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

ABSTRACT: 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 DHP

SUBMITTER: Alaerjan AS 

PROVIDER: S-EPMC12815947 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature