| Paper ID | Megapixels Key | Megapixels Name | Report Link | PDF Link | Journal | Type | Address | 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 | edu | University of Central Florida | 28.59899755 | -81.19712501 | 54% | 999 | 535 | 464 | 73 | 708 | 212 |
| 0e986f51fe45b00633de9fd0c94d082d2be51406 | afw | AFW | Face detection, pose estimation, and landmark localization in the wild | [pdf] | 2012 IEEE Conference on Computer Vision and Pattern Recognition | edu | University of California, Irvine | 33.64319010 | -117.84016494 | 52% | 999 | 521 | 478 | 59 | 607 | 273 |
| 370b5757a5379b15e30d619e4d3fb9e8e13f3256 | 3dddb_unconstrained | 3D Dynamic | Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments | [pdf] | | | | | | 47% | 999 | 472 | 526 | 71 | 619 | 260 |
| 370b5757a5379b15e30d619e4d3fb9e8e13f3256 | ar_facedb | AR Face | Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments | [pdf] | | | | | | 47% | 999 | 472 | 526 | 71 | 619 | 260 |
| 370b5757a5379b15e30d619e4d3fb9e8e13f3256 | m2vtsdb_extended | xm2vtsdb | Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments | [pdf] | | | | | | 47% | 999 | 472 | 526 | 71 | 619 | 260 |
| 370b5757a5379b15e30d619e4d3fb9e8e13f3256 | put_face | Put Face | Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments | [pdf] | | | | | | 47% | 999 | 472 | 526 | 71 | 619 | 260 |
| 759a3b3821d9f0e08e0b0a62c8b693230afc3f8d | pubfig | PubFig | Attribute and simile classifiers for face verification | [pdf] | 2009 IEEE 12th International Conference on Computer Vision | edu | Columbia University | 40.84198360 | -73.94368971 | 51% | 894 | 454 | 440 | 55 | 587 | 222 |
| 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. | | | | | 42% | 999 | 422 | 577 | 80 | 538 | 295 |
| 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. | | | | | 42% | 999 | 422 | 577 | 80 | 538 | 295 |
| 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 | | | | | 41% | 975 | 403 | 572 | 65 | 460 | 345 |
| 2e384f057211426ac5922f1b33d2aa8df5d51f57 | a_pascal_yahoo | aPascal | Describing objects by their attributes | [pdf] | 2009 IEEE Conference on Computer Vision and Pattern Recognition | | | | | 39% | 999 | 393 | 606 | 71 | 727 | 73 |
| 162ea969d1929ed180cc6de9f0bf116993ff6e06 | vgg_faces | VGG Face | Deep Face Recognition | [pdf] | Unknown | | | | | 39% | 999 | 393 | 606 | 71 | 621 | 156 |
| 23fc83c8cfff14a16df7ca497661264fc54ed746 | cohn_kanade | CK | Comprehensive Database for Facial Expression Analysis | [pdf] | | edu | Carnegie Mellon University | 37.41021930 | -122.05965487 | 38% | 999 | 380 | 619 | 75 | 555 | 252 |
| 01959ef569f74c286956024866c1d107099199f7 | vqa | VQA | VQA: Visual Question Answering | [pdf] | 2015 IEEE International Conference on Computer Vision (ICCV) | | | | | 47% | 731 | 344 | 387 | 47 | 628 | 4 |
| 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 | 22.42031295 | 114.20788644 | 42% | 808 | 340 | 468 | 69 | 666 | 50 |
| 4d423acc78273b75134e2afd1777ba6d3a398973 | cmu_pie | CMU PIE | International Conference on Automatic Face and Gesture Recognition The CMU Pose , Illumination , and Expression ( PIE ) Database | [pdf] | | | | | | 44% | 742 | 330 | 412 | 61 | 410 | 232 |
| abe9f3b91fd26fa1b50cd685c0d20debfb372f73 | voc | VOC | The Pascal Visual Object Classes Challenge: A Retrospective | [pdf] | International Journal of Computer Vision | | | | | 32% | 999 | 315 | 684 | 75 | 698 | 6 |
| 45c31cde87258414f33412b3b12fc5bec7cb3ba9 | jaffe | JAFFE | Coding Facial Expressions with Gabor Wavelets | [pdf] | | edu | Kyushu University | 33.59914655 | 130.22359848 | 36% | 848 | 308 | 540 | 56 | 413 | 255 |
| 31b58ced31f22eab10bd3ee2d9174e7c14c27c01 | tiny_images | Tiny Images | Nonparametric Object and Scene Recognition | [pdf] | | | | | | 31% | 999 | 305 | 694 | 93 | 670 | 9 |
| 140438a77a771a8fb656b39a78ff488066eb6b50 | lfw_p | LFWP | Localizing Parts of Faces Using a Consensus of Exemplars | [pdf] | IEEE Transactions on Pattern Analysis and Machine Intelligence | edu | Columbia University | 40.84198360 | -73.94368971 | 53% | 521 | 274 | 247 | 40 | 321 | 144 |
| 18ae7c9a4bbc832b8b14bc4122070d7939f5e00e | frgc | FRGC | Overview of the face recognition grand challenge | [pdf] | 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) | | | | | 25% | 999 | 253 | 746 | 110 | 572 | 64 |
| 32cde90437ab5a70cf003ea36f66f2de0e24b3ab | cityscapes | Cityscapes | The Cityscapes Dataset for Semantic Urban Scene Understanding | [pdf] | 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) | | | | | 33% | 771 | 252 | 519 | 54 | 622 | 0 |
| 32cde90437ab5a70cf003ea36f66f2de0e24b3ab | cityscapes | Cityscapes | The Cityscapes Dataset for Semantic Urban Scene Understanding | [pdf] | 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) | | | | | 33% | 771 | 252 | 519 | 54 | 622 | 0 |
| 560e0e58d0059259ddf86fcec1fa7975dee6a868 | youtube_faces | YouTubeFaces | Face recognition in unconstrained videos with matched background similarity | [pdf] | CVPR 2011 | edu | Open University of Israel | 32.77824165 | 34.99565673 | 50% | 485 | 244 | 240 | 32 | 290 | 140 |
| 3607afdb204de9a5a9300ae98aa4635d9effcda2 | sheffield | Sheffield Face | Face Description with Local Binary Patterns: Application to Face Recognition | [pdf] | IEEE Transactions on Pattern Analysis and Machine Intelligence | | | | | 24% | 999 | 238 | 761 | 65 | 483 | 87 |
| 853bd61bc48a431b9b1c7cab10c603830c488e39 | casia_webface | CASIA Webface | Learning Face Representation from Scratch | [pdf] | CoRR | | | | | 53% | 436 | 233 | 203 | 32 | 284 | 115 |
| 2830fb5282de23d7784b4b4bc37065d27839a412 | h3d | H3D | Poselets: Body part detectors trained using 3D human pose annotations | [pdf] | 2009 IEEE 12th International Conference on Computer Vision | | | | | 32% | 707 | 223 | 484 | 62 | 482 | 18 |
| 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) | | | | | 22% | 999 | 217 | 782 | 67 | 520 | 22 |
| 55206f0b5f57ce17358999145506cd01e570358c | orl | ORL | O M 4 . 1 The Subject Database 4 . 2 Experiment Plan 5 . 1 Varying the Overlap 4 Experimental Setup 5 Parameterisation Results | [pdf] | | | | | | 21% | 999 | 214 | 785 | 96 | 550 | 57 |
| 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) | | | | | 46% | 424 | 195 | 229 | 27 | 231 | 155 |
| 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) | | | | | 46% | 424 | 195 | 229 | 27 | 231 | 155 |
| 93884e46c49f7ae1c7c34046fbc28882f2bd6341 | kdef | KDEF | Gaze fixation and the neural circuitry of face processing in autism | [pdf] | Nature Neuroscience | | | | | 31% | 608 | 190 | 418 | 92 | 463 | 0 |
| 6bd36e9fd0ef20a3074e1430a6cc601e6d407fc3 | cuhk03 | CUHK03 | DeepReID: Deep Filter Pairing Neural Network for Person Re-identification | [pdf] | 2014 IEEE Conference on Computer Vision and Pattern Recognition | | | | | 35% | 512 | 180 | 332 | 29 | 323 | 4 |
| 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 | | | | | 50% | 356 | 179 | 177 | 21 | 299 | 3 |
| 95f12d27c3b4914e0668a268360948bce92f7db3 | helen | Helen | Interactive Facial Feature Localization | [pdf] | | edu | University of Illinois, Urbana-Champaign | 40.11116745 | -88.22587665 | 52% | 339 | 177 | 162 | 27 | 208 | 100 |
