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Improving Face Anti-spoofing via Advanced Multi-perspective Feature Learning

文献类型：期刊论文

作者Wang, Zhuming4; Xu, Yaowen4; Wu, Lifang3,4; Han, Hu1,2,6; Ma, Yukun5; Li, Zun4
刊名ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS
出版日期2023-11-01
卷号19期号:6页码:18
关键词Face anti-spoofing multi-perspective universal cues
ISSN号1551-6857
DOI10.1145/3575660
英文摘要Face anti-spoofing (FAS) plays a vital role in securing face recognition systems. Previous approaches usually learn spoofing features from a single perspective, in which only universal cues shared by all attack types are explored. However, such single-perspective-based approaches ignore the differences among various attacks and commonness between certain attacks and bona fides, thus tending to neglect some non-universal cues that contain strong discernibility against certain types. As a result, when dealing with multiple types of attacks, the above approaches may suffer from the uncomprehensive representation of bona fides and spoof faces. In this work, we propose a novel Advanced Multi-Perspective Feature Learning network (AMPFL), in which multiple perspectives are adopted to learn discriminative features, to improve the performance of FAS. Specifically, the proposed network first learns universal cues and several perspective-specific cues from multiple perspectives, then aggregates the above features and further enhances them to perform face anti-spoofing. In this way, AMPFL obtains features that are difficult to be captured by single-perspective-based methods and provides more comprehensive information on bona fides and spoof faces, thus achieving better performance for FAS. Experimental results show that our AMPFL achieves promising results in public databases, and it effectively solves the issues of single-perspective-based approaches.
资助项目Natural Science Foundation of China[61976010,61732004] ; Natural Science Foundation of China[62176249] ; Natural Science Foundation of China[62106010] ; Natural Science Foundation of China[62176011] ; China Postdoctoral Science Foundation[2022M720318] ; Beijing Postdoctoral Science Foundation[2022-zz-077]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:001035785200035
出版者ASSOC COMPUTING MACHINERY
源URL[http://119.78.100.204/handle/2XEOYT63/21369]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Wu, Lifang
作者单位1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
2.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China
3.Beijing Key Lab Computat Intelligence & Intellige, Beijing, Peoples R China
4.Beijing Univ Technol, Beijing 100124, Peoples R China
5.Henan Inst Sci & Technol, Xinxiang, Henan, Peoples R China
6.Pengcheng Lab, Shenzhen 518055, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Zhuming,Xu, Yaowen,Wu, Lifang,et al. Improving Face Anti-spoofing via Advanced Multi-perspective Feature Learning[J]. ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS,2023,19(6):18.
APA Wang, Zhuming,Xu, Yaowen,Wu, Lifang,Han, Hu,Ma, Yukun,&Li, Zun.(2023).Improving Face Anti-spoofing via Advanced Multi-perspective Feature Learning.ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS,19(6),18.
MLA Wang, Zhuming,et al."Improving Face Anti-spoofing via Advanced Multi-perspective Feature Learning".ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS 19.6(2023):18.

入库方式： OAI收割

来源：计算技术研究所

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