_wildSVWSports Videos in the Wild (SVW): A video dataset for sports analysis[pdf]2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG)83%651151
8627f019882b024aef92e4eb9355c499c733e5b7usedUSED Social Event DatasetUSED: a large-scale social event detection dataset[pdf]UnknowneduUniversity of TrentoItaly46.0658836011.1159894071%752034
56ae6d94fc6097ec4ca861f0daa87941d1c10b70cmdpCMDPDistance Estimation of an Unknown Person from a Portrait[pdf]Unknown44%945063
4b4106614c1d553365bad75d7866bff0de6056edczech_news_agencyUFIUnconstrained Facial Images: Database for Face Recognition Under Real-World Conditions[pdf]Unknown40%1046046
563c940054e4b456661762c1ab858e6f730c3159data_61Data61 PedestrianA Multi-modal Graphical Model for Scene Analysis[pdf]2015 IEEE Winter Conference on Applications of Computer Vision50%844053
137aa2f891d474fce1e7a1d1e9b3aefe21e22b34hrt_transgenderHRT TransgenderIs the eye region more reliable than the face? A preliminary study of face-based recognition on a transgender dataset[pdf]2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS)57%743135
a5a3bc3e5e9753769163cb30b16dbd12e266b93emcgillMcGill Real WorldHierarchical temporal graphical model for head pose estimation and subsequent attribute classification in real-world videos[pdf]Computer Vision and Image Understanding50%844153
079a0a3bf5200994e1f972b1b9197bf2f90e87d4mit_cbclMIT CBCLComponent-Based Face Recognition with 3D Morphable Models[pdf]2004 Conference on Computer Vision and Pattern Recognition Workshop33%1248084
54983972aafc8e149259d913524581357b0f91c3reseedReSEEDReSEED: social event dEtection dataset[pdf]Unknown67%642115
07fcbae86f7a3ad3ea1cf95178459ee9eaf77cb1uccsUCCSLarge scale unconstrained open set face database[pdf]2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS)eduUniversity of Colorado at Colorado SpringsUnited States38.89646790-104.8050594080%541032
7ebb153704706e457ab57b432793d2b6e5d12592vgg_celebs_in_placesCIPFaces in Places: compound query retrieval[pdf]Unknown80%541041
287ddcb3db5562235d83aee318f318b8d5e43fb1erceERCeLearning from Multiple Sources for Video Summarisation[pdf]International Journal of Computer Vision57%743043
287ddcb3db5562235d83aee318f318b8d5e43fb1tisiTimes Square IntersectionLearning from Multiple Sources for Video Summarisation[pdf]International Journal of Computer Vision57%743043
9cc8cf0c7d7fa7607659921b6ff657e17e135eccmafaMAsked FAcesDetecting Masked Faces in the Wild with LLE-CNNs[pdf]2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)67%642151
dd65f71dac86e36eecbd3ed225d016c3336b4a13families_in_the_wildFIWVisual Kinship Recognition of Families in the Wild[pdf]IEEE Transactions on Pattern Analysis and Machine IntelligenceeduUniversity of Massachusetts DartmouthUnited States41.62772475-71.00724501100%330021
57178b36c21fd7f4529ac6748614bb3374714e91ijb_cIJB-CIARPA Janus Benchmark - C: Face Dataset and Protocol[pdf]2018 International Conference on Biometrics (ICB)33%936290
0d2dd4fc016cb6a517d8fb43a7cc3ff62964832elagLAGLarge age-gap face verification by feature injection in deep networks[pdf]Pattern Recognition Letters43%734034
a7fe834a0af614ce6b50dc093132b031dd9a856bmarket1203Market 1203Orientation Driven Bag of Appearances for Person Re-identification[pdf]CoRR43%734044
23e824d1dfc33f3780dd18076284f07bd99f1c43mifsMIFSSpoofing faces using makeup: An investigative study[pdf]2017 IEEE International Conference on Identity, Security and Behavior Analysis (ISBA)eduINRIA MéditerranéeFrance43.615813107.0683800060%532014
a7fe834a0af614ce6b50dc093132b031dd9a856bpku_reidPKU-ReidOrientation Driven Bag of Appearances for Person Re-identification[pdf]CoRR43%734044
17b46e2dad927836c689d6787ddb3387c6159ecegeofacesGeoFacesGeoFaceExplorer: exploring the geo-dependence of facial attributes[pdf]Unknown100%220011
ad01687649d95cd5b56d7399a9603c4b8e2217d7mrp_droneMRP DroneInvestigating Open-World Person Re-identification Using a Drone[pdf]Unknown40%523032
f6c8d5e35d7e4d60a0104f233ac1a3ab757da53fpkuPKUSwiss-System Based Cascade Ranking for Gait-Based Person Re-Identification[pdf]Unknown67%321012
9e5378e7b336c89735d3bb15cf67eff96f86d39aprecariousPrecariousExpecting the Unexpected: Training Detectors for Unusual Pedestrians with Adversarial Imposters[pdf]2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)17%122101111
d4f1eb008eb80595bcfdac368e23ae9754e1e745uccsUCCSUnconstrained Face Detection and Open-Set Face Recognition Challenge[pdf]2017 IEEE International Joint Conference on Biometrics (IJCB)40%523041
4d4bb462c9f1d4e4ab1e4aa6a75cc0bc71b384613dddb_unconstrained3D DynamicA 3 D Dynamic Database for Unconstrained Face Recognition[pdf]Unknown50%211011
65355cbb581a219bd7461d48b3afd115263ea760complex_activitiesOngoing Complex ActivitiesRecognition of ongoing complex activities by sequence prediction over a hierarchical label space[pdf]2016 IEEE Winter Conference on Applications of Computer Vision (WACV)50%211020
3dc3f0b64ef80f573e3a5f96e456e52ee980b877georgia_tech_face_databaseGeorgia Tech FaceMAXIMUM 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[pdf]Unknown33%312021
e58dd160a76349d46f881bd6ddbc2921f08d1050gfwGrouping Face in the WildMerge or Not? Learning to Group Faces via Imitation Learning[pdf]Unknown50%211020
55c40cbcf49a0225e72d911d762c27bb1c2d14aaifadIFADIndian Face Age Database : A Database for Face Recognition with Age Variation[pdf]Unknown50%211020
9e31e77f9543ab42474ba4e9330676e18c242e72imdb_faceIMDb FaceThe Devil of Face Recognition is in the Noise[pdf]UnknowneduNanyang Technological UniversitySingapore1.34841040103.6829796520%514031
d80a3d1f3a438e02a6685e66ee908446766fefa9megaageMegaAgeQuantifying Facial Age by Posterior of Age Comparisons[pdf]CoRR25%413140
578d4ad74818086bb64f182f72e2c8bd31e3d426mr2MR2The MR2: A multi-racial, mega-resolution database of facial stimuli.[pdf]Behavior research methods14%716070
c06b13d0ec3f5c43e2782cd22542588e233733c3nova_emotionsNovaemötions DatasetCrowdsourcing facial expressions for affective-interaction[pdf]Computer Vision and Image Understanding100%110010
922e0a51a3b8c67c4c6ac09a577ff674cbd28b34v47V47Re-identification of pedestrians with variable occlusion and scale[pdf]2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)eduKingston UniversityUnited Kingdom51.42930860-0.2684044010%1019264
5ad4e9f947c1653c247d418f05dad758a3f9277bwlfdbWLFDBWLFDB: Weakly Labeled Face Databases[pdf]Unknown100%110001
4eab317b5ac436a949849ed286baa3de2a541eeflaofiwLAOFIWTurning a Blind Eye: Explicit Removal of Biases and Variation from Deep Neural Network Embeddings[pdf]Unknown50%211020
9696ad8b164f5e10fcfe23aacf74bd6168aebb154dfab4DFAB4DFAB: A Large Scale 4D Facial Expression Database for Biometric Applications[pdf]CoRR0%404022
f0e17f27f029db4ad650ff278fe3c10ecb6cb0c4europersonsEuroCity PersonsThe EuroCity Persons Dataset: A Novel Benchmark for Object Detection[pdf]CoRR0%101010
670637d0303a863c1548d5b19f705860a23e285cface_tracerFaceTracerFace swapping: automatically replacing faces in photographs[pdf]Unknown100%000000
75da1df4ed319926c544eefe17ec8d720feef8c0fddbFDDBFDDB: A benchmark for face detection in unconstrained settings[pdf]Unknown0%101001
bd88bb2e4f351352d88ee7375af834360e223498hda_plusHDA+HDA dataset-DRAFT 1 A Multi-camera video data set for research on High-Definition surveillance[pdf]Unknown0%202012
2b89de1d81cee50552f10e26e865df3365e9bc88ibm_difIBM Diversity in FacesDiversity in Faces[pdf]CoRR100%000000
066d71fcd997033dce4ca58df924397dfe0b5fd1ifdbIFDBIranian Face Database and Evaluation with a New Detection Algorithm[pdf]Unknown100%000000
22909dd19a0ec3b6065334cb5be5392cb24d839dpetsPETS 2017PETS 2017: Dataset and Challenge[pdf]2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)0%808026
c866a2afc871910e3282fd9498dce4ab20f6a332qmul_surv_faceQMUL-SurvFaceSurveillance Face Recognition Challenge[pdf]CoRR100%000000
d3200d49a19a4a4e4e9745ee39649b65d80c834bscut_headSCUT HEADDetecting Heads using Feature Refine Net and Cascaded Multi-scale Architecture[pdf]2018 24th International Conference on Pattern Recognition (ICPR)100%000000
d3f5a1848b0028d8ab51d0b0673732cad2e3c8c9stair_actionsSTAIR ActionSTAIR Actions: A Video Dataset of Everyday Home Actions[pdf]CoRR100%000000
8990cdce3f917dad622e43e033db686b354d057ctiny_facesTinyFaceLow-Resolution Face Recognition[pdf]CoRR100%000000
377f2b65e6a9300448bdccf678cde59449ecd337ufddUFDDPushing the Limits of Unconstrained Face Detection: a Challenge Dataset and Baseline Results[pdf]CoRR0%202020
9b9bf5e623cb8af7407d2d2d857bc3f1b531c182who_goes_thereWGTWho goes there?: approaches to mapping facial appearance diversity[pdf]UnknowneduUniversity of KentuckyUnited States38.03337420-84.50177580100%000000
77c81c13a110a341c140995bedb98101b9e84f7fwildtrackWildTrackWILDTRACK : A Multi-camera HD Dataset for Dense Unscripted Pedestrian Detection[pdf]Unknown100%000000
7b92d1e53cc87f7a4256695de590098a2f30261eappa_realAPPA-REALFrom Apparent to Real Age: Gender, Age, Ethnic, Makeup, and Expression Bias Analysis in Real Age Estimation[pdf]2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)100%000000
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All Papers

Paper IDMegapixels KeyMegapixels NameReport LinkPDF LinkJournalTypeAddressCountryLatLngCoverageTotal CitationsGeocoded CitationsUnknown CitationsEmpty CitationsWith PDFWith DOI
3325860c0c82a93b2eac654f5324dd6a776f609empii_human_poseMPII Human Pose2D Human Pose Estimation: New Benchmark and State of the Art Analysis[pdf]2014 IEEE Conference on Computer Vision and Pattern Recognition62%3562211352130453
e4754afaa15b1b53e70743880484b8d0736990fffiw_300300-W300 Faces In-The-Wild Challenge: database and results[pdf]Image Vision Comput.54%1146153107143
044d9a8c61383312cdafbcc44b9d00d650b21c70fiw_300300-W300 Faces in-the-Wild Challenge: The First Facial Landmark Localization Challenge[pdf]2013 IEEE International Conference on Computer Vision Workshops61%32419912529211118
2e8d0f1802e50cccfd3c0aabac0d0beab3a7846e3dpes3DPeS3DPeS: 3D people dataset for surveillance and forensics[pdf]Unknown48%1225963117151
9696ad8b164f5e10fcfe23aacf74bd6168aebb154dfab4DFAB4DFAB: A Large Scale 4D Facial Expression Database for Biometric Applications[pdf]CoRR0%404022
31b58ced31f22eab10bd3ee2d9174e7c14c27c01tiny_imagesTiny Images80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition[pdf]IEEE Transactions on Pattern Analysis and Machine Intelligence54%99953546394685327
4d4bb462c9f1d4e4ab1e4aa6a75cc0bc71b384613dddb_unconstrained3D DynamicA 3 D Dynamic Database for Unconstrained Face Recognition[pdf]Unknown50%211011
d08cc366a4a0192a01e9a7495af1eb5d9f9e73aeb3d_acB3D(AC)A 3-D Audio-Visual Corpus of Affective Communication[pdf]IEEE Transactions on Multimedia49%39192022712
639937b3a1b8bded3f7e9a40e85bd3770016cf3cbfmBFMA 3D Face Model for Pose and Illumination Invariant Face Recognition[pdf]2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance54%3231761472922698
cc589c499dcf323fe4a143bbef0074c3e31f9b60bu_3dfeBU-3DFEA 3D facial expression database for facial behavior research[pdf]7th International Conference on Automatic Face and Gesture Recognition (FGR06)47%55526329147299270
22646e00a7ba34d1b5fbe3b1efcd91a1e1be3c2bsaivtSAIVT SoftBioA Database for Person Re-Identification in Multi-Camera Surveillance Networks[pdf]2012 International Conference on Digital Image Computing Techniques and Applications (DICTA)45%58263274118
070de852bc6eb275d7ca3a9cdde8f6be8795d1a3d3dfacsD3DFACSA FACS valid 3D dynamic action unit database with applications to 3D dynamic morphable facial modeling[pdf]2011 International Conference on Computer Vision58%52302253715
563c940054e4b456661762c1ab858e6f730c3159data_61Data61 PedestrianA Multi-modal Graphical Model for Scene Analysis[pdf]2015 IEEE Winter Conference on Applications of Computer Vision50%844053
221c18238b829c12b911706947ab38fd017acef7rap_pedestrianRAPA Richly Annotated Dataset for Pedestrian Attribute Recognition[pdf]CoRR67%211470183
013909077ad843eb6df7a3e8e290cfd5575999d2fiw_300300-WA Semi-automatic Methodology for Facial Landmark Annotation[pdf]2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops60%185111741512464
3b4ec8af470948a72a6ed37a9fd226719a874ebcsdu_vidSDU-VIDA Spatio-Temporal Appearance Representation for Video-Based Pedestrian Re-Identification[pdf]2015 IEEE International Conference on Computer Vision (ICCV)62%85533295134
6403117f9c005ae81f1e8e6d1302f4a045e3d99dalert_airportALERT AirportA Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets.[pdf]IEEE transactions on pattern analysis and machine intelligence47%15781105
