| Paper ID | Megapixels Key | Megapixels Name | Report Link | PDF Link | Journal | Type | Address | Country | Lat | Lng | Coverage | Total Citations | Geocoded Citations | Unknown Citations | Empty Citations | With PDF | With DOI | | b5f2846a506fc417e7da43f6a7679146d99c5e96 | ucf_101 | UCF101 | UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild | [pdf] | CoRR | | | | | | 61% | 999 | 607 | 392 | 56 | 628 | 362 |
| 0e986f51fe45b00633de9fd0c94d082d2be51406 | afw | AFW | Face detection, pose estimation, and landmark localization in the wild | [pdf] | 2012 IEEE Conference on Computer Vision and Pattern Recognition | | | | | | 61% | 999 | 605 | 394 | 45 | 576 | 422 |
| 026e3363b7f76b51cc711886597a44d5f1fd1de2 | kitti | KITTI | Vision meets robotics: The KITTI dataset | [pdf] | I. J. Robotics Res. | | | | | | 55% | 999 | 547 | 452 | 37 | 553 | 462 |
| 370b5757a5379b15e30d619e4d3fb9e8e13f3256 | lfw | LFW | Labeled Faces in the Wild: A Database forStudying Face Recognition in Unconstrained Environments | [pdf] | Unknown | | | | | | 55% | 999 | 547 | 452 | 64 | 598 | 382 |
| 162ea969d1929ed180cc6de9f0bf116993ff6e06 | vgg_faces | VGG Face | Deep Face Recognition | [pdf] | Unknown | | | | | | 55% | 999 | 546 | 453 | 58 | 558 | 429 |
| 5e0f8c355a37a5a89351c02f174e7a5ddcb98683 | coco | COCO | Microsoft COCO: Common Objects in Context | [pdf] | Unknown | | | | | | 55% | 999 | 545 | 454 | 26 | 722 | 259 |
| 0ee1916a0cb2dc7d3add086b5f1092c3d4beb38a | voc | VOC | The Pascal Visual Object Classes (VOC) Challenge | [pdf] | International Journal of Computer Vision | | | | | | 54% | 999 | 544 | 454 | 30 | 557 | 422 |
| 4d9a02d080636e9666c4d1cc438b9893391ec6c7 | cohn_kanade_plus | CK+ | 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 - Workshops | edu | University of Pittsburgh | United States | 40.44415295 | -79.96243993 | 54% | 999 | 543 | 456 | 58 | 470 | 518 |
| 759a3b3821d9f0e08e0b0a62c8b693230afc3f8d | pubfig | PubFig | Attribute and simile classifiers for face verification | [pdf] | 2009 IEEE 12th International Conference on Computer Vision | | | | | | 59% | 914 | 535 | 379 | 49 | 586 | 316 |
| 31b58ced31f22eab10bd3ee2d9174e7c14c27c01 | tiny_images | Tiny Images | 80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition | [pdf] | IEEE Transactions on Pattern Analysis and Machine Intelligence | | | | | | 53% | 999 | 529 | 470 | 89 | 644 | 337 |
| 6d96f946aaabc734af7fe3fc4454cf8547fcd5ed | ar_facedb | AR Face | The AR face database | [pdf] | Unknown | | | | | | 52% | 999 | 519 | 480 | 56 | 455 | 530 |
| 18ae7c9a4bbc832b8b14bc4122070d7939f5e00e | frgc | FRGC | Overview of the face recognition grand challenge | [pdf] | 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) | edu | NIST | United States | 39.14004000 | -77.21850600 | 52% | 999 | 519 | 479 | 86 | 549 | 442 |
| 18c72175ddbb7d5956d180b65a96005c100f6014 | yale_faces | YaleFaces | From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose | [pdf] | IEEE Trans. Pattern Anal. Mach. Intell. | | | | | | 52% | 999 | 516 | 483 | 66 | 497 | 462 |
| f72f6a45ee240cc99296a287ff725aaa7e7ebb35 | caltech_pedestrians | Caltech Pedestrians | Pedestrian Detection: An Evaluation of the State of the Art | [pdf] | IEEE Transactions on Pattern Analysis and Machine Intelligence | edu | California Institute of Technology | United States | 34.13710185 | -118.12527487 | 51% | 999 | 511 | 488 | 71 | 526 | 466 |
| 10d6b12fa07c7c8d6c8c3f42c7f1c061c131d4c5 | inria_person | INRIA Pedestrian | Histograms of oriented gradients for human detection | [pdf] | 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) | edu | INRIA Rhone-Alps, Montbonnot, France | France | 45.21788600 | 5.80736900 | 51% | 999 | 510 | 489 | 44 | 419 | 509 |
| 2ad0ee93d029e790ebb50574f403a09854b65b7e | yale_faces | YaleFaces | Acquiring linear subspaces for face recognition under variable lighting | [pdf] | IEEE Transactions on Pattern Analysis and Machine Intelligence | | | | | | 50% | 999 | 498 | 501 | 94 | 495 | 491 |
| 23fc83c8cfff14a16df7ca497661264fc54ed746 | cohn_kanade | CK | Comprehensive Database for Facial Expression Analysis | [pdf] | Unknown | | | | | | 49% | 999 | 492 | 507 | 69 | 539 | 439 |
| b62628ac06bbac998a3ab825324a41a11bc3a988 | m2vtsdb_extended | xm2vtsdb | XM2VTSDB : The extended M2VTS database | [pdf] | Unknown | | | | | | 54% | 864 | 463 | 401 | 40 | 493 | 404 |
| 6424b69f3ff4d35249c0bb7ef912fbc2c86f4ff4 | celeba | CelebA | Deep Learning Face Attributes in the Wild | [pdf] | 2015 IEEE International Conference on Computer Vision (ICCV) | edu | Chinese University of Hong Kong | China | 22.41626320 | 114.21093180 | 50% | 919 | 456 | 462 | 64 | 694 | 201 |
| dc8b25e35a3acb812beb499844734081722319b4 | feret | FERET | The FERET database and evaluation procedure for face-recognition algorithms | [pdf] | Image Vision Comput. | | | | | | 46% | 999 | 455 | 544 | 103 | 590 | 421 |
| 55206f0b5f57ce17358999145506cd01e570358c | orl | ORL | Parameterisation of a stochastic model for human face identification | [pdf] | Unknown | | | | | | 45% | 999 | 452 | 547 | 94 | 543 | 427 |
| 45c31cde87258414f33412b3b12fc5bec7cb3ba9 | jaffe | JAFFE | Coding Facial Expressions with Gabor Wavelets | [pdf] | Unknown | | | | | | 49% | 899 | 443 | 456 | 51 | 431 | 451 |
| 4d423acc78273b75134e2afd1777ba6d3a398973 | cmu_pie | CMU PIE | The CMU Pose, Illumination, and Expression (PIE) Database | [pdf] | Unknown | | | | | | 54% | 760 | 409 | 350 | 50 | 404 | 345 |
| 4d423acc78273b75134e2afd1777ba6d3a398973 | multi_pie | MULTIPIE | The CMU Pose, Illumination, and Expression (PIE) Database | [pdf] | Unknown | | | | | | 54% | 760 | 409 | 350 | 50 | 404 | 345 |
| 2830fb5282de23d7784b4b4bc37065d27839a412 | h3d | H3D | Poselets: Body part detectors trained using 3D human pose annotations | [pdf] | 2009 IEEE 12th International Conference on Computer Vision | | | | | | 52% | 716 | 370 | 346 | 60 | 492 | 222 |
| 6273b3491e94ea4dd1ce42b791d77bdc96ee73a8 | viper | VIPeR | Evaluating Appearance Models for Recognition, Reacquisition, and Tracking | [pdf] | Unknown | | | | | | 56% | 624 | 352 | 272 | 35 | 342 | 276 |
| 6bd36e9fd0ef20a3074e1430a6cc601e6d407fc3 | cuhk_campus_03 | CUHK03 Campus | DeepReID: Deep Filter Pairing Neural Network for Person Re-identification | [pdf] | 2014 IEEE Conference on Computer Vision and Pattern Recognition | | | | | | 57% | 568 | 323 | 245 | 26 | 320 | 235 |
| 2258e01865367018ed6f4262c880df85b94959f8 | mot | MOT | Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics | [pdf] | EURASIP J. Image and Video Processing | | | | | | 50% | 632 | 314 | 316 | 50 | 358 | 264 |
| 560e0e58d0059259ddf86fcec1fa7975dee6a868 | youtube_faces | YouTubeFaces | Face recognition in unconstrained videos with matched background similarity | [pdf] | CVPR 2011 | | | | | | 57% | 509 | 291 | 217 | 28 | 294 | 216 |
| 3316521a5527c7700af8ae6aef32a79a8b83672c | tud_campus | TUD-Campus | People-tracking-by-detection and people-detection-by-tracking | [pdf] | 2008 IEEE Conference on Computer Vision and Pattern Recognition | | | | | | 53% | 545 | 287 | 257 | 40 | 330 | 218 |
| 3316521a5527c7700af8ae6aef32a79a8b83672c | tud_crossing | TUD-Crossing | People-tracking-by-detection and people-detection-by-tracking | [pdf] | 2008 IEEE Conference on Computer Vision and Pattern Recognition | | | | | | 53% | 545 | 287 | 257 | 40 | 330 | 218 |
| 3316521a5527c7700af8ae6aef32a79a8b83672c | tud_pedestrian | TUD-Pedestrian | People-tracking-by-detection and people-detection-by-tracking | [pdf] | 2008 IEEE Conference on Computer Vision and Pattern Recognition | | | | | | 53% | 545 | 287 | 257 | 40 | 330 | 218 |
| 853bd61bc48a431b9b1c7cab10c603830c488e39 | casia_webface | CASIA Webface | Learning Face Representation from Scratch | [pdf] | CoRR | | | | | | 58% | 476 | 277 | 199 | 26 | 290 | 182 |
| cc589c499dcf323fe4a143bbef0074c3e31f9b60 | bu_3dfe | BU-3DFE | A 3D facial expression database for facial behavior research | [pdf] | 7th International Conference on Automatic Face and Gesture Recognition (FGR06) | | | | | | 47% | 588 | 275 | 312 | 45 | 306 | 282 |
