{"url":"/task/face-identification","name":"Face Identification","slug":"face-identification","description_markdown":"Face identification is the task of matching a given face image to one in an existing database of faces. It is the second part of face recognition (the first part being detection). It is a one-to-many mapping: you have to find an unknown person in a database to find who that person is.","categories":[{"name":"Computer Vision","url":"/area/computer-vision"},{"name":"Miscellaneous","url":"/area/miscellaneous"},{"name":"Music","url":"/area/music"}],"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","slug_source":"archive_url"},"counts":{"papers_tagged":144,"papers_with_code":46,"benchmarks":5,"benchmark_tables_in_archive":5,"benchmark_tables_shown":5,"benchmark_tables_withheld_as_spam":0,"benchmark_definition":"a leaderboard table with at least one row; benchmark_tables_shown also counts the zero-row tables; benchmark_tables_in_archive adds the tables withheld as spam","datasets":7,"subtasks":0,"parent_tasks":1},"benchmarks":[{"leaderboard":"/sota/face-identification-on-megaface","slug":"face-identification-on-megaface","dataset":"MegaFace","dataset_url":"/dataset/megaface","rows_in_archive":13,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"Cos+UNPG","paper_title":"Unified Negative Pair Generation toward Well-discriminative Feature Space for Face Recognition","paper_url":"/paper/unified-negative-pair-generation-toward-well","paper_date":"2022-03-22","arxiv_id":"2203.11593","code_links":[{"title":"tomas-gajarsky/facetorch","url":"https://github.com/tomas-gajarsky/facetorch"},{"title":"jung-jun-uk/unpg","url":"https://github.com/jung-jun-uk/unpg"}],"syntology":{"n":14,"n_ran":2,"n_unverified":12,"n_pointer_only":0}}},{"leaderboard":"/sota/face-identification-on-dronesurf","slug":"face-identification-on-dronesurf","dataset":"DroneSURF","dataset_url":"/dataset/dronesurf","rows_in_archive":6,"metrics":["Rank1"],"first_row_in_archive_order":{"model":"CoNAN (Adaface)","paper_title":"CoNAN: Conditional Neural Aggregation Network For Unconstrained Face Feature Fusion","paper_url":"/paper/conan-conditional-neural-aggregation-network","paper_date":"2023-07-16","arxiv_id":"2307.10237","code_links":[],"syntology":null}},{"leaderboard":"/sota/face-identification-on-trillion-pairs-dataset","slug":"face-identification-on-trillion-pairs-dataset","dataset":"Trillion Pairs Dataset","dataset_url":null,"rows_in_archive":6,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"SV-AM-Softmax","paper_title":"Support Vector Guided Softmax Loss for Face Recognition","paper_url":"/paper/support-vector-guided-softmax-loss-for-face","paper_date":"2018-12-29","arxiv_id":"1812.11317","code_links":[{"title":"Recognito-Vision/Linux-FaceRecognition-FaceLivenessDetection","url":"https://github.com/Recognito-Vision/Linux-FaceRecognition-FaceLivenessDetection"},{"title":"xiaoboCASIA/SV-X-Softmax","url":"https://github.com/xiaoboCASIA/SV-X-Softmax"},{"title":"comratvlad/sv_softmax","url":"https://github.com/comratvlad/sv_softmax"},{"title":"SevenZhan/Pytorch","url":"https://github.com/SevenZhan/Pytorch"},{"title":"MindSpore-scientific-2/code-11","url":"https://github.com/MindSpore-scientific-2/code-11/tree/main/SV-X-Softmax"}],"syntology":null}},{"leaderboard":"/sota/face-identification-on-ijb-a","slug":"face-identification-on-ijb-a","dataset":"IJB-A","dataset_url":"/dataset/ijb-a","rows_in_archive":3,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"StyleFNM","paper_title":"Inclusive normalization of face images to passport format","paper_url":"/paper/inclusive-normalization-of-face-images-to","paper_date":"2023-12-22","arxiv_id":"2312.14544","code_links":[],"syntology":null}},{"leaderboard":"/sota/face-identification-on-ijb-b","slug":"face-identification-on-ijb-b","dataset":"IJB-B","dataset_url":"/dataset/ijb-b","rows_in_archive":1,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"FPN","paper_title":"FacePoseNet: Making a Case for Landmark-Free Face Alignment","paper_url":"/paper/faceposenet-making-a-case-for-landmark-free","paper_date":"2017-08-24","arxiv_id":"1708.07517","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"}],"syntology":null}}],"datasets":[{"url":"/dataset/ms-celeb-1m","name":"MS-Celeb-1M","full_name":"","num_papers_in_archive":257},{"url":"/dataset/megaface","name":"MegaFace","full_name":"","num_papers_in_archive":210},{"url":"/dataset/ijb-b","name":"IJB-B","full_name":"IARPA