7ace44190729927e5cb0dd5d363fcae966fe13f7nudedetectionNude DetectionA bag-of-features approach based on Hue-SIFT descriptor for nude detection[pdf]2009 17th European Signal Processing Conference61%51312011833
0d3bb75852098b25d90f31d2f48fd0cb4944702bface_scrubFaceScrubA data-driven approach to cleaning large face datasets[pdf]2014 IEEE International Conference on Image Processing (ICIP)54%123665749627
b91f54e1581fbbf60392364323d00a0cd43e493cbp4d_spontanousBP4D-SpontanousA high-resolution spontaneous 3D dynamic facial expression database[pdf]2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG)eduSUNY BinghamtonUnited States42.08779975-75.9706606651%151777478765
8b56e33f33e582f3e473dba573a16b598ed9bcdcfeiFEIA new ranking method for principal components analysis and its application to face image analysis[pdf]Image Vision Comput.46%1697891672101
2624d84503bc2f8e190e061c5480b6aa4d89277aafew_vaAFEW-VAAFEW-VA database for valence and arousal estimation in-the-wild[pdf]Image Vision Comput.47%15781104
2ad0ee93d029e790ebb50574f403a09854b65b7eyale_facesYaleFacesAcquiring linear subspaces for face recognition under variable lighting[pdf]IEEE Transactions on Pattern Analysis and Machine Intelligence48%999484515110525485
57fe081950f21ca03b5b375ae3e84b399c015861cvc_01_barcelonaCVC-01Adaptive Image Sampling and Windows Classification for On – board Pedestrian Detection[pdf]Unknown39%44172712123
758d7e1be64cc668c59ef33ba8882c8597406e53affectnetAffectNetAffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild[pdf]CoRR53%38201812611
47aeb3b82f54b5ae8142b4bdda7b614433e69b9aam_fedAM-FEDAffectiva-MIT Facial Expression Dataset (AM-FED): Naturalistic and Spontaneous Facial Expressions Collected "In-the-Wild"[pdf]2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops47%73343964134
1be498d4bbc30c3bfd0029114c784bc2114d67c0adienceAdienceAge and Gender Estimation of Unfiltered Faces[pdf]IEEE Transactions on Information Forensics and SecurityeduOpen University of IsraelIsrael32.7782416534.9956567355%168927659478
6dcf418c778f528b5792104760f1fbfe90c6dd6aagedbAgeDBAgeDB: The First Manually Collected, In-the-Wild Age Database[pdf]2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)91%111010101
a74251efa970b92925b89eeef50a5e37d9281ad0aflwAFLWAnnotated Facial Landmarks in the Wild: A large-scale, real-world database for facial landmark localization[pdf]2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)60%2921751173721284
2ce2560cf59db59ce313bbeb004e8ce55c5ce928texas_3dfrdTexas 3DFRDAnthropometric 3D Face Recognition[pdf]International Journal of Computer Vision50%90454556031
0df0d1adea39a5bef318b74faa37de7f3e00b452mpii_gazeMPIIGazeAppearance-based gaze estimation in the wild[pdf]2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)65%138904839742
759a3b3821d9f0e08e0b0a62c8b693230afc3f8dpubfigPubFigAttribute and simile classifiers for face verification[pdf]2009 IEEE 12th International Conference on Computer Vision61%89454435056604300
faf40ce28857aedf183e193486f5b4b0a8c478a2iit_dehli_earIIT Dehli EarAutomated Human Identification Using Ear Imaging[pdf]Unknown37%70264462842
2160788824c4c29ffe213b2cbeb3f52972d73f373d_rma3D-RMAAutomatic 3D face authentication[pdf]Image Vision Comput.46%95445186135
213a579af9e4f57f071b884aa872651372b661fdbbc_poseBBC PoseAutomatic and Efficient Human Pose Estimation for Sign Language Videos[pdf]International Journal of Computer Vision76%251961197
fcc6fe6007c322641796cb8792718641856a22a7miwMIWAutomatic facial makeup detection with application in face recognition[pdf]2013 International Conference on Biometrics (ICB)eduWest Virginia UniversityUnited States39.65404635-79.9647535574%46341211828
fcc6fe6007c322641796cb8792718641856a22a7youtube_makeupYMUAutomatic facial makeup detection with application in face recognition[pdf]2013 International Conference on Biometrics (ICB)eduWest Virginia UniversityUnited States39.65404635-79.9647535574%46341211828
0a85bdff552615643dd74646ac881862a7c7072dpipaPIPABeyond frontal faces: Improving Person Recognition using multiple cues[pdf]2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)companyFacebookUnited States37.39367170-122.0807262072%5036132409
2acf7e58f0a526b957be2099c10aab693f795973bosphorusThe BosphorusBosphorus Database for 3D Face Analysis[pdf]Unknown48%32815817019149183
37d6f0eb074d207b53885bd2eb78ccc8a04be597vmuVMUCan facial cosmetics affect the matching accuracy of face recognition systems?[pdf]2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS)eduWest Virginia UniversityUnited States39.65404635-79.9647535561%49301901831
37d6f0eb074d207b53885bd2eb78ccc8a04be597youtube_makeupYMUCan facial cosmetics affect the matching accuracy of face recognition systems?[pdf]2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS)eduWest Virginia UniversityUnited States39.65404635-79.9647535561%49301901831
8d5998cd984e7cce307da7d46f155f9db99c6590chalearnChaLearnChaLearn looking at people: A review of events and resources[pdf]2017 International Joint Conference on Neural Networks (IJCNN)50%1055164
2bf8541199728262f78d4dced6fb91479b39b738clothing_co_parsingCCPClothing Co-parsing by Joint Image Segmentation and Labeling[pdf]2014 IEEE Conference on Computer Vision and Pattern Recognition70%60421803824
22ad2c8c0f4d6aa4328b38d894b814ec22579761gallagherGallagherClothing cosegmentation for recognizing people[pdf]2008 IEEE Conference on Computer Vision and Pattern RecognitioneduCarnegie Mellon UniversityUnited States37.41021930-122.0596548758%17710374710184
4b1d23d17476fcf78f4cbadf69fb130b1aa627c0leeds_sports_poseLeeds Sports PoseClustered Pose and Nonlinear Appearance Models for Human Pose Estimation[pdf]Unknown65%278180981320878
4b1d23d17476fcf78f4cbadf69fb130b1aa627c0stickmen_buffyBuffy StickmenClustered Pose and Nonlinear Appearance Models for Human Pose Estimation[pdf]Unknown65%278180981320878
45c31cde87258414f33412b3b12fc5bec7cb3ba9jaffeJAFFECoding Facial Expressions with Gabor Wavelets[pdf]Unknown50%84842242655420433
b1f4423c227fa37b9680787be38857069247a307afew_vaAFEW-VACollecting Large, Richly Annotated Facial-Expression Databases from Movies[pdf]IEEE MultiMediaeduAustralian National UniversityAustralia-35.27769990149.1185270060%1821097388699
7f4040b482d16354d5938c1d1b926b544652bf5bnova_emotionsNovaemötions DatasetCompetitive affective gaming: winning with a smile[pdf]UnknowneduUniversidade NOVA de Lisboa, Caparica, PortugalPortugal38.66096400-9.2058130075%862044
079a0a3bf5200994e1f972b1b9197bf2f90e87d4mit_cbclMIT CBCLComponent-Based Face Recognition with 3D Morphable Models[pdf]2004 Conference on Computer Vision and Pattern Recognition Workshop33%1248084
23fc83c8cfff14a16df7ca497661264fc54ed746cohn_kanadeCKComprehensive Database for Facial Expression Analysis[pdf]Unknown50%99950349675572439
09d78009687bec46e70efcf39d4612822e61cb8craidRAiDConsistent Re-identification in a Camera Network[pdf]Unknown51%45232273411
0ceda9dae8b9f322df65ca2ef02caca9758aec6fcasablancaCasablancaContext-Aware CNNs for Person Head Detection[pdf]2015 IEEE International Conference on Computer Vision (ICCV)56%2715121235
0ceda9dae8b9f322df65ca2ef02caca9758aec6fhollywood_headsetHollywoodHeadsContext-Aware CNNs for Person Head Detection[pdf]2015 IEEE International Conference on Computer Vision (ICCV)56%2715121235
c06b13d0ec3f5c43e2782cd22542588e233733c3nova_emotionsNovaemötions DatasetCrowdsourcing facial expressions for affective-interaction[pdf]Computer Vision and Image Understanding100%110010
8355d095d3534ef511a9af68a3b2893339e3f96bimdb_wikiIMDBDEX: Deep EXpectation of Apparent Age from a Single Image[pdf]2015 IEEE International Conference on Computer Vision Workshop (ICCVW)56%120675357447
5a5f0287484f0d480fed1ce585dbf729586f0edcdisfaDISFADISFA: A Spontaneous Facial Action Intensity Database[pdf]IEEE Transactions on Affective ComputingeduUniversity of DenverUnited States39.67665410-104.9622030049%19094961910091
10195a163ab6348eef37213a46f60a3d87f289c5imdb_wikiIMDBDeep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks[pdf]International Journal of Computer VisioneduETH ZurichSwitzerland47.376313008.5476699053%1337162139441
162ea969d1929ed180cc6de9f0bf116993ff6e06vgg_facesVGG FaceDeep Face Recognition[pdf]Unknown54%99954345670635370
6424b69f3ff4d35249c0bb7ef912fbc2c86f4ff4celebaCelebADeep Learning Face Attributes in the Wild[pdf]2015 IEEE International Conference on Computer Vision (ICCV)eduChinese University of Hong KongChina22.41626320114.2109318052%80842138668670118
177bc509dd0c7b8d388bb47403f28d6228c14b5cceleba_plusCelebFaces+Deep Learning Face Representation from Predicting 10,000 Classes[pdf]2014 IEEE Conference on Computer Vision and Pattern Recognition60%66039726325340330
18010284894ed0edcca74e5bf768ee2e15ef7841deep_fashionDeepFashionDeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations[pdf]2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)65%1509753411138
6bd36e9fd0ef20a3074e1430a6cc601e6d407fc3cuhk03CUHK03DeepReID: Deep Filter Pairing Neural Network for Person Re-identification[pdf]2014 IEEE Conference on Computer Vision and Pattern Recognition59%51230420829324180
13f06b08f371ba8b5d31c3e288b4deb61335b462eth_andreas_essETHZ PedestrianDepth and Appearance for Mobile Scene Analysis[pdf]2007 IEEE 11th International Conference on Computer VisioneduETH ZurichSwitzerland47.376313008.5476699055%31917614327195127
4946ba10a4d5a7d0a38372f23e6622bd347ae273coco_actionCOCO-aDescribing Common Human Visual Actions in Images[pdf]Unknown58%2615110251
2e384f057211426ac5922f1b33d2aa8df5d51f57a_pascal_yahoo#N/ADescribing objects by their attributes[pdf]2009 IEEE Conference on Computer Vision and Pattern RecognitioneduUniversity of Illinois, Urbana-ChampaignUnited States40.11116745-88.2258766557%99956543374738264
7808937b46acad36e43c30ae4e9f3fd57462853dbpadBPADDescribing people: A poselet-based approach to attribute classification[pdf]2011 International Conference on Computer Vision57%221125961416559
d3200d49a19a4a4e4e9745ee39649b65d80c834bscut_headSCUT HEADDetecting Heads using Feature Refine Net and Cascaded Multi-scale Architecture[pdf]2018 24th International Conference on Pattern Recognition (ICPR)100%000000
56ae6d94fc6097ec4ca861f0daa87941d1c10b70cmdpCMDPDistance Estimation of an Unknown Person from a Portrait[pdf]Unknown44%945063
2b89de1d81cee50552f10e26e865df3365e9bc88ibm_difIBM Diversity in FacesDiversity in Faces[pdf]CoRR100%000000
84fe5b4ac805af63206012d29523a1e033bc827eawe_earsAWE EarsEar Recognition: More Than a Survey[pdf]Neurocomputing67%2416801113
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6204776d31359d129a582057c2d788a14f8aadebyoutube_celebritiesYouTube CelebritiesFace tracking and recognition with visual constraints in real-world videos[pdf]2008 IEEE Conference on Computer Vision and Pattern Recognition32%3019720218144133
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1aad2da473888cb7ebc1bfaa15bfa0f1502ce005jpl_poseJPL-Interaction datasetFirst-Person Activity Recognition: What Are They Doing to Me?[pdf]2013 IEEE Conference on Computer Vision and Pattern Recognition70%14810345811138
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06f02199690961ba52997cde1527e714d2b3bf8fcolumbia_gazeColumbia GazeGaze locking: passive eye contact detection for human-object interaction[pdf]UnknowneduColumbia UniversityUnited States40.84198360-73.9436897164%80512904935
18858cc936947fc96b5c06bbe3c6c2faa5614540pilot_parliamentPPBGender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification[pdf]Unknown53%5529260477
2eb84aaba316b095d4bb51da1a3e4365bbf9ab1dkin_faceUB KinFaceGenealogical face recognition based on UB KinFace database[pdf]CVPR 2011 WORKSHOPSeduSUNY BuffaloUnited States42.93362780-78.8839447947%30141611021
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a5a3bc3e5e9753769163cb30b16dbd12e266b93emcgillMcGill Real WorldHierarchical temporal graphical model for head pose estimation and subsequent attribute classification in real-world videos[pdf]Computer Vision and Image Understanding50%844153
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10d6b12fa07c7c8d6c8c3f42c7f1c061c131d4c5inria_personINRIA PedestrianHistograms of oriented gradients for human detection[pdf]2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)eduINRIA Rhone-Alps, Montbonnot, FranceFrance45.217886005.8073690054%99953946067537477
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44d23df380af207f5ac5b41459c722c87283e1ebwider_attributeWIDER AttributeHuman Attribute Recognition by Deep Hierarchical Contexts[pdf]Unknown78%181440162