| 4308bd8c28e37e2ed9a3fcfe74d5436cce34b410 | market_1501 | Market 1501 | Scalable Person Re-identification: A Benchmark | [pdf] | 2015 IEEE International Conference on Computer Vision (ICCV) | | | | | | 56% | 460 | 257 | 203 | 16 | 263 | 185 |
| 4053e3423fb70ad9140ca89351df49675197196a | bio_id | BioID Face | Robust Face Detection Using the Hausdorff Distance | [pdf] | Unknown | | | | | | 48% | 511 | 247 | 264 | 51 | 329 | 182 |
| 8a3c5507237957d013a0fe0f082cab7f757af6ee | mafl | MAFL | Facial Landmark Detection by Deep Multi-task Learning | [pdf] | Unknown | | | | | | 59% | 407 | 239 | 168 | 18 | 252 | 153 |
| 8a3c5507237957d013a0fe0f082cab7f757af6ee | mtfl | MTFL | Facial Landmark Detection by Deep Multi-task Learning | [pdf] | Unknown | | | | | | 59% | 407 | 239 | 168 | 18 | 252 | 153 |
| 9055b155cbabdce3b98e16e5ac9c0edf00f9552f | morph | MORPH Commercial | MORPH: a longitudinal image database of normal adult age-progression | [pdf] | 7th International Conference on Automatic Face and Gesture Recognition (FGR06) | | | | | | 51% | 437 | 225 | 211 | 24 | 228 | 203 |
| 9055b155cbabdce3b98e16e5ac9c0edf00f9552f | morph_nc | MORPH Non-Commercial | MORPH: a longitudinal image database of normal adult age-progression | [pdf] | 7th International Conference on Automatic Face and Gesture Recognition (FGR06) | | | | | | 51% | 437 | 225 | 211 | 24 | 228 | 203 |
| 3325860c0c82a93b2eac654f5324dd6a776f609e | mpii_human_pose | MPII Human Pose | 2D Human Pose Estimation: New Benchmark and State of the Art Analysis | [pdf] | 2014 IEEE Conference on Computer Vision and Pattern Recognition | | | | | | 58% | 387 | 224 | 163 | 21 | 291 | 96 |
| 16c7c31a7553d99f1837fc6e88e77b5ccbb346b8 | prid | PRID | Person Re-identification by Descriptive and Discriminative Classification | [pdf] | Unknown | | | | | | 57% | 386 | 221 | 165 | 25 | 204 | 180 |
| 2485c98aa44131d1a2f7d1355b1e372f2bb148ad | cas_peal | CAS-PEAL | The CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations | [pdf] | IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans | | | | | | 51% | 429 | 218 | 211 | 39 | 198 | 234 |
| 2a75f34663a60ab1b04a0049ed1d14335129e908 | mmi_facial_expression | MMI Facial Expression Dataset | Web-based database for facial expression analysis | [pdf] | 2005 IEEE International Conference on Multimedia and Expo | | | | | | 47% | 464 | 217 | 247 | 45 | 282 | 188 |
| 75da1df4ed319926c544eefe17ec8d720feef8c0 | fddb | FDDB | FDDB: A benchmark for face detection in unconstrained settings | [pdf] | Unknown | | | | | | 56% | 380 | 214 | 166 | 22 | 202 | 164 |
| 6dd0597f8513dc100cd0bc1b493768cde45098a9 | stickmen_buffy | Buffy Stickmen | Learning to parse images of articulated bodies | [pdf] | Unknown | | | | | | 56% | 369 | 207 | 161 | 32 | 237 | 131 |
| 6dd0597f8513dc100cd0bc1b493768cde45098a9 | stickmen_pascal | Stickmen PASCAL | Learning to parse images of articulated bodies | [pdf] | Unknown | | | | | | 56% | 369 | 207 | 161 | 32 | 237 | 131 |
| 6dd0597f8513dc100cd0bc1b493768cde45098a9 | stickmen_pascal | Stickmen PASCAL | Learning to parse images of articulated bodies | [pdf] | Unknown | | | | | | 56% | 369 | 207 | 161 | 32 | 237 | 131 |
| 95f12d27c3b4914e0668a268360948bce92f7db3 | helen | Helen | Interactive Facial Feature Localization | [pdf] | Unknown | company | Facebook | United States | 37.39367170 | -122.08072620 | 57% | 352 | 200 | 152 | 26 | 212 | 146 |
| 044d9a8c61383312cdafbcc44b9d00d650b21c70 | fiw_300 | 300-W | 300 Faces in-the-Wild Challenge: The First Facial Landmark Localization Challenge | [pdf] | 2013 IEEE International Conference on Computer Vision Workshops | | | | | | 61% | 323 | 198 | 125 | 27 | 208 | 120 |
| 3765df816dc5a061bc261e190acc8bdd9d47bec0 | rafd | RaFD | Presentation and validation of the Radboud Faces Database | [pdf] | Unknown | | | | | | 39% | 487 | 192 | 295 | 39 | 342 | 144 |
| 2724ba85ec4a66de18da33925e537f3902f21249 | cofw | COFW | Robust Face Landmark Estimation under Occlusion | [pdf] | 2013 IEEE International Conference on Computer Vision | edu | California Institute of Technology | United States | 34.13710185 | -118.12527487 | 57% | 325 | 185 | 140 | 19 | 194 | 133 |
| 639937b3a1b8bded3f7e9a40e85bd3770016cf3c | bfm | BFM | A 3D Face Model for Pose and Illumination Invariant Face Recognition | [pdf] | 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance | | | | | | 53% | 343 | 183 | 160 | 25 | 223 | 114 |
| 13f06b08f371ba8b5d31c3e288b4deb61335b462 | eth_andreas_ess | ETHZ Pedestrian | Depth and Appearance for Mobile Scene Analysis | [pdf] | 2007 IEEE 11th International Conference on Computer Vision | edu | ETH Zurich | Switzerland | 47.37631300 | 8.54766990 | 55% | 324 | 179 | 145 | 26 | 193 | 127 |
| a74251efa970b92925b89eeef50a5e37d9281ad0 | aflw | AFLW | Annotated 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) | | | | | | 56% | 318 | 177 | 141 | 35 | 211 | 107 |
| 4b1d23d17476fcf78f4cbadf69fb130b1aa627c0 | leeds_sports_pose | Leeds Sports Pose | Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation | [pdf] | Unknown | | | | | | 61% | 285 | 174 | 111 | 13 | 197 | 93 |
| 4b1d23d17476fcf78f4cbadf69fb130b1aa627c0 | stickmen_buffy | Buffy Stickmen | Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation | [pdf] | Unknown | | | | | | 61% | 285 | 174 | 111 | 13 | 197 | 93 |
| 5981e6479c3fd4e31644db35d236bfb84ae46514 | mot | MOT | Learning to associate: HybridBoosted multi-target tracker for crowded scene | [pdf] | 2009 IEEE Conference on Computer Vision and Pattern Recognition | edu | University of Southern California | United States | 34.02241490 | -118.28634407 | 53% | 326 | 172 | 153 | 27 | 190 | 137 |
| 436f798d1a4e54e5947c1e7d7375c31b2bdb4064 | tud_multiview | TUD-Multiview | Monocular 3D pose estimation and tracking by detection | [pdf] | 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition | edu | TU Darmstadt | Germany | 49.87482770 | 8.65632810 | 55% | 311 | 172 | 139 | 35 | 208 | 105 |
| 436f798d1a4e54e5947c1e7d7375c31b2bdb4064 | tud_stadtmitte | TUD-Stadtmitte | Monocular 3D pose estimation and tracking by detection | [pdf] | 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition | edu | TU Darmstadt | Germany | 49.87482770 | 8.65632810 | 55% | 311 | 172 | 139 | 35 | 208 | 105 |
| 2acf7e58f0a526b957be2099c10aab693f795973 | bosphorus | The Bosphorus | Bosphorus Database for 3D Face Analysis | [pdf] | Unknown | | | | | | 49% | 352 | 171 | 181 | 17 | 162 | 188 |
| 44484d2866f222bbb9b6b0870890f9eea1ffb2d0 | cuhk_campus_03 | CUHK03 Campus | Human Reidentification with Transferred Metric Learning | [pdf] | Unknown | | | | | | 60% | 280 | 168 | 112 | 12 | 139 | 137 |
| 833fa04463d90aab4a9fe2870d480f0b40df446e | sun_attributes | SUN | SUN attribute database: Discovering, annotating, and recognizing scene attributes | [pdf] | 2012 IEEE Conference on Computer Vision and Pattern Recognition | edu | Brown University | United States | 41.82686820 | -71.40123146 | 58% | 264 | 154 | 110 | 27 | 206 | 56 |
| 010f0f4929e6a6644fb01f0e43820f91d0fad292 | yfcc_100m | YFCC100M | YFCC100M: the new data in multimedia research | [pdf] | Commun. ACM | | | | | | 56% | 274 | 154 | 120 | 24 | 172 | 100 |
| 9361b784e73e9238d5cefbea5ac40d35d1e3103f | town_centre | TownCentre | Stable multi-target tracking in real-time surveillance video | [pdf] | CVPR 2011 | | | | | | 47% | 328 | 153 | 175 | 24 | 186 | 140 |
| 6204776d31359d129a582057c2d788a14f8aadeb | youtube_celebrities | YouTube Celebrities | Face tracking and recognition with visual constraints in real-world videos | [pdf] | 2008 IEEE Conference on Computer Vision and Pattern Recognition | edu | Rutgers University | United States | 40.47913175 | -74.43168868 | 53% | 267 | 142 | 124 | 14 | 125 | 121 |
| 2eed184680edcdec8a3b605ad1a3ba8e8f7cc2e9 | graz | Graz Pedestrian | Generic object recognition with boosting | [pdf] | IEEE Transactions on Pattern Analysis and Machine Intelligence | edu | TU Graz | Austria | 47.07071400 | 15.43950400 | 47% | 293 | 138 | 155 | 16 | 195 | 97 |
