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Volume 20 Issue 5
October  2023
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Feng-Zhao Lian, Jun-Duan Huang, Ji-Xin Liu, Guang Chen, Jun-Hong Zhao, Wen-Xiong Kang. FedFV: A Personalized Federated Learning Framework for Finger Vein Authentication. Machine Intelligence Research, vol. 20, no. 5, pp.683-696, 2023. https://doi.org/10.1007/s11633-022-1341-4
Citation: Feng-Zhao Lian, Jun-Duan Huang, Ji-Xin Liu, Guang Chen, Jun-Hong Zhao, Wen-Xiong Kang. FedFV: A Personalized Federated Learning Framework for Finger Vein Authentication. Machine Intelligence Research, vol. 20, no. 5, pp.683-696, 2023. https://doi.org/10.1007/s11633-022-1341-4
shu

FedFV: A Personalized Federated Learning Framework for Finger Vein Authentication

doi: 10.1007/s11633-022-1341-4
  • 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

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  • Author Bio:

    Feng-Zhao Lian received the B. Sc. degree in automation from South China University of Technology, China in 2019. He is currently a master student at School of Automation Science and Engineering, South China University of Technology, China. His research interests include biometrics, federated learning, computer vision and deep learning. E-mail: lianfengzhaoscut@qq.com ORCID iD: 0000-0002-4219-067X

    Jun-Duan Huang received the B. Sc. degree in automation, and M. Sc. degree in agricultural electrification and automation, from South China Agriculture University, Guangzhou, China, in 2017 and 2020, respectively. He is currently a doctoral candidate in electronic and information at South China University of Technology, Guangzhou, China. His research interests include biometrics, computer vision, audio signal processing, deep learning and agricultural engineering. E-mail: runrunjun@163.com ORCID iD: 0000-0002-5510-7046

    Ji-Xin Liu received the M. Sc. degree in power electronics and power drives from Northeast Petroleum University, China in 2004, and the Ph. D. degree in information and communication engineering from Harbin Institute of Technology, China in 2010. He is currently an associate professor with School of Automation, Guangdong University of Petrochemical Technology, China. His research interests include biometric identification, privacy preserving machine learning, pattern recognition and fault diagnosis of petrochemical equipment. E-mail: ljxfrog@qq.com

    Guang Chen received the B. Sc. degree in mechatronics engineering from Xi′an University of Architecture and Technology, China in 2008. His research interests include biometric recognition, machine learning and federated learning. E-mail: cguang1@grgbanking.com

    Jun-Hong Zhao received the M. Sc. degree in pattern recognition and intelligent system from Chongqing University, China in 2003, and the Ph. D. degree in pattern recognition and intelligent system from South China University of Technology, China in 2011. She is currently a lecturer with School of Automation Science and Engineering, South China University of Technology, China. Her research interests include image processing, image forensics and biometrics identification. E-mail: jhzhao@scut.edu.cn (Corresponding author)

    Wen-Xiong Kang received the Ph. D. degree in systems engineering from South China University of Technology, China in 2009. He is currently a professor with School of Automation Science and Engineering, South China University of Technology, China.His research interests include biometrics identification, image processing, pattern recognition, and computer vision.E-mail: auwxkang@scut.edu.cn (Corresponding author) ORCID iD: 0000-0001-9023-7252

  • Received Date: 2022-04-17
  • Accepted Date: 2022-05-27
  • Publish Online: 2023-01-11
  • Publish Date: 2023-10-01
    Abstract
  • 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.

     

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