{"url":"/dataset/ijb-b","name":"IJB-B","full_name":"IARPA Janus Benchmark-B","description_markdown":"The **IJB-B** dataset is a template-based face dataset that contains 1845 subjects with 11,754 images, 55,025 frames and 7,011 videos where a template consists of a varying number of still images and video frames from different sources. These images and videos are collected from the Internet and are totally unconstrained, with large variations in pose, illumination, image quality etc. In addition, the dataset comes with protocols for 1-to-1 template-based face verification, 1-to-N template-based open-set face identification, and 1-to-N open-set video face identification.\r\n\r\nSource: [An Automatic System for Unconstrained Video-Based Face Recognition](https://arxiv.org/abs/1812.04058)\r\nImage Source: [https://www.vislab.ucr.edu/Biometrics2017/program_slides/Noblis_CVPRW_IJBB.pdf](https://www.vislab.ucr.edu/Biometrics2017/program_slides/Noblis_CVPRW_IJBB.pdf)","description_withheld":null,"homepage":"https://www.nist.gov/programs-projects/face-challenges","introduced_date":"2017-01-01","introduced_date_note":null,"introduced_by":{"paper":null,"title":"IARPA Janus Benchmark-B Face Dataset","first_author":null,"url":"https://doi.org/10.1109/CVPRW.2017.87"},"license":{"name":"Custom","url":"https://www.nist.gov/system/files/documents/2017/05/30/readme.pdf"},"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Videos","url":"/datasets/modality/videos"}],"tasks":[{"name":"Face Recognition","url":"/task/face-recognition","datasets_with_task":"/datasets/task/face-recognition"},{"name":"Face Verification","url":"/task/face-verification","datasets_with_task":"/datasets/task/face-verification"},{"name":"Quantization","url":"/task/quantization","datasets_with_task":"/datasets/task/quantization"},{"name":"Lightweight Face Recognition","url":"/task/lightweight-face-recognition","datasets_with_task":"/datasets/task/lightweight-face-recognition"},{"name":"Face Identification","url":"/task/face-identification","datasets_with_task":"/datasets/task/face-identification"}],"languages":[],"variants":["IJB-B"],"data_loaders":[],"num_papers_in_archive":163,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/face-verification-on-ijb-b","task":"Face Verification","dataset_variant":"IJB-B","rows":12,"metrics":["TAR @ FAR=0.01","TAR @ FAR=0.001","TAR@FAR=0.0001","TAR @ FAR=1e-5","TAR @ FAR=0.0001"],"first_row_in_archive_order":{"model":"QMagFace","paper":"/paper/qmagface-simple-and-accurate-quality-aware","metrics":{"TAR @ FAR=0.0001":"94.7","TAR @ FAR=0.001":"96.48","TAR @ FAR=0.01":"97.72%","TAR@FAR=0.0001":"94.7"},"code_links":[{"title":"pterhoer/QMagFace","url":"https://github.com/pterhoer/QMagFace"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/face-recognition-on-ijb-b","task":"Face Recognition","dataset_variant":"IJB-B","rows":4,"metrics":["Rank-1","Rank-5","TAR @ FAR=0.0001","TAR @ FAR=1e-3","TAR @ FAR=1e-4","TAR @ FAR=1e-5"],"first_row_in_archive_order":{"model":"ArcFace+CSFM","paper":"/paper/controllable-and-guided-face-synthesis-for","metrics":{"Rank-1":"0.9496","Rank-5":"0.9684","TAR @ FAR=1e-3":"0.9621","TAR @ FAR=1e-4":"0.9461","TAR @ FAR=1e-5":"0.9095"},"code_links":[{"title":"Faceplugin-ltd/FaceRecognition-Android","url":"https://github.com/Faceplugin-ltd/FaceRecognition-Android"},{"title":"FaceOnLive/Face-Recognition-SDK-Android","url":"https://github.com/FaceOnLive/Face-Recognition-SDK-Android"},{"title":"liuf1990/CFSM","url":"https://github.com/liuf1990/CFSM"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/lightweight-face-recognition-on-ijb-b","task":"Lightweight Face Recognition","dataset_variant":"IJB-B","rows":3,"metrics":["TAR @ FAR=0.01","MFLOPs","MParams"],"first_row_in_archive_order":{"model":"EdgeFace - S (g=0.5)","paper":"/paper/edgeface-efficient-face-recognition-model-for","metrics":{"MFLOPs":"306.11","MParams":"3.65","TAR @ FAR=0.01":"0.9358"},"code_links":[{"title":"otroshi/edgeface","url":"https://github.com/otroshi/edgeface"},{"title":"anjith2006/bob.paper.tbiom2023_edgeface","url":"https://github.com/anjith2006/bob.paper.tbiom2023_edgeface"},{"title":"bob/bob.paper.tbiom2023_edgeface","url":"https://gitlab.idiap.ch/bob/bob.paper.tbiom2023_edgeface"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/face-identification-on-ijb-b","task":"Face