| key | name | our title | found title | | | address | s2 id | | 10k_US_adult_faces | 10K US Adult Faces | The intrinsic memorability of face images | The intrinsic memorability of face photographs. | [pdf] | [s2] | | 8b2dd5c61b23ead5ae5508bb8ce808b5ea266730 |
| afad | AFAD | Ordinal Regression with a Multiple Output CNN for Age Estimation | Ordinal Regression with Multiple Output CNN for Age Estimation | [pdf] | [s2] | | 6618cff7f2ed440a0d2fa9e74ad5469df5cdbe4c |
| afw | AFW | Face detection, pose estimation and landmark localization in the wild | Face detection, pose estimation, and landmark localization in the wild | [pdf] | [s2] | | 0e986f51fe45b00633de9fd0c94d082d2be51406 |
| am_fed | AM-FED | Affectiva MIT Facial Expression Dataset (AM-FED): Naturalistic and Spontaneous Facial Expressions Collected “In the Wild” | Affectiva-MIT Facial Expression Dataset (AM-FED): Naturalistic and Spontaneous Facial Expressions Collected "In-the-Wild" | [pdf] | [s2] | | 47aeb3b82f54b5ae8142b4bdda7b614433e69b9a |
| bp4d_spontanous | BP4D-Spontanous | A high resolution spontaneous 3D dynamic facial expression database | A high-resolution spontaneous 3D dynamic facial expression database | [pdf] | [s2] | SUNY Binghamton | b91f54e1581fbbf60392364323d00a0cd43e493c |
| casablanca | Casablanca | Context-aware {CNNs} for person head detection | Context-Aware CNNs for Person Head Detection | [pdf] | [s2] | | 0ceda9dae8b9f322df65ca2ef02caca9758aec6f |
| cfd | CFD | The Chicago face database: A free stimulus set of faces and norming data | The Chicago face database: A free stimulus set of faces and norming data. | [pdf] | [s2] | | 4df3143922bcdf7db78eb91e6b5359d6ada004d2 |
| cmu_pie | CMU PIE | The CMU Pose, Illumination, and Expression Database | The CMU Pose, Illumination, and Expression (PIE) Database | [pdf] | [s2] | | 4d423acc78273b75134e2afd1777ba6d3a398973 |
| columbia_gaze | Columbia Gaze | Gaze Locking: Passive Eye Contact Detection for Human–Object Interaction | Gaze locking: passive eye contact detection for human-object interaction | [pdf] | [s2] | Columbia University | 06f02199690961ba52997cde1527e714d2b3bf8f |
| d3dfacs | D3DFACS | A FACS Valid 3D Dynamic Action Unit database with Applications to 3D Dynamic Morphable Facial Modelling | A FACS valid 3D dynamic action unit database with applications to 3D dynamic morphable facial modeling | [pdf] | [s2] | | 070de852bc6eb275d7ca3a9cdde8f6be8795d1a3 |
| dartmouth_children | Dartmouth Children | The Dartmouth Database of Children's Faces: Acquisition and validation of a new face stimulus set | The Dartmouth Database of Children’s Faces: Acquisition and Validation of a New Face Stimulus Set | [pdf] | [s2] | | 4e6ee936eb50dd032f7138702fa39b7c18ee8907 |
| disfa | DISFA | 1 | DISFA: A Spontaneous Facial Action Intensity Database | [pdf] | [s2] | University of Denver | 5a5f0287484f0d480fed1ce585dbf729586f0edc |
| fei | FEI | Captura e Alinhamento de Imagens: Um Banco de Faces Brasileiro | A new ranking method for principal components analysis and its application to face image analysis | [pdf] | [s2] | | 8b56e33f33e582f3e473dba573a16b598ed9bcdc |
| frgc | FRGC | Overview of the Face Recognition Grand Challenge | Overview of the face recognition grand challenge | [pdf] | [s2] | NIST | 18ae7c9a4bbc832b8b14bc4122070d7939f5e00e |
| geofaces | GeoFaces | FACE2GPS: Estimating geographic location from facial features | Exploring the geo-dependence of human face appearance | [pdf] | [s2] | | 2cd7821fcf5fae53a185624f7eeda007434ae037 |
| hda_plus | HDA+ | A Multi-camera video data set for research on High-Definition surveillance | HDA dataset-DRAFT 1 A Multi-camera video data set for research on High-Definition surveillance | [pdf] | [s2] | | bd88bb2e4f351352d88ee7375af834360e223498 |
| ibm_dif | IBM Diversity in Faces | Diversity in Faces | Facial Coding Scheme Reference 1 Craniofacial Distances | [pdf] | [s2] | | 0ab7cff2ccda7269b73ff6efd9d37e1318f7db25 |
| ijb_c | IJB-C | IARPA Janus Benchmark C | IARPA Janus Benchmark - C: Face Dataset and Protocol | [pdf] | [s2] | | 57178b36c21fd7f4529ac6748614bb3374714e91 |