44484d2866f222bbb9b6b0870890f9eea1ffb2d0cuhk01CUHK01Human Reidentification with Transferred Metric Learning[pdf]Unknown62%2581609812142115
57178b36c21fd7f4529ac6748614bb3374714e91ijb_cIJB-CIARPA Janus Benchmark - C: Face Dataset and Protocol[pdf]2018 International Conference on Biometrics (ICB)33%936290
0cb2dd5f178e3a297a0c33068961018659d0f443ijb_cIJB-CIARPA Janus Benchmark-B Face Dataset[pdf]2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)eduMichigan State UniversityUnited States42.71856800-84.4779157128%257186214
0297448f3ed948e136bb06ceff10eccb34e5bb77ilids_mctsi-LIDS Multiple-CameraImagery Library for Intelligent Detection Systems (i-LIDS); A Standard for Testing Video Based Detection Systems[pdf]Proceedings 40th Annual 2006 International Carnahan Conference on Security Technology38%32122021815
55c40cbcf49a0225e72d911d762c27bb1c2d14aaifadIFADIndian Face Age Database : A Database for Face Recognition with Age Variation[pdf]Unknown50%211020
ca3e88d87e1344d076c964ea89d91a75c417f5eeimfdbIMFDBIndian Movie Face Database: A benchmark for face recognition under wide variations[pdf]2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG)eduCVIT, IIITH, IndiaIndia17.4459581078.3495994060%15960105
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ad01687649d95cd5b56d7399a9603c4b8e2217d7mrp_droneMRP DroneInvestigating Open-World Person Re-identification Using a Drone[pdf]Unknown40%523032
2f43b614607163abf41dfe5d17ef6749a1b61304hrt_transgenderHRT TransgenderInvestigating the Periocular-Based Face Recognition Across Gender Transformation[pdf]IEEE Transactions on Information Forensics and SecurityeduUniversity of North Carolina at WilmingtonUnited States34.22498270-77.8690774469%1394068
066d71fcd997033dce4ca58df924397dfe0b5fd1ifdbIFDBIranian Face Database and Evaluation with a New Detection Algorithm[pdf]Unknown100%000000
b71d1aa90dcbe3638888725314c0d56640c1fef1ifdbIFDBIranian Face Database with age, pose and expression[pdf]2007 International Conference on Machine VisioneduIslamic Azad UniversityIran34.8452999048.5596212035%207132129
137aa2f891d474fce1e7a1d1e9b3aefe21e22b34hrt_transgenderHRT TransgenderIs the eye region more reliable than the face? A preliminary study of face-based recognition on a transgender dataset[pdf]2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS)57%743135
0b440695c822a8e35184fb2f60dcdaa8a6de84aekinectfaceKinectFaceDBKinectFaceDB: A Kinect Database for Face Recognition[pdf]IEEE Transactions on Systems, Man, and Cybernetics: SystemseduUniversity of North Carolina at Chapel HillUnited States35.91139710-79.0504529061%75462962650
4793f11fbca4a7dba898b9fff68f70d868e2497ckin_faceUB KinFaceKinship verification through transfer learning[pdf]Unknown55%71393222943
370b5757a5379b15e30d619e4d3fb9e8e13f3256lfwLFWLabeled Faces in the Wild : A Database for Studying Face Recognition in Unconstrained Environments[pdf]Unknown58%99957542271639371
7de6e81d775e9cd7becbfd1bd685f4e2a5eebb22lfwLFWLabeled Faces in the Wild : A Survey[pdf]UnknowneduStevens Institute of TechnologyUnited States40.74225200-74.0270949045%99455486336
2d3482dcff69c7417c7b933f22de606a0e8e42d4lfwLFWLabeled Faces in the Wild : Updates and New Reporting Procedures[pdf]UnknowneduUniversity of MassachusettsUnited States42.38897850-72.5286987058%123715237250
0d2dd4fc016cb6a517d8fb43a7cc3ff62964832elagLAGLarge age-gap face verification by feature injection in deep networks[pdf]Pattern Recognition Letters43%734034
07fcbae86f7a3ad3ea1cf95178459ee9eaf77cb1uccsUCCSLarge scale unconstrained open set face database[pdf]2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS)eduUniversity of Colorado at Colorado SpringsUnited States38.89646790-104.8050594080%541032
a0fd85b3400c7b3e11122f44dc5870ae2de9009amaflMAFLLearning Deep Representation for Face Alignment with Auxiliary Attributes[pdf]IEEE Transactions on Pattern Analysis and Machine Intelligence55%1106050126943
a0fd85b3400c7b3e11122f44dc5870ae2de9009amtflMTFLLearning Deep Representation for Face Alignment with Auxiliary Attributes[pdf]IEEE Transactions on Pattern Analysis and Machine Intelligence55%1106050126943
853bd61bc48a431b9b1c7cab10c603830c488e39casia_webfaceCASIA WebfaceLearning Face Representation from Scratch[pdf]CoRReduChinese Academy of SciencesChina40.00447950116.3702380060%43626017630288150
2a171f8d14b6b8735001a11c217af9587d095848social_relationSocial RelationLearning Social Relation Traits from Face Images[pdf]2015 IEEE International Conference on Computer Vision (ICCV)40%208125155
4e4746094bf60ee83e40d8597a6191e463b57f76leeds_sports_pose_extendedLeeds Sports Pose ExtendedLearning effective human pose estimation from inaccurate annotation[pdf]CVPR 2011eduUniversity of LeedsUnited Kingdom53.80387185-1.5524571264%173111621012256
5981e6479c3fd4e31644db35d236bfb84ae46514motMOTLearning to associate: HybridBoosted multi-target tracker for crowded scene[pdf]2009 IEEE Conference on Computer Vision and Pattern RecognitioneduUniversity of Southern CaliforniaUnited States34.02241490-118.2863440752%33017215727196139
6dd0597f8513dc100cd0bc1b493768cde45098a9stickmen_buffyBuffy StickmenLearning to parse images of articulated bodies[pdf]Unknown58%37321515735251129
6dd0597f8513dc100cd0bc1b493768cde45098a9stickmen_pascalStickmen PASCALLearning to parse images of articulated bodies[pdf]Unknown58%37321515735251129
6dd0597f8513dc100cd0bc1b493768cde45098a9stickmen_pascalStickmen PASCALLearning to parse images of articulated bodies[pdf]Unknown58%37321515735251129
28d4e027c7e90b51b7d8908fce68128d1964668amegafaceMegaFaceLevel Playing Field for Million Scale Face Recognition[pdf]2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)eduUniversity of WashingtonUnited States47.65432380-122.3080089439%3815232298
46a01565e6afe7c074affb752e7069ee3bf2e4efsdu_vidSDU-VIDLocal Descriptors Encoded by Fisher Vectors for Person Re-identification[pdf]Unknown58%198114841611188
140438a77a771a8fb656b39a78ff488066eb6b50lfpwLFWPLocalizing Parts of Faces Using a Consensus of Exemplars[pdf]IEEE Transactions on Pattern Analysis and Machine Intelligence60%52131520642337195
38b55d95189c5e69cf4ab45098a48fba407609b4cuhk02CUHK02Locally Aligned Feature Transforms across Views[pdf]2013 IEEE Conference on Computer Vision and Pattern Recognition53%24212911317139102
8990cdce3f917dad622e43e033db686b354d057ctiny_facesTinyFaceLow-Resolution Face Recognition[pdf]CoRR100%000000
c0387e788a52f10bf35d4d50659cfa515d89fbecmarsMARSMARS: A Video Benchmark for Large-Scale Person Re-Identification[pdf]Unknown58%146856169749
3dc3f0b64ef80f573e3a5f96e456e52ee980b877georgia_tech_face_databaseGeorgia Tech FaceMAXIMUM 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[pdf]Unknown33%312021
9055b155cbabdce3b98e16e5ac9c0edf00f9552fmorphMORPH CommercialMORPH: a longitudinal image database of normal adult age-progression[pdf]7th International Conference on Automatic Face and Gesture Recognition (FGR06)53%42422519826239190
9055b155cbabdce3b98e16e5ac9c0edf00f9552fmorph_ncMORPH Non-CommercialMORPH: a longitudinal image database of normal adult age-progression[pdf]7th International Conference on Automatic Face and Gesture Recognition (FGR06)53%42422519826239190
291265db88023e92bb8c8e6390438e5da148e8f5mscelebMsCelebMS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition[pdf]Unknown54%16791761413136
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5753b2b5e442eaa3be066daa4a2ca8d8a0bb172550_people_one_question50 People One QuestionMerging Pose Estimates Across Space and Time[pdf]Unknown87%151320124
5e0f8c355a37a5a89351c02f174e7a5ddcb98683cocoCOCOMicrosoft COCO: Common Objects in Context[pdf]Unknown57%99956943029799193
a5a44a32a91474f00a3cda671a802e87c899fbb4moments_in_timeMoments in TimeMoments in Time Dataset: one million videos for event understanding[pdf]CoRR64%251692250
436f798d1a4e54e5947c1e7d7375c31b2bdb4064tud_multiviewTUD-MultiviewMonocular 3D pose estimation and tracking by detection[pdf]2010 IEEE Computer Society Conference on Computer Vision and Pattern RecognitioneduTU DarmstadtGermany49.874827708.6563281054%30216413834207100
436f798d1a4e54e5947c1e7d7375c31b2bdb4064tud_stadtmitteTUD-StadtmitteMonocular 3D pose estimation and tracking by detection[pdf]2010 IEEE Computer Society Conference on Computer Vision and Pattern RecognitioneduTU DarmstadtGermany49.874827708.6563281054%30216413834207100
3b5b6d19d4733ab606c39c69a889f9e67967f151qmul_gridGRIDMulti-camera activity correlation analysis[pdf]2009 IEEE Conference on Computer Vision and Pattern RecognitioneduQueen Mary University of LondonUnited Kingdom51.52472720-0.0393103562%138865287961
6ad5a38df8dd4cdddd74f31996ce096d41219f72tud_brusselsTUD-BrusselsMulti-cue onboard pedestrian detection[pdf]2009 IEEE Conference on Computer Vision and Pattern Recognition56%217121961413386
6ad5a38df8dd4cdddd74f31996ce096d41219f72tud_motionpairsTUD-MotionparisMulti-cue onboard pedestrian detection[pdf]2009 IEEE Conference on Computer Vision and Pattern Recognition56%217121961413386
32c801cb7fbeb742edfd94cccfca4934baec71daucf_crowdUCF-CC-50Multi-source Multi-scale Counting in Extremely Dense Crowd Images[pdf]2013 IEEE Conference on Computer Vision and Pattern Recognition70%125883767352
1e3df3ca8feab0b36fd293fe689f93bb2aaac591immediacyImmediacyMulti-task Recurrent Neural Network for Immediacy Prediction[pdf]2015 IEEE International Conference on Computer Vision (ICCV)64%251692215
2b926b3586399d028b46315d7d9fb9d879e4f79cfrav3dFRAV3DMultimodal 2D, 2.5D & 3D Face Verification[pdf]2006 International Conference on Image ProcessingeduUniversidad Rey Juan Carlos, Spain40.33586610-3.8769432050%14770212
53ae38a6bb2b21b42bac4f0c4c8ed1f9fa02f9d4bp4d_plusBP4D+Multimodal Spontaneous Emotion Corpus for Human Behavior Analysis[pdf]2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)50%40202002120
2fda164863a06a92d3a910b96eef927269aeb730names_and_facesNews DatasetNames and faces in the news[pdf]Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004.51%2941501432921582
4156b7e88f2e0ab0a7c095b9bab199ae2b23bd06distance_nighttimeLong Distance Heterogeneous FaceNighttime Face Recognition at Long Distance: Cross-Distance and Cross-Spectral Matching[pdf]Unknown43%2191231110
3394168ff0719b03ff65bcea35336a76b21fe5e4penn_fudanPenn FudanObject Detection Combining Recognition and Segmentation[pdf]Unknown50%1015051115842
12ad3b5bbbf407f8e54ea692c07633d1a867c566grazGraz PedestrianObject recognition using segmentation for feature detection[pdf]Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004.eduInst. of Comput. Sci., Univ. of Leoben, AustriaAustria47.3847372015.0930201041%2912171218
4f93cd09785c6e77bf4bc5a788e079df524c8d21sotonSOTON HiDOn a Large Sequence-Based Human Gait Database[pdf]Unknown58%14886621710449
6618cff7f2ed440a0d2fa9e74ad5469df5cdbe4cafadAFADOrdinal Regression with Multiple Output CNN for Age Estimation[pdf]2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)53%68363284917
a7fe834a0af614ce6b50dc093132b031dd9a856bmarket1203Market 1203Orientation Driven Bag of Appearances for Person Re-identification[pdf]CoRR43%734044
a7fe834a0af614ce6b50dc093132b031dd9a856bpku_reidPKU-ReidOrientation Driven Bag of Appearances for Person Re-identification[pdf]CoRR43%734044
18ae7c9a4bbc832b8b14bc4122070d7939f5e00efrgcFRGCOverview of the face recognition grand challenge[pdf]2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)eduNISTUnited States39.14004000-77.2185060050%999497501114594424
22909dd19a0ec3b6065334cb5be5392cb24d839dpetsPETS 2017PETS 2017: Dataset and Challenge[pdf]2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)0%808026
56ffa7d906b08d02d6d5a12c7377a57e24ef3391unbc_shoulder_painUNBC-McMaster PainPainful data: The UNBC-McMaster shoulder pain expression archive database[pdf]Face and Gesture 2011eduCarnegie Mellon UniversityUnited States37.41021930-122.0596548752%18495892311271
55206f0b5f57ce17358999145506cd01e570358corlORLParameterisation of a stochastic model for human face identification[pdf]Unknown44%99944255797569445
0486214fb58ee9a04edfe7d6a74c6d0f661a7668chokepointChokePointPatch-based probabilistic image quality assessment for face selection and improved video-based face recognition[pdf]CVPR 2011 WORKSHOPS53%128686067360
488e475eeb3bb39a145f23ede197cd3620f1d98aapisAPiS1.0Pedestrian Attribute Classification in Surveillance: Database and Evaluation[pdf]2013 IEEE International Conference on Computer Vision Workshops69%2618811313
488e475eeb3bb39a145f23ede197cd3620f1d98asvsSVSPedestrian Attribute Classification in Surveillance: Database and Evaluation[pdf]2013 IEEE International Conference on Computer Vision Workshops69%2618811313
