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Physics-Driven Spectrum-Consistent Federated Learning for Palmprint Verification

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Abstract

Palmprint as biometrics has gained increasing attention recently due to its discriminative ability and robustness. However, existing methods mainly improve palmprint verification within one spectrum, which is challenging to verify across different spectrums. Additionally, in distributed server-client-based deployment, palmprint verification systems predominantly necessitate clients to transmit private data for model training on the centralized server, thereby engendering privacy apprehensions. To alleviate the above issues, in this paper, we propose a physics-driven spectrum-consistent federated learning method for palmprint verification, dubbed as PSFed-Palm. PSFed-Palm draws upon the inherent physical properties of distinct wavelength spectrums, wherein images acquired under similar wavelengths display heightened resemblances. Our approach first partitions clients into short- and long-spectrum groups according to the wavelength range of their local spectrum images. Subsequently, we introduce anchor models for short- and long-spectrum, which constrain the optimization directions of local models associated with long- and short-spectrum images. Specifically, a spectrum-consistent loss that enforces the model parameters and feature representation to align with their corresponding anchor models is designed. Finally, we impose constraints on the local models to ensure their consistency with the global model, effectively preventing model drift. This measure guarantees spectrum consistency while protecting data privacy, as there is no need to share local data. Extensive experiments are conducted to validate the efficacy of our proposed PSFed-Palm approach. The proposed PSFed-Palm demonstrates compelling performance despite only a limited number of training data. The codes have been released at https://github.com/Zi-YuanYang/PSFed-Palm.

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Data availability statement

The data that support the findings of this study are available from PolyU (Zhang et al., 2010) and CASIA (Hao et al., 2008) but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of PolyU (Zhang et al., 2010) and CASIA (Hao et al., 2008).

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grants 62271335; in part by the Sichuan Science and Technology Program under Grant 2021JDJQ0024; and in part by the Sichuan University “From 0 to 1” Innovative Research Program under Grant 2022SCUH0016. The Science and Technology Development Fund, Macao S.A.R (FDCT) 0028/2023/RIA1.

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Authors and Affiliations

  1. College of Computer Science, Sichuan University, Chengdu, China

    Ziyuan Yang

  2. School of Electrical and Electronic Engineering, College of Engineering, Yonsei University, Seoul, Republic of Korea

    Andrew Beng Jin Teoh

  3. Pattern Analysis and Machine Intelligence Group, Department of Computer and Information Science, University of Macau, Macau, China

    Bob Zhang

  4. School of Software, Nanchang Hangkong University, Nanchang, China

    Lu Leng

  5. School of Cyber Science and Engineering, Sichuan University, Chengdu, China

    Yi Zhang

  6. Key Laboratory of Data Protection and Intelligent Management, Ministry of Education, Sichuan University, Chengdu, China

    Ziyuan Yang & Yi Zhang

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  1. Ziyuan Yang
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Correspondence to Yi Zhang.

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Communicated by Segio Escalera.

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Yang, Z., Teoh, A.B.J., Zhang, B. et al. Physics-Driven Spectrum-Consistent Federated Learning for Palmprint Verification. Int J Comput Vis (2024). https://doi.org/10.1007/s11263-024-02077-9

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