Deep Learning for Face Anti-Spoofing: A Survey
文献类型：期刊论文
| 作者 | Yu, Zitong2; Qin, Yunxiao3; Li, Xiaobai2; Zhao, Chenxu6; Lei, Zhen1,4,5 ; Zhao, Guoying2
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| 刊名 | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
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| 出版日期 | 2023-05-01 |
| 卷号 | 45期号:5页码:5609-5631 |
| ISSN号 | 0162-8828 |
| 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 |
| 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 However, existing reviews in this field mainly focus on the handcrafted features, which are outdated and uninspiring for the progress of FAS community.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. |
| WOS关键词 | PRESENTATION ATTACK DETECTION ; LOCAL BINARY PATTERNS ; DOMAIN ADAPTATION ; RECOGNITION ; NETWORKS ; SYSTEMS ; IMAGE |
| 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) |
| 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.Chinese Acad Sci CASIA, Inst Automat, Ctr Biometr & Secur Res CBSR, Natl Lab Pattern Recognit NLPR, Beijing 100190, Peoples R China 2.Univ Oulu, Ctr Machine Vis & Signal Anal, Oulu 90014, Finland 3.Commun Univ China, State Key Lab Media Convergence & Commun, Beijing 100024, Peoples R China 4.Chinese Acad Sci, Hong Kong Inst Sci & Innovat, Ctr Artificial Intelligence & Robot, Hong Kong, Peoples R China 5.Univ Chinese Acad Sci UCAS, Sch Artificial Intelligence, Beijing 100049, Peoples R China 6.SailYond Technol, Beijing 100000, 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. |
入库方式： OAI收割
来源：自动化研究所
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