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FedPSFV: Personalized Federated Learning via Prototype Sharing for Finger Vein Recognition.
Haoyan Xu1,2, Yuyang Guo1,2, Yunzan Qu1,2
1School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China.
Sensors (Basel, Switzerland)
|November 13, 2025
Summary
This study introduces FedPSFV, a novel federated learning algorithm for finger vein recognition. It enhances model accuracy and generalizability by addressing data heterogeneity and improving feature differentiation, crucial for privacy-preserving biometric systems.
Area of Science:
- Biometrics and Pattern Recognition
- Machine Learning and Artificial Intelligence
Background:
- Deep learning for finger vein recognition faces challenges due to data privacy concerns and limited public datasets.
- Federated learning (FL) offers a solution for privacy but struggles with data heterogeneity across clients, impacting model performance, especially with small datasets.
Purpose of the Study:
- To propose a novel federated finger vein recognition algorithm (FedPSFV) that overcomes data heterogeneity and enhances model performance.
- To improve feature differentiation and interclass distance within the federated learning framework for finger vein recognition.
Main Methods:
- FedPSFV utilizes a federated learning framework incorporating prototype sharing among clients to increase interclass distance.
- The algorithm integrates an improved margin-based loss function to enhance the model's feature differentiation capabilities.
Main Results:
- Comparative experiments on six public datasets (SDUMLA, MMCBNU, USM, UTFVP, VERA, NUPT) demonstrate FedPSFV's superior accuracy and generalizability.
- FedPSFV achieved a 5.00-11.25% improvement in TAR@FAR=0.01 and an 81.48-90.22% reduction in EER compared to existing methods.
Conclusions:
- FedPSFV effectively addresses data heterogeneity in federated finger vein recognition through prototype sharing and improved loss functions.
- The proposed algorithm significantly enhances recognition accuracy and generalizability, offering a promising solution for privacy-preserving biometric identification.