| 2724ba85ec4a66de18da33925e537f3902f21249 | cofw | COFW | Robust Face Landmark Estimation under Occlusion | [pdf] | 2013 IEEE International Conference on Computer Vision | | | | | 55% | 305 | 167 | 138 | 16 | 186 | 95 |
| 6273b3491e94ea4dd1ce42b791d77bdc96ee73a8 | viper | VIPeR | Evaluating Appearance Models for Recognition, Reacquisition, and Tracking | [pdf] | | | | | | 27% | 584 | 159 | 425 | 38 | 336 | 9 |
| 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) | | | | | 53% | 292 | 155 | 137 | 38 | 207 | 59 |
| a74251efa970b92925b89eeef50a5e37d9281ad0 | imm_face | IMM Face Dataset | 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) | | | | | 53% | 292 | 155 | 137 | 38 | 207 | 59 |
| a74251efa970b92925b89eeef50a5e37d9281ad0 | muct | MUCT | 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) | | | | | 53% | 292 | 155 | 137 | 38 | 207 | 59 |
| 4308bd8c28e37e2ed9a3fcfe74d5436cce34b410 | market_1501 | Market 1501 | Scalable Person Re-identification: A Benchmark | [pdf] | 2015 IEEE International Conference on Computer Vision (ICCV) | | | | | 38% | 394 | 149 | 245 | 18 | 271 | 3 |
| 2a75f34663a60ab1b04a0049ed1d14335129e908 | mmi_facial_expression | MMI Facial Expression Dataset | Web-based database for facial expression analysis | [pdf] | 2005 IEEE International Conference on Multimedia and Expo | | | | | 32% | 440 | 142 | 298 | 44 | 258 | 82 |
| 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 | | | | | 41% | 323 | 131 | 192 | 29 | 221 | 25 |
| 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) | edu | SUNY Binghamton | 42.08779975 | -75.97066066 | 24% | 555 | 131 | 424 | 47 | 283 | 48 |
| 696ca58d93f6404fea0fc75c62d1d7b378f47628 | coco | COCO | Microsoft COCO Captions: Data Collection and Evaluation Server | [pdf] | CoRR | | | | | 46% | 283 | 129 | 154 | 16 | 231 | 4 |
| 4053e3423fb70ad9140ca89351df49675197196a | bio_id | BioID Face | Robust Face Detection Using the Hausdorff Distance | [pdf] | | | | | | 26% | 498 | 127 | 371 | 55 | 319 | 32 |
| 3765df816dc5a061bc261e190acc8bdd9d47bec0 | rafd | RaFD | Presentation and validation of the Radboud Faces Database | [pdf] | Unknown | | | | | 28% | 446 | 127 | 319 | 43 | 307 | 19 |
| 2fda164863a06a92d3a910b96eef927269aeb730 | names_and_faces_news | News Dataset | Names and faces in the news | [pdf] | Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004. | | | | | 41% | 294 | 120 | 174 | 24 | 207 | 45 |
| 4b1d23d17476fcf78f4cbadf69fb130b1aa627c0 | leeds_sports_pose | Leeds Sports Pose | Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation | [pdf] | | | | | | 43% | 278 | 119 | 159 | 12 | 198 | 2 |
| 4b1d23d17476fcf78f4cbadf69fb130b1aa627c0 | stickmen_buffy | Buffy Stickmen | Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation | [pdf] | | | | | | 43% | 278 | 119 | 159 | 12 | 198 | 2 |
| 4b1d23d17476fcf78f4cbadf69fb130b1aa627c0 | stickmen_buffy | Buffy Stickmen | Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation | [pdf] | | | | | | 43% | 278 | 119 | 159 | 12 | 198 | 2 |
| 6dd0597f8513dc100cd0bc1b493768cde45098a9 | stickmen_pascal | Stickmen PASCAL | Learning to parse images of articulated bodies | [pdf] | | | | | | 31% | 373 | 117 | 256 | 30 | 238 | 2 |
| 6dd0597f8513dc100cd0bc1b493768cde45098a9 | stickmen_pascal | Stickmen PASCAL | Learning to parse images of articulated bodies | [pdf] | | | | | | 31% | 373 | 117 | 256 | 30 | 238 | 2 |
| 3316521a5527c7700af8ae6aef32a79a8b83672c | tud_campus | TUD-Campus | People-tracking-by-detection and people-detection-by-tracking | [pdf] | 2008 IEEE Conference on Computer Vision and Pattern Recognition | | | | | 22% | 529 | 116 | 413 | 41 | 316 | 1 |
| 3316521a5527c7700af8ae6aef32a79a8b83672c | tud_crossing | TUD-Crossing | People-tracking-by-detection and people-detection-by-tracking | [pdf] | 2008 IEEE Conference on Computer Vision and Pattern Recognition | | | | | 22% | 529 | 116 | 413 | 41 | 316 | 1 |
| 3316521a5527c7700af8ae6aef32a79a8b83672c | tud_pedestrian | TUD-Pedestrian | People-tracking-by-detection and people-detection-by-tracking | [pdf] | 2008 IEEE Conference on Computer Vision and Pattern Recognition | | | | | 22% | 529 | 116 | 413 | 41 | 316 | 1 |
| 0f0fcf041559703998abf310e56f8a2f90ee6f21 | feret | FERET | The FERET Evaluation Methodology for Face-Recognition Algorithms | [pdf] | IEEE Trans. Pattern Anal. Mach. Intell. | | | | | 11% | 999 | 109 | 259 | 32 | 213 | 51 |
| 0f0fcf041559703998abf310e56f8a2f90ee6f21 | feret | FERET | The FERET Evaluation Methodology for Face-Recognition Algorithms | [pdf] | IEEE Trans. Pattern Anal. Mach. Intell. | | | | | 11% | 999 | 109 | 259 | 32 | 213 | 51 |
| 0f0fcf041559703998abf310e56f8a2f90ee6f21 | feret | FERET | The FERET Evaluation Methodology for Face-Recognition Algorithms | [pdf] | IEEE Trans. Pattern Anal. Mach. Intell. | | | | | 11% | 999 | 109 | 259 | 32 | 213 | 51 |
| 0f0fcf041559703998abf310e56f8a2f90ee6f21 | feret | FERET | The FERET Evaluation Methodology for Face-Recognition Algorithms | [pdf] | IEEE Trans. Pattern Anal. Mach. Intell. | | | | | 11% | 999 | 109 | 259 | 32 | 213 | 51 |
| 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) | | | | | 48% | 222 | 107 | 115 | 21 | 158 | 48 |
| 28312c3a47c1be3a67365700744d3d6665b86f22 | hrt_transgender | HRT Transgender | Face Recognition: A Literature Survey1 | [pdf] | | | | | | 11% | 999 | 105 | 265 | 32 | 217 | 39 |
| 28312c3a47c1be3a67365700744d3d6665b86f22 | hrt_transgender | HRT Transgender | Face Recognition: A Literature Survey1 | [pdf] | | | | | | 11% | 999 | 105 | 265 | 32 | 217 | 39 |
| 28312c3a47c1be3a67365700744d3d6665b86f22 | hrt_transgender | HRT Transgender | Face Recognition: A Literature Survey1 | [pdf] | | | | | | 11% | 999 | 105 | 265 | 32 | 217 | 39 |
| 013909077ad843eb6df7a3e8e290cfd5575999d2 | fiw_300 | 300-W | A Semi-automatic Methodology for Facial Landmark Annotation | [pdf] | 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops | edu | University of Twente | 52.23801390 | 6.85667610 | 59% | 169 | 100 | 69 | 14 | 112 | 49 |
| 013909077ad843eb6df7a3e8e290cfd5575999d2 | fiw_300 | 300-W | A Semi-automatic Methodology for Facial Landmark Annotation | [pdf] | 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops | edu | University of Twente | 52.23801390 | 6.85667610 | 59% | 169 | 100 | 69 | 14 | 112 | 49 |
| 013909077ad843eb6df7a3e8e290cfd5575999d2 | fiw_300 | 300-W | A Semi-automatic Methodology for Facial Landmark Annotation | [pdf] | 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops | edu | University of Twente | 52.23801390 | 6.85667610 | 59% | 169 | 100 | 69 | 14 | 112 | 49 |
| 7808937b46acad36e43c30ae4e9f3fd57462853d | berkeley_pose | BPAD | Describing people: A poselet-based approach to attribute classification | [pdf] | 2011 International Conference on Computer Vision | | | | | 43% | 221 | 96 | 125 | 14 | 160 | 23 |
| f72f6a45ee240cc99296a287ff725aaa7e7ebb35 | caltech_pedestrians | Caltech Pedestrians | Pedestrian Detection: An Evaluation of the State of the Art | [pdf] | IEEE Transactions on Pattern Analysis and Machine Intelligence | | | | | 10% | 999 | 96 | 355 | 26 | 252 | 5 |
