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

文献类型：期刊论文

作者Liu, Xing2; Su, Anyang1,4; Wu, Minghui1; Yu, Zitong3; Wu, Kangle4; An, Da4; Hao, Jie1; Xu, Mengzhen5; Zhao, Chenxu1,4; Lei, Zhen6,7,8
刊名INTERNATIONAL JOURNAL OF COMPUTER VISION
出版日期2024-06-10
页码16
关键词Face Anti-Spoofing Data augmentation Generative model Face editing
ISSN号0920-5691
DOI10.1007/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
WOS记录号WOS:001242698400001
出版者SPRINGER
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
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.Mininglamp Technol, Beijing, Peoples R China
2.Zelos Technol, Beijing, Peoples R China
3.Great Bay Univ, Dongguan, Peoples R China
4.Shanghai Artificial Intelligence Lab, Shanghai, Peoples R China
5.Tsinghua Univ, Dept Hydraul Engn, Beijing, Peoples R China
6.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China
7.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China
8.Chinese Acad Sci, Ctr Artificial Intelligence & Robot, Hong Kong Inst Sci & Innovat, Hong Kong, 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.

入库方式： OAI收割

来源：自动化研究所

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