Deep Learning for Face Anti-Spoofing: A Survey

doi:10.1109/TPAMI.2022.3215850

CORC > 自动化研究所 > 中国科学院自动化研究所 > 多模态人工智能系统全国重点实验室

	Deep Learning for Face Anti-Spoofing: A Survey
	Yu, Zitong 5; Qin, Yunxiao 4; Li, Xiaobai 5; Zhao, Chenxu 3; Lei, Zhen 1,2,6; Zhao, Guoying 5
刊名	IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
	2023-05-01
卷号	45 期号:5 页码:5609-5631
关键词	Face recognition Deep learning Cameras Task analysis Sensors Protocols Three-dimensional displays Face anti-spoofing presentation attack deep learning pixel-wise supervision multi-modal domain generalization
ISSN号	0162-8828
DOI	10.1109/TPAMI.2022.3215850
通讯作者	Zhao, Guoying(guoying.zhao@oulu.fi)
英文摘要	Face anti-spoofing (FAS) has lately attracted increasing attention due to its vital role in securing face recognition systems from presentation attacks (PAs). As more and more realistic PAs with novel types spring up, early-stage FAS methods based on handcrafted features become unreliable due to their limited representation capacity. With the emergence of large-scale academic datasets in the recent decade, deep learning based FAS achieves remarkable performance and dominates this area. However, existing reviews in this field mainly focus on the handcrafted features, which are outdated and uninspiring for the progress of FAS community. In this paper, to stimulate future research, we present the first comprehensive review of recent advances in deep learning based FAS. It covers several novel and insightful components: 1) besides supervision with binary label (e.g., '0' for bonafide versus '1' for PAs), we also investigate recent methods with pixel-wise supervision (e.g., pseudo depth map); 2) in addition to traditional intra-dataset evaluation, we collect and analyze the latest methods specially designed for domain generalization and open-set FAS; and 3) besides commercial RGB camera, we summarize the deep learning applications under multi-modal (e.g., depth and infrared) or specialized (e.g., light field and flash) sensors. We conclude this survey by emphasizing current open issues and highlighting potential prospects.
资助项目	Academy of Finland[336116] ; Academy of Finland[345122] ; Academy of Finland[ICT2023] ; Academy of Finland[345948] ; Chinese National Natural Science Foundation[62276254] ; Chinese National Natural Science Foundation[61976229] ; Chinese National Natural Science Foundation[62106264] ; InnoHK program, and (BAAI)
WOS关键词	PRESENTATION ATTACK DETECTION ; LOCAL BINARY PATTERNS ; DOMAIN ADAPTATION ; RECOGNITION ; NETWORKS ; SYSTEMS ; IMAGE
WOS研究方向	Computer Science ; Engineering
语种	英语
出版者	IEEE COMPUTER SOC
WOS记录号	WOS:000964792800018
资助机构	Academy of Finland ; Chinese National Natural Science Foundation ; InnoHK program, and (BAAI)
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/53212]
专题	多模态人工智能系统全国重点实验室
通讯作者	Zhao, Guoying
作者单位	1.Univ Chinese Acad Sci UCAS, Sch Artificial Intelligence, Beijing 100049, Peoples R China 2.Chinese Acad Sci CASIA, Inst Automat, Ctr Biometr & Secur Res CBSR, Natl Lab Pattern Recognit NLPR, Beijing 100190, Peoples R China 3.SailYond Technol, Beijing 100000, Peoples R China 4.Commun Univ China, State Key Lab Media Convergence & Commun, Beijing 100024, Peoples R China 5.Univ Oulu, Ctr Machine Vis & Signal Anal, Oulu 90014, Finland 6.Chinese Acad Sci, Hong Kong Inst Sci & Innovat, Ctr Artificial Intelligence & Robot, Hong Kong, Peoples R China
推荐引用方式 GB/T 7714	Yu, Zitong,Qin, Yunxiao,Li, Xiaobai,et al. Deep Learning for Face Anti-Spoofing: A Survey[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2023,45(5):5609-5631.
APA	Yu, Zitong,Qin, Yunxiao,Li, Xiaobai,Zhao, Chenxu,Lei, Zhen,&Zhao, Guoying.(2023).Deep Learning for Face Anti-Spoofing: A Survey.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,45(5),5609-5631.
MLA	Yu, Zitong,et al."Deep Learning for Face Anti-Spoofing: A Survey".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 45.5(2023):5609-5631.