| f72f6a45ee240cc99296a287ff725aaa7e7ebb35 | caltech_pedestrians | Caltech Pedestrians | Pedestrian Detection: An Evaluation of the State of the Art | [pdf] | IEEE Transactions on Pattern Analysis and Machine Intelligence | | | | | 10% | 999 | 96 | 355 | 26 | 252 | 5 |
| e8de844fefd54541b71c9823416daa238be65546 | visual_phrases | Phrasal Recognition | Recognition using visual phrases | [pdf] | CVPR 2011 | | | | | 41% | 233 | 95 | 138 | 18 | 174 | 5 |
| 16c7c31a7553d99f1837fc6e88e77b5ccbb346b8 | prid | PRID | Person Re-identification by Descriptive and Discriminative Classification | [pdf] | | | | | | 27% | 352 | 94 | 258 | 26 | 195 | 3 |
| 833fa04463d90aab4a9fe2870d480f0b40df446e | sun_attributes | SUN | SUN attribute database: Discovering, annotating, and recognizing scene attributes | [pdf] | 2012 IEEE Conference on Computer Vision and Pattern Recognition | | | | | 32% | 260 | 84 | 85 | 18 | 143 | 10 |
| 833fa04463d90aab4a9fe2870d480f0b40df446e | sun_attributes | SUN | SUN attribute database: Discovering, annotating, and recognizing scene attributes | [pdf] | 2012 IEEE Conference on Computer Vision and Pattern Recognition | | | | | 32% | 260 | 84 | 85 | 18 | 143 | 10 |
| 35b0331dfcd2897abd5749b49ff5e2b8ba0f7a62 | coco_qa | COCO QA | Exploring Models and Data for Image Question Answering | [pdf] | | | | | | 43% | 191 | 83 | 108 | 12 | 163 | 1 |
| 291265db88023e92bb8c8e6390438e5da148e8f5 | msceleb | MsCeleb | MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition | [pdf] | | | | | | 50% | 167 | 83 | 84 | 15 | 131 | 27 |
| 13f06b08f371ba8b5d31c3e288b4deb61335b462 | eth_andreas_ess | ETHZ Pedestrian | Depth and Appearance for Mobile Scene Analysis | [pdf] | 2007 IEEE 11th International Conference on Computer Vision | | | | | 25% | 319 | 79 | 240 | 27 | 192 | 0 |
| 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 | 22.42031295 | 114.20788644 | 53% | 148 | 78 | 70 | 16 | 107 | 34 |
| 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 | | | | | 18% | 415 | 76 | 339 | 39 | 182 | 35 |
| 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 | | | | | 51% | 148 | 76 | 72 | 8 | 109 | 3 |
| 2485c98aa44131d1a2f7d1355b1e372f2bb148ad | m2vts | m2vts | The CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations | [pdf] | IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans | | | | | 18% | 415 | 76 | 339 | 39 | 182 | 35 |
| 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 | | | | | 25% | 302 | 76 | 226 | 32 | 199 | 1 |
| 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 | | | | | 25% | 302 | 76 | 226 | 32 | 199 | 1 |
| 133f01aec1534604d184d56de866a4bd531dac87 | lfw_a | LFW-a | Effective Unconstrained Face Recognition by Combining Multiple Descriptors and Learned Background Statistics | [pdf] | IEEE Transactions on Pattern Analysis and Machine Intelligence | | | | | 42% | 177 | 75 | 102 | 15 | 102 | 54 |
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| 21d9d0deed16f0ad62a4865e9acf0686f4f15492 | images_of_groups | Images of Groups | Understanding images of groups of people | [pdf] | 2009 IEEE Conference on Computer Vision and Pattern Recognition | | | | | 36% | 202 | 72 | 130 | 12 | 126 | 24 |
| 4e4746094bf60ee83e40d8597a6191e463b57f76 | leeds_sports_pose_extended | Leeds Sports Pose Extended | Learning effective human pose estimation from inaccurate annotation | [pdf] | CVPR 2011 | | | | | 40% | 173 | 70 | 103 | 9 | 116 | 2 |
| 44484d2866f222bbb9b6b0870890f9eea1ffb2d0 | cuhk01 | CUHK01 | Human Reidentification with Transferred Metric Learning | [pdf] | | | | | | 26% | 258 | 67 | 191 | 12 | 141 | 1 |
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| b1f4423c227fa37b9680787be38857069247a307 | afew_va | AFEW-VA | Collecting Large, Richly Annotated Facial-Expression Databases from Movies | [pdf] | IEEE MultiMedia | edu | Australian National University | -35.27769990 | 149.11852700 | 33% | 175 | 58 | 66 | 7 | 54 | 49 |
| 2acf7e58f0a526b957be2099c10aab693f795973 | bosphorus | The Bosphorus | Bosphorus Database for 3D Face Analysis | [pdf] | | | | | | 18% | 328 | 58 | 270 | 18 | 143 | 37 |
| 27a2fad58dd8727e280f97036e0d2bc55ef5424c | duke_mtmc | Duke MTMC | Performance Measures and a Data Set for Multi-target, Multi-camera Tracking | [pdf] | | | | | | 43% | 136 | 58 | 78 | 6 | 107 | 0 |
| 56ffa7d906b08d02d6d5a12c7377a57e24ef3391 | unbc_shoulder_pain | UNBC-McMaster Pain | Painful data: The UNBC-McMaster shoulder pain expression archive database | [pdf] | Face and Gesture 2011 | | | | | 32% | 184 | 58 | 126 | 23 | 112 | 23 |
| 38b55d95189c5e69cf4ab45098a48fba407609b4 | cuhk02 | CUHK02 | Locally Aligned Feature Transforms across Views | [pdf] | 2013 IEEE Conference on Computer Vision and Pattern Recognition | | | | | 24% | 242 | 57 | 185 | 17 | 139 | 1 |
| 2f5d44dc3e1b5955942133ff872ebd31716ec604 | frav3d | FRAV3D | 2D and 3D face recognition: A survey | [pdf] | Pattern Recognition Letters | | | | | 15% | 389 | 57 | 332 | 28 | 198 | 17 |
| a6e695ddd07aad719001c0fc1129328452385949 | yfcc_100m | YFCC100M | The New Data and New Challenges in Multimedia Research | [pdf] | CoRR | | | | | 36% | 160 | 57 | 103 | 11 | 105 | 4 |
| 0d3bb75852098b25d90f31d2f48fd0cb4944702b | face_scrub | FaceScrub | A data-driven approach to cleaning large face datasets | [pdf] | 2014 IEEE International Conference on Image Processing (ICIP) | edu | University of Illinois, Urbana-Champaign | 40.11116745 | -88.22587665 | 46% | 123 | 56 | 67 | 6 | 95 | 21 |
| 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 | 42.08779975 | -75.97066066 | 36% | 151 | 54 | 97 | 7 | 85 | 26 |
| 4f93cd09785c6e77bf4bc5a788e079df524c8d21 | soton | SOTON HiD | On a large sequence-based human gait database | [pdf] | | | | | | 36% | 148 | 54 | 94 | 16 | 98 | 0 |
| 0df0d1adea39a5bef318b74faa37de7f3e00b452 | mpii_gaze | MPIIGaze | Appearance-based gaze estimation in the wild | [pdf] | 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) | edu | Max Planck Institute for Informatics | 49.25795660 | 7.04577417 | 38% | 138 | 52 | 86 | 3 | 94 | 7 |
| c0387e788a52f10bf35d4d50659cfa515d89fbec | mars | MARS | MARS: A Video Benchmark for Large-Scale Person Re-Identification | [pdf] | | | | | | 34% | 146 | 49 | 97 | 6 | 96 | 0 |
| 98bb029afe2a1239c3fdab517323066f0957b81b | sdu_vid | SDU-VID | Person Re-identification by Video Ranking | [pdf] | Unknown | | | | | 25% | 196 | 49 | 124 | 11 | 98 | 1 |
| 98bb029afe2a1239c3fdab517323066f0957b81b | sdu_vid | SDU-VID | Person Re-identification by Video Ranking | [pdf] | Unknown | | | | | 25% | 196 | 49 | 124 | 11 | 98 | 1 |
| 98bb029afe2a1239c3fdab517323066f0957b81b | sdu_vid | SDU-VID | Person Re-identification by Video Ranking | [pdf] | Unknown | | | | | 25% | 196 | 49 | 124 | 11 | 98 | 1 |
| 04c2cda00e5536f4b1508cbd80041e9552880e67 | hipsterwars | Hipsterwars | Hipster Wars: Discovering Elements of Fashion Styles | [pdf] | | edu | Tohoku University | 38.25309450 | 140.87365930 | 53% | 91 | 48 | 43 | 5 | 60 | 15 |