Janus Benchmark-B","num_papers_in_archive":163},{"url":"/dataset/ijb-a","name":"IJB-A","full_name":"IARPA Janus Benchmark A","num_papers_in_archive":156},{"url":"/dataset/dronesurf","name":"DroneSURF","full_name":"DroneSURF: Benchmark Dataset for Drone-based Face Recognition","num_papers_in_archive":6},{"url":"/dataset/petface","name":"PetFace","full_name":"","num_papers_in_archive":2},{"url":"/dataset/ltft","name":"LTFT","full_name":"Long-Term Face Tracking","num_papers_in_archive":1}],"subtasks":[],"parent_tasks":[{"url":"/task/facial-recognition-and-modelling","name":"Facial Recognition and Modelling"}],"papers":{"order":"repositories listed in the archive (desc), then date (desc); the archive holds no stars","population":"papers tagged with this task that list at least one repository in the archive","shown":30,"of":46,"tagged_in_all":144,"items":[{"url":"/paper/facenet-a-unified-embedding-for-face","title":"FaceNet: A Unified Embedding for Face Recognition and Clustering","date":"2015-03-12","arxiv_id":"1503.03832","repositories_listed":183,"syntology":{"n":154,"n_ran":65,"n_unverified":89,"n_pointer_only":45}},{"url":"/paper/arcface-additive-angular-margin-loss-for-deep","title":"ArcFace: Additive Angular Margin Loss for Deep Face Recognition","date":"2018-01-23","arxiv_id":"1801.07698","repositories_listed":100,"syntology":{"n":21,"n_ran":16,"n_unverified":5,"n_pointer_only":14}},{"url":"/paper/sphereface-deep-hypersphere-embedding-for","title":"SphereFace: Deep Hypersphere Embedding for Face Recognition","date":"2017-04-26","arxiv_id":"1704.08063","repositories_listed":22,"syntology":{"n":2,"n_ran":1,"n_unverified":1,"n_pointer_only":1}},{"url":"/paper/a-light-cnn-for-deep-face-representation-with","title":"A Light CNN for Deep Face Representation with Noisy Labels","date":"2015-11-09","arxiv_id":"1511.02683","repositories_listed":19,"syntology":{"n":2,"n_ran":2,"n_unverified":0,"n_pointer_only":1}},{"url":"/paper/network-in-network","title":"Network In Network","date":"2013-12-16","arxiv_id":"1312.4400","repositories_listed":17,"syntology":{"n":7,"n_ran":1,"n_unverified":6,"n_pointer_only":1}},{"url":"/paper/cosface-large-margin-cosine-loss-for-deep","title":"CosFace: Large Margin Cosine Loss for Deep Face Recognition","date":"2018-01-29","arxiv_id":"1801.09414","repositories_listed":11,"syntology":{"n":2,"n_ran":1,"n_unverified":1,"n_pointer_only":2}},{"url":"/paper/deepid3-face-recognition-with-very-deep","title":"DeepID3: Face Recognition with Very Deep Neural Networks","date":"2015-02-03","arxiv_id":"1502.00873","repositories_listed":9,"syntology":null},{"url":"/paper/ghostfacenets-lightweight-face-recognition","title":"GhostFaceNets: Lightweight Face Recognition Model From Cheap Operations","date":"2023-04-10","arxiv_id":null,"repositories_listed":7,"syntology":null},{"url":"/paper/partial-fc-training-10-million-identities-on","title":"Partial FC: Training 10 Million Identities on a Single Machine","date":"2020-10-11","arxiv_id":"2010.05222","repositories_listed":7,"syntology":null},{"url":"/paper/deep-polynomial-neural-networks","title":"Deep Polynomial Neural Networks","date":"2020-06-20","arxiv_id":"2006.13026","repositories_listed":5,"syntology":null},{"url":"/paper/editable-neural-networks-1","title":"Editable Neural Networks","date":"2020-04-01","arxiv_id":"2004.00345","repositories_listed":5,"syntology":{"n":17,"n_ran":1,"n_unverified":16,"n_pointer_only":1}},{"url":"/paper/faceposenet-making-a-case-for-landmark-free","title":"FacePoseNet: Making a Case for Landmark-Free Face Alignment","date":"2017-08-24","arxiv_id":"1708.07517","repositories_listed":5,"syntology":null},{"url":"/paper/groupface-learning-latent-groups-and","title":"GroupFace: Learning Latent Groups and Constructing Group-based Representations for Face Recognition","date":"2020-05-21","arxiv_id":"2005.10497","repositories_listed":4,"syntology":null},{"url":"/paper/deep-learning-face-representation-from-1","title":"Deep Learning Face Representation from Predicting 10,000 Classes","date":"2014-01-01","arxiv_id":null,"repositories_listed":4,"syntology":null},{"url":"/paper/lad-rcnn-a-powerful-tool-for-livestock-face","title":"LAD-RCNN:A Powerful Tool for Livestock Face Detection and Normalization","date":"2022-10-31","arxiv_id":"2210.17146","repositories_listed":3,"syntology":null},{"url":"/paper/vargfacenet-an-efficient-variable-group","title":"VarGFaceNet: An Efficient Variable Group Convolutional Neural Network for Lightweight Face Recognition","date":"2019-10-11","arxiv_id":"1910.04985","repositories_listed":3,"syntology":null},{"url":"/paper/deep-learning-face-representation-by-joint","title":"Deep Learning Face Representation by Joint Identification-Verification","date":"2014-06-18","arxiv_id":"1406.4773","repositories_listed":3,"syntology":null},{"url":"/paper/large-scale-correlation-clustering","title":"Large Scale Correlation Clustering Optimization","date":"2011-12-13","arxiv_id":"1112.2903","repositories_listed":3,"syntology":null},{"url":"/paper/proxyfusion-face-feature-aggregation-through","title":"ProxyFusion: Face Feature Aggregation Through Sparse Experts","date":"2025-09-24","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/petface-a-large-scale-dataset-and-benchmark","title":"PetFace: A Large-Scale Dataset and Benchmark for Animal Identification","date":"2024-07-18","arxiv_id":"2407.13555","repositories_listed":2,"syntology":null},{"url":"/paper/learning-unified-representations-for-multi","title":"Learning Unified Representations for Multi-Resolution Face Recognition","date":"2023-10-14","arxiv_id":"2310.09563","repositories_listed":2,"syntology":null},{"url":"/paper/rethinking-bias-mitigation-fairer","title":"Rethinking Bias Mitigation: Fairer Architectures Make for Fairer Face Recognition","date":"2022-10-18","arxiv_id":"2210.09943","repositories_listed":2,"syntology":{"n":3,"n_ran":3,"n_unverified":0,"n_pointer_only":3}},{"url":"/paper/classifying-emotions-and-engagement-in-online","title":"Classifying emotions and engagement in online learning based on a single facial expression recognition neural network","date":"2022-07-04","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/unified-negative-pair-generation-toward-well","title":"Unified Negative Pair Generation toward Well-discriminative Feature Space for Face Recognition","date":"2022-03-22","arxiv_id":"2203.11593","repositories_listed":2,"syntology":{"n":14,"n_ran":2,"n_unverified":12,"n_pointer_only":0}},{"url":"/paper/facial-expression-and-attributes-recognition","title":"Facial expression and attributes recognition based on multi-task learning of lightweight neural networks","date":"2021-03-31","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/facial-expression-and-attributes-recognition-1","title":"Facial expression and attributes recognition based on multi-task learning of lightweight neural networks","date":"2021-03-31","arxiv_id":"2103.17107","repositories_listed":2,"syntology":null},{"url":"/paper/campro-camera-based-anti-facial-recognition","title":"CamPro: Camera-based Anti-Facial Recognition","date":"2023-12-30","arxiv_id":"2401.00151","repositories_listed":1,"syntology":null},{"url":"/paper/fast-and-interpretable-face-identification","title":"Fast and Interpretable Face Identification for Out-Of-Distribution Data Using Vision Transformers","date":"2023-11-06","arxiv_id":"2311.02803","repositories_listed":1,"syntology":null},{"url":"/paper/open-set-face-identification-on-few-shot","title":"Open-Set Face Identification on Few-Shot Gallery by Fine-Tuning","date":"2023-01-05","arxiv_id":"2301.01922","repositories_listed":1,"syntology":null},{"url":"/paper/histogram-of-oriented-gradients-meet-deep","title":"Histogram of Oriented Gradients Meet Deep Learning: A Novel Multi-task Deep Network for Medical Image Semantic Segmentation","date":"2022-04-02","arxiv_id":"2204.01712","repositories_listed":1,"syntology":null}],"syntology_records":9,"syntology_note":"a paper without a record is not a recorded non-run: it may lack an arXiv id or simply be absent from the graph layer"},"description_links":{"kept":0,"unwrapped_to_text":0,"bare_urls_linked":0,"relative_images_dropped":0,"rule":"internal links are kept only when the target slug exists in the catalog"},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per-sample execution status on synthesized fixtures ('ran N of M samples'); not a correctness claim and not a ranking signal.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"}},"not_shown":{"libraries":"the archive has no per-task library table","trend_sparklines":"the Trend column of the benchmarks table was a rendered image; it is not in the archive","social_and_latest_sorts":"stars and social signals are not in the archive"}}