| 38b55d95189c5e69cf4ab45098a48fba407609b4 | cuhk_campus_03 | CUHK03 Campus | Locally Aligned Feature Transforms across Views | [pdf] | 2013 IEEE Conference on Computer Vision and Pattern Recognition | | | | | | 52% | 258 | 135 | 123 | 17 | 136 | 117 |
| e8de844fefd54541b71c9823416daa238be65546 | visual_phrases | Phrasal Recognition | Recognition using visual phrases | [pdf] | CVPR 2011 | edu | University of Illinois, Urbana-Champaign | United States | 40.11116745 | -88.22587665 | 55% | 246 | 135 | 111 | 18 | 170 | 68 |
| 7808937b46acad36e43c30ae4e9f3fd57462853d | bpad | BPAD | Describing people: A poselet-based approach to attribute classification | [pdf] | 2011 International Conference on Computer Vision | | | | | | 56% | 230 | 128 | 102 | 14 | 163 | 66 |
| 0c91808994a250d7be332400a534a9291ca3b60e | graz | Graz Pedestrian | Weak Hypotheses and Boosting for Generic Object Detection and Recognition | [pdf] | Unknown | | | | | | 54% | 236 | 127 | 109 | 17 | 161 | 77 |
| 4c170a0dcc8de75587dae21ca508dab2f9343974 | face_tracer | FaceTracer | FaceTracer: A Search Engine for Large Collections of Images with Faces | [pdf] | Unknown | | | | | | 56% | 225 | 126 | 99 | 17 | 146 | 77 |
| 140c95e53c619eac594d70f6369f518adfea12ef | ijb_a | IJB-A | Pushing the frontiers of unconstrained face detection and recognition: IARPA Janus Benchmark A | [pdf] | 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) | | | | | | 52% | 237 | 123 | 114 | 22 | 159 | 76 |
| 98bb029afe2a1239c3fdab517323066f0957b81b | ilids_mcts_vid | iLIDS-VID | Person Re-identification by Video Ranking | [pdf] | Unknown | | | | | | 59% | 209 | 123 | 86 | 9 | 111 | 97 |
| 98bb029afe2a1239c3fdab517323066f0957b81b | sdu_vid | SDU-VID | Person Re-identification by Video Ranking | [pdf] | Unknown | | | | | | 59% | 209 | 123 | 86 | 9 | 111 | 97 |
| 35b0331dfcd2897abd5749b49ff5e2b8ba0f7a62 | coco_qa | COCO QA | Exploring Models and Data for Image Question Answering | [pdf] | Unknown | | | | | | 56% | 206 | 115 | 91 | 11 | 162 | 39 |
| 46a01565e6afe7c074affb752e7069ee3bf2e4ef | sdu_vid | SDU-VID | Local Descriptors Encoded by Fisher Vectors for Person Re-identification | [pdf] | Unknown | | | | | | 58% | 197 | 114 | 83 | 16 | 108 | 88 |
| b1f4423c227fa37b9680787be38857069247a307 | afew_va | AFEW-VA | Collecting Large, Richly Annotated Facial-Expression Databases from Movies | [pdf] | IEEE MultiMedia | edu | Australian National University | Australia | -35.27769990 | 149.11852700 | 61% | 181 | 111 | 70 | 8 | 87 | 97 |
| 570f37ed63142312e6ccdf00ecc376341ec72b9f | stanford_drone | Stanford Drone | Social LSTM: Human Trajectory Prediction in Crowded Spaces | [pdf] | 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) | | | | | | 49% | 224 | 110 | 114 | 3 | 140 | 81 |
| 013909077ad843eb6df7a3e8e290cfd5575999d2 | fiw_300 | 300-W | A Semi-automatic Methodology for Facial Landmark Annotation | [pdf] | 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops | | | | | | 58% | 184 | 107 | 77 | 14 | 120 | 67 |
| 18010284894ed0edcca74e5bf768ee2e15ef7841 | deep_fashion | DeepFashion | DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations | [pdf] | 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) | | | | | | 60% | 176 | 105 | 71 | 2 | 113 | 62 |
| 22ad2c8c0f4d6aa4328b38d894b814ec22579761 | gallagher | Gallagher | Clothing cosegmentation for recognizing people | [pdf] | 2008 IEEE Conference on Computer Vision and Pattern Recognition | edu | Carnegie Mellon University Silicon Valley | United States | 37.41021930 | -122.05965487 | 59% | 178 | 105 | 73 | 7 | 100 | 86 |
| 52d7eb0fbc3522434c13cc247549f74bb9609c5d | wider_face | WIDER FACE | WIDER FACE: A Face Detection Benchmark | [pdf] | 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) | edu | Chinese University of Hong Kong | China | 22.41626320 | 114.21093180 | 58% | 178 | 104 | 74 | 13 | 112 | 66 |
| 4e4746094bf60ee83e40d8597a6191e463b57f76 | leeds_sports_pose_extended | Leeds Sports Pose Extended | Learning effective human pose estimation from inaccurate annotation | [pdf] | CVPR 2011 | edu | University of Leeds | United Kingdom | 53.80387185 | -1.55245712 | 61% | 169 | 103 | 66 | 8 | 108 | 65 |
| 133f01aec1534604d184d56de866a4bd531dac87 | lfw | LFW | Effective Unconstrained Face Recognition by Combining Multiple Descriptors and Learned Background Statistics | [pdf] | IEEE Transactions on Pattern Analysis and Machine Intelligence | | | | | | 55% | 183 | 101 | 82 | 14 | 103 | 77 |
| 1aad2da473888cb7ebc1bfaa15bfa0f1502ce005 | jpl_pose | JPL-Interaction dataset | First-Person Activity Recognition: What Are They Doing to Me? | [pdf] | 2013 IEEE Conference on Computer Vision and Pattern Recognition | | | | | | 66% | 148 | 98 | 50 | 7 | 105 | 43 |
| 56ffa7d906b08d02d6d5a12c7377a57e24ef3391 | unbc_shoulder_pain | UNBC-McMaster Pain | Painful data: The UNBC-McMaster shoulder pain expression archive database | [pdf] | Face and Gesture 2011 | edu | Carnegie Mellon University Silicon Valley | United States | 37.41021930 | -122.05965487 | 51% | 189 | 97 | 92 | 22 | 108 | 78 |
| 0df0d1adea39a5bef318b74faa37de7f3e00b452 | mpii_gaze | MPIIGaze | Appearance-based gaze estimation in the wild | [pdf] | 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) | | | | | | 64% | 149 | 95 | 54 | 3 | 94 | 54 |
| 32c801cb7fbeb742edfd94cccfca4934baec71da | ucf_crowd | UCF-CC-50 | Multi-source Multi-scale Counting in Extremely Dense Crowd Images | [pdf] | 2013 IEEE Conference on Computer Vision and Pattern Recognition | | | | | | 64% | 148 | 95 | 53 | 5 | 80 | 65 |
| 29a705a5fa76641e0d8963f1fdd67ee4c0d92d3d | scface | SCface | SCface – surveillance cameras face database | [pdf] | Multimedia Tools and Applications | | | | | | 53% | 179 | 94 | 85 | 15 | 88 | 89 |
| 1be498d4bbc30c3bfd0029114c784bc2114d67c0 | adience | Adience | Age and Gender Estimation of Unfiltered Faces | [pdf] | IEEE Transactions on Information Forensics and Security | | | | | | 52% | 179 | 93 | 86 | 7 | 98 | 80 |
| 27a2fad58dd8727e280f97036e0d2bc55ef5424c | duke_mtmc | Duke MTMC | Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking | [pdf] | Unknown | edu | Duke University | United States | 35.99905220 | -78.92906290 | 55% | 169 | 93 | 76 | 6 | 113 | 54 |
| 27a2fad58dd8727e280f97036e0d2bc55ef5424c | mot | MOT | Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking | [pdf] | Unknown | edu | Duke University | United States | 35.99905220 | -78.92906290 | 55% | 169 | 93 | 76 | 6 | 113 | 54 |
| 5a5f0287484f0d480fed1ce585dbf729586f0edc | disfa | DISFA | DISFA: A Spontaneous Facial Action Intensity Database | [pdf] | IEEE Transactions on Affective Computing | edu | University of Denver | United States | 39.67665410 | -104.96220300 | 49% | 184 | 90 | 94 | 19 | 96 | 89 |
| 4f93cd09785c6e77bf4bc5a788e079df524c8d21 | soton | SOTON HiD | On a Large Sequence-Based Human Gait Database | [pdf] | Unknown | | | | | | 60% | 150 | 90 | 60 | 17 | 103 | 51 |
| 291265db88023e92bb8c8e6390438e5da148e8f5 | msceleb | MsCeleb | MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition | [pdf] | Unknown | | | | | | 49% | 180 | 88 | 92 | 16 | 120 | 59 |
| 3b5b6d19d4733ab606c39c69a889f9e67967f151 | qmul_grid | GRID | Multi-camera activity correlation analysis | [pdf] | 2009 IEEE Conference on Computer Vision and Pattern Recognition | edu | Queen Mary University of London | United Kingdom | 51.52472720 | -0.03931035 | 62% | 142 | 88 | 54 | 7 | 77 | 64 |
| c0387e788a52f10bf35d4d50659cfa515d89fbec | mars | MARS | MARS: A Video Benchmark for Large-Scale Person Re-Identification | [pdf] | Unknown | | | | | | 51% | 168 | 85 | 83 | 5 | 97 | 69 |
| 066000d44d6691d27202896691f08b27117918b9 | psu | PSU | Vision-Based Analysis of Small Groups in Pedestrian Crowds | [pdf] | IEEE Transactions on Pattern Analysis and Machine Intelligence | | | | | | 49% | 168 | 83 | 85 | 11 | 85 | 79 |
| 8b56e33f33e582f3e473dba573a16b598ed9bcdc | fei | FEI | A new ranking method for principal components analysis and its application to face image analysis | [pdf] | Image Vision Comput. | | | | | | 47% | 169 | 80 | 89 | 6 | 69 | 102 |