| 109df0e8e5969ddf01e073143e83599228a1163f | multi_pie | MULTIPIE | Scheduling heterogeneous multi-cores through performance impact estimation (PIE) | [pdf] | 2012 39th Annual International Symposium on Computer Architecture (ISCA) | | | | | 25% | 192 | 48 | 144 | 8 | 99 | 0 |
| 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 | | | | | 38% | 125 | 48 | 77 | 6 | 72 | 1 |
| 2d3482dcff69c7417c7b933f22de606a0e8e42d4 | lfw | LFW | Labeled Faces in the Wild : Updates and New Reporting Procedures | [pdf] | | edu | University of Massachusetts | 42.38897850 | -72.52869870 | 41% | 116 | 47 | 46 | 4 | 62 | 19 |
| 2d3482dcff69c7417c7b933f22de606a0e8e42d4 | lfw | LFW | Labeled Faces in the Wild : Updates and New Reporting Procedures | [pdf] | | edu | University of Massachusetts | 42.38897850 | -72.52869870 | 41% | 116 | 47 | 46 | 4 | 62 | 19 |
| 2d3482dcff69c7417c7b933f22de606a0e8e42d4 | lfw | LFW | Labeled Faces in the Wild : Updates and New Reporting Procedures | [pdf] | | edu | University of Massachusetts | 42.38897850 | -72.52869870 | 41% | 116 | 47 | 46 | 4 | 62 | 19 |
| 8355d095d3534ef511a9af68a3b2893339e3f96b | imdb_wiki | IMDB | DEX: Deep EXpectation of Apparent Age from a Single Image | [pdf] | 2015 IEEE International Conference on Computer Vision Workshop (ICCVW) | | | | | 44% | 102 | 45 | 54 | 6 | 62 | 28 |
| 8355d095d3534ef511a9af68a3b2893339e3f96b | imdb_wiki | IMDB | DEX: Deep EXpectation of Apparent Age from a Single Image | [pdf] | 2015 IEEE International Conference on Computer Vision Workshop (ICCVW) | | | | | 44% | 102 | 45 | 54 | 6 | 62 | 28 |
| 6ad5a38df8dd4cdddd74f31996ce096d41219f72 | tud_brussels | TUD-Brussels | Multi-cue onboard pedestrian detection | [pdf] | 2009 IEEE Conference on Computer Vision and Pattern Recognition | | | | | 19% | 217 | 41 | 176 | 14 | 131 | 1 |
| 6ad5a38df8dd4cdddd74f31996ce096d41219f72 | tud_motionpairs | TUD-Motionparis | Multi-cue onboard pedestrian detection | [pdf] | 2009 IEEE Conference on Computer Vision and Pattern Recognition | | | | | 19% | 217 | 41 | 176 | 14 | 131 | 1 |
| 0486214fb58ee9a04edfe7d6a74c6d0f661a7668 | chokepoint | ChokePoint | Patch-based probabilistic image quality assessment for face selection and improved video-based face recognition | [pdf] | CVPR 2011 WORKSHOPS | edu | University of Queensland | -27.49741805 | 153.01316956 | 30% | 128 | 39 | 89 | 6 | 68 | 14 |
| 2a4bbee0b4cf52d5aadbbc662164f7efba89566c | peta | PETA | Pedestrian Attribute Recognition At Far Distance | [pdf] | | | | | | 46% | 80 | 37 | 43 | 2 | 51 | 3 |
| 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) | | | | | 58% | 60 | 35 | 25 | 5 | 42 | 12 |
| a0fd85b3400c7b3e11122f44dc5870ae2de9009a | mafl | MAFL | Learning Deep Representation for Face Alignment with Auxiliary Attributes | [pdf] | IEEE Transactions on Pattern Analysis and Machine Intelligence | | | | | 33% | 105 | 35 | 50 | 8 | 55 | 16 |
| a0fd85b3400c7b3e11122f44dc5870ae2de9009a | mafl | MAFL | Learning Deep Representation for Face Alignment with Auxiliary Attributes | [pdf] | IEEE Transactions on Pattern Analysis and Machine Intelligence | | | | | 33% | 105 | 35 | 50 | 8 | 55 | 16 |
| a0fd85b3400c7b3e11122f44dc5870ae2de9009a | mtfl | MTFL | Learning Deep Representation for Face Alignment with Auxiliary Attributes | [pdf] | IEEE Transactions on Pattern Analysis and Machine Intelligence | | | | | 33% | 105 | 35 | 50 | 8 | 55 | 16 |
| a0fd85b3400c7b3e11122f44dc5870ae2de9009a | mtfl | MTFL | Learning Deep Representation for Face Alignment with Auxiliary Attributes | [pdf] | IEEE Transactions on Pattern Analysis and Machine Intelligence | | | | | 33% | 105 | 35 | 50 | 8 | 55 | 16 |
| 214c966d1f9c2a4b66f4535d9a0d4078e63a5867 | brainwash | Brainwash | Brainwash: A Data System for Feature Engineering | [pdf] | | | | | | 60% | 57 | 34 | 23 | 2 | 50 | 0 |
| 0dc11a37cadda92886c56a6fb5191ded62099c28 | stickmen_family | We Are Family Stickmen | We Are Family: Joint Pose Estimation of Multiple Persons | [pdf] | | | | | | 44% | 77 | 34 | 43 | 4 | 57 | 1 |
| 4df3143922bcdf7db78eb91e6b5359d6ada004d2 | cfd | CFD | The Chicago face database: A free stimulus set of faces and norming data. | [pdf] | Behavior research methods | | | | | 39% | 83 | 32 | 51 | 2 | 62 | 3 |
| 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) | | | | | 44% | 72 | 32 | 40 | 7 | 54 | 8 |
| 3cd40bfa1ff193a96bde0207e5140a399476466c | tvhi | TVHI | High Five: Recognising human interactions in TV shows | [pdf] | | | | | | 34% | 91 | 31 | 60 | 11 | 64 | 1 |
| 6618cff7f2ed440a0d2fa9e74ad5469df5cdbe4c | afad | AFAD | Ordinal Regression with Multiple Output CNN for Age Estimation | [pdf] | 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) | | | | | 44% | 68 | 30 | 38 | 8 | 49 | 7 |
| fcc6fe6007c322641796cb8792718641856a22a7 | miw | MIW | Automatic facial makeup detection with application in face recognition | [pdf] | 2013 International Conference on Biometrics (ICB) | edu | West Virginia University | 39.65404635 | -79.96475355 | 65% | 46 | 30 | 16 | 1 | 18 | 21 |
| 0a85bdff552615643dd74646ac881862a7c7072d | pipa | PIPA | Beyond frontal faces: Improving Person Recognition using multiple cues | [pdf] | 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) | | | | | 60% | 50 | 30 | 19 | 2 | 40 | 4 |
| fcc6fe6007c322641796cb8792718641856a22a7 | youtube_makeup | YMU | Automatic facial makeup detection with application in face recognition | [pdf] | 2013 International Conference on Biometrics (ICB) | edu | West Virginia University | 39.65404635 | -79.96475355 | 65% | 46 | 30 | 16 | 1 | 18 | 21 |
| fcc6fe6007c322641796cb8792718641856a22a7 | youtube_makeup | YMU | Automatic facial makeup detection with application in face recognition | [pdf] | 2013 International Conference on Biometrics (ICB) | edu | West Virginia University | 39.65404635 | -79.96475355 | 65% | 46 | 30 | 16 | 1 | 18 | 21 |
| 51eba481dac6b229a7490f650dff7b17ce05df73 | imsitu | imSitu | Situation Recognition: Visual Semantic Role Labeling for Image Understanding | [pdf] | 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) | edu | University of Washington | 47.65432380 | -122.30800894 | 60% | 48 | 29 | 19 | 2 | 45 | 2 |
| 2bf8541199728262f78d4dced6fb91479b39b738 | clothing_co_parsing | CCP | Clothing Co-parsing by Joint Image Segmentation and Labeling | [pdf] | 2014 IEEE Conference on Computer Vision and Pattern Recognition | | | | | 47% | 60 | 28 | 32 | 0 | 36 | 6 |
| 42505464808dfb446f521fc6ff2cfeffd4d68ff1 | gavab_db | Gavab | Expression invariant 3D face recognition with a Morphable Model | [pdf] | 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition | | | | | 29% | 94 | 27 | 67 | 10 | 57 | 5 |
| 066000d44d6691d27202896691f08b27117918b9 | psu | PSU | Vision-Based Analysis of Small Groups in Pedestrian Crowds | [pdf] | IEEE Transactions on Pattern Analysis and Machine Intelligence | | | | | 18% | 151 | 27 | 124 | 9 | 78 | 2 |
| 47aeb3b82f54b5ae8142b4bdda7b614433e69b9a | am_fed | AM-FED | Affectiva-MIT Facial Expression Dataset (AM-FED): Naturalistic and Spontaneous Facial Expressions Collected "In-the-Wild" | [pdf] | | | | | | 36% | 73 | 26 | 47 | 6 | 39 | 16 |