2a4bbee0b4cf52d5aadbbc662164f7efba89566cpetaPETAPedestrian Attribute Recognition At Far Distance[pdf]Unknown68%80542625128
f72f6a45ee240cc99296a287ff725aaa7e7ebb35caltech_pedestriansCaltech PedestriansPedestrian Detection: An Evaluation of the State of the Art[pdf]IEEE Transactions on Pattern Analysis and Machine IntelligenceeduCalifornia Institute of TechnologyUnited States34.13710185-118.1252748749%99948551471541464
1dc35905a1deff8bc74688f2d7e2f48fd2273275caltech_pedestriansCaltech PedestriansPedestrian detection: A benchmark[pdf]2009 IEEE Conference on Computer Vision and Pattern Recognition50%51926125827289233
3316521a5527c7700af8ae6aef32a79a8b83672ctud_campusTUD-CampusPeople-tracking-by-detection and people-detection-by-tracking[pdf]2008 IEEE Conference on Computer Vision and Pattern Recognition53%52928024840324213
3316521a5527c7700af8ae6aef32a79a8b83672ctud_crossingTUD-CrossingPeople-tracking-by-detection and people-detection-by-tracking[pdf]2008 IEEE Conference on Computer Vision and Pattern Recognition53%52928024840324213
3316521a5527c7700af8ae6aef32a79a8b83672ctud_pedestrianTUD-PedestrianPeople-tracking-by-detection and people-detection-by-tracking[pdf]2008 IEEE Conference on Computer Vision and Pattern Recognition53%52928024840324213
27a2fad58dd8727e280f97036e0d2bc55ef5424cduke_mtmcDuke MTMCPerformance Measures and a Data Set for Multi-Target, Multi-Camera Tracking[pdf]Unknown58%1367957710827
27a2fad58dd8727e280f97036e0d2bc55ef5424cmotMOTPerformance Measures and a Data Set for Multi-Target, Multi-Camera Tracking[pdf]Unknown58%1367957710827
16c7c31a7553d99f1837fc6e88e77b5ccbb346b8pridPRIDPerson Re-identification by Descriptive and Discriminative Classification[pdf]Unknown58%35220414827196157
98bb029afe2a1239c3fdab517323066f0957b81bilids_vid_reidiLIDS-VIDPerson Re-identification by Video Ranking[pdf]Unknown57%210120901011594
98bb029afe2a1239c3fdab517323066f0957b81bsdu_vidSDU-VIDPerson Re-identification by Video Ranking[pdf]Unknown57%210120901011594
0b84f07af44f964817675ad961def8a51406dd2eprwPRWPerson Re-identification in the Wild[pdf]2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)51%65333214617
ec792ad2433b6579f2566c932ee414111e194537msmt_17MSMT17Person Transfer GAN to Bridge Domain Gap for Person Re-Identification[pdf]CoRR71%141041113
1c2802c2199b6d15ecefe7ba0c39bfe44363de38youtube_posesYouTube PosePersonalizing Human Video Pose Estimation[pdf]2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)eduOxford UniversityUnited Kingdom51.75208490-1.2516646066%3221112295
b92a1ed9622b8268ae3ac9090e25789fc41cc9b8pornodb#N/APooling in image representation: The visual codeword point of view[pdf]Computer Vision and Image Understanding32%77255274634
2830fb5282de23d7784b4b4bc37065d27839a412h3dH3DPoselets: Body part detectors trained using 3D human pose annotations[pdf]2009 IEEE 12th International Conference on Computer Vision52%70736833967509215
3765df816dc5a061bc261e190acc8bdd9d47bec0rafdRaFDPresentation and validation of the Radboud Faces Database[pdf]Unknown39%44617527143322136
636b8ffc09b1b23ff714ac8350bb35635e49fa3ccaltech_10k_web_facesCaltech 10K Web FacesPruning training sets for learning of object categories[pdf]2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)65%60392154317
377f2b65e6a9300448bdccf678cde59449ecd337ufddUFDDPushing the Limits of Unconstrained Face Detection: a Challenge Dataset and Baseline Results[pdf]CoRR0%202020
140c95e53c619eac594d70f6369f518adfea12efijb_cIJB-CPushing the frontiers of unconstrained face detection and recognition: IARPA Janus Benchmark A[pdf]2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)55%222123991916162
d80a3d1f3a438e02a6685e66ee908446766fefa9megaageMegaAgeQuantifying Facial Age by Posterior of Age Comparisons[pdf]CoRR25%413140
922e0a51a3b8c67c4c6ac09a577ff674cbd28b34v47V47Re-identification of pedestrians with variable occlusion and scale[pdf]2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)eduKingston UniversityUnited Kingdom51.42930860-0.2684044010%1019264
6f3c76b7c0bd8e1d122c6ea808a271fd4749c951wardWARDRe-identify people in wide area camera network[pdf]2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops38%55213423519
54983972aafc8e149259d913524581357b0f91c3reseedReSEEDReSEED: social event dEtection dataset[pdf]Unknown67%642115
65355cbb581a219bd7461d48b3afd115263ea760complex_activitiesOngoing Complex ActivitiesRecognition of ongoing complex activities by sequence prediction over a hierarchical label space[pdf]2016 IEEE Winter Conference on Applications of Computer Vision (WACV)50%211020
e8de844fefd54541b71c9823416daa238be65546visual_phrasesPhrasal RecognitionRecognition using visual phrases[pdf]CVPR 2011eduUniversity of Illinois, Urbana-ChampaignUnited States40.11116745-88.2258766558%233135981817758
356b431d4f7a2a0a38cf971c84568207dcdbf189widerWIDERRecognize complex events from static images by fusing deep channels[pdf]2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)64%45291613015
25474c21613607f6bb7687a281d5f9d4ffa1f9f3faceplaceFace PlaceRecognizing disguised faces[pdf]Unknown33%248160186
4053e3423fb70ad9140ca89351df49675197196abio_idBioID FaceRobust Face Detection Using the Hausdorff Distance[pdf]Unknown48%49823726156330179
2724ba85ec4a66de18da33925e537f3902f21249cofwCOFWRobust Face Landmark Estimation under Occlusion[pdf]2013 IEEE International Conference on Computer VisioneduCalifornia Institute of TechnologyUnited States34.13710185-118.1252748761%30518611916192116
c570d1247e337f91e555c3be0e8c8a5aba539d9fmcgillMcGill Real WorldRobust semi-automatic head pose labeling for real-world face video sequences[pdf]Multimedia Tools and ApplicationseduMcGill UniversityCanada45.50397610-73.5749687044%188100137
e27ef52c641c2b5100a1b34fd0b819e84a31b4dfsarc3dSarc3DSARC3D: A New 3D Body Model for People Tracking and Re-identification[pdf]Unknown41%29121731712
bd26dabab576adb6af30484183c9c9c8379bf2e0scut_fbpSCUT-FBPSCUT-FBP: A Benchmark Dataset for Facial Beauty Perception[pdf]2015 IEEE International Conference on Systems, Man, and Cybernetics43%1468359
29a705a5fa76641e0d8963f1fdd67ee4c0d92d3dscfaceSCfaceSCface – surveillance cameras face database[pdf]Multimedia Tools and Applications51%1789088159089
d3f5a1848b0028d8ab51d0b0673732cad2e3c8c9stair_actionsSTAIR ActionSTAIR Actions: A Video Dataset of Everyday Home Actions[pdf]CoRR100%000000
833fa04463d90aab4a9fe2870d480f0b40df446esun_attributesSUNSUN attribute database: Discovering, annotating, and recognizing scene attributes[pdf]2012 IEEE Conference on Computer Vision and Pattern RecognitioneduBrown UniversityUnited States41.82686820-71.4012314658%2691561132921557
4308bd8c28e37e2ed9a3fcfe74d5436cce34b410market_1501Market 1501Scalable Person Re-identification: A Benchmark[pdf]2015 IEEE International Conference on Computer Vision (ICCV)60%39423815618272116
9c23859ec7313f2e756a3e85575735e0c52249f4facebook_100Facebook100Scaling up biologically-inspired computer vision: A case study in unconstrained face recognition on facebook[pdf]CVPR 2011 WORKSHOPSeduHarvard UniversityUnited States42.36782045-71.1266665358%50292133911
9c23859ec7313f2e756a3e85575735e0c52249f4pubfig_83pubfig83Scaling up biologically-inspired computer vision: A case study in unconstrained face recognition on facebook[pdf]CVPR 2011 WORKSHOPSeduHarvard UniversityUnited States42.36782045-71.1266665358%50292133911
51eba481dac6b229a7490f650dff7b17ce05df73imsituimSituSituation Recognition: Visual Semantic Role Labeling for Image Understanding[pdf]2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)62%4830182462
570f37ed63142312e6ccdf00ecc376341ec72b9fstanford_droneStanford DroneSocial LSTM: Human Trajectory Prediction in Crowded Spaces[pdf]2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)46%229106123515079
23e824d1dfc33f3780dd18076284f07bd99f1c43mifsMIFSSpoofing faces using makeup: An investigative study[pdf]2017 IEEE International Conference on Identity, Security and Behavior Analysis (ISBA)eduINRIA MéditerranéeFrance43.615813107.0683800060%532014
1a40092b493c6b8840257ab7f96051d1a4dbfeb2sports_videos_in_the_wildSVWSports Videos in the Wild (SVW): A video dataset for sports analysis[pdf]2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG)83%651151
9361b784e73e9238d5cefbea5ac40d35d1e3103ftowncenterTownCenterStable multi-target tracking in real-time surveillance video[pdf]CVPR 201144%31013717324180131
c866a2afc871910e3282fd9498dce4ab20f6a332qmul_surv_faceQMUL-SurvFaceSurveillance Face Recognition Challenge[pdf]CoRR100%000000
f6c8d5e35d7e4d60a0104f233ac1a3ab757da53fpkuPKUSwiss-System Based Cascade Ranking for Gait-Based Person Re-Identification[pdf]Unknown67%321012
4d58f886f5150b2d5e48fd1b5a49e09799bf895dtexas_3dfrdTexas 3DFRDTexas 3D Face Recognition Database[pdf]2010 IEEE Southwest Symposium on Image Analysis & Interpretation (SSIAI)39%61243733725
6d96f946aaabc734af7fe3fc4454cf8547fcd5edar_facedbAR FaceThe AR face database[pdf]53%99952647351459573
2485c98aa44131d1a2f7d1355b1e372f2bb148adcas_pealCAS-PEALThe CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations[pdf]IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans50%41520920639189232
47662d1a368daf70ba70ef2d59eb6209f98b675dfiaCMU FiAThe CMU Face In Action (FIA) Database[pdf]Unknown44%55243154117
4d423acc78273b75134e2afd1777ba6d3a398973cmu_pieCMU PIEThe CMU Pose, Illumination, and Expression (PIE) Database of Human Faces[pdf]Unknown53%74239634459416329
4d423acc78273b75134e2afd1777ba6d3a398973multi_pieMULTIPIEThe CMU Pose, Illumination, and Expression (PIE) Database of Human Faces[pdf]Unknown53%74239634459416329
4df3143922bcdf7db78eb91e6b5359d6ada004d2cfdCFDThe Chicago face database: A free stimulus set of faces and norming data.[pdf]Behavior research methods48%83404326319
20388099cc415c772926e47bcbbe554e133343d1cafeCAFEThe Child Affective Facial Expression (CAFE) set: validity and reliability from untrained adults[pdf]48%3316173285
5ffd74d2873b7cba2cbc5fd295cc7fbdedca22a2cityscapesCityscapesThe Cityscapes Dataset[pdf]Unknown59%54322234014
32cde90437ab5a70cf003ea36f66f2de0e24b3abcityscapesCityscapesThe Cityscapes Dataset for Semantic Urban Scene Understanding[pdf]2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)52%77140336854624138
4e6ee936eb50dd032f7138702fa39b7c18ee8907dartmouth_childrenDartmouth ChildrenThe Dartmouth Database of Children’s Faces: Acquisition and Validation of a New Face Stimulus Set[pdf]45%209112174
9e31e77f9543ab42474ba4e9330676e18c242e72imdb_faceIMDb FaceThe Devil of Face Recognition is in the Noise[pdf]UnknowneduNanyang Technological UniversitySingapore1.34841040103.6829796520%514031
71b7fc715e2f1bb24c0030af8d7e7b6e7cd128a6umd_facesUMDThe Do’s and Don’ts for CNN-Based Face Verification[pdf]2017 IEEE International Conference on Computer Vision Workshops (ICCVW)36%259163176
f0e17f27f029db4ad650ff278fe3c10ecb6cb0c4europersonsEuroCity PersonsThe EuroCity Persons Dataset: A Novel Benchmark for Object Detection[pdf]CoRR0%101010
4d9a02d080636e9666c4d1cc438b9893391ec6c7cohn_kanade_plusCK+The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression[pdf]2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - WorkshopseduUniversity of PittsburghUnited States40.44415295-79.9624399355%97553543967475510
0f0fcf041559703998abf310e56f8a2f90ee6f21feretFERETThe FERET Evaluation Methodology for Face-Recognition Algorithms[pdf]Unknown48%999482517103560454
0c4a139bb87c6743c7905b29a3cfec27a5130652feretFERETThe FERET Verification Testing Protocol for Face Recognition Algorithms[pdf]Unknown44%1124963117935
dc8b25e35a3acb812beb499844734081722319b4feretFERETThe FERET database and evaluation procedure for face-recognition algorithms[pdf]Image Vision Comput.44%999443556106606413
8f02ec0be21461fbcedf51d864f944cfc42c875fhda_plusHDA+The HDA+ Data Set for Research on Fully Automated Re-identification Systems[pdf]Unknown35%176112116
9a9877791945c6fa4c1743ec6d3fb32570ef8481m2vtsm2vtsThe M2VTS Multimodal Face Database (Release 1.00)[pdf]UnknowneduLaboratoire de Télécommunications et Télédétection, UCL, Louvain-La-Neuve, BelgiumBelgium50.669687504.6155909043%129557448054
ea050801199f98a1c7c1df6769f23f658299a3aempi_largeLarge MPI Facial ExpressionThe MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial Expressions[pdf]46%2813154244
ea050801199f98a1c7c1df6769f23f658299a3aempi_smallSmall MPI Facial ExpressionThe MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial Expressions[pdf]46%2813154244
