{"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/face-anti-spoofing-based-on-color-texture","title":"face anti-spoofing based on color texture analysis","arxiv_id":"1511.06316","date":"2015-11-19","proceeding":null,"authors":["Zinelabidine Boulkenafet","Jukka Komulainen","Abdenour Hadid"],"abstract":"Research on face spoofing detection has mainly been focused on analyzing the\nluminance of the face images, hence discarding the chrominance information\nwhich can be useful for discriminating fake faces from genuine ones. In this\nwork, we propose a new face anti-spoofing method based on color texture\nanalysis. We analyze the joint color-texture information from the luminance and\nthe chrominance channels using a color local binary pattern descriptor. More\nspecifically, the feature histograms are extracted from each image band\nseparately. Extensive experiments on two benchmark datasets, namely CASIA face\nanti-spoofing and Replay-Attack databases, showed excellent results compared to\nthe state-of-the-art. Most importantly, our inter-database evaluation depicts\nthat the proposed approach showed very promising generalization capabilities.","url_abs":"http://arxiv.org/abs/1511.06316v1","url_pdf":"http://arxiv.org/pdf/1511.06316v1.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":"face-anti-spoofing-based-on-color-texture","repo_url":"https://github.com/coderwangson/Face-anti-spoofing-based-on-color-texture-analysis","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"face-anti-spoofing","task_name":"Face Anti-Spoofing"},{"task_slug":"texture-classification","task_name":"Texture Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/face-anti-spoofing-on-msu-mfsd","task":"Face Anti-Spoofing","dataset":"MSU-MFSD","model":"Color LBP","rank_in_archive_order":3,"of":3,"metrics":{"Equal Error Rate":"10.8%"},"uses_additional_data":false},{"leaderboard":"/sota/face-anti-spoofing-on-replay-attack","task":"Face Anti-Spoofing","dataset":"Replay-Attack","model":"YCbCr+HSV-LBP","rank_in_archive_order":3,"of":4,"metrics":{"EER":"0.40","HTER":"2.90"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/1511.06316","atlas_url":"https://app.syntology.ai/?focus=1511.06316","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}