| 10195a163ab6348eef37213a46f60a3d87f289c5 | imdb_wiki | IMDB | Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks | [pdf] | International Journal of Computer Vision | edu | ETH Zurich | Switzerland | 47.37631300 | 8.54766990 | 52% | 145 | 76 | 69 | 11 | 93 | 51 |
| 96e0cfcd81cdeb8282e29ef9ec9962b125f379b0 | megaface | MegaFace | The MegaFace Benchmark: 1 Million Faces for Recognition at Scale | [pdf] | 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) | | | | | | 53% | 139 | 74 | 65 | 9 | 100 | 37 |
| 5a4df9bef1872865f0b619ac3aacc97f49e4a035 | cuhk_train_station | CUHK Train Station Dataset | Understanding collective crowd behaviors: Learning a Mixture model of Dynamic pedestrian-Agents | [pdf] | 2012 IEEE Conference on Computer Vision and Pattern Recognition | edu | Chinese University of Hong Kong | China | 22.41626320 | 114.21093180 | 52% | 141 | 73 | 68 | 6 | 60 | 75 |
| 0486214fb58ee9a04edfe7d6a74c6d0f661a7668 | chokepoint | ChokePoint | Patch-based probabilistic image quality assessment for face selection and improved video-based face recognition | [pdf] | CVPR 2011 WORKSHOPS | | | | | | 52% | 138 | 72 | 66 | 6 | 76 | 63 |
| b91f54e1581fbbf60392364323d00a0cd43e493c | bp4d_spontanous | BP4D-Spontanous | A high-resolution spontaneous 3D dynamic facial expression database | [pdf] | 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG) | edu | SUNY Binghamton | United States | 42.08779975 | -75.97066066 | 46% | 154 | 71 | 83 | 7 | 80 | 75 |
| 2d3482dcff69c7417c7b933f22de606a0e8e42d4 | lfw | LFW | Labeled Faces in the Wild : Updates and New Reporting Procedures | [pdf] | Unknown | | | | | | 58% | 123 | 71 | 52 | 3 | 71 | 51 |
| 66e6f08873325d37e0ec20a4769ce881e04e964e | sun_attributes | SUN | The SUN Attribute Database: Beyond Categories for Deeper Scene Understanding | [pdf] | International Journal of Computer Vision | | | | | | 59% | 116 | 69 | 47 | 14 | 84 | 31 |
| 8355d095d3534ef511a9af68a3b2893339e3f96b | imdb_wiki | IMDB | DEX: Deep EXpectation of Apparent Age from a Single Image | [pdf] | 2015 IEEE International Conference on Computer Vision Workshop (ICCVW) | | | | | | 56% | 122 | 68 | 54 | 6 | 75 | 48 |
| 0d3bb75852098b25d90f31d2f48fd0cb4944702b | face_scrub | FaceScrub | A data-driven approach to cleaning large face datasets | [pdf] | 2014 IEEE International Conference on Image Processing (ICIP) | | | | | | 49% | 138 | 67 | 71 | 5 | 95 | 41 |
| e4754afaa15b1b53e70743880484b8d0736990ff | fiw_300 | 300-W | 300 Faces In-The-Wild Challenge: database and results | [pdf] | Image Vision Comput. | | | | | | 51% | 129 | 66 | 63 | 9 | 74 | 55 |
| 2e8d0f1802e50cccfd3c0aabac0d0beab3a7846e | 3dpes | 3DPeS | 3DPeS: 3D people dataset for surveillance and forensics | [pdf] | Unknown | | | | | | 49% | 133 | 65 | 68 | 11 | 73 | 58 |
| 04c2cda00e5536f4b1508cbd80041e9552880e67 | hipsterwars | Hipsterwars | Hipster Wars: Discovering Elements of Fashion Styles | [pdf] | Unknown | | | | | | 63% | 95 | 60 | 35 | 4 | 59 | 35 |
| 3b4ec8af470948a72a6ed37a9fd226719a874ebc | sdu_vid | SDU-VID | A Spatio-Temporal Appearance Representation for Video-Based Pedestrian Re-Identification | [pdf] | 2015 IEEE International Conference on Computer Vision (ICCV) | | | | | | 60% | 95 | 57 | 38 | 7 | 50 | 45 |
| a0fd85b3400c7b3e11122f44dc5870ae2de9009a | mafl | MAFL | Learning Deep Representation for Face Alignment with Auxiliary Attributes | [pdf] | IEEE Transactions on Pattern Analysis and Machine Intelligence | | | | | | 52% | 108 | 56 | 52 | 11 | 66 | 44 |
| a0fd85b3400c7b3e11122f44dc5870ae2de9009a | mtfl | MTFL | Learning Deep Representation for Face Alignment with Auxiliary Attributes | [pdf] | IEEE Transactions on Pattern Analysis and Machine Intelligence | | | | | | 52% | 108 | 56 | 52 | 11 | 66 | 44 |
| 06f02199690961ba52997cde1527e714d2b3bf8f | columbia_gaze | Columbia Gaze | Gaze locking: passive eye contact detection for human-object interaction | [pdf] | Unknown | edu | Columbia University | United States | 40.84198360 | -73.94368971 | 70% | 79 | 55 | 24 | 0 | 49 | 34 |
| 08f6745bc6c1b0fb68953ea61054bdcdde6d2fc7 | kin_face | UB KinFace | Understanding Kin Relationships in a Photo | [pdf] | IEEE Transactions on Multimedia | | | | | | 59% | 94 | 55 | 39 | 1 | 33 | 61 |
| 2a4bbee0b4cf52d5aadbbc662164f7efba89566c | peta | PETA | Pedestrian Attribute Recognition At Far Distance | [pdf] | Unknown | | | | | | 62% | 88 | 55 | 33 | 1 | 50 | 36 |
| 3394168ff0719b03ff65bcea35336a76b21fe5e4 | penn_fudan | Penn Fudan | Object Detection Combining Recognition and Segmentation | [pdf] | Unknown | | | | | | 50% | 105 | 53 | 52 | 9 | 58 | 43 |
| 4df3143922bcdf7db78eb91e6b5359d6ada004d2 | cfd | CFD | The Chicago face database: A free stimulus set of faces and norming data. | [pdf] | Behavior research methods | | | | | | 53% | 99 | 52 | 47 | 1 | 73 | 21 |
| 0c4a139bb87c6743c7905b29a3cfec27a5130652 | feret | FERET | The FERET Verification Testing Protocol for Face Recognition Algorithms | [pdf] | Unknown | | | | | | 43% | 115 | 49 | 66 | 8 | 75 | 37 |
| 3cd40bfa1ff193a96bde0207e5140a399476466c | tvhi | TVHI | High Five: Recognising human interactions in TV shows | [pdf] | Unknown | | | | | | 50% | 98 | 49 | 49 | 10 | 66 | 28 |
| 5194cbd51f9769ab25260446b4fa17204752e799 | violent_flows | Violent Flows | Violent flows: Real-time detection of violent crowd behavior | [pdf] | 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops | edu | Open University of Israel | Israel | 32.77824165 | 34.99565673 | 55% | 88 | 48 | 40 | 6 | 45 | 44 |
| 2160788824c4c29ffe213b2cbeb3f52972d73f37 | 3d_rma | 3D-RMA | Automatic 3D face authentication | [pdf] | Image Vision Comput. | | | | | | 47% | 100 | 47 | 53 | 8 | 63 | 36 |
| 7de6e81d775e9cd7becbfd1bd685f4e2a5eebb22 | lfw | LFW | Labeled Faces in the Wild: A Survey | [pdf] | Unknown | | | | | | 43% | 109 | 47 | 62 | 9 | 66 | 43 |
| 2ce2560cf59db59ce313bbeb004e8ce55c5ce928 | texas_3dfrd | Texas 3DFRD | Anthropometric 3D Face Recognition | [pdf] | International Journal of Computer Vision | | | | | | 52% | 91 | 47 | 44 | 5 | 60 | 31 |
| 0b440695c822a8e35184fb2f60dcdaa8a6de84ae | kinectface | KinectFaceDB | KinectFaceDB: A Kinect Database for Face Recognition | [pdf] | IEEE Transactions on Systems, Man, and Cybernetics: Systems | edu | University of North Carolina at Chapel Hill | United States | 35.91139710 | -79.05045290 | 56% | 82 | 46 | 36 | 6 | 28 | 52 |
| 0dc11a37cadda92886c56a6fb5191ded62099c28 | stickmen_family | We Are Family Stickmen | We Are Family: Joint Pose Estimation of Multiple Persons | [pdf] | Unknown | | | | | | 58% | 78 | 45 | 33 | 6 | 54 | 23 |
| 7f23a4bb0c777dd72cca7665a5f370ac7980217e | duke_mtmc | Duke MTMC | Improving Person Re-identification by Attribute and Identity Learning | [pdf] | CoRR | | | | | | 51% | 87 | 44 | 43 | 0 | 43 | 42 |
| 2edb87494278ad11641b6cf7a3f8996de12b8e14 | qmul_grid | GRID | Time-Delayed Correlation Analysis for Multi-Camera Activity Understanding | [pdf] | International Journal of Computer Vision | edu | Queen Mary University of London | United Kingdom | 51.52472720 | -0.03931035 | 52% | 84 | 44 | 40 | 6 | 51 | 33 |
| ae0aee03d946efffdc7af2362a42d3750e7dd48a | put_face | Put Face | The put face database | [pdf] | Unknown | | | | | | 43% | 99 | 43 | 56 | 7 | 54 | 48 |
| 2bf8541199728262f78d4dced6fb91479b39b738 | clothing_co_parsing | CCP | Clothing Co-parsing by Joint Image Segmentation and Labeling | [pdf] | 2014 IEEE Conference on Computer Vision and Pattern Recognition | | | | | | 70% | 60 | 42 | 18 | 0 | 34 | 28 |
| 70c59dc3470ae867016f6ab0e008ac8ba03774a1 | vgg_faces2 | VGG Face2 | VGGFace2: A Dataset for Recognising Faces across Pose and Age | [pdf] | 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018) | | | | | | 49% | 83 | 41 | 42 | 6 | 61 | 20 |
| 636b8ffc09b1b23ff714ac8350bb35635e49fa3c | caltech_10k_web_faces | Caltech 10K Web Faces | Pruning training sets for learning of object categories | [pdf] | 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) | | | | | | 63% | 63 | 40 | 23 | 5 | 42 | 20 |
