Dynamic Personalized Federated Learning for Cross-spectral Palmprint Recognition.

Journal: IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Published Date:

Abstract

Palmprint recognition has recently garnered attention due to its high accuracy, strong robustness, and high security. Existing deep learning-based palmprint recognition methods usually require large amounts of data for centralized training, facing the challenge of privacy disclosure. In addition, the non-independent and identically distributed (non-IID) issue in the multi-spectral palmprint images generally leads to the degradation of recognition performance. To tackle these problems, this paper proposes a dynamic personalized federated learning model for cross-spectral palmprint recognition, called DPFed-Palm. Specifically, for each client's local training, we present a new combination of loss functions to enforce the constraints of local models and effectively enhance the feature representation capability of models. Subsequently, DPFed-Palm aggregates the above-trained local models by using the combined aggregation strategies of the Federated Averaging (FedAvg) and Personalized Federated Learning (PFL) to obtain the best personalized global model of each client. For the selection of the best personalized global model, we develop a dynamic weight selection strategy to obtain the optimal weights of the local and global models by cross-spectral (cross-client) testing. Extensive experimental results on three public PolyU multispectral, IITD, and CASIA datasets show that the proposed method outperforms the existing techniques in privacy-preserving and recognition performance.

Authors

  • Shuyi Li
    School of Pharmacy, Shenyang Pharmaceutical University, Shenyang 110016, China.
  • Jianian Hu
  • Bob Zhang
  • Xin Ning
    Institute of Semiconductors, Chinese Academy of Sciences, Beijing, 100083, China; School of Integrated Circuits, University of Chinese Academy of Sciences, Beijing, 100083, China; Beijing Key Laboratory of Semiconductor Neural Network Intelligent Sensing and Computing Technology, Beijing, 100083, China. Electronic address: ningxin@semi.ac.cn.
  • Lifang Wu
    Science Island Branch, University of Science and Technology of China, Hefei, Anhui, China.

Keywords

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