Institute of Computing Technology, Chinese Academy IR
Improving Face Anti-spoofing via Advanced Multi-perspective Feature Learning | |
Wang, Zhuming1; Xu, Yaowen1; Wu, Lifang1,2; Han, Hu3,4,5; Ma, Yukun6; Li, Zun1 | |
2023-11-01 | |
发表期刊 | ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS |
ISSN | 1551-6857 |
卷号 | 19期号:6页码:18 |
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. |
关键词 | Face anti-spoofing multi-perspective universal cues |
DOI | 10.1145/3575660 |
收录类别 | SCI |
语种 | 英语 |
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 |
WOS类目 | Computer Science, Information Systems ; Computer Science, Software Engineering ; Computer Science, Theory & Methods |
WOS记录号 | WOS:001035785200035 |
出版者 | ASSOC COMPUTING MACHINERY |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.204/handle/2XEOYT63/21370 |
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.Beijing Univ Technol, Beijing 100124, Peoples R China 2.Beijing Key Lab Computat Intelligence & Intellige, Beijing, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China 4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 5.Pengcheng Lab, Shenzhen 518055, Peoples R China 6.Henan Inst Sci & Technol, Xinxiang, Henan, 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. |
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