578d4ad74818086bb64f182f72e2c8bd31e3d426mr2MR2The MR2: A multi-racial, mega-resolution database of facial stimuli.[pdf]Behavior research methods14%716070
f1af714b92372c8e606485a3982eab2f16772ad8mug_facesMUG FacesThe MUG facial expression database[pdf]11th International Workshop on Image Analysis for Multimedia Interactive Services WIAMIS 10eduAristotle University of ThessalonikiGreece40.6298414522.9588935043%68293952840
79828e6e9f137a583082b8b5a9dfce0c301989b8mapillaryMapillaryThe Mapillary Vistas Dataset for Semantic Understanding of Street Scenes[pdf]2017 IEEE International Conference on Computer Vision (ICCV)52%4423210367
96e0cfcd81cdeb8282e29ef9ec9962b125f379b0megafaceMegaFaceThe MegaFace Benchmark: 1 Million Faces for Recognition at Scale[pdf]2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)59%121715099822
0ee1916a0cb2dc7d3add086b5f1092c3d4beb38avocVOCThe Pascal Visual Object Classes (VOC) Challenge[pdf]International Journal of Computer VisioneduOxford UniversityUnited Kingdom51.75208490-1.2516646058%99957542435613414
66e6f08873325d37e0ec20a4769ce881e04e964esun_attributesSUNThe SUN Attribute Database: Beyond Categories for Deeper Scene Understanding[pdf]International Journal of Computer Vision62%1127042148429
8b2dd5c61b23ead5ae5508bb8ce808b5ea26673010k_US_adult_faces10K US Adult FacesThe intrinsic memorability of face photographs.[pdf]Journal of experimental psychology. General51%47242333413
ae0aee03d946efffdc7af2362a42d3750e7dd48aput_facePut FaceThe put face database[pdf]Unknown42%100425875648
19d1b811df60f86cbd5e04a094b07f32fff7a32ayork_3dUOY 3D Face DatabaseThree-dimensional face recognition: an eigensurface approach[pdf]2004 International Conference on Image Processing, 2004. ICIP '04.33%36122442511
2edb87494278ad11641b6cf7a3f8996de12b8e14qmul_gridGRIDTime-Delayed Correlation Analysis for Multi-Camera Activity Understanding[pdf]International Journal of Computer VisioneduQueen Mary University of LondonUnited Kingdom51.52472720-0.0393103549%83414265133
298cbc3dfbbb3a20af4eed97906650a4ea1c29e0ferplusFER+Training deep networks for facial expression recognition with crowd-sourced label distribution[pdf]Unknown62%29181101514
b5f2846a506fc417e7da43f6a7679146d99c5e96ucf_101UCF101UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild[pdf]CoRR61%99961138873716283
16e8b0a1e8451d5f697b94c0c2b32a00abee1d52umbUMBUMB-DB: A database of partially occluded 3D faces[pdf]2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)60%45271822024
31b05f65405534a696a847dd19c621b7b8588263umd_facesUMDUMDFaces: An annotated face dataset for training deep networks[pdf]2017 IEEE International Joint Conference on Biometrics (IJCB)eduUniversity of MarylandUnited States39.28996850-76.6219610357%3520154287
8627f019882b024aef92e4eb9355c499c733e5b7usedUSED Social Event DatasetUSED: a large-scale social event detection dataset[pdf]UnknowneduUniversity of TrentoItaly46.0658836011.1159894071%752034
d4f1eb008eb80595bcfdac368e23ae9754e1e745uccsUCCSUnconstrained Face Detection and Open-Set Face Recognition Challenge[pdf]2017 IEEE International Joint Conference on Biometrics (IJCB)40%523041
4b4106614c1d553365bad75d7866bff0de6056edczech_news_agencyUFIUnconstrained Facial Images: Database for Face Recognition Under Real-World Conditions[pdf]Unknown40%1046046
08f6745bc6c1b0fb68953ea61054bdcdde6d2fc7kin_faceUB KinFaceUnderstanding Kin Relationships in a Photo[pdf]IEEE Transactions on Multimedia57%96554123463
21d9d0deed16f0ad62a4865e9acf0686f4f15492images_of_groupsImages of GroupsUnderstanding images of groups of people[pdf]2009 IEEE Conference on Computer Vision and Pattern RecognitioneduCarnegie Mellon UniversityUnited States37.41021930-122.0596548754%202110921213275
fd8168f1c50de85bac58a8d328df0a50248b16aend_2006ND-2006Using a Multi-Instance Enrollment Representation to Improve 3D Face Recognition[pdf]2007 First IEEE International Conference on Biometrics: Theory, Applications, and SystemseduUniversity of Notre DameUnited States41.70456775-86.2382202656%32181431715
4563b46d42079242f06567b3f2e2f7a80cb3befevadanaVADANAVADANA: A dense dataset for facial image analysis[pdf]2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)eduUniversity of DelawareUnited States39.68103280-75.7540184044%16790610
eb027969f9310e0ae941e2adee2d42cdf07d938cvgg_faces2VGG Face2VGGFace2: A Dataset for Recognising Faces across Pose and Age[pdf]2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018)eduOxford UniversityUnited Kingdom51.75208490-1.2516646045%5625316506
01959ef569f74c286956024866c1d107099199f7vqaVQAVQA: Visual Question Answering[pdf]2015 IEEE International Conference on Computer Vision (ICCV)61%7314442874762996
5194cbd51f9769ab25260446b4fa17204752e799violent_flowsViolent FlowsViolent flows: Real-time detection of violent crowd behavior[pdf]2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition WorkshopseduOpen University of IsraelIsrael32.7782416534.9956567355%83463764441
026e3363b7f76b51cc711886597a44d5f1fd1de2kittiKITTIVision meets robotics: The KITTI dataset[pdf]I. J. Robotics Res.53%99953246737571448
066000d44d6691d27202896691f08b27117918b9psuPSUVision-Based Analysis of Small Groups in Pedestrian Crowds[pdf]IEEE Transactions on Pattern Analysis and Machine Intelligence49%151747797973
dd65f71dac86e36eecbd3ed225d016c3336b4a13families_in_the_wildFIWVisual Kinship Recognition of Families in the Wild[pdf]IEEE Transactions on Pattern Analysis and Machine IntelligenceeduUniversity of Massachusetts DartmouthUnited States41.62772475-71.00724501100%330021
52d7eb0fbc3522434c13cc247549f74bb9609c5dwider_faceWIDER FACEWIDER FACE: A Face Detection Benchmark[pdf]2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)eduChinese University of Hong KongChina22.41626320114.2109318057%14885631510841
77c81c13a110a341c140995bedb98101b9e84f7fwildtrackWildTrackWILDTRACK : A Multi-camera HD Dataset for Dense Unscripted Pedestrian Detection[pdf]Unknown100%000000
5ad4e9f947c1653c247d418f05dad758a3f9277bwlfdbWLFDBWLFDB: Weakly Labeled Face Databases[pdf]Unknown100%110001
0dc11a37cadda92886c56a6fb5191ded62099c28stickmen_familyWe Are Family StickmenWe are family: joint pose estimation of multiple persons[pdf]Unknown66%77512656019
0c91808994a250d7be332400a534a9291ca3b60egrazGraz PedestrianWeak Hypotheses and Boosting for Generic Object Detection and Recognition[pdf]Unknown51%2471251221817778
2a75f34663a60ab1b04a0049ed1d14335129e908mmi_facial_expressionMMI Facial Expression DatasetWeb-based database for facial expression analysis[pdf]2005 IEEE International Conference on Multimedia and Expo48%44021222844267181
9b9bf5e623cb8af7407d2d2d857bc3f1b531c182who_goes_thereWGTWho goes there?: approaches to mapping facial appearance diversity[pdf]UnknowneduUniversity of KentuckyUnited States38.03337420-84.50177580100%000000
b62628ac06bbac998a3ab825324a41a11bc3a988m2vtsdb_extendedxm2vtsdbXm2vtsdb: the Extended M2vts Database[pdf]Unknown53%90648142544542408
010f0f4929e6a6644fb01f0e43820f91d0fad292yfcc_100mYFCC100MYFCC100M: the new data in multimedia research[pdf]Commun. ACMeduCarnegie Mellon UniversityUnited States37.41021930-122.0596548756%2761551212317599
a94cae786d515d3450d48267e12ca954aab791c4yawddYawDDYawDD: a yawning detection dataset[pdf]Unknown57%14861212
\ No newline at end of file +All Papers

All Papers

Paper IDMegapixels KeyMegapixels NameReport LinkPDF LinkJournalTypeAddressCountryLatLngCoverageTotal CitationsGeocoded CitationsUnknown CitationsEmpty CitationsWith PDFWith DOI
3325860c0c82a93b2eac654f5324dd6a776f609empii_human_poseMPII Human Pose2D Human Pose Estimation: New Benchmark and State of the Art Analysis[pdf]2014 IEEE Conference on Computer Vision and Pattern Recognition62%3562211352130453
e4754afaa15b1b53e70743880484b8d0736990fffiw_300300-W300 Faces In-The-Wild Challenge: database and results[pdf]Image Vision Comput.54%1146153107143
044d9a8c61383312cdafbcc44b9d00d650b21c70fiw_300300-W300 Faces in-the-Wild Challenge: The First Facial Landmark Localization Challenge[pdf]2013 IEEE International Conference on Computer Vision Workshops61%32419912529211118
2e8d0f1802e50cccfd3c0aabac0d0beab3a7846e3dpes3DPeS3DPeS: 3D people dataset for surveillance and forensics[pdf]Unknown48%1225963117151
9696ad8b164f5e10fcfe23aacf74bd6168aebb154dfab4DFAB4DFAB: A Large Scale 4D Facial Expression Database for Biometric Applications[pdf]CoRR0%404022
31b58ced31f22eab10bd3ee2d9174e7c14c27c01tiny_imagesTiny Images80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition[pdf]IEEE Transactions on Pattern Analysis and Machine Intelligence54%99953546394685327
4d4bb462c9f1d4e4ab1e4aa6a75cc0bc71b384613dddb_unconstrained3D DynamicA 3 D Dynamic Database for Unconstrained Face Recognition[pdf]Unknown50%211011
d08cc366a4a0192a01e9a7495af1eb5d9f9e73aeb3d_acB3D(AC)A 3-D Audio-Visual Corpus of Affective Communication[pdf]IEEE Transactions on Multimedia49%39192022712
639937b3a1b8bded3f7e9a40e85bd3770016cf3cbfmBFMA 3D Face Model for Pose and Illumination Invariant Face Recognition[pdf]2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance54%3231761472922698
cc589c499dcf323fe4a143bbef0074c3e31f9b60bu_3dfeBU-3DFEA 3D facial expression database for facial behavior research[pdf]7th International Conference on Automatic Face and Gesture Recognition (FGR06)47%55526329147299270
22646e00a7ba34d1b5fbe3b1efcd91a1e1be3c2bsaivtSAIVT SoftBioA Database for Person Re-Identification in Multi-Camera Surveillance Networks[pdf]2012 International Conference on Digital Image Computing Techniques and Applications (DICTA)45%58263274118
070de852bc6eb275d7ca3a9cdde8f6be8795d1a3d3dfacsD3DFACSA FACS valid 3D dynamic action unit database with applications to 3D dynamic morphable facial modeling[pdf]2011 International Conference on Computer Vision58%52302253715
563c940054e4b456661762c1ab858e6f730c3159data_61Data61 PedestrianA Multi-modal Graphical Model for Scene Analysis[pdf]2015 IEEE Winter Conference on Applications of Computer Vision50%844053
221c18238b829c12b911706947ab38fd017acef7rap_pedestrianRAPA Richly Annotated Dataset for Pedestrian Attribute Recognition[pdf]CoRR67%211470183
013909077ad843eb6df7a3e8e290cfd5575999d2fiw_300300-WA Semi-automatic Methodology for Facial Landmark Annotation[pdf]2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops60%185111741512464
3b4ec8af470948a72a6ed37a9fd226719a874ebcsdu_vidSDU-VIDA Spatio-Temporal Appearance Representation for Video-Based Pedestrian Re-Identification[pdf]2015 IEEE International Conference on Computer Vision (ICCV)62%85533295134
6403117f9c005ae81f1e8e6d1302f4a045e3d99dalert_airportALERT AirportA Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets.[pdf]IEEE transactions on pattern analysis and machine intelligence47%15781105
7ace44190729927e5cb0dd5d363fcae966fe13f7nudedetection#N/AA bag-of-features approach based on Hue-SIFT descriptor for nude detection[pdf]2009 17th European Signal Processing Conference61%51312011833
0d3bb75852098b25d90f31d2f48fd0cb4944702bface_scrubFaceScrubA data-driven approach to cleaning large face datasets[pdf]2014 IEEE International Conference on Image Processing (ICIP)54%123665749627
b91f54e1581fbbf60392364323d00a0cd43e493cbp4d_spontanousBP4D-SpontanousA high-resolution spontaneous 3D dynamic facial expression database[pdf]2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG)eduSUNY BinghamtonUnited States42.08779975-75.9706606651%151777478765
8b56e33f33e582f3e473dba573a16b598ed9bcdcfeiFEIA new ranking method for principal components analysis and its application to face image analysis[pdf]Image Vision Comput.46%1697891672101
2624d84503bc2f8e190e061c5480b6aa4d89277aafew_vaAFEW-VAAFEW-VA database for valence and arousal estimation in-the-wild[pdf]Image Vision Comput.47%15781104
2ad0ee93d029e790ebb50574f403a09854b65b7eyale_facesYaleFacesAcquiring linear subspaces for face recognition under variable lighting[pdf]IEEE Transactions on Pattern Analysis and Machine Intelligence48%999484515110525485
57fe081950f21ca03b5b375ae3e84b399c015861cvc_01_barcelonaCVC-01Adaptive Image Sampling and Windows Classification for On – board Pedestrian Detection[pdf]Unknown39%44172712123
758d7e1be64cc668c59ef33ba8882c8597406e53affectnetAffectNetAffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild[pdf]CoRR53%38201812611
47aeb3b82f54b5ae8142b4bdda7b614433e69b9aam_fedAM-FEDAffectiva-MIT Facial Expression Dataset (AM-FED): Naturalistic and Spontaneous Facial Expressions Collected "In-the-Wild"[pdf]2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops47%73343964134
