Dual-Branch Meta-Learning Network With Distribution Alignment for Face Anti-Spoofing

doi:10.1109/TIFS.2021.3134869

	Dual-Branch Meta-Learning Network With Distribution Alignment for Face Anti-Spoofing
	Jia, Yunpei 1,2; Zhang, Jie 1,2; Shan, Shiguang 1,2,3
	2022
发表期刊	IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
ISSN	1556-6013
卷号	17 页码:138-151
摘要	Existing face anti-spoofing (FAS) methods fail to generalize well to unseen domains with different data distribution from the training domains, due to the distribution discrepancies between various domains. To extract domain-invariant features for unseen domains, this work proposes a Dual-Branch Meta-learning Network (DBMNet) with distribution alignment for face anti-spoofing. Specifically, DBMNet consists of a feature embedding (FE) branch and a depth estimating (DE) branch for real and fake face discrimination. Each branch acts as a meta-learner and is optimized by step-adjusted meta-learning that can adaptively select the best number of meta-train steps. In order to mitigate distribution discrepancies between domains, we introduce two distribution alignment losses to directly regularize the two meta-learners, i.e., the triplet loss for FE branch and the depth loss for DE branch, respectively. Both of them are designed as part of the meta-train and meta-test objectives, which contribute to higher-order derivatives on the parameters during the meta-optimization for further seeking domain-invariant features. Extensive ablation studies and comparisons with the state-of-the-art methods show the effectiveness of our method for better generalization.
关键词	Faces Feature extraction Testing Face recognition Databases Training Task analysis Face anti-spoofing face presentation attack detection domain generalization meta-learning distribution alignment deep learning
DOI	10.1109/TIFS.2021.3134869
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2017YFA0700800] ; Natural Science Foundation of China[61806188] ; Natural Science Foundation of China[61976219] ; Shanghai Municipal Science and Technology Major Project[2017SHZDZX01]
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号	WOS:000736739100004
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：21[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/18332
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Zhang, Jie
作者单位	1.Chinese Acad Sci, Inst Comp Technol ICT, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci UCAS, Beijing 100049, Peoples R China 3.Chinese Acad Sci, CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Jia, Yunpei,Zhang, Jie,Shan, Shiguang. Dual-Branch Meta-Learning Network With Distribution Alignment for Face Anti-Spoofing[J]. IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY,2022,17:138-151.
APA	Jia, Yunpei,Zhang, Jie,&Shan, Shiguang.(2022).Dual-Branch Meta-Learning Network With Distribution Alignment for Face Anti-Spoofing.IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY,17,138-151.
MLA	Jia, Yunpei,et al."Dual-Branch Meta-Learning Network With Distribution Alignment for Face Anti-Spoofing".IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 17(2022):138-151.