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

doi:10.1145/3575660

	Improving Face Anti-spoofing via Advanced Multi-perspective Feature Learning
	Wang, Zhuming 1; Xu, Yaowen 1; Wu, Lifang 1,2; Han, Hu 3,4,5; Ma, Yukun 6; Li, Zun 1
	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.