{"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/improving-face-anti-spoofing-by-3d-virtual","title":"Improving Face Anti-Spoofing by 3D Virtual Synthesis","arxiv_id":"1901.00488","date":"2019-01-02","proceeding":null,"authors":["Jianzhu Guo","Xiangyu Zhu","Jinchuan Xiao","Zhen Lei","Genxun Wan","Stan Z. Li"],"abstract":"Face anti-spoofing is crucial for the security of face recognition systems. Learning based methods especially deep learning based methods need large-scale training samples to reduce overfitting. However, acquiring spoof data is very expensive since the live faces should be re-printed and re-captured in many views. In this paper, we present a method to synthesize virtual spoof data in 3D space to alleviate this problem. Specifically, we consider a printed photo as a flat surface and mesh it into a 3D object, which is then randomly bent and rotated in 3D space. Afterward, the transformed 3D photo is rendered through perspective projection as a virtual sample. The synthetic virtual samples can significantly boost the anti-spoofing performance when combined with a proposed data balancing strategy. Our promising results open up new possibilities for advancing face anti-spoofing using cheap and large-scale synthetic data.","url_abs":"https://arxiv.org/abs/1901.00488v3","url_pdf":"https://arxiv.org/pdf/1901.00488v3.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":"improving-face-anti-spoofing-by-3d-virtual","repo_url":"https://github.com/FaceOnLive/Face-Liveness-Detection-SDK-Linux","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"improving-face-anti-spoofing-by-3d-virtual","repo_url":"https://github.com/Recognito-Vision/Android-FaceRecognition-FaceLivenessDetection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"improving-face-anti-spoofing-by-3d-virtual","repo_url":"https://github.com/sicxu/Deep3DFaceRecon_pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"face-anti-spoofing","task_name":"Face Anti-Spoofing"},{"task_slug":"face-recognition","task_name":"Face Recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/face-anti-spoofing-on-casia-mfsd","task":"Face Anti-Spoofing","dataset":"CASIA-MFSD","model":"3D Synthesis (balancing sampling)","rank_in_archive_order":1,"of":2,"metrics":{"EER":"2.22","HTER":"1.67"},"uses_additional_data":false},{"leaderboard":"/sota/face-anti-spoofing-on-replay-attack","task":"Face Anti-Spoofing","dataset":"Replay-Attack","model":"3D Synthesis (balancing sampling)","rank_in_archive_order":2,"of":4,"metrics":{"EER":"0.25","HTER":"0.63"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}