| c900e0ad4c95948baaf0acd8449fde26f9b4952a | emotio_net | EmotioNet Database | EmotioNet: 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) | | | | | | 47% | 86 | 40 | 46 | 7 | 54 | 29 |
| 4793f11fbca4a7dba898b9fff68f70d868e2497c | kin_face | UB KinFace | Kinship Verification through Transfer Learning | [pdf] | Unknown | | | | | | 56% | 71 | 40 | 31 | 2 | 29 | 42 |
| 47aeb3b82f54b5ae8142b4bdda7b614433e69b9a | am_fed | AM-FED | Affectiva-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 Workshops | | | | | | 45% | 83 | 37 | 46 | 6 | 43 | 39 |
| 0a85bdff552615643dd74646ac881862a7c7072d | pipa | PIPA | Beyond frontal faces: Improving Person Recognition using multiple cues | [pdf] | 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) | company | Facebook | United States | 37.39367170 | -122.08072620 | 69% | 54 | 37 | 16 | 1 | 41 | 12 |
| 6618cff7f2ed440a0d2fa9e74ad5469df5cdbe4c | afad | AFAD | Ordinal Regression with Multiple Output CNN for Age Estimation | [pdf] | 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) | | | | | | 45% | 78 | 35 | 43 | 8 | 44 | 31 |
| 0b84f07af44f964817675ad961def8a51406dd2e | prw | PRW | Person Re-identification in the Wild | [pdf] | 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) | | | | | | 45% | 77 | 35 | 42 | 2 | 47 | 27 |
| 31de9b3dd6106ce6eec9a35991b2b9083395fd0b | feret | FERET | FERET ( Face Recognition Technology ) Recognition Algorithm Development and Test Results | [pdf] | Unknown | | | | | | 45% | 75 | 34 | 41 | 5 | 54 | 20 |
| fcc6fe6007c322641796cb8792718641856a22a7 | miw | MIW | Automatic facial makeup detection with application in face recognition | [pdf] | 2013 International Conference on Biometrics (ICB) | edu | West Virginia University | United States | 39.65404635 | -79.96475355 | 69% | 49 | 34 | 15 | 1 | 19 | 29 |
| f1af714b92372c8e606485a3982eab2f16772ad8 | mug_faces | MUG Faces | The MUG facial expression database | [pdf] | 11th International Workshop on Image Analysis for Multimedia Interactive Services WIAMIS 10 | edu | Aristotle University of Thessaloniki | Greece | 40.62984145 | 22.95889350 | 41% | 82 | 34 | 48 | 5 | 34 | 47 |
| fcc6fe6007c322641796cb8792718641856a22a7 | youtube_makeup | YMU | Automatic facial makeup detection with application in face recognition | [pdf] | 2013 International Conference on Biometrics (ICB) | edu | West Virginia University | United States | 39.65404635 | -79.96475355 | 69% | 49 | 34 | 15 | 1 | 19 | 29 |
| 51eba481dac6b229a7490f650dff7b17ce05df73 | imsitu | imSitu | Situation Recognition: Visual Semantic Role Labeling for Image Understanding | [pdf] | 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) | | | | | | 62% | 52 | 32 | 20 | 1 | 46 | 6 |
| 8be57cdad86fdf8c8290df4ca3149592f3c46dd3 | m2vts | m2vts | The M2VTS Multimodal Face Database (Release 1.00) | [pdf] | Unknown | | | | | | 44% | 73 | 32 | 41 | 2 | 39 | 33 |
| 37d6f0eb074d207b53885bd2eb78ccc8a04be597 | vmu | VMU | Can facial cosmetics affect the matching accuracy of face recognition systems? | [pdf] | 2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS) | edu | West Virginia University | United States | 39.65404635 | -79.96475355 | 60% | 53 | 32 | 21 | 0 | 19 | 31 |
| 37d6f0eb074d207b53885bd2eb78ccc8a04be597 | youtube_makeup | YMU | Can facial cosmetics affect the matching accuracy of face recognition systems? | [pdf] | 2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS) | edu | West Virginia University | United States | 39.65404635 | -79.96475355 | 60% | 53 | 32 | 21 | 0 | 19 | 31 |
| f41c7bb02fc97d5fb9cadd7a49c3e558a1c58a44 | pa_100k | PA-100K | HydraPlus-Net: Attentive Deep Features for Pedestrian Analysis | [pdf] | 2017 IEEE International Conference on Computer Vision (ICCV) | | | | | | 56% | 55 | 31 | 24 | 0 | 36 | 17 |
| faf40ce28857aedf183e193486f5b4b0a8c478a2 | iit_dehli_ear | IIT Dehli Ear | Automated Human Identification Using Ear Imaging | [pdf] | Unknown | | | | | | 38% | 80 | 30 | 50 | 6 | 35 | 44 |
| 18858cc936947fc96b5c06bbe3c6c2faa5614540 | pilot_parliament | PPB | Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification | [pdf] | Unknown | | | | | | 49% | 59 | 29 | 30 | 0 | 47 | 10 |
| 09d78009687bec46e70efcf39d4612822e61cb8c | raid | RAiD | Consistent Re-identification in a Camera Network | [pdf] | Unknown | | | | | | 59% | 49 | 29 | 20 | 5 | 34 | 13 |
| 4d58f886f5150b2d5e48fd1b5a49e09799bf895d | texas_3dfrd | Texas 3DFRD | Texas 3D Face Recognition Database | [pdf] | 2010 IEEE Southwest Symposium on Image Analysis & Interpretation (SSIAI) | | | | | | 44% | 66 | 29 | 37 | 3 | 40 | 27 |
| 16e8b0a1e8451d5f697b94c0c2b32a00abee1d52 | umb | UMB | UMB-DB: A database of partially occluded 3D faces | [pdf] | 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops) | | | | | | 62% | 47 | 29 | 18 | 2 | 22 | 24 |
| 356b431d4f7a2a0a38cf971c84568207dcdbf189 | wider | WIDER | Recognize complex events from static images by fusing deep channels | [pdf] | 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) | | | | | | 66% | 44 | 29 | 15 | 1 | 29 | 15 |
| 3dc3f0b64ef80f573e3a5f96e456e52ee980b877 | georgia_tech_face_database | Georgia Tech Face | Maximum Likelihood Training of the Embedded HMM for Face Detection and Recognition | [pdf] | Unknown | | | | | | 42% | 67 | 28 | 39 | 4 | 29 | 28 |
| 79828e6e9f137a583082b8b5a9dfce0c301989b8 | mapillary | Mapillary | The Mapillary Vistas Dataset for Semantic Understanding of Street Scenes | [pdf] | 2017 IEEE International Conference on Computer Vision (ICCV) | | | | | | 46% | 61 | 28 | 33 | 0 | 43 | 16 |
| 8b2dd5c61b23ead5ae5508bb8ce808b5ea266730 | 10k_US_adult_faces | 10K US Adult Faces | The intrinsic memorability of face photographs. | [pdf] | Journal of experimental psychology. General | | | | | | 52% | 52 | 27 | 25 | 2 | 36 | 14 |
| 9c23859ec7313f2e756a3e85575735e0c52249f4 | facebook_100 | Facebook100 | Scaling up biologically-inspired computer vision: A case study in unconstrained face recognition on facebook | [pdf] | CVPR 2011 WORKSHOPS | edu | Harvard University | United States | 42.36782045 | -71.12666653 | 52% | 52 | 27 | 25 | 3 | 38 | 13 |
| 9c23859ec7313f2e756a3e85575735e0c52249f4 | pubfig_83 | pubfig83 | Scaling up biologically-inspired computer vision: A case study in unconstrained face recognition on facebook | [pdf] | CVPR 2011 WORKSHOPS | edu | Harvard University | United States | 42.36782045 | -71.12666653 | 52% | 52 | 27 | 25 | 3 | 38 | 13 |
| 22646e00a7ba34d1b5fbe3b1efcd91a1e1be3c2b | saivt | SAIVT SoftBio | A Database for Person Re-Identification in Multi-Camera Surveillance Networks | [pdf] | 2012 International Conference on Digital Image Computing Techniques and Applications (DICTA) | | | | | | 42% | 65 | 27 | 38 | 7 | 45 | 20 |
| 070de852bc6eb275d7ca3a9cdde8f6be8795d1a3 | d3dfacs | D3DFACS | A FACS valid 3D dynamic action unit database with applications to 3D dynamic morphable facial modeling | [pdf] | 2011 International Conference on Computer Vision | | | | | | 50% | 50 | 25 | 25 | 5 | 31 | 18 |
| 298cbc3dfbbb3a20af4eed97906650a4ea1c29e0 | ferplus | FER+ | Training deep networks for facial expression recognition with crowd-sourced label distribution | [pdf] | Unknown | | | | | | 71% | 34 | 24 | 10 | 0 | 18 | 16 |
| 2161f6b7ee3c0acc81603b01dc0df689683577b9 | large_scale_person_search | Large Scale Person Search | End-to-End Deep Learning for Person Search | [pdf] | CoRR | | | | | | 52% | 46 | 24 | 22 | 1 | 27 | 16 |
| 6f3c76b7c0bd8e1d122c6ea808a271fd4749c951 | ward | WARD | Re-identify people in wide area camera network | [pdf] | 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops | edu | University of Udine | Italy | 46.08107230 | 13.21194740 | 40% | 60 | 24 | 36 | 1 | 38 | 21 |
| 758d7e1be64cc668c59ef33ba8882c8597406e53 | affectnet | AffectNet | AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild | [pdf] | CoRR | | | | | | 59% | 37 | 22 | 15 | 0 | 25 | 11 |
| 1bd1645a629f1b612960ab9bba276afd4cf7c666 | brainwash | Brainwash | End-to-End People Detection in Crowded Scenes | [pdf] | 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) | | | | | | 50% | 42 | 21 | 21 | 1 | 19 | 19 |