| 356b431d4f7a2a0a38cf971c84568207dcdbf189 | wider | WIDER | Recognize complex events from static images by fusing deep channels | [pdf] | 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) | edu | Shenzhen Institutes of Advanced Technology | 22.59805605 | 113.98533784 | 58% | 45 | 26 | 19 | 1 | 30 | 12 |
| 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 | 42.36782045 | -71.12666653 | 50% | 50 | 25 | 25 | 3 | 39 | 4 |
| 0b84f07af44f964817675ad961def8a51406dd2e | prw | PRW | Person Re-identification in the Wild | [pdf] | 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) | edu | University of Technology Sydney | -33.88096510 | 151.20107299 | 38% | 65 | 25 | 40 | 1 | 46 | 0 |
| 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 | 42.36782045 | -71.12666653 | 50% | 50 | 25 | 25 | 3 | 39 | 4 |
| 2160788824c4c29ffe213b2cbeb3f52972d73f37 | 3d_rma | 3D-RMA | Automatic 3D face authentication | [pdf] | Image Vision Comput. | | | | | 25% | 95 | 24 | 71 | 8 | 60 | 2 |
| 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) | | | | | 49% | 49 | 24 | 25 | 0 | 18 | 22 |
| 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 | edu | Jacobs University | 53.41291480 | -2.96897915 | 44% | 52 | 23 | 29 | 5 | 37 | 4 |
| 3394168ff0719b03ff65bcea35336a76b21fe5e4 | penn_fudan | Penn Fudan | Object Detection Combining Recognition and Segmentation | [pdf] | | | | | | 23% | 101 | 23 | 78 | 11 | 58 | 0 |
| 2e8d0f1802e50cccfd3c0aabac0d0beab3a7846e | 3dpes | 3DPeS | 3DPeS: 3D people dataset for surveillance and forensics | [pdf] | | | | | | 18% | 122 | 22 | 100 | 11 | 71 | 1 |
| 08f6745bc6c1b0fb68953ea61054bdcdde6d2fc7 | kin_face | UB KinFace | Understanding Kin Relationships in a Photo | [pdf] | IEEE Transactions on Multimedia | | | | | 24% | 89 | 21 | 46 | 2 | 20 | 14 |
| 08f6745bc6c1b0fb68953ea61054bdcdde6d2fc7 | kin_face | UB KinFace | Understanding Kin Relationships in a Photo | [pdf] | IEEE Transactions on Multimedia | | | | | 24% | 89 | 21 | 46 | 2 | 20 | 14 |
| 08f6745bc6c1b0fb68953ea61054bdcdde6d2fc7 | kin_face | UB KinFace | Understanding Kin Relationships in a Photo | [pdf] | IEEE Transactions on Multimedia | | | | | 24% | 89 | 21 | 46 | 2 | 20 | 14 |
| eb027969f9310e0ae941e2adee2d42cdf07d938c | 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) | edu | University of Oxford | 51.75345380 | -1.25400997 | 38% | 56 | 21 | 35 | 6 | 50 | 3 |
| 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 | 40.62984145 | 22.95889350 | 28% | 68 | 19 | 49 | 5 | 28 | 19 |
| 2ce2560cf59db59ce313bbeb004e8ce55c5ce928 | texas_3dfrd | Texas 3DFRD | Anthropometric 3D Face Recognition | [pdf] | International Journal of Computer Vision | | | | | 22% | 88 | 19 | 43 | 4 | 42 | 2 |
| 2ce2560cf59db59ce313bbeb004e8ce55c5ce928 | texas_3dfrd | Texas 3DFRD | Anthropometric 3D Face Recognition | [pdf] | International Journal of Computer Vision | | | | | 22% | 88 | 19 | 43 | 4 | 42 | 2 |
| 8b2dd5c61b23ead5ae5508bb8ce808b5ea266730 | 10k_US_adult_faces | 10K US Adult Faces | The intrinsic memorability of face photographs. | [pdf] | Journal of experimental psychology. General | | | | | 36% | 47 | 17 | 30 | 3 | 33 | 1 |
| 69a68f9cf874c69e2232f47808016c2736b90c35 | celeba_plus | CelebFaces+ | Learning Deep Representation for Imbalanced Classification | [pdf] | 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) | edu | Shenzhen Institutes of Advanced Technology | 22.59805605 | 113.98533784 | 33% | 51 | 17 | 34 | 1 | 39 | 2 |
| 79828e6e9f137a583082b8b5a9dfce0c301989b8 | mapillary | Mapillary | The Mapillary Vistas Dataset for Semantic Understanding of Street Scenes | [pdf] | 2017 IEEE International Conference on Computer Vision (ICCV) | | | | | 39% | 44 | 17 | 27 | 0 | 36 | 0 |
| 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 | | | | | 20% | 83 | 17 | 66 | 6 | 42 | 2 |
| 20388099cc415c772926e47bcbbe554e133343d1 | cafe | CAFE | The Child Affective Facial Expression (CAFE) set: validity and reliability from untrained adults | [pdf] | | | | | | 48% | 33 | 16 | 17 | 3 | 28 | 1 |
| c34532fe6bfbd1e6df477c9ffdbb043b77e7804d | columbia_gaze | Columbia Gaze | A 3D Morphable Eye Region Model for Gaze Estimation | [pdf] | Unknown | edu | Carnegie Mellon University | 37.41021930 | -122.05965487 | 67% | 24 | 16 | 8 | 0 | 18 | 4 |
| 47662d1a368daf70ba70ef2d59eb6209f98b675d | fia | CMU FiA | The CMU Face In Action (FIA) Database | [pdf] | | | | | | 29% | 55 | 16 | 39 | 5 | 38 | 7 |
| 213a579af9e4f57f071b884aa872651372b661fd | bbc_pose | BBC Pose | Automatic and Efficient Human Pose Estimation for Sign Language Videos | [pdf] | International Journal of Computer Vision | | | | | 60% | 25 | 15 | 10 | 1 | 18 | 1 |
| 1e3df3ca8feab0b36fd293fe689f93bb2aaac591 | immediacy | Immediacy | Multi-task Recurrent Neural Network for Immediacy Prediction | [pdf] | 2015 IEEE International Conference on Computer Vision (ICCV) | | | | | 60% | 25 | 15 | 10 | 2 | 20 | 0 |
| a5a44a32a91474f00a3cda671a802e87c899fbb4 | moments_in_time | Moments in Time | Moments in Time Dataset: one million videos for event understanding | [pdf] | CoRR | | | | | 60% | 25 | 15 | 10 | 2 | 25 | 0 |
| 4946ba10a4d5a7d0a38372f23e6622bd347ae273 | coco_action | COCO-a | RONCHI AND PERONA: DESCRIBING COMMON HUMAN VISUAL ACTIONS IN IMAGES 1 Describing Common Human Visual Actions in Images | [pdf] | | | | | | 54% | 26 | 14 | 12 | 0 | 25 | 0 |
| 2161f6b7ee3c0acc81603b01dc0df689683577b9 | large_scale_person_search | Large Scale Person Search | End-to-End Deep Learning for Person Search | [pdf] | CoRR | | | | | 34% | 41 | 14 | 27 | 2 | 27 | 0 |
| 1c2802c2199b6d15ecefe7ba0c39bfe44363de38 | youtube_poses | YouTube Pose | Personalizing Human Video Pose Estimation | [pdf] | 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) | | | | | 44% | 32 | 14 | 18 | 2 | 27 | 0 |
| ea050801199f98a1c7c1df6769f23f658299a3ae | mpi_large | Large MPI Facial Expression | The MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial Expressions | [pdf] | | | | | | 46% | 28 | 13 | 15 | 4 | 24 | 3 |
| ea050801199f98a1c7c1df6769f23f658299a3ae | mpi_small | Small MPI Facial Expression | The MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial Expressions | [pdf] | | | | | | 46% | 28 | 13 | 15 | 4 | 24 | 3 |
| 16e8b0a1e8451d5f697b94c0c2b32a00abee1d52 | umb | UMB | UMB-DB: A database of partially occluded 3D faces | [pdf] | 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops) | | | | | 29% | 45 | 13 | 32 | 2 | 20 | 3 |
| d08cc366a4a0192a01e9a7495af1eb5d9f9e73ae | b3d_ac | B3D(AC) | A 3-D Audio-Visual Corpus of Affective Communication | [pdf] | IEEE Transactions on Multimedia | | | | | 31% | 39 | 12 | 27 | 2 | 27 | 7 |
| 0b440695c822a8e35184fb2f60dcdaa8a6de84ae | kinectface | KinectFaceDB | KinectFaceDB: A Kinect Database for Face Recognition | [pdf] | IEEE Transactions on Systems, Man, and Cybernetics: Systems | | | | | 16% | 75 | 12 | 63 | 6 | 25 | 8 |
| 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) | edu | Chinese Academy of Sciences | 40.00447950 | 116.37023800 | 71% | 17 | 12 | 5 | 0 | 13 | 4 |
| 2edb87494278ad11641b6cf7a3f8996de12b8e14 | qmul_grid | GRID | Time-Delayed Correlation Analysis for Multi-Camera Activity Understanding | [pdf] | International Journal of Computer Vision | | | | | 16% | 77 | 12 | 39 | 3 | 32 | 0 |
