{"url":"/dataset/ijb-a","name":"IJB-A","full_name":"IARPA Janus Benchmark A","description_markdown":"The **IARPA Janus Benchmark A** (**IJB-A**) database is developed with the aim to augment more challenges to the face recognition task by collecting facial images with a wide variations in pose, illumination, expression, resolution and occlusion. IJB-A is constructed by collecting 5,712 images and 2,085 videos from 500 identities, with an average of 11.4 images and 4.2 videos per identity.\r\n\r\nSource: [von Mises-Fisher Mixture Model-based Deep learning: Application to Face Verification](https://arxiv.org/abs/1706.04264)\r\n\r\nImage Source: [Ruan et al](https://www.researchgate.net/figure/The-IARPA-Janus-Benchmark-A-IJB-A-dataset-face-verification-11-test-protocol-a_fig12_342756996)","description_withheld":null,"homepage":"https://www.nist.gov/programs-projects/face-challenges","introduced_date":"2015-01-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/pushing-the-frontiers-of-unconstrained-face","title":"Pushing the Frontiers of Unconstrained Face Detection and Recognition: IARPA Janus Benchmark A","first_author":"Brendan F. Klare","url":null},"license":{"name":"Custom","url":"https://nigos.nist.gov/datasets/ijbc/request"},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Face Verification","url":"/task/face-verification","datasets_with_task":"/datasets/task/face-verification"},{"name":"Face Identification","url":"/task/face-identification","datasets_with_task":"/datasets/task/face-identification"}],"languages":[],"variants":["IJB-A"],"data_loaders":[],"num_papers_in_archive":156,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/face-verification-on-ijb-a","task":"Face Verification","dataset_variant":"IJB-A","rows":17,"metrics":["TAR @ FAR=0.01","TAR @ FAR=0.001","TAR @ FAR=0.1"],"first_row_in_archive_order":{"model":"Dual-Agent GANs","paper":"/paper/dual-agent-gans-for-photorealistic-and","metrics":{"TAR @ FAR=0.01":"97.60%"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/face-identification-on-ijb-a","task":"Face Identification","dataset_variant":"IJB-A","rows":3,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"StyleFNM","paper":"/paper/inclusive-normalization-of-face-images-to","metrics":{"Accuracy":"94.90%"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/inclusive-normalization-of-face-images-to","title":"Inclusive normalization of face images to passport format","date":"2023-12-22","rows_on_this_dataset":2,"code_links":0,"syntology":null},{"paper":"/paper/probabilistic-face-embeddings","title":"Probabilistic Face Embeddings","date":"2019-04-21","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":1,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/ghostvlad-for-set-based-face-recognition","title":"GhostVLAD for set-based face recognition","date":"2018-10-23","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/semi-supervised-adversarial-learning-to","title":"Semi-supervised Adversarial Learning to Generate Photorealistic Face Images of New Identities from 3D Morphable Model","date":"2018-04-10","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/pose-robust-face-recognition-via-deep","title":"Pose-Robust Face Recognition via Deep Residual Equivariant Mapping","date":"2018-03-02","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":15,"samples_ran":1,"samples_unverified":14,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/dual-agent-gans-for-photorealistic-and","title":"Dual-Agent GANs for Photorealistic and Identity Preserving Profile Face Synthesis","date":"2017-12-01","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/vggface2-a-dataset-for-recognising-faces","title":"VGGFace2: A dataset for recognising faces across pose and age","date":"2017-10-23","rows_on_this_dataset":1,"code_links":24,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":8,"samples_ran":5,"samples_unverified":3,"pointer_only_for_licence":5,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/faceposenet-making-a-case-for-landmark-free","title":"FacePoseNet: Making a Case for Landmark-Free Face Alignment","date":"2017-08-24","rows_on_this_dataset":2,"code_links":5,"syntology":null},{"paper":"/paper/l2-constrained-softmax-loss-for","title":"L2-constrained Softmax Loss for Discriminative Face Verification","date":"2017-03-28","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/an-all-in-one-convolutional-neural-network","title":"An All-In-One Convolutional Neural Network for Face Analysis","date":"2016-11-03","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/triplet-probabilistic-embedding-for-face","title":"Triplet Probabilistic Embedding for Face Verification and Clustering","date":"2016-04-19","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/face-recognition-using-deep-multi-pose","title":"Face Recognition Using Deep Multi-Pose Representations","date":"2016-03-23","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/do-we-really-need-to-collect-millions-of","title":"Do We Really Need to Collect Millions of Faces for Effective Face Recognition?","date":"2016-03-23","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/neural-aggregation-network-for-video-face","title":"Neural Aggregation Network for Video Face Recognition","date":"2016-03-17","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/template-adaptation-for-face-verification-and","title":"Template Adaptation for Face Verification and Identification","date":"2016-03-12","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/unconstrained-face-verification-using-deep","title":"Unconstrained Face Verification using Deep CNN Features","date":"2015-08-07","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/face-search-at-scale-80-million-gallery","title":"Face Search at Scale: 80 Million Gallery","date":"2015-07-26","rows_on_this_dataset":1,"code_links":0,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":3,"samples_harvested":24,"samples_ran":7,"samples_unverified":17,"pointer_only_for_licence":5,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}