| 47662d1a368daf70ba70ef2d59eb6209f98b675d | fia | CMU FiA | The CMU Face In Action (FIA) Database | [pdf] | Unknown | | | | | | 39% | 54 | 21 | 33 | 5 | 40 | 16 |
| fd8168f1c50de85bac58a8d328df0a50248b16ae | nd_2006 | ND-2006 | Using a Multi-Instance Enrollment Representation to Improve 3D Face Recognition | [pdf] | 2007 First IEEE International Conference on Biometrics: Theory, Applications, and Systems | edu | University of Notre Dame | United States | 41.70456775 | -86.23822026 | 60% | 35 | 21 | 14 | 3 | 18 | 15 |
| 31b05f65405534a696a847dd19c621b7b8588263 | umd_faces | UMD | UMDFaces: An annotated face dataset for training deep networks | [pdf] | 2017 IEEE International Joint Conference on Biometrics (IJCB) | | | | | | 50% | 42 | 21 | 21 | 5 | 30 | 11 |
| 53ae38a6bb2b21b42bac4f0c4c8ed1f9fa02f9d4 | bp4d_plus | BP4D+ | Multimodal Spontaneous Emotion Corpus for Human Behavior Analysis | [pdf] | 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) | | | | | | 48% | 42 | 20 | 22 | 0 | 17 | 26 |
| 1c2802c2199b6d15ecefe7ba0c39bfe44363de38 | youtube_poses | YouTube Pose | Personalizing Human Video Pose Estimation | [pdf] | 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) | | | | | | 56% | 36 | 20 | 16 | 2 | 30 | 8 |
| 488e475eeb3bb39a145f23ede197cd3620f1d98a | apis | APiS1.0 | Pedestrian Attribute Classification in Surveillance: Database and Evaluation | [pdf] | 2013 IEEE International Conference on Computer Vision Workshops | | | | | | 68% | 28 | 19 | 9 | 0 | 13 | 15 |
| d08cc366a4a0192a01e9a7495af1eb5d9f9e73ae | b3d_ac | B3D(AC) | A 3-D Audio-Visual Corpus of Affective Communication | [pdf] | IEEE Transactions on Multimedia | | | | | | 45% | 42 | 19 | 23 | 2 | 26 | 15 |
| 57fe081950f21ca03b5b375ae3e84b399c015861 | cvc_01_barcelona | CVC-01 | Adaptive Image Sampling and Windows Classification for On-board Pedestrian Detection | [pdf] | Unknown | | | | | | 40% | 47 | 19 | 28 | 1 | 23 | 24 |
| 28d4e027c7e90b51b7d8908fce68128d1964668a | megaface | MegaFace | Level Playing Field for Million Scale Face Recognition | [pdf] | 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) | | | | | | 49% | 39 | 19 | 20 | 2 | 29 | 9 |
| 41976ebc8ab76d9a6861487c97cc7fcbe3b6015f | moments_in_time | Moments in Time | Moments in Time Dataset: one million videos for event understanding | [pdf] | CoRR | | | | | | 66% | 29 | 19 | 10 | 2 | 27 | 2 |
| 488e475eeb3bb39a145f23ede197cd3620f1d98a | svs | SVS | Pedestrian Attribute Classification in Surveillance: Database and Evaluation | [pdf] | 2013 IEEE International Conference on Computer Vision Workshops | | | | | | 68% | 28 | 19 | 9 | 0 | 13 | 15 |
| 0ceda9dae8b9f322df65ca2ef02caca9758aec6f | casablanca | Casablanca | Context-Aware CNNs for Person Head Detection | [pdf] | 2015 IEEE International Conference on Computer Vision (ICCV) | | | | | | 55% | 33 | 18 | 15 | 1 | 23 | 11 |
| 4fefd1bc8dc4e0ab37ee3324ddfa43ad9d6a04a7 | deep_fashion | DeepFashion | Fashion Landmark Detection in the Wild | [pdf] | Unknown | | | | | | 69% | 26 | 18 | 8 | 1 | 16 | 10 |
| 0ceda9dae8b9f322df65ca2ef02caca9758aec6f | hollywood_headset | HollywoodHeads | Context-Aware CNNs for Person Head Detection | [pdf] | 2015 IEEE International Conference on Computer Vision (ICCV) | | | | | | 55% | 33 | 18 | 15 | 1 | 23 | 11 |
| 84fe5b4ac805af63206012d29523a1e033bc827e | awe_ears | AWE Ears | Ear Recognition: More Than a Survey | [pdf] | Neurocomputing | | | | | | 65% | 26 | 17 | 9 | 0 | 10 | 16 |
| 213a579af9e4f57f071b884aa872651372b661fd | bbc_pose | BBC Pose | Automatic and Efficient Human Pose Estimation for Sign Language Videos | [pdf] | International Journal of Computer Vision | | | | | | 65% | 26 | 17 | 9 | 1 | 16 | 11 |
| 20388099cc415c772926e47bcbbe554e133343d1 | cafe | #N/A | The Child Affective Facial Expression (CAFE) set: validity and reliability from untrained adults | [pdf] | | | | | | | 46% | 37 | 17 | 20 | 3 | 30 | 7 |
| d178cde92ab3dc0dd2ebee5a76a33d556c39448b | jiku_mobile | Jiku Mobile Video Dataset | The jiku mobile video dataset | [pdf] | Unknown | edu | National University of Singapore | Singapore | 1.29620180 | 103.77689944 | 71% | 24 | 17 | 7 | 0 | 6 | 19 |
| 2eb84aaba316b095d4bb51da1a3e4365bbf9ab1d | kin_face | UB KinFace | Genealogical face recognition based on UB KinFace database | [pdf] | CVPR 2011 WORKSHOPS | edu | SUNY Buffalo | United States | 42.93362780 | -78.88394479 | 55% | 31 | 17 | 14 | 0 | 11 | 21 |
| a0cc5f73a37723a6dd465924143f1cb4976d0169 | msmt_17 | MSMT17 | Person Transfer GAN to Bridge Domain Gap for Person Re-identification | [pdf] | 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition | | | | | | 71% | 24 | 17 | 7 | 1 | 20 | 4 |
| e27ef52c641c2b5100a1b34fd0b819e84a31b4df | sarc3d | Sarc3D | SARC3D: A New 3D Body Model for People Tracking and Re-identification | [pdf] | Unknown | | | | | | 50% | 34 | 17 | 17 | 3 | 21 | 12 |
| 4946ba10a4d5a7d0a38372f23e6622bd347ae273 | coco_action | COCO-a | Describing Common Human Visual Actions in Images | [pdf] | Unknown | | | | | | 64% | 25 | 16 | 9 | 0 | 23 | 2 |
| b6c293f0420f7e945b5916ae44269fb53e139275 | erce | ERCe | Video Synopsis by Heterogeneous Multi-source Correlation | [pdf] | 2013 IEEE International Conference on Computer Vision | | | | | | 55% | 29 | 16 | 13 | 2 | 14 | 13 |
| b6c293f0420f7e945b5916ae44269fb53e139275 | tisi | Times Square Intersection | Video Synopsis by Heterogeneous Multi-source Correlation | [pdf] | 2013 IEEE International Conference on Computer Vision | | | | | | 55% | 29 | 16 | 13 | 2 | 14 | 13 |
| 1e3df3ca8feab0b36fd293fe689f93bb2aaac591 | immediacy | Immediacy | Multi-task Recurrent Neural Network for Immediacy Prediction | [pdf] | 2015 IEEE International Conference on Computer Vision (ICCV) | | | | | | 58% | 26 | 15 | 11 | 2 | 21 | 6 |
| ea050801199f98a1c7c1df6769f23f658299a3ae | mpi_large | Large MPI Facial Expression | The MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial Expressions | [pdf] | | | | | | | 45% | 33 | 15 | 18 | 4 | 29 | 4 |
| ea050801199f98a1c7c1df6769f23f658299a3ae | mpi_small | Small MPI Facial Expression | The MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial Expressions | [pdf] | | | | | | | 45% | 33 | 15 | 18 | 4 | 29 | 4 |
| d818568838433a6d6831adde49a58cef05e0c89f | agedb | AgeDB | AgeDB: The First Manually Collected, In-the-Wild Age Database | [pdf] | 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) | | | | | | 78% | 18 | 14 | 4 | 0 | 14 | 3 |
| 0297448f3ed948e136bb06ceff10eccb34e5bb77 | ilids_mcts | i-LIDS Multiple-Camera | Imagery 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 Technology | | | | | | 40% | 35 | 14 | 21 | 2 | 21 | 14 |
| 5753b2b5e442eaa3be066daa4a2ca8d8a0bb1725 | 50_people_one_question | 50 People One Question | Merging Pose Estimates Across Space and Time | [pdf] | Unknown | | | | | | 81% | 16 | 13 | 3 | 0 | 13 | 4 |
| 19d1b811df60f86cbd5e04a094b07f32fff7a32a | york_3d | UOY 3D Face Database | Three-dimensional face recognition: an eigensurface approach | [pdf] | 2004 International Conference on Image Processing, 2004. ICIP '04. | | | | | | 34% | 38 | 13 | 25 | 4 | 24 | 13 |
| 0cb2dd5f178e3a297a0c33068961018659d0f443 | ijb_b | IJB-B | IARPA Janus Benchmark-B Face Dataset | [pdf] | 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) | | | | | | 34% | 35 | 12 | 23 | 6 | 25 | 8 |
| 221c18238b829c12b911706947ab38fd017acef7 | rap_pedestrian | RAP | A Richly Annotated Dataset for Pedestrian Attribute Recognition | [pdf] | CoRR | | | | | | 46% | 26 | 12 | 14 | 0 | 16 | 10 |
| 774cbb45968607a027ae4729077734db000a1ec5 | urban_tribes | Urban Tribes | From Bikers to Surfers: Visual Recognition of Urban Tribes | [pdf] | Unknown | | | | | | 67% | 18 | 12 | 6 | 1 | 12 | 6 |
| 44d23df380af207f5ac5b41459c722c87283e1eb | wider_attribute | WIDER Attribute | Human Attribute Recognition by Deep Hierarchical Contexts | [pdf] | Unknown | | | | | | 67% | 18 | 12 | 6 | 0 | 14 | 4 |