1be498d4bbc30c3bfd0029114c784bc2114d67c0adienceAdienceAge and Gender Estimation of Unfiltered Faces[pdf]IEEE Transactions on Information Forensics and SecurityeduOpen University of IsraelIsrael32.7782416534.9956567355%168927659478
6dcf418c778f528b5792104760f1fbfe90c6dd6aagedbAgeDBAgeDB: The First Manually Collected, In-the-Wild Age Database[pdf]2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)91%111010101
a74251efa970b92925b89eeef50a5e37d9281ad0aflwAFLWAnnotated Facial Landmarks in the Wild: A large-scale, real-world database for facial landmark localization[pdf]2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)60%2921751173721284
2ce2560cf59db59ce313bbeb004e8ce55c5ce928texas_3dfrdTexas 3DFRDAnthropometric 3D Face Recognition[pdf]International Journal of Computer Vision50%90454556031
633c851ebf625ad7abdda2324e9de093cf623141appa_realAPPA-REALApparent and Real Age Estimation in Still Images with Deep Residual Regressors on Appa-Real Database[pdf]2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017)46%13670113
0df0d1adea39a5bef318b74faa37de7f3e00b452mpii_gazeMPIIGazeAppearance-based gaze estimation in the wild[pdf]2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)65%138904839742
759a3b3821d9f0e08e0b0a62c8b693230afc3f8dpubfigPubFigAttribute and simile classifiers for face verification[pdf]2009 IEEE 12th International Conference on Computer Vision61%89454435056604300
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0a85bdff552615643dd74646ac881862a7c7072dpipaPIPABeyond frontal faces: Improving Person Recognition using multiple cues[pdf]2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)companyFacebookUnited States37.39367170-122.0807262072%5036132409
2acf7e58f0a526b957be2099c10aab693f795973bosphorusThe BosphorusBosphorus Database for 3D Face Analysis[pdf]Unknown48%32815817019149183
37d6f0eb074d207b53885bd2eb78ccc8a04be597vmuVMUCan facial cosmetics affect the matching accuracy of face recognition systems?[pdf]2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS)eduWest Virginia UniversityUnited States39.65404635-79.9647535561%49301901831
37d6f0eb074d207b53885bd2eb78ccc8a04be597youtube_makeupYMUCan facial cosmetics affect the matching accuracy of face recognition systems?[pdf]2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS)eduWest Virginia UniversityUnited States39.65404635-79.9647535561%49301901831
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2bf8541199728262f78d4dced6fb91479b39b738clothing_co_parsingCCPClothing Co-parsing by Joint Image Segmentation and Labeling[pdf]2014 IEEE Conference on Computer Vision and Pattern Recognition70%60421803824
22ad2c8c0f4d6aa4328b38d894b814ec22579761gallagherGallagherClothing cosegmentation for recognizing people[pdf]2008 IEEE Conference on Computer Vision and Pattern RecognitioneduCarnegie Mellon University Silicon ValleyUnited States37.41021930-122.0596548758%17710374710184
4b1d23d17476fcf78f4cbadf69fb130b1aa627c0leeds_sports_poseLeeds Sports PoseClustered Pose and Nonlinear Appearance Models for Human Pose Estimation[pdf]Unknown65%278180981320878
4b1d23d17476fcf78f4cbadf69fb130b1aa627c0stickmen_buffyBuffy StickmenClustered Pose and Nonlinear Appearance Models for Human Pose Estimation[pdf]Unknown65%278180981320878
45c31cde87258414f33412b3b12fc5bec7cb3ba9jaffeJAFFECoding Facial Expressions with Gabor Wavelets[pdf]Unknown50%84842242655420433
b1f4423c227fa37b9680787be38857069247a307afew_vaAFEW-VACollecting Large, Richly Annotated Facial-Expression Databases from Movies[pdf]IEEE MultiMediaeduAustralian National UniversityAustralia-35.27769990149.1185270060%1821097388699
7f4040b482d16354d5938c1d1b926b544652bf5bnova_emotionsNovaemötions DatasetCompetitive affective gaming: winning with a smile[pdf]UnknowneduUniversidade NOVA de Lisboa, Caparica, PortugalPortugal38.66096400-9.2058130075%862044
079a0a3bf5200994e1f972b1b9197bf2f90e87d4mit_cbclMIT CBCLComponent-Based Face Recognition with 3D Morphable Models[pdf]2004 Conference on Computer Vision and Pattern Recognition Workshop33%1248084
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09d78009687bec46e70efcf39d4612822e61cb8craidRAiDConsistent Re-identification in a Camera Network[pdf]Unknown51%45232273411
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c06b13d0ec3f5c43e2782cd22542588e233733c3nova_emotionsNovaemötions DatasetCrowdsourcing facial expressions for affective-interaction[pdf]Computer Vision and Image Understanding100%110010
8355d095d3534ef511a9af68a3b2893339e3f96bimdb_wikiIMDBDEX: Deep EXpectation of Apparent Age from a Single Image[pdf]2015 IEEE International Conference on Computer Vision Workshop (ICCVW)56%120675357447
5a5f0287484f0d480fed1ce585dbf729586f0edcdisfaDISFADISFA: A Spontaneous Facial Action Intensity Database[pdf]IEEE Transactions on Affective ComputingeduUniversity of DenverUnited States39.67665410-104.9622030049%19094961910091
10195a163ab6348eef37213a46f60a3d87f289c5imdb_wikiIMDBDeep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks[pdf]International Journal of Computer VisioneduETH ZurichSwitzerland47.376313008.5476699053%1337162139441
162ea969d1929ed180cc6de9f0bf116993ff6e06vgg_facesVGG FaceDeep Face Recognition[pdf]Unknown54%99954345670635370
6424b69f3ff4d35249c0bb7ef912fbc2c86f4ff4celebaCelebADeep Learning Face Attributes in the Wild[pdf]2015 IEEE International Conference on Computer Vision (ICCV)eduChinese University of Hong KongChina22.41626320114.2109318052%80842138668670118
18010284894ed0edcca74e5bf768ee2e15ef7841deep_fashionDeepFashionDeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations[pdf]2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)65%1509753411138
6bd36e9fd0ef20a3074e1430a6cc601e6d407fc3cuhk03CUHK03DeepReID: Deep Filter Pairing Neural Network for Person Re-identification[pdf]2014 IEEE Conference on Computer Vision and Pattern Recognition59%51230420829324180
13f06b08f371ba8b5d31c3e288b4deb61335b462eth_andreas_essETHZ PedestrianDepth and Appearance for Mobile Scene Analysis[pdf]2007 IEEE 11th International Conference on Computer VisioneduETH ZurichSwitzerland47.376313008.5476699055%31917614327195127
4946ba10a4d5a7d0a38372f23e6622bd347ae273coco_actionCOCO-aDescribing Common Human Visual Actions in Images[pdf]Unknown58%2615110251
2e384f057211426ac5922f1b33d2aa8df5d51f57a_pascal_yahoo#N/ADescribing objects by their attributes[pdf]2009 IEEE Conference on Computer Vision and Pattern RecognitioneduUniversity of Illinois, Urbana-ChampaignUnited States40.11116745-88.2258766557%99956543374738264
7808937b46acad36e43c30ae4e9f3fd57462853dbpadBPADDescribing people: A poselet-based approach to attribute classification[pdf]2011 International Conference on Computer Vision57%221125961416559
d3200d49a19a4a4e4e9745ee39649b65d80c834bscut_headSCUT HEADDetecting Heads using Feature Refine Net and Cascaded Multi-scale Architecture[pdf]2018 24th International Conference on Pattern Recognition (ICPR)100%000000
9cc8cf0c7d7fa7607659921b6ff657e17e135eccmafaMAsked FAcesDetecting Masked Faces in the Wild with LLE-CNNs[pdf]2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)67%642151
56ae6d94fc6097ec4ca861f0daa87941d1c10b70cmdpCMDPDistance Estimation of an Unknown Person from a Portrait[pdf]Unknown44%945063
2b89de1d81cee50552f10e26e865df3365e9bc88ibm_difIBM Diversity in FacesDiversity in Faces[pdf]CoRR100%000000
84fe5b4ac805af63206012d29523a1e033bc827eawe_earsAWE EarsEar Recognition: More Than a Survey[pdf]Neurocomputing67%2416801113
133f01aec1534604d184d56de866a4bd531dac87lfwLFWEffective Unconstrained Face Recognition by Combining Multiple Descriptors and Learned Background Statistics[pdf]IEEE Transactions on Pattern Analysis and Machine Intelligence55%17798791510475
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2161f6b7ee3c0acc81603b01dc0df689683577b9large_scale_person_searchLarge Scale Person SearchEnd-to-End Deep Learning for Person Search[pdf]CoRR54%41221922712
1bd1645a629f1b612960ab9bba276afd4cf7c666brainwashBrainwashEnd-to-End People Detection in Crowded Scenes[pdf]2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)eduMax Planck Institute for InformaticsGermany49.257956607.0457741737%49183112321
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9e5378e7b336c89735d3bb15cf67eff96f86d39aprecariousPrecariousExpecting the Unexpected: Training Detectors for Unusual Pedestrians with Adversarial Imposters[pdf]2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)17%122101111
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560e0e58d0059259ddf86fcec1fa7975dee6a868youtube_facesYouTubeFacesFace recognition in unconstrained videos with matched background similarity[pdf]CVPR 2011eduTel Aviv UniversityIsrael32.1119889034.8045970260%48529219230298193
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1aad2da473888cb7ebc1bfaa15bfa0f1502ce005jpl_poseJPL-Interaction datasetFirst-Person Activity Recognition: What Are They Doing to Me?[pdf]2013 IEEE Conference on Computer Vision and Pattern Recognition70%14810345811138
7b92d1e53cc87f7a4256695de590098a2f30261eappa_realAPPA-REALFrom Apparent to Real Age: Gender, Age, Ethnic, Makeup, and Expression Bias Analysis in Real Age Estimation[pdf]2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)100%000000
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066d71fcd997033dce4ca58df924397dfe0b5fd1ifdbIFDBIranian Face Database and Evaluation with a New Detection Algorithm[pdf]Unknown100%000000
b71d1aa90dcbe3638888725314c0d56640c1fef1ifdbIFDBIranian Face Database with age, pose and expression[pdf]2007 International Conference on Machine VisioneduIslamic Azad UniversityIran34.8452999048.5596212035%207132129
137aa2f891d474fce1e7a1d1e9b3aefe21e22b34hrt_transgenderHRT TransgenderIs the eye region more reliable than the face? A preliminary study of face-based recognition on a transgender dataset[pdf]2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS)57%743135
0b440695c822a8e35184fb2f60dcdaa8a6de84aekinectfaceKinectFaceDBKinectFaceDB: A Kinect Database for Face Recognition[pdf]IEEE Transactions on Systems, Man, and Cybernetics: SystemseduUniversity of North Carolina at Chapel HillUnited States35.91139710-79.0504529061%75462962650
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7de6e81d775e9cd7becbfd1bd685f4e2a5eebb22lfwLFWLabeled Faces in the Wild : A Survey[pdf]UnknowneduStevens Institute of TechnologyUnited States40.74225200-74.0270949045%99455486336
2d3482dcff69c7417c7b933f22de606a0e8e42d4lfwLFWLabeled Faces in the Wild : Updates and New Reporting Procedures[pdf]UnknowneduUniversity of MassachusettsUnited States42.38897850-72.5286987058%123715237250
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07fcbae86f7a3ad3ea1cf95178459ee9eaf77cb1uccsUCCSLarge scale unconstrained open set face database[pdf]2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS)eduUniversity of Colorado at Colorado SpringsUnited States38.89646790-104.8050594080%541032
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853bd61bc48a431b9b1c7cab10c603830c488e39casia_webfaceCASIA WebfaceLearning Face Representation from Scratch[pdf]CoRReduChinese Academy of SciencesChina40.00447950116.3702380060%43626017630288150
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4e4746094bf60ee83e40d8597a6191e463b57f76leeds_sports_pose_extendedLeeds Sports Pose ExtendedLearning effective human pose estimation from inaccurate annotation[pdf]CVPR 2011eduUniversity of LeedsUnited Kingdom53.80387185-1.5524571264%173111621012256
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6dd0597f8513dc100cd0bc1b493768cde45098a9stickmen_buffyBuffy StickmenLearning to parse images of articulated bodies[pdf]Unknown58%37321515735251129
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6dd0597f8513dc100cd0bc1b493768cde45098a9stickmen_pascalStickmen PASCALLearning to parse images of articulated bodies[pdf]Unknown58%37321515735251129
28d4e027c7e90b51b7d8908fce68128d1964668amegafaceMegaFaceLevel Playing Field for Million Scale Face Recognition[pdf]2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)eduUniversity of WashingtonUnited States47.65432380-122.3080089439%3815232298
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38b55d95189c5e69cf4ab45098a48fba407609b4cuhk02CUHK02Locally Aligned Feature Transforms across Views[pdf]2013 IEEE Conference on Computer Vision and Pattern Recognition53%24212911317139102
8990cdce3f917dad622e43e033db686b354d057ctiny_facesTinyFaceLow-Resolution Face Recognition[pdf]CoRR100%000000
c0387e788a52f10bf35d4d50659cfa515d89fbecmarsMARSMARS: A Video Benchmark for Large-Scale Person Re-Identification[pdf]Unknown58%146856169749
