FedFV: A Personalized Federated Learning Framework for Finger Vein Authentication

doi:10.1007/s11633-022-1341-4

CASIA OpenIR > 学术期刊 > Machine Intelligence Research

	FedFV: A Personalized Federated Learning Framework for Finger Vein Authentication
	Feng-Zhao Lian 1,2; Jun-Duan Huang 1; Ji-Xin Liu 3; Guang Chen 1,2; Jun-Hong Zhao 1; Wen-Xiong Kang 1
发表期刊	Machine Intelligence Research
ISSN	2731-538X
	2023
卷号	20 期号:5 页码:683-696
摘要	Most finger vein authentication systems suffer from the problem of small sample size. However, the data augmentation can alleviate this problem to a certain extent but did not fundamentally solve the problem of category diversity. So the researchers resort to pre-training or multi-source data joint training methods, but these methods will lead to the problem of user privacy leakage. In view of the above issues, this paper proposes a federated learning-based finger vein authentication framework (FedFV) to solve the problem of small sample size and category diversity while protecting user privacy. Through training under FedFV, each client can share the knowledge learned from its user's finger vein data with the federated client without causing template leaks. In addition, we further propose an efficient personalized federated aggregation algorithm, named federated weighted proportion reduction (FedWPR), to tackle the problem of non-independent identically distribution caused by client diversity, thus achieving the best performance for each client. To thoroughly evaluate the effectiveness of FedFV, comprehensive experiments are conducted on nine publicly available finger vein datasets. Experimental results show that FedFV can improve the performance of the finger vein authentication system without directly using other client data. To the best of our knowledge, FedFV is the first personalized federated finger vein authentication framework, which has some reference value for subsequent biometric privacy protection research.
关键词	Finger vein, personalized federated learning, privacy protection, biometric, authentication
DOI	10.1007/s11633-022-1341-4
引用统计	被引频次：2[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/56003
专题	学术期刊_Machine Intelligence Research
作者单位	1.School of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, China 2.GRG Banking Equipment Co. Ltd., Guangzhou 510663, China 3.School of Automation, Guangdong University of Petrochemical Technology, Maoming 525000, China
推荐引用方式 GB/T 7714	Feng-Zhao Lian,Jun-Duan Huang,Ji-Xin Liu,et al. FedFV: A Personalized Federated Learning Framework for Finger Vein Authentication[J]. Machine Intelligence Research,2023,20(5):683-696.
APA	Feng-Zhao Lian,Jun-Duan Huang,Ji-Xin Liu, Guang Chen,Jun-Hong Zhao,&Wen-Xiong Kang.(2023).FedFV: A Personalized Federated Learning Framework for Finger Vein Authentication.Machine Intelligence Research,20(5),683-696.
MLA	Feng-Zhao Lian,et al."FedFV: A Personalized Federated Learning Framework for Finger Vein Authentication".Machine Intelligence Research 20.5(2023):683-696.

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