{"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/triplet-probabilistic-embedding-for-face","title":"Triplet Probabilistic Embedding for Face Verification and Clustering","arxiv_id":"1604.05417","date":"2016-04-19","proceeding":null,"authors":["Swami Sankaranarayanan","Azadeh Alavi","Carlos Castillo","Rama Chellappa"],"abstract":"Despite significant progress made over the past twenty five years,\nunconstrained face verification remains a challenging problem. This paper\nproposes an approach that couples a deep CNN-based approach with a\nlow-dimensional discriminative embedding learned using triplet probability\nconstraints to solve the unconstrained face verification problem. Aside from\nyielding performance improvements, this embedding provides significant\nadvantages in terms of memory and for post-processing operations like subject\nspecific clustering. Experiments on the challenging IJB-A dataset show that the\nproposed algorithm performs comparably or better than the state of the art\nmethods in verification and identification metrics, while requiring much less\ntraining data and training time. The superior performance of the proposed\nmethod on the CFP dataset shows that the representation learned by our deep CNN\nis robust to extreme pose variation. Furthermore, we demonstrate the robustness\nof the deep features to challenges including age, pose, blur and clutter by\nperforming simple clustering experiments on both IJB-A and LFW datasets.","url_abs":"http://arxiv.org/abs/1604.05417v3","url_pdf":"http://arxiv.org/pdf/1604.05417v3.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":"triplet-probabilistic-embedding-for-face","repo_url":"https://github.com/Ananaskelly/TPE","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"triplet-probabilistic-embedding-for-face","repo_url":"https://github.com/obj2vec/obj2vec","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"face-verification","task_name":"Face Verification"},{"task_slug":null,"task_name":"Triplet"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/face-verification-on-ijb-a","task":"Face Verification","dataset":"IJB-A","model":"Triplet probabilistic embedding","rank_in_archive_order":12,"of":17,"metrics":{"TAR @ FAR=0.01":"90%"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1604.05417","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}