Identification","dataset_variant":"IJB-B","rows":1,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"FPN","paper":"/paper/faceposenet-making-a-case-for-landmark-free","metrics":{"Accuracy":"91.1%"},"code_links":[{"title":"fengju514/Expression-Net","url":"https://github.com/fengju514/Expression-Net"},{"title":"fengju514/Face-Pose-Net","url":"https://github.com/fengju514/Face-Pose-Net"},{"title":"iacopomasi/face_specific_augm","url":"https://github.com/iacopomasi/face_specific_augm"},{"title":"Recognito-Vision/Android-FaceRecognition-FaceLivenessDetection","url":"https://github.com/Recognito-Vision/Android-FaceRecognition-FaceLivenessDetection"},{"title":"nova26/facePoseEstimation","url":"https://github.com/nova26/facePoseEstimation"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/quantization-on-ijb-b","task":"Quantization","dataset_variant":"IJB-B","rows":1,"metrics":["TAR @ FAR=1e-4"],"first_row_in_archive_order":{"model":"","paper":"/paper/quantface-towards-lightweight-face","metrics":{"TAR @ FAR=1e-4":"95.13"},"code_links":[{"title":"fdbtrs/QuantFace","url":"https://github.com/fdbtrs/QuantFace"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/edgeface-efficient-face-recognition-model-for","title":"EdgeFace: Efficient Face Recognition Model for Edge Devices","date":"2023-07-04","rows_on_this_dataset":2,"code_links":3,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":2,"samples_ran":2,"samples_unverified":0,"pointer_only_for_licence":2,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/cluster-and-aggregate-face-recognition-with","title":"Cluster and Aggregate: Face Recognition with Large Probe Set","date":"2022-10-19","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":9,"samples_ran":7,"samples_unverified":2,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/controllable-and-guided-face-synthesis-for","title":"Controllable and Guided Face Synthesis for Unconstrained Face Recognition","date":"2022-07-20","rows_on_this_dataset":1,"code_links":3,"syntology":null},{"paper":"/paper/quantface-towards-lightweight-face","title":"QuantFace: Towards Lightweight Face Recognition by Synthetic Data Low-bit Quantization","date":"2022-06-21","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/adaface-quality-adaptive-margin-for-face","title":"AdaFace: Quality Adaptive Margin for Face Recognition","date":"2022-04-03","rows_on_this_dataset":5,"code_links":9,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":22,"samples_ran":17,"samples_unverified":5,"pointer_only_for_licence":11,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/killing-two-birds-with-one-stone-efficient","title":"Killing Two Birds with One Stone:Efficient and Robust Training of Face Recognition CNNs by Partial FC","date":"2022-03-28","rows_on_this_dataset":1,"code_links":6,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":12,"samples_ran":9,"samples_unverified":3,"pointer_only_for_licence":4,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/unified-negative-pair-generation-toward-well","title":"Unified Negative Pair Generation toward Well-discriminative Feature Space for Face Recognition","date":"2022-03-22","rows_on_this_dataset":3,"code_links":2,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":14,"samples_ran":9,"samples_unverified":5,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/qmagface-simple-and-accurate-quality-aware","title":"QMagFace: Simple and Accurate Quality-Aware Face Recognition","date":"2021-11-26","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/elasticface-elastic-margin-loss-for-deep-face","title":"ElasticFace: Elastic Margin Loss for Deep Face Recognition","date":"2021-09-20","rows_on_this_dataset":1,"code_links":3,"syntology":null},{"paper":"/paper/mixfacenets-extremely-efficient-face","title":"MixFaceNets: Extremely Efficient Face Recognition Networks","date":"2021-07-27","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"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/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":"2026-09-25T09:33:49+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}],"syntology_totals":{"read_at":"2026-09-25T09:33:49+00:00","papers_with_samples":6,"samples_harvested":67,"samples_ran":49,"samples_unverified":18,"pointer_only_for_licence":22,"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."}