CG-FAS: Cross-label Generative Augmentation for Face Anti-Spoofing

doi:10.1007/s11263-024-02132-5

CORC > 自动化研究所 > 中国科学院自动化研究所 > 模式识别国家重点实验室 > 生物识别与安全技术研究中心

	CG-FAS: Cross-label Generative Augmentation for Face Anti-Spoofing
	Liu, Xing 7; Su, Anyang 5,8; Wu, Minghui 8; Yu, Zitong 6; Wu, Kangle 5; An, Da 5; Hao, Jie 8; Xu, Mengzhen 4; Zhao, Chenxu 5,8; Lei, Zhen 1,2,3
刊名	INTERNATIONAL JOURNAL OF COMPUTER VISION
	2024-06-10
页码	16
关键词	Face Anti-Spoofing Data augmentation Generative model Face editing
ISSN号	0920-5691
DOI	10.1007/s11263-024-02132-5
通讯作者	Zhao, Chenxu(zhaochenxu@mininglamp.com) ; Lei, Zhen(zlei@nlpr.ia.ac.cn)
英文摘要	Face Anti-Spoofing (FAS) is essential to secure face recognition systems from various physical attacks. A sufficient and diverse training set helps to build robust FAS models. To exploit the potential of FAS datasets, we propose to generate high-quality data including live and diverse presentation attacks (PAs) faces, for data augmentation during the model training stage. Our method is called Cross-label Generative augmentation for Face Anti-Spoofing (CG-FAS), which could convert a live face into a 3D high-fidelity mask, replay, print, or other extra physical PAs. Correspondingly, CG-FAS can also restore a specific physical presentation attack into a live face. This function is realized by innovatively building an Interchange Bridge matrix, which stores disentangled spoof clues between PAs and live faces. To verify the effects of these generated data, we utilize them as augmentation data and conduct experiments on several typical FAS benchmarks. Extensive experimental results demonstrate the superior performance gain with CG-FAS for off-the-shelf data-driven FAS models. We hope the CG-FAS can shine a light on the deep FAS community to alleviate the data-hungry issue. The code will be released soon at: https://github.com/liuxingwt/CG-FAS.
资助项目	Brain-like General Vision Model and Applications project[2022ZD0160402] ; Chinese National Natural Science Foundation[62276254] ; Chinese National Natural Science Foundation[U23B2054] ; Frontier Interdiscipline Project of Tsinghua University[20221080082] ; National Natural Science Foundation of China[62306061] ; Guangdong Basic and Applied Basic Research Foundation[2023A1515140037]
WOS研究方向	Computer Science
语种	英语
出版者	SPRINGER
WOS记录号	WOS:001242698400001
资助机构	Brain-like General Vision Model and Applications project ; Chinese National Natural Science Foundation ; Frontier Interdiscipline Project of Tsinghua University ; National Natural Science Foundation of China ; Guangdong Basic and Applied Basic Research Foundation
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/58667]
专题	自动化研究所_模式识别国家重点实验室_生物识别与安全技术研究中心
通讯作者	Zhao, Chenxu; Lei, Zhen
作者单位	1.Chinese Acad Sci, Ctr Artificial Intelligence & Robot, Hong Kong Inst Sci & Innovat, Hong Kong, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China 3.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China 4.Tsinghua Univ, Dept Hydraul Engn, Beijing, Peoples R China 5.Shanghai Artificial Intelligence Lab, Shanghai, Peoples R China 6.Great Bay Univ, Dongguan, Peoples R China 7.Zelos Technol, Beijing, Peoples R China 8.Mininglamp Technol, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Liu, Xing,Su, Anyang,Wu, Minghui,et al. CG-FAS: Cross-label Generative Augmentation for Face Anti-Spoofing[J]. INTERNATIONAL JOURNAL OF COMPUTER VISION,2024:16.
APA	Liu, Xing.,Su, Anyang.,Wu, Minghui.,Yu, Zitong.,Wu, Kangle.,...&Lei, Zhen.(2024).CG-FAS: Cross-label Generative Augmentation for Face Anti-Spoofing.INTERNATIONAL JOURNAL OF COMPUTER VISION,16.
MLA	Liu, Xing,et al."CG-FAS: Cross-label Generative Augmentation for Face Anti-Spoofing".INTERNATIONAL JOURNAL OF COMPUTER VISION (2024):16.