Efficient multi-finger vein recognition using layer-wise progressive MobileNet fine-tuning and a Dense-Head Probabilistic Siamese Network

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Finger-vein recognition offers a highly secure and non-intrusive biometric modality ideal for modern authentication systems, yet its deployment on resource-limited devices remains challenging due to the high computational cost of deep models and the rigidity of single-finger enrollment. We present a new two-stage deep learning framework that solves these problems by combining a lightweight MobileNet feature extractor that gets better over time with a new Dense-Head Probabilistic Siamese (DHPS) matcher. Our method is the only one that allows for multi-finger recognition, so users can authenticate with any finger without losing accuracy or speed. Layer-wise unfreezing carefully adjusts the feature extractor to find the right balance between model compactness and discriminative power. The DHPS matcher replaces traditional margin-based losses with a calibrated probabilistic output that is optimized through binary cross-entropy. Our system gets the best results on three different public finger-vein datasets, with Equal Error Rates (EER) of 0.002, 0.067, and 0.075 on the FV-USM, UTFVP, and VERA datasets, respectively. It also has F1-scores of 99.8%, 95.6%, and 91.3% on their test sets. These results are possible because the model is small and can be used on embedded platforms with fast inference. In addition, the pre-trained extractor will be made available to the public to encourage more research in the future. This work improves practical finger-vein biometrics by making them more accurate, flexible, and efficient, which gets around major obstacles to their use in the real world.

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