| 45e616093a92e5f1e61a7c6037d5f637aa8964af | malf | MALF | Fine-grained evaluation on face detection in the wild | [pdf] | 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG) | | | | | | 65% | 17 | 11 | 6 | 0 | 12 | 5 |
| 4156b7e88f2e0ab0a7c095b9bab199ae2b23bd06 | distance_nighttime | Long Distance Heterogeneous Face | Nighttime Face Recognition at Long Distance: Cross-Distance and Cross-Spectral Matching | [pdf] | Unknown | | | | | | 45% | 22 | 10 | 12 | 3 | 11 | 10 |
| 64e0690dd176a93de9d4328f6e31fc4afe1e7536 | duke_mtmc | Duke MTMC | Tracking Multiple People Online and in Real Time | [pdf] | Unknown | | | | | | 43% | 23 | 10 | 13 | 2 | 12 | 10 |
| ca3e88d87e1344d076c964ea89d91a75c417f5ee | imfdb | IMFDB | Indian 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) | edu | BVBCET, Hubli, India | India | 15.36883320 | 75.12137960 | 59% | 17 | 10 | 7 | 0 | 11 | 5 |
| 2a171f8d14b6b8735001a11c217af9587d095848 | social_relation | Social Relation | Learning Social Relation Traits from Face Images | [pdf] | 2015 IEEE International Conference on Computer Vision (ICCV) | | | | | | 43% | 23 | 10 | 13 | 4 | 16 | 7 |
| 71b7fc715e2f1bb24c0030af8d7e7b6e7cd128a6 | umd_faces | UMD | The Do’s and Don’ts for CNN-Based Face Verification | [pdf] | 2017 IEEE International Conference on Computer Vision Workshops (ICCVW) | | | | | | 38% | 26 | 10 | 16 | 3 | 16 | 8 |
| 25474c21613607f6bb7687a281d5f9d4ffa1f9f3 | faceplace | Face Place | Recognizing disguised faces | [pdf] | Unknown | | | | | | 31% | 29 | 9 | 20 | 0 | 18 | 10 |
| a8d0b149c2eadaa02204d3e4356fbc8eccf3b315 | hi4d_adsip | Hi4D-ADSIP | Hi4D-ADSIP 3-D dynamic facial articulation database | [pdf] | Image Vision Comput. | | | | | | 60% | 15 | 9 | 6 | 1 | 4 | 11 |
| 2f43b614607163abf41dfe5d17ef6749a1b61304 | hrt_transgender | HRT Transgender | Investigating the Periocular-Based Face Recognition Across Gender Transformation | [pdf] | IEEE Transactions on Information Forensics and Security | edu | University of North Carolina at Wilmington | United States | 34.22498270 | -77.86907744 | 69% | 13 | 9 | 4 | 0 | 6 | 8 |
| bd26dabab576adb6af30484183c9c9c8379bf2e0 | scut_fbp | SCUT-FBP | SCUT-FBP: A Benchmark Dataset for Facial Beauty Perception | [pdf] | 2015 IEEE International Conference on Systems, Man, and Cybernetics | | | | | | 47% | 19 | 9 | 10 | 2 | 6 | 13 |
| 2624d84503bc2f8e190e061c5480b6aa4d89277a | afew_va | AFEW-VA | AFEW-VA database for valence and arousal estimation in-the-wild | [pdf] | Image Vision Comput. | | | | | | 44% | 18 | 8 | 10 | 0 | 12 | 5 |
| 4e6ee936eb50dd032f7138702fa39b7c18ee8907 | dartmouth_children | Dartmouth Children | The Dartmouth Database of Children’s Faces: Acquisition and Validation of a New Face Stimulus Set | [pdf] | | | | | | | 38% | 21 | 8 | 13 | 2 | 18 | 3 |
| 0f0fcf041559703998abf310e56f8a2f90ee6f21 | feret | FERET | The FERET Evaluation Methodology for Face-Recognition Algorithms | [pdf] | Unknown | | | | | | 28% | 29 | 8 | 21 | 3 | 18 | 9 |
| c570d1247e337f91e555c3be0e8c8a5aba539d9f | mcgill | McGill Real World | Robust semi-automatic head pose labeling for real-world face video sequences | [pdf] | Multimedia Tools and Applications | edu | McGill University | Canada | 45.50397610 | -73.57496870 | 44% | 18 | 8 | 10 | 0 | 13 | 7 |
| 041d3eedf5e45ce5c5229f0181c5c576ed1fafd6 | ucf_selfie | UCF Selfie | How to Take a Good Selfie? | [pdf] | Unknown | | | | | | 73% | 11 | 8 | 3 | 0 | 7 | 5 |
| a94cae786d515d3450d48267e12ca954aab791c4 | yawdd | YawDD | YawDD: a yawning detection dataset | [pdf] | Unknown | | | | | | 53% | 15 | 8 | 7 | 1 | 2 | 13 |
| 6403117f9c005ae81f1e8e6d1302f4a045e3d99d | alert_airport | ALERT Airport | A Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets | [pdf] | IEEE Transactions on Pattern Analysis and Machine Intelligence | | | | | | 35% | 20 | 7 | 13 | 0 | 9 | 11 |
| 2b926b3586399d028b46315d7d9fb9d879e4f79c | frav3d | FRAV3D | Multimodal 2D, 2.5D & 3D Face Verification | [pdf] | 2006 International Conference on Image Processing | edu | Universidad Rey Juan Carlos, Spain | Spain | 40.33586610 | -3.87694320 | 50% | 14 | 7 | 7 | 0 | 2 | 12 |
| 2cd7821fcf5fae53a185624f7eeda007434ae037 | geofaces | GeoFaces | Exploring the geo-dependence of human face appearance | [pdf] | IEEE Winter Conference on Applications of Computer Vision | | | | | | 88% | 8 | 7 | 1 | 0 | 5 | 3 |
| 2cd7821fcf5fae53a185624f7eeda007434ae037 | geofaces | GeoFaces | Exploring the geo-dependence of human face appearance | [pdf] | IEEE Winter Conference on Applications of Computer Vision | | | | | | 88% | 8 | 7 | 1 | 0 | 5 | 3 |
| b71d1aa90dcbe3638888725314c0d56640c1fef1 | ifdb | IFDB | Iranian Face Database with age, pose and expression | [pdf] | 2007 International Conference on Machine Vision | edu | Islamic Azad University | Iran | 34.84529990 | 48.55962120 | 30% | 23 | 7 | 16 | 2 | 14 | 9 |
| 4563b46d42079242f06567b3f2e2f7a80cb3befe | vadana | VADANA | VADANA: A dense dataset for facial image analysis | [pdf] | 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops) | edu | University of Delaware | United States | 39.68103280 | -75.75401840 | 47% | 15 | 7 | 8 | 0 | 5 | 10 |
| 060820f110a72cbf02c14a6d1085bd6e1d994f6a | caltech_crp | Caltech CRP | Fine-grained classification of pedestrians in video: Benchmark and state of the art | [pdf] | 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) | | | | | | 35% | 17 | 6 | 11 | 0 | 9 | 8 |
| 8d5998cd984e7cce307da7d46f155f9db99c6590 | chalearn | ChaLearn | ChaLearn looking at people: A review of events and resources | [pdf] | 2017 International Joint Conference on Neural Networks (IJCNN) | | | | | | 46% | 13 | 6 | 7 | 1 | 8 | 4 |
| 22f656d0f8426c84a33a267977f511f127bfd7f3 | expw | ExpW | From Facial Expression Recognition to Interpersonal Relation Prediction | [pdf] | International Journal of Computer Vision | | | | | | 55% | 11 | 6 | 5 | 0 | 5 | 4 |
| 7f4040b482d16354d5938c1d1b926b544652bf5b | nova_emotions | Novaemötions Dataset | Competitive affective gaming: winning with a smile | [pdf] | Unknown | edu | Universidade NOVA de Lisboa, Caparica, Portugal | Portugal | 38.66096400 | -9.20581300 | 67% | 9 | 6 | 3 | 0 | 5 | 4 |
| 1a40092b493c6b8840257ab7f96051d1a4dbfeb2 | sports_videos_in_the_wild | SVW | Sports 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) | | | | | | 86% | 7 | 6 | 1 | 1 | 5 | 2 |
| 633c851ebf625ad7abdda2324e9de093cf623141 | appa_real | APPA-REAL | Apparent 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) | | | | | | 50% | 10 | 5 | 5 | 0 | 8 | 3 |
| 4af89578ac237278be310f7660a408b03f12d603 | geofaces | GeoFaces | Large-scale geo-facial image analysis | [pdf] | EURASIP J. Image and Video Processing | | | | | | 83% | 6 | 5 | 1 | 0 | 4 | 2 |
| 8f02ec0be21461fbcedf51d864f944cfc42c875f | hda_plus | HDA+ | The HDA+ Data Set for Research on Fully Automated Re-identification Systems | [pdf] | Unknown | | | | | | 31% | 16 | 5 | 11 | 2 | 10 | 6 |
| 57178b36c21fd7f4529ac6748614bb3374714e91 | ijb_c | IJB-C | IARPA Janus Benchmark - C: Face Dataset and Protocol | [pdf] | 2018 International Conference on Biometrics (ICB) | | | | | | 36% | 14 | 5 | 9 | 2 | 12 | 1 |
| 07fcbae86f7a3ad3ea1cf95178459ee9eaf77cb1 | uccs | UCCS | Large scale unconstrained open set face database | [pdf] | 2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS) | | | | | | 83% | 6 | 5 | 1 | 0 | 4 | 2 |
| d4f1eb008eb80595bcfdac368e23ae9754e1e745 | uccs | UCCS | Unconstrained Face Detection and Open-Set Face Recognition Challenge | [pdf] | 2017 IEEE International Joint Conference on Biometrics (IJCB) | | | | | | 100% | 5 | 5 | 0 | 0 | 4 | 1 |
| 8627f019882b024aef92e4eb9355c499c733e5b7 | used | USED Social Event Dataset | USED: a large-scale social event detection dataset | [pdf] | Unknown | edu | University of Trento | Italy | 46.06588360 | 11.11598940 | 71% | 7 | 5 | 2 | 0 | 3 | 4 |
| 56ae6d94fc6097ec4ca861f0daa87941d1c10b70 | cmdp | CMDP | Distance Estimation of an Unknown Person from a Portrait | [pdf] | Unknown | | | | | | 44% | 9 | 4 | 5 | 0 | 6 | 3 |
| 4b4106614c1d553365bad75d7866bff0de6056ed | ufi | UFI | Unconstrained Facial Images: Database for Face Recognition Under Real-World Conditions | [pdf] | Unknown | | | | | | 33% | 12 | 4 | 8 | 0 | 4 | 6 |