| 2edb87494278ad11641b6cf7a3f8996de12b8e14 | qmul_grid | GRID | Time-Delayed Correlation Analysis for Multi-Camera Activity Understanding | [pdf] | International Journal of Computer Vision | | | | | 16% | 77 | 12 | 39 | 3 | 32 | 0 |
| 09d78009687bec46e70efcf39d4612822e61cb8c | raid | RAiD | Consistent Re-identification in a Camera Network | [pdf] | | | | | | 27% | 45 | 12 | 33 | 7 | 34 | 1 |
| 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) | | | | | 21% | 58 | 12 | 46 | 7 | 40 | 1 |
| 44d23df380af207f5ac5b41459c722c87283e1eb | wider_attribute | WIDER Attribute | Human Attribute Recognition by Deep Hierarchical Contexts | [pdf] | Unknown | edu | Chinese University of Hong Kong | 22.42031295 | 114.20788644 | 67% | 18 | 12 | 6 | 0 | 16 | 0 |
| 28d4e027c7e90b51b7d8908fce68128d1964668a | megaface | MegaFace | Level Playing Field for Million Scale Face Recognition | [pdf] | 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) | | | | | 41% | 27 | 11 | 16 | 2 | 22 | 4 |
| 28d4e027c7e90b51b7d8908fce68128d1964668a | megaface | MegaFace | Level Playing Field for Million Scale Face Recognition | [pdf] | 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) | | | | | 41% | 27 | 11 | 16 | 2 | 22 | 4 |
| 221c18238b829c12b911706947ab38fd017acef7 | rap_pedestrian | RAP | A Richly Annotated Dataset for Pedestrian Attribute Recognition | [pdf] | CoRR | | | | | 52% | 21 | 11 | 10 | 0 | 18 | 0 |
| 488e475eeb3bb39a145f23ede197cd3620f1d98a | apis | APiS1.0 | Pedestrian Attribute Classification in Surveillance: Database and Evaluation | [pdf] | 2013 IEEE International Conference on Computer Vision Workshops | | | | | 38% | 26 | 10 | 16 | 1 | 13 | 2 |
| 53ae38a6bb2b21b42bac4f0c4c8ed1f9fa02f9d4 | bp4d_plus | BP4D+ | Multimodal Spontaneous Emotion Corpus for Human Behavior Analysis | [pdf] | 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) | | | | | 25% | 40 | 10 | 30 | 0 | 20 | 6 |
| 298cbc3dfbbb3a20af4eed97906650a4ea1c29e0 | ferplus | FER+ | Training deep networks for facial expression recognition with crowd-sourced label distribution | [pdf] | | | | | | 34% | 29 | 10 | 19 | 0 | 15 | 3 |
| 488e475eeb3bb39a145f23ede197cd3620f1d98a | svs | SVS | Pedestrian Attribute Classification in Surveillance: Database and Evaluation | [pdf] | 2013 IEEE International Conference on Computer Vision Workshops | | | | | 38% | 26 | 10 | 16 | 1 | 13 | 2 |
| 5753b2b5e442eaa3be066daa4a2ca8d8a0bb1725 | 50_people_one_question | 50 People One Question | Merging Pose Estimates Across Space and Time | [pdf] | | | | | | 60% | 15 | 9 | 6 | 0 | 11 | 2 |
| 6dcf418c778f528b5792104760f1fbfe90c6dd6a | agedb | AgeDB | AgeDB: The First Manually Collected, In-the-Wild Age Database | [pdf] | 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) | | | | | 82% | 11 | 9 | 2 | 0 | 10 | 0 |
| 0ceda9dae8b9f322df65ca2ef02caca9758aec6f | casablanca | Casablanca | Context-Aware CNNs for Person Head Detection | [pdf] | 2015 IEEE International Conference on Computer Vision (ICCV) | | | | | 33% | 27 | 9 | 18 | 1 | 22 | 0 |
| 4fefd1bc8dc4e0ab37ee3324ddfa43ad9d6a04a7 | deep_fashion | DeepFashion | Fashion Landmark Detection in the Wild | [pdf] | | edu | Chinese University of Hong Kong | 22.42031295 | 114.20788644 | 39% | 23 | 9 | 8 | 0 | 15 | 0 |
| 4fefd1bc8dc4e0ab37ee3324ddfa43ad9d6a04a7 | deep_fashion | DeepFashion | Fashion Landmark Detection in the Wild | [pdf] | | edu | Chinese University of Hong Kong | 22.42031295 | 114.20788644 | 39% | 23 | 9 | 8 | 0 | 15 | 0 |
| 0ceda9dae8b9f322df65ca2ef02caca9758aec6f | hollywood_headset | HollywoodHeads | Context-Aware CNNs for Person Head Detection | [pdf] | 2015 IEEE International Conference on Computer Vision (ICCV) | | | | | 33% | 27 | 9 | 18 | 1 | 22 | 0 |
| faf40ce28857aedf183e193486f5b4b0a8c478a2 | iit_dehli_ear | IIT Dehli Ear | Automated Human Identification Using Ear Imaging | [pdf] | Unknown | | | | | 13% | 70 | 9 | 61 | 6 | 28 | 1 |
| 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) | | | | | 60% | 15 | 9 | 6 | 0 | 10 | 4 |
| 7ace44190729927e5cb0dd5d363fcae966fe13f7 | nudedetection | Nude Detection | A bag-of-features approach based on Hue-SIFT descriptor for nude detection | [pdf] | 2009 17th European Signal Processing Conference | | | | | 18% | 51 | 9 | 42 | 1 | 18 | 0 |
| 4e6ee936eb50dd032f7138702fa39b7c18ee8907 | dartmouth_children | Dartmouth Children | The Dartmouth Database of Children’s Faces: Acquisition and Validation of a New Face Stimulus Set | [pdf] | | | | | | 40% | 20 | 8 | 12 | 2 | 16 | 0 |
| 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 | | | | | 25% | 32 | 8 | 24 | 3 | 16 | 1 |
| 774cbb45968607a027ae4729077734db000a1ec5 | urban_tribes | Urban Tribes | From Bikers to Surfers: Visual Recognition of Urban Tribes | [pdf] | | edu | Columbia University | 40.84198360 | -73.94368971 | 47% | 17 | 8 | 9 | 1 | 12 | 1 |
| 84fe5b4ac805af63206012d29523a1e033bc827e | awe_ears | AWE Ears | Ear recognition: More than a survey | [pdf] | Neurocomputing | | | | | 29% | 24 | 7 | 17 | 0 | 11 | 0 |
| 5801690199c1917fa58c35c3dead177c0b8f9f2d | camel | CAMEL | Application of Object Based Classification and High Resolution Satellite Imagery for Savanna Ecosystem Analysis | [pdf] | Remote Sensing | | | | | 37% | 19 | 7 | 12 | 1 | 16 | 0 |
| 0297448f3ed948e136bb06ceff10eccb34e5bb77 | ilids_mcts | | 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 | | | | | 22% | 32 | 7 | 25 | 2 | 17 | 0 |
| ec792ad2433b6579f2566c932ee414111e194537 | msmt_17 | MSMT17 | Person Transfer GAN to Bridge Domain Gap for Person Re-Identification | [pdf] | CoRR | | | | | 50% | 14 | 7 | 7 | 1 | 11 | 0 |
| b92a1ed9622b8268ae3ac9090e25789fc41cc9b8 | pornodb | Pornography DB | Pooling in image representation: The visual codeword point of view | [pdf] | Computer Vision and Image Understanding | | | | | 9% | 77 | 7 | 70 | 7 | 43 | 2 |
| 19d1b811df60f86cbd5e04a094b07f32fff7a32a | york_3d | UOY 3D Face Database | Three-dimensional face recognition: an eigensurface approach | [pdf] | 2004 International Conference on Image Processing, 2004. ICIP '04. | | | | | 19% | 36 | 7 | 29 | 4 | 25 | 1 |
| 25474c21613607f6bb7687a281d5f9d4ffa1f9f3 | faceplace | Face Place | Recognizing disguised faces | [pdf] | | | | | | 25% | 24 | 6 | 18 | 0 | 16 | 1 |
| 0cb2dd5f178e3a297a0c33068961018659d0f443 | ijb_b | IJB-B | IARPA Janus Benchmark-B Face Dataset | [pdf] | 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) | | | | | 24% | 25 | 6 | 19 | 6 | 21 | 3 |
| bd26dabab576adb6af30484183c9c9c8379bf2e0 | scut_fbp | SCUT-FBP | SCUT-FBP: A Benchmark Dataset for Facial Beauty Perception | [pdf] | 2015 IEEE International Conference on Systems, Man, and Cybernetics | edu | South China University of Technology | 23.05020420 | 113.39880323 | 43% | 14 | 6 | 8 | 3 | 5 | 7 |
| 2a171f8d14b6b8735001a11c217af9587d095848 | social_relation | Social Relation | Learning Social Relation Traits from Face Images | [pdf] | 2015 IEEE International Conference on Computer Vision (ICCV) | edu | Chinese University of Hong Kong | 22.42031295 | 114.20788644 | 30% | 20 | 6 | 14 | 5 | 15 | 0 |