3dc3f0b64ef80f573e3a5f96e456e52ee980b877georgia_tech_face_databaseGeorgia Tech FaceMAXIMUM 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[pdf]Unknown33%312021
9055b155cbabdce3b98e16e5ac9c0edf00f9552fmorphMORPH CommercialMORPH: a longitudinal image database of normal adult age-progression[pdf]7th International Conference on Automatic Face and Gesture Recognition (FGR06)53%42422519826239190
9055b155cbabdce3b98e16e5ac9c0edf00f9552fmorph_ncMORPH Non-CommercialMORPH: a longitudinal image database of normal adult age-progression[pdf]7th International Conference on Automatic Face and Gesture Recognition (FGR06)53%42422519826239190
291265db88023e92bb8c8e6390438e5da148e8f5mscelebMsCelebMS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition[pdf]Unknown54%16791761413136
e58dd160a76349d46f881bd6ddbc2921f08d1050gfwGrouping Face in the WildMerge or Not? Learning to Group Faces via Imitation Learning[pdf]Unknown50%211020
5753b2b5e442eaa3be066daa4a2ca8d8a0bb172550_people_one_question50 People One QuestionMerging Pose Estimates Across Space and Time[pdf]Unknown87%151320124
5e0f8c355a37a5a89351c02f174e7a5ddcb98683cocoCOCOMicrosoft COCO: Common Objects in Context[pdf]Unknown57%99956943029799193
a5a44a32a91474f00a3cda671a802e87c899fbb4moments_in_timeMoments in TimeMoments in Time Dataset: one million videos for event understanding[pdf]CoRR64%251692250
436f798d1a4e54e5947c1e7d7375c31b2bdb4064tud_multiviewTUD-MultiviewMonocular 3D pose estimation and tracking by detection[pdf]2010 IEEE Computer Society Conference on Computer Vision and Pattern RecognitioneduTU DarmstadtGermany49.874827708.6563281054%30216413834207100
436f798d1a4e54e5947c1e7d7375c31b2bdb4064tud_stadtmitteTUD-StadtmitteMonocular 3D pose estimation and tracking by detection[pdf]2010 IEEE Computer Society Conference on Computer Vision and Pattern RecognitioneduTU DarmstadtGermany49.874827708.6563281054%30216413834207100
3b5b6d19d4733ab606c39c69a889f9e67967f151qmul_gridGRIDMulti-camera activity correlation analysis[pdf]2009 IEEE Conference on Computer Vision and Pattern RecognitioneduQueen Mary University of LondonUnited Kingdom51.52472720-0.0393103562%138865287961
6ad5a38df8dd4cdddd74f31996ce096d41219f72tud_brusselsTUD-BrusselsMulti-cue onboard pedestrian detection[pdf]2009 IEEE Conference on Computer Vision and Pattern Recognition56%217121961413386
6ad5a38df8dd4cdddd74f31996ce096d41219f72tud_motionpairsTUD-MotionparisMulti-cue onboard pedestrian detection[pdf]2009 IEEE Conference on Computer Vision and Pattern Recognition56%217121961413386
32c801cb7fbeb742edfd94cccfca4934baec71daucf_crowdUCF-CC-50Multi-source Multi-scale Counting in Extremely Dense Crowd Images[pdf]2013 IEEE Conference on Computer Vision and Pattern Recognition70%125883767352
1e3df3ca8feab0b36fd293fe689f93bb2aaac591immediacyImmediacyMulti-task Recurrent Neural Network for Immediacy Prediction[pdf]2015 IEEE International Conference on Computer Vision (ICCV)64%251692215
2b926b3586399d028b46315d7d9fb9d879e4f79cfrav3dFRAV3DMultimodal 2D, 2.5D & 3D Face Verification[pdf]2006 International Conference on Image ProcessingeduUniversidad Rey Juan Carlos, Spain40.33586610-3.8769432050%14770212
53ae38a6bb2b21b42bac4f0c4c8ed1f9fa02f9d4bp4d_plusBP4D+Multimodal Spontaneous Emotion Corpus for Human Behavior Analysis[pdf]2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)50%40202002120
2fda164863a06a92d3a910b96eef927269aeb730names_and_facesNews DatasetNames and faces in the news[pdf]Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004.51%2941501432921582
4156b7e88f2e0ab0a7c095b9bab199ae2b23bd06distance_nighttimeLong Distance Heterogeneous FaceNighttime Face Recognition at Long Distance: Cross-Distance and Cross-Spectral Matching[pdf]Unknown43%2191231110
3394168ff0719b03ff65bcea35336a76b21fe5e4penn_fudanPenn FudanObject Detection Combining Recognition and Segmentation[pdf]Unknown50%1015051115842
12ad3b5bbbf407f8e54ea692c07633d1a867c566grazGraz PedestrianObject recognition using segmentation for feature detection[pdf]Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004.eduInst. of Comput. Sci., Univ. of Leoben, AustriaAustria47.3847372015.0930201041%2912171218
4f93cd09785c6e77bf4bc5a788e079df524c8d21sotonSOTON HiDOn a Large Sequence-Based Human Gait Database[pdf]Unknown58%14886621710449
6618cff7f2ed440a0d2fa9e74ad5469df5cdbe4cafadAFADOrdinal Regression with Multiple Output CNN for Age Estimation[pdf]2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)53%68363284917
a7fe834a0af614ce6b50dc093132b031dd9a856bmarket1203Market 1203Orientation Driven Bag of Appearances for Person Re-identification[pdf]CoRR43%734044
a7fe834a0af614ce6b50dc093132b031dd9a856bpku_reidPKU-ReidOrientation Driven Bag of Appearances for Person Re-identification[pdf]CoRR43%734044
18ae7c9a4bbc832b8b14bc4122070d7939f5e00efrgcFRGCOverview of the face recognition grand challenge[pdf]2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)eduNISTUnited States39.14004000-77.2185060050%999497501114594424
22909dd19a0ec3b6065334cb5be5392cb24d839dpetsPETS 2017PETS 2017: Dataset and Challenge[pdf]2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)0%808026
56ffa7d906b08d02d6d5a12c7377a57e24ef3391unbc_shoulder_painUNBC-McMaster PainPainful data: The UNBC-McMaster shoulder pain expression archive database[pdf]Face and Gesture 2011eduCarnegie Mellon University Silicon ValleyUnited States37.41021930-122.0596548752%18495892311271
55206f0b5f57ce17358999145506cd01e570358corlORLParameterisation of a stochastic model for human face identification[pdf]Unknown44%99944255797569445
0486214fb58ee9a04edfe7d6a74c6d0f661a7668chokepointChokePointPatch-based probabilistic image quality assessment for face selection and improved video-based face recognition[pdf]CVPR 2011 WORKSHOPS53%128686067360
488e475eeb3bb39a145f23ede197cd3620f1d98aapisAPiS1.0Pedestrian Attribute Classification in Surveillance: Database and Evaluation[pdf]2013 IEEE International Conference on Computer Vision Workshops69%2618811313
488e475eeb3bb39a145f23ede197cd3620f1d98asvsSVSPedestrian Attribute Classification in Surveillance: Database and Evaluation[pdf]2013 IEEE International Conference on Computer Vision Workshops69%2618811313
2a4bbee0b4cf52d5aadbbc662164f7efba89566cpetaPETAPedestrian Attribute Recognition At Far Distance[pdf]Unknown68%80542625128
f72f6a45ee240cc99296a287ff725aaa7e7ebb35caltech_pedestriansCaltech PedestriansPedestrian Detection: An Evaluation of the State of the Art[pdf]IEEE Transactions on Pattern Analysis and Machine IntelligenceeduCalifornia Institute of TechnologyUnited States34.13710185-118.1252748749%99948551471541464
1dc35905a1deff8bc74688f2d7e2f48fd2273275caltech_pedestriansCaltech PedestriansPedestrian detection: A benchmark[pdf]2009 IEEE Conference on Computer Vision and Pattern Recognition50%51926125827289233
3316521a5527c7700af8ae6aef32a79a8b83672ctud_campusTUD-CampusPeople-tracking-by-detection and people-detection-by-tracking[pdf]2008 IEEE Conference on Computer Vision and Pattern Recognition53%52928024840324213
3316521a5527c7700af8ae6aef32a79a8b83672ctud_crossingTUD-CrossingPeople-tracking-by-detection and people-detection-by-tracking[pdf]2008 IEEE Conference on Computer Vision and Pattern Recognition53%52928024840324213
3316521a5527c7700af8ae6aef32a79a8b83672ctud_pedestrianTUD-PedestrianPeople-tracking-by-detection and people-detection-by-tracking[pdf]2008 IEEE Conference on Computer Vision and Pattern Recognition53%52928024840324213
27a2fad58dd8727e280f97036e0d2bc55ef5424cduke_mtmcDuke MTMCPerformance Measures and a Data Set for Multi-Target, Multi-Camera Tracking[pdf]Unknown58%1367957710827
27a2fad58dd8727e280f97036e0d2bc55ef5424cmotMOTPerformance Measures and a Data Set for Multi-Target, Multi-Camera Tracking[pdf]Unknown58%1367957710827
16c7c31a7553d99f1837fc6e88e77b5ccbb346b8pridPRIDPerson Re-identification by Descriptive and Discriminative Classification[pdf]Unknown58%35220414827196157
98bb029afe2a1239c3fdab517323066f0957b81bilids_vid_reidiLIDS-VIDPerson Re-identification by Video Ranking[pdf]Unknown57%210120901011594
98bb029afe2a1239c3fdab517323066f0957b81bsdu_vidSDU-VIDPerson Re-identification by Video Ranking[pdf]Unknown57%210120901011594
0b84f07af44f964817675ad961def8a51406dd2eprwPRWPerson Re-identification in the Wild[pdf]2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)51%65333214617
ec792ad2433b6579f2566c932ee414111e194537msmt_17MSMT17Person Transfer GAN to Bridge Domain Gap for Person Re-Identification[pdf]CoRR71%141041113
1c2802c2199b6d15ecefe7ba0c39bfe44363de38youtube_posesYouTube PosePersonalizing Human Video Pose Estimation[pdf]2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)eduOxford UniversityUnited Kingdom51.75208490-1.2516646066%3221112295
b92a1ed9622b8268ae3ac9090e25789fc41cc9b8pornodb#N/APooling in image representation: The visual codeword point of view[pdf]Computer Vision and Image Understanding32%77255274634
2830fb5282de23d7784b4b4bc37065d27839a412h3dH3DPoselets: Body part detectors trained using 3D human pose annotations[pdf]2009 IEEE 12th International Conference on Computer Vision52%70736833967509215
3765df816dc5a061bc261e190acc8bdd9d47bec0rafdRaFDPresentation and validation of the Radboud Faces Database[pdf]Unknown39%44617527143322136
636b8ffc09b1b23ff714ac8350bb35635e49fa3ccaltech_10k_web_facesCaltech 10K Web FacesPruning training sets for learning of object categories[pdf]2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)65%60392154317
377f2b65e6a9300448bdccf678cde59449ecd337ufddUFDDPushing the Limits of Unconstrained Face Detection: a Challenge Dataset and Baseline Results[pdf]CoRR0%202020
140c95e53c619eac594d70f6369f518adfea12efijb_cIJB-CPushing the frontiers of unconstrained face detection and recognition: IARPA Janus Benchmark A[pdf]2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)55%222123991916162
d80a3d1f3a438e02a6685e66ee908446766fefa9megaageMegaAgeQuantifying Facial Age by Posterior of Age Comparisons[pdf]CoRR25%413140
922e0a51a3b8c67c4c6ac09a577ff674cbd28b34v47V47Re-identification of pedestrians with variable occlusion and scale[pdf]2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)eduKingston UniversityUnited Kingdom51.42930860-0.2684044010%1019264
6f3c76b7c0bd8e1d122c6ea808a271fd4749c951wardWARDRe-identify people in wide area camera network[pdf]2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops38%55213423519
54983972aafc8e149259d913524581357b0f91c3reseedReSEEDReSEED: social event dEtection dataset[pdf]Unknown67%642115
65355cbb581a219bd7461d48b3afd115263ea760complex_activitiesOngoing Complex ActivitiesRecognition of ongoing complex activities by sequence prediction over a hierarchical label space[pdf]2016 IEEE Winter Conference on Applications of Computer Vision (WACV)50%211020
e8de844fefd54541b71c9823416daa238be65546visual_phrasesPhrasal RecognitionRecognition using visual phrases[pdf]CVPR 2011eduUniversity of Illinois, Urbana-ChampaignUnited States40.11116745-88.2258766558%233135981817758
356b431d4f7a2a0a38cf971c84568207dcdbf189widerWIDERRecognize complex events from static images by fusing deep channels[pdf]2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)64%45291613015
25474c21613607f6bb7687a281d5f9d4ffa1f9f3faceplaceFace PlaceRecognizing disguised faces[pdf]Unknown33%248160186
4053e3423fb70ad9140ca89351df49675197196abio_idBioID FaceRobust Face Detection Using the Hausdorff Distance[pdf]Unknown48%49823726156330179
2724ba85ec4a66de18da33925e537f3902f21249cofwCOFWRobust Face Landmark Estimation under Occlusion[pdf]2013 IEEE International Conference on Computer VisioneduCalifornia Institute of TechnologyUnited States34.13710185-118.1252748761%30518611916192116
c570d1247e337f91e555c3be0e8c8a5aba539d9fmcgillMcGill Real WorldRobust semi-automatic head pose labeling for real-world face video sequences[pdf]Multimedia Tools and ApplicationseduMcGill UniversityCanada45.50397610-73.5749687044%188100137
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bd26dabab576adb6af30484183c9c9c8379bf2e0scut_fbpSCUT-FBPSCUT-FBP: A Benchmark Dataset for Facial Beauty Perception[pdf]2015 IEEE International Conference on Systems, Man, and Cybernetics43%1468359
29a705a5fa76641e0d8963f1fdd67ee4c0d92d3dscfaceSCfaceSCface – surveillance cameras face database[pdf]Multimedia Tools and Applications51%1789088159089
d3f5a1848b0028d8ab51d0b0673732cad2e3c8c9stair_actionsSTAIR ActionSTAIR Actions: A Video Dataset of Everyday Home Actions[pdf]CoRR100%000000
