{"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/joint-statistical-and-causal-feature","title":"Joint Statistical and Causal Feature Modulated Face Anti-Spoofing","arxiv_id":null,"date":"2023-07-04","proceeding":"journal 2023 7","authors":["Xin Dong; Tao Wang; Zhendong Li; Hao Liu"],"abstract":"In this paper, we propose a hierarchical feature modulation (HFM) approach for stable face anti-spoofing in unseen domains and unseen attacks. The conventional multi-domain based generalizable approaches likely lead to local optima due to the complicated or heuristic learning paradigm. Inspired by the fact that high-level semantic disturbances and low-level miscellaneous bias jointly cause the distribution shift, HFM aims to modulate the fine-grained feature in a hierarchical manner. Specifically, we complement the structural feature with patch-wise learnable statistical information, i.e. local difference histogram, to relieve the overfitting on high-level semantics. We further introduce the structural causal model (SCM) with imaging color model to reveal that presenting mediums and capturing devices destroy the liveness-relevant information from the low level. Thus we model this hidden entanglement as a distribution mixture problem and propose the expectation-maximization (EM) based causal intervention to remove these miscellanies. Experimental results on public datasets demonstrate the effectiveness of HFM, especially in out-of-distribution settings.","url_abs":"https://ieeexplore.ieee.org/abstract/document/10219753","url_pdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10219753","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":"joint-statistical-and-causal-feature","repo_url":"https://github.com/2023-MindSpore-4/Code12/tree/main/liuhao/Joint%20Statistical%20and%20Causal%20Feature%20Modulated%20Face%20Anti-Spoofing","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"joint-statistical-and-causal-feature","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":"joint-statistical-and-causal-feature","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"}}],"tasks":[{"task_slug":"face-anti-spoofing","task_name":"Face Anti-Spoofing"},{"task_slug":"miscellaneous","task_name":"Miscellaneous"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}