| 2a171f8d14b6b8735001a11c217af9587d095848 | social_relation | Social Relation | Learning Social Relation Traits from Face Images | [pdf] | 2015 IEEE International Conference on Computer Vision (ICCV) | edu | Chinese University of Hong Kong | 22.42031295 | 114.20788644 | 30% | 20 | 6 | 14 | 5 | 15 | 0 |
| 4156b7e88f2e0ab0a7c095b9bab199ae2b23bd06 | distance_nighttime | Long Distance Heterogeneous Face | Nighttime Face Recognition at Long Distance: Cross-Distance and Cross-Spectral Matching | [pdf] | | | | | | 24% | 21 | 5 | 16 | 3 | 11 | 1 |
| 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 | 45.50397610 | -73.57496870 | 28% | 18 | 5 | 6 | 0 | 7 | 4 |
| 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 | 45.50397610 | -73.57496870 | 28% | 18 | 5 | 6 | 0 | 7 | 4 |
| 6f3c76b7c0bd8e1d122c6ea808a271fd4749c951 | ward | WARD | Re-identify people in wide area camera network | [pdf] | 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops | | | | | 9% | 55 | 5 | 50 | 2 | 35 | 0 |
| 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 | | | | | 27% | 15 | 4 | 11 | 1 | 10 | 0 |
| 014b8df0180f33b9fea98f34ae611c6447d761d2 | buhmap_db | BUHMAP-DB | Facial feature tracking and expression recognition for sign language | [pdf] | 2008 23rd International Symposium on Computer and Information Sciences | | | | | 16% | 25 | 4 | 21 | 1 | 10 | 2 |
| 57fe081950f21ca03b5b375ae3e84b399c015861 | cvc_01_barcelona | CVC-01 | Adaptive Image Sampling and Windows Classification for On–board Pedestrian Detection | [pdf] | | | | | | 9% | 44 | 4 | 40 | 1 | 21 | 0 |
| 22f656d0f8426c84a33a267977f511f127bfd7f3 | expw | ExpW | From Facial Expression Recognition to Interpersonal Relation Prediction | [pdf] | International Journal of Computer Vision | | | | | 44% | 9 | 4 | 5 | 0 | 5 | 1 |
| 22f656d0f8426c84a33a267977f511f127bfd7f3 | expw | ExpW | From Facial Expression Recognition to Interpersonal Relation Prediction | [pdf] | International Journal of Computer Vision | | | | | 44% | 9 | 4 | 5 | 0 | 5 | 1 |
| e27ef52c641c2b5100a1b34fd0b819e84a31b4df | sarc3d | Sarc3D | SARC3D: A New 3D Body Model for People Tracking and Re-identification | [pdf] | Unknown | | | | | 14% | 29 | 4 | 25 | 3 | 17 | 0 |
| 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) | edu | Michigan State University | 42.71856800 | -84.47791571 | 67% | 6 | 4 | 2 | 1 | 5 | 0 |
| 7ebb153704706e457ab57b432793d2b6e5d12592 | vgg_celebs_in_places | CIP | Faces in Places: compound query retrieval | [pdf] | Unknown | edu | University of Oxford | 51.75345380 | -1.25400997 | 80% | 5 | 4 | 1 | 0 | 4 | 0 |
| 8d5998cd984e7cce307da7d46f155f9db99c6590 | chalearn | ChaLearn | ChaLearn looking at people: A review of events and resources | [pdf] | 2017 International Joint Conference on Neural Networks (IJCNN) | | | | | 30% | 10 | 3 | 7 | 1 | 6 | 0 |
| a5acda0e8c0937bfed013e6382da127103e41395 | disfa | DISFA | Extended DISFA Dataset: Investigating Posed and Spontaneous Facial Expressions | [pdf] | 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) | | | | | 38% | 8 | 3 | 5 | 1 | 5 | 0 |
| 57178b36c21fd7f4529ac6748614bb3374714e91 | ijb_c | IJB-C | IARPA Janus Benchmark - C: Face Dataset and Protocol | [pdf] | 2018 International Conference on Biometrics (ICB) | | | | | 33% | 9 | 3 | 6 | 2 | 9 | 0 |
| 07fcbae86f7a3ad3ea1cf95178459ee9eaf77cb1 | uccs | UCCS | Large scale unconstrained open set face database | [pdf] | 2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS) | edu | University of Colorado at Colorado Springs | 38.89646790 | -104.80505940 | 60% | 5 | 3 | 2 | 0 | 3 | 0 |
| 8627f019882b024aef92e4eb9355c499c733e5b7 | used | USED Social Event Dataset | USED: a large-scale social event detection dataset | [pdf] | | | | | | 43% | 7 | 3 | 4 | 0 | 3 | 2 |
| 4563b46d42079242f06567b3f2e2f7a80cb3befe | vadana | VADANA | VADANA: A dense dataset for facial image analysis | [pdf] | 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops) | | | | | 19% | 16 | 3 | 13 | 0 | 6 | 6 |
| 56ae6d94fc6097ec4ca861f0daa87941d1c10b70 | cmdp | CMDP | Distance Estimation of an Unknown Person from a Portrait | [pdf] | | edu | California Institute of Technology | 34.13710185 | -118.12527487 | 22% | 9 | 2 | 7 | 0 | 6 | 1 |
| 670637d0303a863c1548d5b19f705860a23e285c | face_tracer | FaceTracer | Face swapping: automatically replacing faces in photographs | [pdf] | ACM Trans. Graph. | edu | Columbia University | 40.84198360 | -73.94368971 | 100% | 2 | 2 | 0 | 0 | 1 | 1 |
| 670637d0303a863c1548d5b19f705860a23e285c | face_tracer | FaceTracer | Face swapping: automatically replacing faces in photographs | [pdf] | ACM Trans. Graph. | edu | Columbia University | 40.84198360 | -73.94368971 | 100% | 2 | 2 | 0 | 0 | 1 | 1 |
| dd65f71dac86e36eecbd3ed225d016c3336b4a13 | families_in_the_wild | FIW | Visual Kinship Recognition of Families in the Wild | [pdf] | IEEE Transactions on Pattern Analysis and Machine Intelligence | | | | | 67% | 3 | 2 | 1 | 0 | 2 | 0 |
| 6dbe8e5121c534339d6e41f8683e85f87e6abf81 | gallagher | Gallagher | Clothing Cosegmentation for Shopping Images With Cluttered Background | [pdf] | IEEE Transactions on Multimedia | | | | | 33% | 6 | 2 | 4 | 0 | 3 | 2 |
| 99eb4cea0d9bc9fe777a5c5172f8638a37a7f262 | ilids_vid_reid | iLIDS-VID | Person Re-identification by Exploiting Spatio-Temporal Cues and Multi-view Metric Learning | [pdf] | IEEE Signal Processing Letters | | | | | 29% | 7 | 2 | 5 | 0 | 4 | 0 |
| 0d2dd4fc016cb6a517d8fb43a7cc3ff62964832e | lag | LAG | Large Age-Gap face verification by feature injection in deep networks | [pdf] | Pattern Recognition Letters | | | | | 29% | 7 | 2 | 5 | 0 | 3 | 2 |
| a7fe834a0af614ce6b50dc093132b031dd9a856b | market1203 | Market 1203 | Orientation Driven Bag of Appearances for Person Re-identification | [pdf] | CoRR | | | | | 29% | 7 | 2 | 5 | 0 | 3 | 0 |
| ad01687649d95cd5b56d7399a9603c4b8e2217d7 | mrp_drone | MRP Drone | Investigating Open-World Person Re-identi cation Using a Drone | [pdf] | | | | | | 40% | 5 | 2 | 3 | 0 | 3 | 0 |
| a7fe834a0af614ce6b50dc093132b031dd9a856b | pku_reid | PKU-Reid | Orientation Driven Bag of Appearances for Person Re-identification | [pdf] | CoRR | | | | | 29% | 7 | 2 | 5 | 0 | 3 | 0 |
| 041d3eedf5e45ce5c5229f0181c5c576ed1fafd6 | ucf_selfie | UCF Selfie | How to Take a Good Selfie? | [pdf] | | | | | | 22% | 9 | 2 | 7 | 0 | 5 | 0 |
| 4b4106614c1d553365bad75d7866bff0de6056ed | czech_news_agency | UFI | Unconstrained Facial Images: Database for Face Recognition Under Real-World Conditions | [pdf] | | | | | | 10% | 10 | 1 | 9 | 0 | 4 | 2 |
| 563c940054e4b456661762c1ab858e6f730c3159 | data_61 | Data61 Pedestrian | A Multi-modal Graphical Model for Scene Analysis | [pdf] | 2015 IEEE Winter Conference on Applications of Computer Vision | | | | | 12% | 8 | 1 | 7 | 0 | 5 | 0 |
| c6526dd3060d63a6c90e8b7ff340383c4e0e0dd8 | face_research_lab | Face Research Lab London | Anxiety promotes memory for mood-congruent faces but does not alter loss aversion. | [pdf] | Scientific reports | edu | University College London | 51.52316070 | -0.12820370 | 25% | 4 | 1 | 3 | 0 | 2 | 2 |