833fa04463d90aab4a9fe2870d480f0b40df446esun_attributesSUNSUN attribute database: Discovering, annotating, and recognizing scene attributes[pdf]2012 IEEE Conference on Computer Vision and Pattern RecognitioneduBrown UniversityUnited States41.82686820-71.4012314658%2691561132921557
4308bd8c28e37e2ed9a3fcfe74d5436cce34b410market_1501Market 1501Scalable Person Re-identification: A Benchmark[pdf]2015 IEEE International Conference on Computer Vision (ICCV)60%39423815618272116
9c23859ec7313f2e756a3e85575735e0c52249f4facebook_100Facebook100Scaling up biologically-inspired computer vision: A case study in unconstrained face recognition on facebook[pdf]CVPR 2011 WORKSHOPSeduHarvard UniversityUnited States42.36782045-71.1266665358%50292133911
9c23859ec7313f2e756a3e85575735e0c52249f4pubfig_83pubfig83Scaling up biologically-inspired computer vision: A case study in unconstrained face recognition on facebook[pdf]CVPR 2011 WORKSHOPSeduHarvard UniversityUnited States42.36782045-71.1266665358%50292133911
51eba481dac6b229a7490f650dff7b17ce05df73imsituimSituSituation Recognition: Visual Semantic Role Labeling for Image Understanding[pdf]2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)62%4830182462
570f37ed63142312e6ccdf00ecc376341ec72b9fstanford_droneStanford DroneSocial LSTM: Human Trajectory Prediction in Crowded Spaces[pdf]2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)46%229106123515079
23e824d1dfc33f3780dd18076284f07bd99f1c43mifsMIFSSpoofing faces using makeup: An investigative study[pdf]2017 IEEE International Conference on Identity, Security and Behavior Analysis (ISBA)eduINRIA MéditerranéeFrance43.615813107.0683800060%532014
1a40092b493c6b8840257ab7f96051d1a4dbfeb2sports_videos_in_the_wildSVWSports Videos in the Wild (SVW): A video dataset for sports analysis[pdf]2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG)83%651151
9361b784e73e9238d5cefbea5ac40d35d1e3103ftowncenterTownCenterStable multi-target tracking in real-time surveillance video[pdf]CVPR 201144%31013717324180131
c866a2afc871910e3282fd9498dce4ab20f6a332qmul_surv_faceQMUL-SurvFaceSurveillance Face Recognition Challenge[pdf]CoRR100%000000
f6c8d5e35d7e4d60a0104f233ac1a3ab757da53fpkuPKUSwiss-System Based Cascade Ranking for Gait-Based Person Re-Identification[pdf]Unknown67%321012
4d58f886f5150b2d5e48fd1b5a49e09799bf895dtexas_3dfrdTexas 3DFRDTexas 3D Face Recognition Database[pdf]2010 IEEE Southwest Symposium on Image Analysis & Interpretation (SSIAI)39%61243733725
6d96f946aaabc734af7fe3fc4454cf8547fcd5edar_facedbAR FaceThe AR face database[pdf]53%99952647351459573
2485c98aa44131d1a2f7d1355b1e372f2bb148adcas_pealCAS-PEALThe CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations[pdf]IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans50%41520920639189232
47662d1a368daf70ba70ef2d59eb6209f98b675dfiaCMU FiAThe CMU Face In Action (FIA) Database[pdf]Unknown44%55243154117
4d423acc78273b75134e2afd1777ba6d3a398973cmu_pieCMU PIEThe CMU Pose, Illumination, and Expression (PIE) Database of Human Faces[pdf]Unknown53%74239634459416329
4d423acc78273b75134e2afd1777ba6d3a398973multi_pieMULTIPIEThe CMU Pose, Illumination, and Expression (PIE) Database of Human Faces[pdf]Unknown53%74239634459416329
4df3143922bcdf7db78eb91e6b5359d6ada004d2cfdCFDThe Chicago face database: A free stimulus set of faces and norming data.[pdf]Behavior research methods48%83404326319
20388099cc415c772926e47bcbbe554e133343d1cafe#N/AThe Child Affective Facial Expression (CAFE) set: validity and reliability from untrained adults[pdf]48%3316173285
4e6ee936eb50dd032f7138702fa39b7c18ee8907dartmouth_childrenDartmouth ChildrenThe Dartmouth Database of Children’s Faces: Acquisition and Validation of a New Face Stimulus Set[pdf]45%209112174
9e31e77f9543ab42474ba4e9330676e18c242e72imdb_faceIMDb FaceThe Devil of Face Recognition is in the Noise[pdf]UnknowneduNanyang Technological UniversitySingapore1.34841040103.6829796520%514031
71b7fc715e2f1bb24c0030af8d7e7b6e7cd128a6umd_facesUMDThe Do’s and Don’ts for CNN-Based Face Verification[pdf]2017 IEEE International Conference on Computer Vision Workshops (ICCVW)36%259163176
f0e17f27f029db4ad650ff278fe3c10ecb6cb0c4europersonsEuroCity PersonsThe EuroCity Persons Dataset: A Novel Benchmark for Object Detection[pdf]CoRR0%101010
4d9a02d080636e9666c4d1cc438b9893391ec6c7cohn_kanade_plusCK+The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression[pdf]2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - WorkshopseduUniversity of PittsburghUnited States40.44415295-79.9624399355%97553543967475510
0f0fcf041559703998abf310e56f8a2f90ee6f21feretFERETThe FERET Evaluation Methodology for Face-Recognition Algorithms[pdf]Unknown48%999482517103560454
0c4a139bb87c6743c7905b29a3cfec27a5130652feretFERETThe FERET Verification Testing Protocol for Face Recognition Algorithms[pdf]Unknown44%1124963117935
dc8b25e35a3acb812beb499844734081722319b4feretFERETThe FERET database and evaluation procedure for face-recognition algorithms[pdf]Image Vision Comput.44%999443556106606413
8f02ec0be21461fbcedf51d864f944cfc42c875fhda_plusHDA+The HDA+ Data Set for Research on Fully Automated Re-identification Systems[pdf]Unknown35%176112116
9a9877791945c6fa4c1743ec6d3fb32570ef8481m2vtsm2vtsThe M2VTS Multimodal Face Database (Release 1.00)[pdf]UnknowneduLaboratoire de Télécommunications et Télédétection, UCL, Louvain-La-Neuve, BelgiumBelgium50.669687504.6155909043%129557448054
ea050801199f98a1c7c1df6769f23f658299a3aempi_largeLarge MPI Facial ExpressionThe MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial Expressions[pdf]46%2813154244
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578d4ad74818086bb64f182f72e2c8bd31e3d426mr2MR2The MR2: A multi-racial, mega-resolution database of facial stimuli.[pdf]Behavior research methods14%716070
f1af714b92372c8e606485a3982eab2f16772ad8mug_facesMUG FacesThe MUG facial expression database[pdf]11th International Workshop on Image Analysis for Multimedia Interactive Services WIAMIS 10eduAristotle University of ThessalonikiGreece40.6298414522.9588935043%68293952840
79828e6e9f137a583082b8b5a9dfce0c301989b8mapillaryMapillaryThe Mapillary Vistas Dataset for Semantic Understanding of Street Scenes[pdf]2017 IEEE International Conference on Computer Vision (ICCV)52%4423210367
96e0cfcd81cdeb8282e29ef9ec9962b125f379b0megafaceMegaFaceThe MegaFace Benchmark: 1 Million Faces for Recognition at Scale[pdf]2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)59%121715099822
0ee1916a0cb2dc7d3add086b5f1092c3d4beb38avocVOCThe Pascal Visual Object Classes (VOC) Challenge[pdf]International Journal of Computer VisioneduOxford UniversityUnited Kingdom51.75208490-1.2516646058%99957542435613414
66e6f08873325d37e0ec20a4769ce881e04e964esun_attributesSUNThe SUN Attribute Database: Beyond Categories for Deeper Scene Understanding[pdf]International Journal of Computer Vision62%1127042148429
8b2dd5c61b23ead5ae5508bb8ce808b5ea26673010k_US_adult_faces10K US Adult FacesThe intrinsic memorability of face photographs.[pdf]Journal of experimental psychology. General51%47242333413
ae0aee03d946efffdc7af2362a42d3750e7dd48aput_facePut FaceThe put face database[pdf]Unknown42%100425875648
19d1b811df60f86cbd5e04a094b07f32fff7a32ayork_3dUOY 3D Face DatabaseThree-dimensional face recognition: an eigensurface approach[pdf]2004 International Conference on Image Processing, 2004. ICIP '04.33%36122442511
2edb87494278ad11641b6cf7a3f8996de12b8e14qmul_gridGRIDTime-Delayed Correlation Analysis for Multi-Camera Activity Understanding[pdf]International Journal of Computer VisioneduQueen Mary University of LondonUnited Kingdom51.52472720-0.0393103549%83414265133
298cbc3dfbbb3a20af4eed97906650a4ea1c29e0ferplusFER+Training deep networks for facial expression recognition with crowd-sourced label distribution[pdf]Unknown62%29181101514
4eab317b5ac436a949849ed286baa3de2a541eeflaofiwLAOFIWTurning a Blind Eye: Explicit Removal of Biases and Variation from Deep Neural Network Embeddings[pdf]Unknown50%211020
b5f2846a506fc417e7da43f6a7679146d99c5e96ucf_101UCF101UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild[pdf]CoRR61%99961138873716283
16e8b0a1e8451d5f697b94c0c2b32a00abee1d52umbUMBUMB-DB: A database of partially occluded 3D faces[pdf]2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)60%45271822024
31b05f65405534a696a847dd19c621b7b8588263umd_facesUMDUMDFaces: An annotated face dataset for training deep networks[pdf]2017 IEEE International Joint Conference on Biometrics (IJCB)eduUniversity of MarylandUnited States39.28996850-76.6219610357%3520154287
8627f019882b024aef92e4eb9355c499c733e5b7usedUSED Social Event DatasetUSED: a large-scale social event detection dataset[pdf]UnknowneduUniversity of TrentoItaly46.0658836011.1159894071%752034
d4f1eb008eb80595bcfdac368e23ae9754e1e745uccsUCCSUnconstrained Face Detection and Open-Set Face Recognition Challenge[pdf]2017 IEEE International Joint Conference on Biometrics (IJCB)40%523041
4b4106614c1d553365bad75d7866bff0de6056edczech_news_agencyUFIUnconstrained Facial Images: Database for Face Recognition Under Real-World Conditions[pdf]Unknown40%1046046
08f6745bc6c1b0fb68953ea61054bdcdde6d2fc7kin_faceUB KinFaceUnderstanding Kin Relationships in a Photo[pdf]IEEE Transactions on Multimedia57%96554123463
21d9d0deed16f0ad62a4865e9acf0686f4f15492images_of_groupsImages of GroupsUnderstanding images of groups of people[pdf]2009 IEEE Conference on Computer Vision and Pattern RecognitioneduCarnegie Mellon University Silicon ValleyUnited States37.41021930-122.0596548754%202110921213275
fd8168f1c50de85bac58a8d328df0a50248b16aend_2006ND-2006Using a Multi-Instance Enrollment Representation to Improve 3D Face Recognition[pdf]2007 First IEEE International Conference on Biometrics: Theory, Applications, and SystemseduUniversity of Notre DameUnited States41.70456775-86.2382202656%32181431715
4563b46d42079242f06567b3f2e2f7a80cb3befevadanaVADANAVADANA: A dense dataset for facial image analysis[pdf]2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)eduUniversity of DelawareUnited States39.68103280-75.7540184044%16790610
eb027969f9310e0ae941e2adee2d42cdf07d938cvgg_faces2VGG Face2VGGFace2: A Dataset for Recognising Faces across Pose and Age[pdf]2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018)eduOxford UniversityUnited Kingdom51.75208490-1.2516646045%5625316506
01959ef569f74c286956024866c1d107099199f7vqaVQAVQA: Visual Question Answering[pdf]2015 IEEE International Conference on Computer Vision (ICCV)61%7314442874762996
b6c293f0420f7e945b5916ae44269fb53e139275erceERCeVideo Synopsis by Heterogeneous Multi-source Correlation[pdf]2013 IEEE International Conference on Computer Vision29%3192221513
b6c293f0420f7e945b5916ae44269fb53e139275tisiTimes Square IntersectionVideo Synopsis by Heterogeneous Multi-source Correlation[pdf]2013 IEEE International Conference on Computer Vision29%3192221513
5194cbd51f9769ab25260446b4fa17204752e799violent_flowsViolent FlowsViolent flows: Real-time detection of violent crowd behavior[pdf]2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition WorkshopseduOpen University of IsraelIsrael32.7782416534.9956567355%83463764441
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066000d44d6691d27202896691f08b27117918b9psuPSUVision-Based Analysis of Small Groups in Pedestrian Crowds[pdf]IEEE Transactions on Pattern Analysis and Machine Intelligence49%151747797973
dd65f71dac86e36eecbd3ed225d016c3336b4a13families_in_the_wildFIWVisual Kinship Recognition of Families in the Wild[pdf]IEEE Transactions on Pattern Analysis and Machine IntelligenceeduUniversity of Massachusetts DartmouthUnited States41.62772475-71.00724501100%330021
52d7eb0fbc3522434c13cc247549f74bb9609c5dwider_faceWIDER FACEWIDER FACE: A Face Detection Benchmark[pdf]2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)eduChinese University of Hong KongChina22.41626320114.2109318057%14885631510841
77c81c13a110a341c140995bedb98101b9e84f7fwildtrackWildTrackWILDTRACK : A Multi-camera HD Dataset for Dense Unscripted Pedestrian Detection[pdf]Unknown100%000000
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2a75f34663a60ab1b04a0049ed1d14335129e908mmi_facial_expressionMMI Facial Expression DatasetWeb-based database for facial expression analysis[pdf]2005 IEEE International Conference on Multimedia and Expo48%44021222844267181
9b9bf5e623cb8af7407d2d2d857bc3f1b531c182who_goes_thereWGTWho goes there?: approaches to mapping facial appearance diversity[pdf]UnknowneduUniversity of KentuckyUnited States38.03337420-84.50177580100%000000
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