{"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/deepface-closing-the-gap-to-human-level-1","title":"DeepFace: Closing the Gap to Human-Level Performance in Face Verification","arxiv_id":null,"date":"2014-06-24","proceeding":"Conference on Computer Vision and Pattern Recognition (CVPR) 2014 6","authors":["Yaniv Taigman","Ming Yang","Marc’ Aurelio Ranzato","Lior Wolf"],"abstract":"In modern face recognition, the conventional pipeline consists of four stages: detect => align => represent => classify. We revisit both the alignment step and the representation step by employing explicit 3D face modeling in order to apply a piecewise affine transformation, and derive a face representation from a nine-layer deep neural network. This deep network involves more than 120 million parameters using several locally connected layers without weight sharing, rather than the standard convolutional layers. Thus we trained it on the largest facial dataset to-date, an identity labeled dataset of four million facial images belonging to more than 4,000 identities.\r\n\r\nThe learned representations coupling the accurate model-based alignment with the large facial database generalize remarkably well to faces in unconstrained environments, even with a simple classifier. Our method reaches an accuracy of 97.35% on the Labeled Faces in the Wild (LFW) dataset, reducing the error of the current state of the art by more than 27%, closely approaching human-level performance.","url_abs":"https://research.fb.com/publications/deepface-closing-the-gap-to-human-level-performance-in-face-verification/","url_pdf":"https://research.fb.com/wp-content/uploads/2016/11/deepface-closing-the-gap-to-human-level-performance-in-face-verification.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":"deepface-closing-the-gap-to-human-level-1","repo_url":"https://github.com/Recognito-Vision/Linux-FaceRecognition-FaceLivenessDetection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"deepface-closing-the-gap-to-human-level-1","repo_url":"https://github.com/melgor/pyface","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"deepface-closing-the-gap-to-human-level-1","repo_url":"https://github.com/serengil/deepface","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"deepface-closing-the-gap-to-human-level-1","repo_url":"https://github.com/swghosh/DeepFace","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"3d-face-modeling","task_name":"3D Face Modelling"},{"task_slug":"face-recognition","task_name":"Face Recognition"},{"task_slug":"face-verification","task_name":"Face Verification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/3d-face-modeling-on-lfw","task":"3D Face Modelling","dataset":"LFW","model":"DeepFace","rank_in_archive_order":1,"of":1,"metrics":{"1-of-100 Accuracy":"70"},"uses_additional_data":false},{"leaderboard":"/sota/face-verification-on-labeled-faces-in-the","task":"Face Verification","dataset":"Labeled Faces in the Wild","model":"DeepFace","rank_in_archive_order":5,"of":7,"metrics":{"Accuracy":"98.37%"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}