| 17b46e2dad927836c689d6787ddb3387c6159ece | geofaces | GeoFaces | GeoFaceExplorer: exploring the geo-dependence of facial attributes | [pdf] | | edu | University of Kentucky | 38.03337420 | -84.50177580 | 50% | 2 | 1 | 1 | 0 | 1 | 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 |
| d80a3d1f3a438e02a6685e66ee908446766fefa9 | megaage | MegaAge | Quantifying Facial Age by Posterior of Age Comparisons | [pdf] | CoRR | edu | Chinese University of Hong Kong | 22.42031295 | 114.20788644 | 25% | 4 | 1 | 3 | 1 | 4 | 0 |
| 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 | 43.61581310 | 7.06838000 | 20% | 5 | 1 | 4 | 0 | 1 | 2 |
| fb82681ac5d3487bd8e52dbb3d1fa220eeac855e | pilot_parliament | PPB | 1 Network Notebook | [pdf] | | | | | | 9% | 11 | 1 | 10 | 1 | 10 | 0 |
| 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) | | | | | 8% | 12 | 1 | 11 | 1 | 10 | 0 |
| 54983972aafc8e149259d913524581357b0f91c3 | reseed | ReSEED | ReSEED: social event dEtection dataset | [pdf] | | | | | | 17% | 6 | 1 | 5 | 1 | 1 | 1 |
| c9bda86e23cab9e4f15ea0c4cbb6cc02b9dfb709 | stanford_drone | Stanford Drone | Learning to predict human behaviour in crowded scenes | [pdf] | | | | | | 20% | 5 | 1 | 4 | 1 | 5 | 0 |
| 9696ad8b164f5e10fcfe23aacf74bd6168aebb15 | 4dfab | 4DFAB | 4DFAB: A Large Scale 4D Facial Expression Database for Biometric Applications | [pdf] | CoRR | | | | | 0% | 4 | 0 | 4 | 0 | 2 | 0 |
| f152b6ee251cca940dd853c54e6a7b78fbc6b235 | affectnet | AffectNet | Skybiometry and AffectNet on Facial Emotion Recognition Using Supervised Machine Learning Algorithms | [pdf] | Unknown | | | | | 100% | 0 | 0 | 0 | 0 | 0 | 0 |
| 1ed1a49534ad8dd00f81939449f6389cfbc25321 | bjut_3d | BJUT-3D | A novel face recognition method based on 3D face model | [pdf] | 2007 IEEE International Conference on Robotics and Biomimetics (ROBIO) | | | | | 0% | 2 | 0 | 2 | 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) | | | | | 0% | 2 | 0 | 2 | 0 | 2 | 0 |
| f0e17f27f029db4ad650ff278fe3c10ecb6cb0c4 | europersons | EuroCity Persons | The EuroCity Persons Dataset: A Novel Benchmark for Object Detection | [pdf] | CoRR | | | | | 0% | 1 | 0 | 1 | 0 | 1 | 0 |
| 75da1df4ed319926c544eefe17ec8d720feef8c0 | fddb | FDDB | FDDB: A Benchmark for Face Detection in Unconstrained Settings | [pdf] | | edu | University of Massachusetts | 42.38897850 | -72.52869870 | 0% | 1 | 0 | 1 | 0 | 0 | 0 |
| b6b1b0632eb9d4ab1427278f5e5c46f97753c73d | fei | FEI | Generalização cartográfica automatizada para um banco de dados cadastral | [pdf] | Unknown | | | | | 100% | 0 | 0 | 0 | 0 | 0 | 0 |
| 3dc3f0b64ef80f573e3a5f96e456e52ee980b877 | georgia_tech_face_database | Georgia Tech Face | Maximum Likelihood Training of the Embedded HMM for Face Detection and Recognition | [pdf] | | | | | | 0% | 3 | 0 | 3 | 0 | 2 | 0 |
| bd88bb2e4f351352d88ee7375af834360e223498 | hda_plus | HDA+ | A Multi - camera video data set for research on High - Definition surveillance | [pdf] | | | | | | 0% | 1 | 0 | 0 | 0 | 0 | 0 |
| bd88bb2e4f351352d88ee7375af834360e223498 | hda_plus | HDA+ | A Multi - camera video data set for research on High - Definition surveillance | [pdf] | | | | | | 0% | 1 | 0 | 0 | 0 | 0 | 0 |
| 24830e3979d4ed01b9fd0feebf4a8fd22e0c35fd | hi4d_adsip | Hi4D-ADSIP | High-resolution comprehensive 3-D dynamic database for facial articulation analysis | [pdf] | 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops) | | | | | 0% | 5 | 0 | 5 | 0 | 1 | 0 |
| 066d71fcd997033dce4ca58df924397dfe0b5fd1 | ifdb | IFDB | Iranian Face Database and Evaluation with a New Detection Algorithm | [pdf] | | | | | | 100% | 0 | 0 | 0 | 0 | 0 | 0 |
| 066d71fcd997033dce4ca58df924397dfe0b5fd1 | ifdb | IFDB | Iranian Face Database and Evaluation with a New Detection Algorithm | [pdf] | | | | | | 100% | 0 | 0 | 0 | 0 | 0 | 0 |
| ad62c6e17bc39b4dec20d32f6ac667ae42d2c118 | jiku_mobile | Jiku Mobile Video Dataset | A Synchronization Ground Truth for the Jiku Mobile Video Dataset | [pdf] | | | | | | 0% | 1 | 0 | 1 | 0 | 0 | 0 |
| 079a0a3bf5200994e1f972b1b9197bf2f90e87d4 | mit_cbcl | MIT CBCL | Component-Based Face Recognition with 3D Morphable Models | [pdf] | 2004 Conference on Computer Vision and Pattern Recognition Workshop | | | | | 0% | 12 | 0 | 12 | 0 | 8 | 0 |
| 7f4040b482d16354d5938c1d1b926b544652bf5b | nova_emotions | Novaemötions Dataset | Competitive affective gaming: winning with a smile | [pdf] | | | | | | 0% | 8 | 0 | 3 | 0 | 2 | 0 |
| 7f4040b482d16354d5938c1d1b926b544652bf5b | nova_emotions | Novaemötions Dataset | Competitive affective gaming: winning with a smile | [pdf] | | | | | | 0% | 8 | 0 | 3 | 0 | 2 | 0 |
| 22909dd19a0ec3b6065334cb5be5392cb24d839d | pets | PETS 2017 | PETS 2017: Dataset and Challenge | [pdf] | 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) | | | | | 0% | 8 | 0 | 8 | 0 | 2 | 0 |
| f6c8d5e35d7e4d60a0104f233ac1a3ab757da53f | pku | PKU | Swiss-System Based Cascade Ranking for Gait-Based Person Re-Identification | [pdf] | | | | | | 0% | 3 | 0 | 3 | 0 | 1 | 0 |
| c866a2afc871910e3282fd9498dce4ab20f6a332 | qmul_surv_face | QMUL-SurvFace | Surveillance Face Recognition Challenge | [pdf] | CoRR | | | | | 100% | 0 | 0 | 0 | 0 | 0 | 0 |
| f3b84a03985de3890b400b68e2a92c0a00afd9d0 | scface | SCface | Large Variability Surveillance Camera Face Database | [pdf] | 2015 Seventh International Conference on Computational Intelligence, Modelling and Simulation (CIMSim) | | | | | 0% | 1 | 0 | 1 | 0 | 0 | 0 |
| d3f5a1848b0028d8ab51d0b0673732cad2e3c8c9 | stair_actions | STAIR Action | STAIR Actions: A Video Dataset of Everyday Home Actions | [pdf] | CoRR | | | | | 100% | 0 | 0 | 0 | 0 | 0 | 0 |
| 377f2b65e6a9300448bdccf678cde59449ecd337 | ufdd | UFDD | Pushing the Limits of Unconstrained Face Detection: a Challenge Dataset and Baseline Results | [pdf] | CoRR | edu | Johns Hopkins University | 39.32905300 | -76.61942500 | 0% | 2 | 0 | 2 | 0 | 2 | 0 |
| 447d8893a4bdc29fa1214e53499ffe67b28a6db5 | umd_faces | UMD | Electronic Transport in Quantum Confined Systems | [pdf] | | | | | | 0% | 1 | 0 | 1 | 0 | 1 | 0 |
| 447d8893a4bdc29fa1214e53499ffe67b28a6db5 | umd_faces | UMD | Electronic Transport in Quantum Confined Systems | [pdf] | | | | | | 0% | 1 | 0 | 1 | 0 | 1 | 0 |
| 922e0a51a3b8c67c4c6ac09a577ff674cbd28b34 | v47 | V47 | Re-identification of pedestrians with variable occlusion and scale | [pdf] | 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops) | | | | | 0% | 10 | 0 | 10 | 2 | 6 | 0 |
| 9b9bf5e623cb8af7407d2d2d857bc3f1b531c182 | who_goes_there | WGT | Who goes there?: approaches to mapping facial appearance diversity | [pdf] | | | | | | 100% | 0 | 0 | 0 | 0 | 0 | 0 |
| 77c81c13a110a341c140995bedb98101b9e84f7f | wildtrack | WildTrack | WILDTRACK : A Multi-camera HD Dataset for Dense Unscripted Pedestrian Detection | [pdf] | Unknown | | | | | 100% | 0 | 0 | 0 | 0 | 0 | 0 |
| 5ad4e9f947c1653c247d418f05dad758a3f9277b | wlfdb | | WLFDB: Weakly Labeled Face Databases | [pdf] | Unknown | | | | | 0% | 1 | 0 | 1 | 0 | 0 | 0 |
| a94cae786d515d3450d48267e12ca954aab791c4 | yawdd | YawDD | YawDD: a yawning detection dataset | [pdf] | | | | | | 0% | 14 | 0 | 14 | 1 | 2 | 1 |