{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/probabilistic-face-embeddings","title":"Probabilistic Face Embeddings","arxiv_id":"1904.09658","date":"2019-04-21","proceeding":"ICCV 2019 10","authors":["Yichun Shi","Anil K. Jain"],"abstract":"Embedding methods have achieved success in face recognition by comparing facial features in a latent semantic space. However, in a fully unconstrained face setting, the facial features learned by the embedding model could be ambiguous or may not even be present in the input face, leading to noisy representations. We propose Probabilistic Face Embeddings (PFEs), which represent each face image as a Gaussian distribution in the latent space. The mean of the distribution estimates the most likely feature values while the variance shows the uncertainty in the feature values. Probabilistic solutions can then be naturally derived for matching and fusing PFEs using the uncertainty information. Empirical evaluation on different baseline models, training datasets and benchmarks show that the proposed method can improve the face recognition performance of deterministic embeddings by converting them into PFEs. The uncertainties estimated by PFEs also serve as good indicators of the potential matching accuracy, which are important for a risk-controlled recognition system.","url_abs":"https://arxiv.org/abs/1904.09658v4","url_pdf":"https://arxiv.org/pdf/1904.09658v4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"probabilistic-face-embeddings","repo_url":"https://github.com/seasonSH/Probabilistic-Face-Embeddings","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"face-recognition","task_name":"Face Recognition"},{"task_slug":"face-verification","task_name":"Face Verification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/face-verification-on-ijb-a","task":"Face Verification","dataset":"IJB-A","model":"PFEfuse + match","rank_in_archive_order":2,"of":17,"metrics":{"TAR @ FAR=0.001":"95.25","TAR @ FAR=0.01":"97.5%"},"uses_additional_data":false},{"leaderboard":"/sota/face-verification-on-ijb-c","task":"Face Verification","dataset":"IJB-C","model":"PFEfuse + match","rank_in_archive_order":22,"of":26,"metrics":{"TAR @ FAR=1e-2":"97.17%","TAR @ FAR=1e-3":"95.49%","model":"SphereFace64","training dataset":"MS1M V2"},"uses_additional_data":false},{"leaderboard":"/sota/face-verification-on-megaface","task":"Face Verification","dataset":"MegaFace","model":"PFEfuse + match","rank_in_archive_order":9,"of":12,"metrics":{"Accuracy":"92.51%"},"uses_additional_data":false},{"leaderboard":"/sota/face-verification-on-youtube-faces-db","task":"Face Verification","dataset":"YouTube Faces DB","model":"PFEfuse+match","rank_in_archive_order":5,"of":12,"metrics":{"Accuracy":"97.36%"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1904.09658","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.09658"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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