| ilids_mcts | i-LIDS Multiple-Camera | Imagery Library for Intelligent Detection Systems: The i-LIDS User Guide | Imagery Library for Intelligent Detection Systems (i-LIDS); A Standard for Testing Video Based Detection Systems | [pdf] | [s2] | | 0297448f3ed948e136bb06ceff10eccb34e5bb77 |
| ilids_mcts_vid | iLIDS-VID | Person Re-Identi cation by Video Ranking | Person Re-identification by Video Ranking | [pdf] | [s2] | | 98bb029afe2a1239c3fdab517323066f0957b81b |
| images_of_groups | Images of Groups | Understanding Groups of Images of People | Understanding images of groups of people | [pdf] | [s2] | Carnegie Mellon University | 21d9d0deed16f0ad62a4865e9acf0686f4f15492 |
| lfw | LFW | Labeled Faces in the Wild: Updates and New Reporting Procedures | Labeled Faces in the Wild : Updates and New Reporting Procedures | [pdf] | [s2] | | 2d3482dcff69c7417c7b933f22de606a0e8e42d4 |
| lfw | LFW | Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments | Labeled Faces in the Wild: A Database forStudying Face Recognition in Unconstrained Environments | [pdf] | [s2] | | 370b5757a5379b15e30d619e4d3fb9e8e13f3256 |
| m2vtsdb_extended | xm2vtsdb | XM2VTSDB: The Extended M2VTS Database | XM2VTSDB : The extended M2VTS database | [pdf] | [s2] | | b62628ac06bbac998a3ab825324a41a11bc3a988 |
| malf | MALF | Fine-grained Evaluation on Face Detection in the Wild. | Fine-grained evaluation on face detection in the wild | [pdf] | [s2] | | 45e616093a92e5f1e61a7c6037d5f637aa8964af |
| mr2 | MR2 | The MR2: A multi-racial mega-resolution database of facial stimuli | The MR2: A multi-racial, mega-resolution database of facial stimuli. | [pdf] | [s2] | | 578d4ad74818086bb64f182f72e2c8bd31e3d426 |
| multi_pie | MULTIPIE | Multi-PIE | The CMU Pose, Illumination, and Expression (PIE) Database | [pdf] | [s2] | | 4d423acc78273b75134e2afd1777ba6d3a398973 |
| names_and_faces | News Dataset | Names and Faces | Names and faces in the news | [pdf] | [s2] | | 2fda164863a06a92d3a910b96eef927269aeb730 |
| nova_emotions | Novaemötions Dataset | Competitive affective gamming: Winning with a smile | Competitive affective gaming: winning with a smile | [pdf] | [s2] | Universidade NOVA de Lisboa, Caparica, Portugal | 7f4040b482d16354d5938c1d1b926b544652bf5b |
| sdu_vid | SDU-VID | Person reidentification by video ranking | Person Re-identification by Video Ranking | [pdf] | [s2] | | 98bb029afe2a1239c3fdab517323066f0957b81b |
| stanford_drone | Stanford Drone | Learning Social Etiquette: Human Trajectory Prediction In Crowded Scenes | Social LSTM: Human Trajectory Prediction in Crowded Spaces | [pdf] | [s2] | | 570f37ed63142312e6ccdf00ecc376341ec72b9f |
| stickmen_buffy | Buffy Stickmen | Learning to Parse Images of Articulated Objects | Learning to parse images of articulated bodies | [pdf] | [s2] | | 6dd0597f8513dc100cd0bc1b493768cde45098a9 |
| stickmen_pascal | Stickmen PASCAL | Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation | Learning to parse images of articulated bodies | [pdf] | [s2] | | 6dd0597f8513dc100cd0bc1b493768cde45098a9 |
| stickmen_pascal | Stickmen PASCAL | Learning to Parse Images of Articulated Objects | Learning to parse images of articulated bodies | [pdf] | [s2] | | 6dd0597f8513dc100cd0bc1b493768cde45098a9 |
| tiny_images | #N/A | 80 million tiny images: a large dataset for non-parametric object and scene recognition | 80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition | [pdf] | [s2] | | 31b58ced31f22eab10bd3ee2d9174e7c14c27c01 |
| umd_faces | UMD | The Do's and Don'ts for CNN-based Face Verification | The Do’s and Don’ts for CNN-Based Face Verification | [pdf] | [s2] | | 71b7fc715e2f1bb24c0030af8d7e7b6e7cd128a6 |
| voxceleb2 | VoxCeleb2 | VoxCeleb2: Deep Speaker Recognition | VoxCeleb2: Deep Speaker Recognition. | [pdf] | [s2] | | 8875ae233bc074f5cd6c4ebba447b536a7e847a5 |
| who_goes_there | WGT | Who Goes There? Approaches to Mapping Facial Appearance Diversity | Who goes there?: approaches to mapping facial appearance diversity | [pdf] | [s2] | University of Kentucky | 9b9bf5e623cb8af7407d2d2d857bc3f1b531c182 |
| wlfdb | WLFDB | WLFDB: Weakly Labeled Face Databases | WLFDB : Weakly Labeled Face Databases | [pdf] | [s2] | | 5ad4e9f947c1653c247d418f05dad758a3f9277b |