| key | name | our title | found title | | | address | s2 id | | ilids_mcts | i-LIDS | 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 |
| 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] | | 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 |
| casablanca | Casablanca | Context-aware {CNNs} for person head detection | Context-Aware CNNs for Person Head Detection | [pdf] | [s2] | | 0ceda9dae8b9f322df65ca2ef02caca9758aec6f |
| 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 |
| frgc | FRGC | Overview of the Face Recognition Grand Challenge | Overview of the face recognition grand challenge | [pdf] | [s2] | | 18ae7c9a4bbc832b8b14bc4122070d7939f5e00e |
| images_of_groups | Images of Groups | Understanding Groups of Images of People | Understanding images of groups of people | [pdf] | [s2] | | 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 |
| names_and_faces_news | News Dataset | Names and Faces | Names and faces in the news | [pdf] | [s2] | | 2fda164863a06a92d3a910b96eef927269aeb730 |
| tiny_images | Tiny Images | 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 |
| lfw | LFW | Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments | Labeled Faces in the Wild : A Database for Studying Face Recognition in Unconstrained Environments | [pdf] | [s2] | | 370b5757a5379b15e30d619e4d3fb9e8e13f3256 |
| georgia_tech_face_database | Georgia Tech Face | Maximum likelihood training of the embedded HMM for face detection and recognition | MAXIMUM LIKELIHOOD TRAINING OF THE EMBEDDED HMM FOR FACE DETECTION AND RECOGNITION Ara V. Ne an and Monson H. Hayes III Center for Signal and Image Processing School of Electrical and Computer Engineering | [pdf] | [s2] | | 3dc3f0b64ef80f573e3a5f96e456e52ee980b877 |
| malf | MALF | Fine-grained Evaluation on Face Detection in the Wild. | Fine-grained evaluation on face detection in the wild | [pdf] | [s2] | | 45e616093a92e5f1e61a7c6037d5f637aa8964af |
| 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 |
| cmu_pie | CMU PIE | The CMU Pose, Illumination, and Expression Database | The CMU Pose, Illumination, and Expression (PIE) Database | [pdf] | [s2] | | 4d423acc78273b75134e2afd1777ba6d3a398973 |
| multi_pie | MULTIPIE | Multi-PIE | The CMU Pose, Illumination, and Expression (PIE) Database | [pdf] | [s2] | | 4d423acc78273b75134e2afd1777ba6d3a398973 |
| 3dddb_unconstrained | 3D Dynamic | A 3D Dynamic Database for Unconstrained Face Recognition | A 3 D Dynamic Database for Unconstrained Face Recognition | [pdf] | [s2] | | 4d4bb462c9f1d4e4ab1e4aa6a75cc0bc71b38461 |
| 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 |
| 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 |
| ifad | IFAD | Indian Face Age Database: A Database for Face Recognition with Age Variation | Indian Face Age Database : A Database for Face Recognition with Age Variation | [pdf] | [s2] | | 55c40cbcf49a0225e72d911d762c27bb1c2d14aa |
| stanford_drone | Stanford Drone | Learning Social Etiquette: Human Trajectory Prediction In Crowded Scenes | Social LSTM: Human Trajectory Prediction in Crowded Spaces | [pdf] | [s2] | | 570f37ed63142312e6ccdf00ecc376341ec72b9f |
| ijb_c | IJB-C | IARPA Janus Benchmark C | IARPA Janus Benchmark - C: Face Dataset and Protocol | [pdf] | [s2] | | 57178b36c21fd7f4529ac6748614bb3374714e91 |
| 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 |
| cvc_01_barcelona | CVC-01 | Adaptive Image Sampling and Windows Classification for On-board Pedestrian Detection | Adaptive Image Sampling and Windows Classification for On – board Pedestrian Detection | [pdf] | [s2] | | 57fe081950f21ca03b5b375ae3e84b399c015861 |
| viper | VIPeR | Evaluating Appearance Models for Recognition, Reacquisition, and Tracking | Evaluating Appearance Models for Recognition , Reacquisition , and Tracking | [pdf] | [s2] | | 6273b3491e94ea4dd1ce42b791d77bdc96ee73a8 |
| alert_airport | ALERT Airport | A Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets | A Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets. | [pdf] | [s2] | | 6403117f9c005ae81f1e8e6d1302f4a045e3d99d |
| afad | AFAD | Ordinal Regression with a Multiple Output CNN for Age Estimation | Ordinal Regression with Multiple Output CNN for Age Estimation | [pdf] | [s2] | | 6618cff7f2ed440a0d2fa9e74ad5469df5cdbe4c |
| 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 |
| 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 |
| fddb | FDDB | FDDB: A Benchmark for Face Detection in Unconstrained Settings | A Benchmark for Face Detection in Unconstrained Settings | [pdf] | [s2] | | 75da1df4ed319926c544eefe17ec8d720feef8c0 |
| wildtrack | WildTrack | WILDTRACK: A Multi-camera HD Dataset for Dense Unscripted Pedestrian Detection | WILDTRACK : A Multi-camera HD Dataset for Dense Unscripted Pedestrian Detection | [pdf] | [s2] | | 77c81c13a110a341c140995bedb98101b9e84f7f |
| lfw | LFW | Labeled Faces in the Wild: A Survey | Labeled Faces in the Wild : A Survey | [pdf] | [s2] | | 7de6e81d775e9cd7becbfd1bd685f4e2a5eebb22 |
| nova_emotions | Novaemötions Dataset | Competitive affective gamming: Winning with a smile | Competitive affective gaming: winning with a smile | [pdf] | [s2] | | 7f4040b482d16354d5938c1d1b926b544652bf5b |
| sun_attributes | SUN | SUN Attribute Database:
Discovering, Annotating, and Recognizing Scene Attributes | SUN attribute database: Discovering, annotating, and recognizing scene attributes | [pdf] | [s2] | | 833fa04463d90aab4a9fe2870d480f0b40df446e |
| 10k_US_adult_faces | 10K US Adult Faces | The intrinsic memorability of face images | The intrinsic memorability of face photographs. | [pdf] | [s2] | | 8b2dd5c61b23ead5ae5508bb8ce808b5ea266730 |
| 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 |
| ilids_vid_reid | iLIDS-VID | Person Re-Identi cation by Video Ranking | Person Re-identification by Video Ranking | [pdf] | [s2] | | 98bb029afe2a1239c3fdab517323066f0957b81b |
| sdu_vid | SDU-VID | Person reidentification by video ranking | Person Re-identification by Video Ranking | [pdf] | [s2] | | 98bb029afe2a1239c3fdab517323066f0957b81b |
| who_goes_there | WGT | Who Goes There? Approaches to Mapping Facial Appearance Diversity | Who goes there?: approaches to mapping facial appearance diversity | [pdf] | [s2] | | 9b9bf5e623cb8af7407d2d2d857bc3f1b531c182 |
| bp4d_spontanous | BP4D-Spontanous | A high resolution spontaneous 3D dynamic facial expression database | A high-resolution spontaneous 3D dynamic facial expression database | [pdf] | [s2] | | b91f54e1581fbbf60392364323d00a0cd43e493c |
| 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 |
| imdb_face | | | The Devil of Face Recognition is in the Noise | [pdf] | [s2] | | 9e31e77f9543ab42474ba4e9330676e18c242e72 |