| 563c940054e4b456661762c1ab858e6f730c3159 | data_61 | Data61 Pedestrian | A Multi-modal Graphical Model for Scene Analysis | [pdf] | 2015 IEEE Winter Conference on Applications of Computer Vision | | | | | | 50% | 8 | 4 | 4 | 0 | 5 | 3 |
| 287ddcb3db5562235d83aee318f318b8d5e43fb1 | erce | ERCe | Learning from Multiple Sources for Video Summarisation | [pdf] | International Journal of Computer Vision | | | | | | 57% | 7 | 4 | 3 | 0 | 4 | 3 |
| dd65f71dac86e36eecbd3ed225d016c3336b4a13 | families_in_the_wild | FIW | Visual Kinship Recognition of Families in the Wild | [pdf] | IEEE Transactions on Pattern Analysis and Machine Intelligence | edu | University of Massachusetts Dartmouth | United States | 41.62772475 | -71.00724501 | 80% | 5 | 4 | 1 | 0 | 2 | 3 |
| 137aa2f891d474fce1e7a1d1e9b3aefe21e22b34 | hrt_transgender | HRT Transgender | Is 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% | 7 | 4 | 3 | 1 | 3 | 5 |
| 2d45cfd838016a6e39f6b766ffe85acd649440c7 | mcgill | McGill Real World | Hierarchical temporal graphical model for head pose estimation and subsequent attribute classification in real-world videos | [pdf] | Computer Vision and Image Understanding | | | | | | 50% | 8 | 4 | 4 | 1 | 5 | 3 |
| 23e824d1dfc33f3780dd18076284f07bd99f1c43 | mifs | MIFS | Spoofing faces using makeup: An investigative study | [pdf] | 2017 IEEE International Conference on Identity, Security and Behavior Analysis (ISBA) | edu | INRIA Méditerranée | France | 43.61581310 | 7.06838000 | 67% | 6 | 4 | 2 | 0 | 1 | 5 |
| 54983972aafc8e149259d913524581357b0f91c3 | reseed | ReSEED | ReSEED: social event dEtection dataset | [pdf] | Unknown | | | | | | 67% | 6 | 4 | 2 | 1 | 1 | 5 |
| 287ddcb3db5562235d83aee318f318b8d5e43fb1 | tisi | Times Square Intersection | Learning from Multiple Sources for Video Summarisation | [pdf] | International Journal of Computer Vision | | | | | | 57% | 7 | 4 | 3 | 0 | 4 | 3 |
| 7ebb153704706e457ab57b432793d2b6e5d12592 | vgg_celebs_in_places | CIP | Faces in Places: compound query retrieval | [pdf] | Unknown | | | | | | 80% | 5 | 4 | 1 | 0 | 3 | 2 |
| 0d2dd4fc016cb6a517d8fb43a7cc3ff62964832e | lag | LAG | Large age-gap face verification by feature injection in deep networks | [pdf] | Pattern Recognition Letters | | | | | | 43% | 7 | 3 | 4 | 0 | 3 | 4 |
| 9cc8cf0c7d7fa7607659921b6ff657e17e135ecc | mafa | MAsked FAces | Detecting Masked Faces in the Wild with LLE-CNNs | [pdf] | 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) | | | | | | 60% | 5 | 3 | 2 | 1 | 4 | 1 |
| a7fe834a0af614ce6b50dc093132b031dd9a856b | market_1501 | Market 1501 | Orientation Driven Bag of Appearances for Person Re-identification | [pdf] | CoRR | | | | | | 43% | 7 | 3 | 4 | 0 | 4 | 4 |
| a7fe834a0af614ce6b50dc093132b031dd9a856b | pku_reid | PKU-Reid | Orientation Driven Bag of Appearances for Person Re-identification | [pdf] | CoRR | | | | | | 43% | 7 | 3 | 4 | 0 | 4 | 4 |
| 9e5378e7b336c89735d3bb15cf67eff96f86d39a | precarious | Precarious | Expecting the Unexpected: Training Detectors for Unusual Pedestrians with Adversarial Imposters | [pdf] | 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) | | | | | | 21% | 14 | 3 | 11 | 1 | 12 | 1 |
| 17b46e2dad927836c689d6787ddb3387c6159ece | geofaces | GeoFaces | GeoFaceExplorer: exploring the geo-dependence of facial attributes | [pdf] | Unknown | | | | | | 100% | 2 | 2 | 0 | 0 | 1 | 1 |
| ad01687649d95cd5b56d7399a9603c4b8e2217d7 | mrp_drone | MRP Drone | Investigating Open-World Person Re-identification Using a Drone | [pdf] | Unknown | | | | | | 29% | 7 | 2 | 5 | 1 | 5 | 2 |
| f6c8d5e35d7e4d60a0104f233ac1a3ab757da53f | pku_reid | PKU-Reid | Swiss-System Based Cascade Ranking for Gait-Based Person Re-Identification | [pdf] | Unknown | | | | | | 50% | 4 | 2 | 2 | 0 | 1 | 2 |
| 922e0a51a3b8c67c4c6ac09a577ff674cbd28b34 | v47 | V47 | Re-identification of pedestrians with variable occlusion and scale | [pdf] | 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops) | edu | Kingston University | United Kingdom | 51.42930860 | -0.26840440 | 22% | 9 | 2 | 7 | 2 | 5 | 4 |
| 4d4bb462c9f1d4e4ab1e4aa6a75cc0bc71b38461 | 3dddb_unconstrained | 3D Dynamic | A 3D Dynamic Database for Unconstrained Face Recognition | [pdf] | Unknown | | | | | | 50% | 2 | 1 | 1 | 0 | 1 | 1 |
| 65355cbb581a219bd7461d48b3afd115263ea760 | complex_activities | Ongoing Complex Activities | Recognition of ongoing complex activities by sequence prediction over a hierarchical label space | [pdf] | 2016 IEEE Winter Conference on Applications of Computer Vision (WACV) | | | | | | 33% | 3 | 1 | 2 | 0 | 3 | 0 |
| e58dd160a76349d46f881bd6ddbc2921f08d1050 | gfw | Grouping Face in the Wild | Merge or Not? Learning to Group Faces via Imitation Learning | [pdf] | Unknown | | | | | | 50% | 2 | 1 | 1 | 0 | 2 | 0 |
| 55c40cbcf49a0225e72d911d762c27bb1c2d14aa | ifad | IFAD | Indian Face Age Database: A Database for Face Recognition with Age Variation | [pdf] | Unknown | | | | | | 50% | 2 | 1 | 1 | 0 | 2 | 0 |
| 9e31e77f9543ab42474ba4e9330676e18c242e72 | imdb_face | IMDb Face | The Devil of Face Recognition is in the Noise | [pdf] | Unknown | company | SenseTime | China | 39.99300800 | 116.32988200 | 17% | 6 | 1 | 5 | 0 | 4 | 1 |
| 4eab317b5ac436a949849ed286baa3de2a541eef | laofiw | LAOFIW | Turning a Blind Eye: Explicit Removal of Biases and Variation from Deep Neural Network Embeddings | [pdf] | Unknown | | | | | | 50% | 2 | 1 | 1 | 0 | 2 | 0 |
| c72a2ea819df9b0e8cd267eebcc6528b8741e03d | megaage | MegaAge | Quantifying Facial Age by Posterior of Age Comparisons | [pdf] | CoRR | | | | | | 25% | 4 | 1 | 3 | 1 | 4 | 0 |
| 578d4ad74818086bb64f182f72e2c8bd31e3d426 | mr2 | MR2 | The MR2: A multi-racial, mega-resolution database of facial stimuli. | [pdf] | Behavior research methods | | | | | | 14% | 7 | 1 | 6 | 0 | 7 | 0 |
| c06b13d0ec3f5c43e2782cd22542588e233733c3 | nova_emotions | Novaemötions Dataset | Crowdsourcing facial expressions for affective-interaction | [pdf] | Computer Vision and Image Understanding | | | | | | 100% | 1 | 1 | 0 | 0 | 1 | 0 |
| 22909dd19a0ec3b6065334cb5be5392cb24d839d | pets | PETS 2017 | PETS 2017: Dataset and Challenge | [pdf] | 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) | | | | | | 11% | 9 | 1 | 8 | 0 | 1 | 8 |
| 2306b2a8fba28539306052764a77a0d0f5d1236a | qmul_surv_face | QMUL-SurvFace | Surveillance Face Recognition Challenge | [pdf] | CoRR | edu | Queen Mary University of London | United Kingdom | 51.52472720 | -0.03931035 | 100% | 1 | 1 | 0 | 0 | 1 | 0 |
| d3f5a1848b0028d8ab51d0b0673732cad2e3c8c9 | stair_actions | STAIR Action | STAIR Actions: A Video Dataset of Everyday Home Actions | [pdf] | CoRR | | | | | | 100% | 1 | 1 | 0 | 0 | 1 | 0 |
| 3531332efe19be21e7401ba1f04570a142617236 | ufdd | UFDD | Pushing the Limits of Unconstrained Face Detection: a Challenge Dataset and Baseline Results | [pdf] | CoRR | | | | | | 25% | 4 | 1 | 3 | 1 | 4 | 0 |
| 5ad4e9f947c1653c247d418f05dad758a3f9277b | wlfdb | WLFDB | WLFDB : Weakly Labeled Face Databases | [pdf] | Unknown | | | | | | 100% | 1 | 1 | 0 | 0 | 0 | 1 |
| a40f9bfd3c45658ee8da70e1f2dfbe1f0c744d43 | 4dfab | 4DFAB | 4DFAB: A Large Scale 4D Facial Expression Database for Biometric Applications | [pdf] | CoRR | | | | | | 0% | 4 | 0 | 4 | 0 | 2 | 2 |
| 7b92d1e53cc87f7a4256695de590098a2f30261e | appa_real | APPA-REAL | From 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% | 0 | 0 | 0 | 0 | 0 | 0 |
| 1dc35905a1deff8bc74688f2d7e2f48fd2273275 | caltech_pedestrians | Caltech Pedestrians | Pedestrian detection: A benchmark | [pdf] | 2009 IEEE Conference on Computer Vision and Pattern Recognition | | | | | | 100% | 0 | 0 | 0 | 0 | 0 | 0 |
| 15e1af79939dbf90790b03d8aa02477783fb1d0f | duke_mtmc | Duke MTMC | Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in Vitro | [pdf] | 2017 IEEE International Conference on Computer Vision (ICCV) | | | | | | 100% | 0 | 0 | 0 | 0 | 0 | 0 |
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