SIAMESE FUSION U-NET FOR FINGER VEIN BIOMETRIC RECOGNITION

Abstract

Finger vein recognition (FVR) is a promising biometric modality due to its resistance to spoofing,contactless usage, and lifelong stability. However, reliable recognition remains challenging becausefinger vein images often suffer from low contrast, light scattering, and noise introduced by biologicaltissues. This study proposes a novel deep learning-based framework that jointly enhances segmentationand classification performance through two key innovations. First, introduce Atten-D3Net, anenhanced U-Net variant that integrates dilated fused convolutional layers, depth-wise separable fusedconvolutions, and a spatial attention mechanism to capture fine-grained vein patterns underchallenging imaging conditions. Second, develop the Siamese Cross-Folded Deep Fusion (S-CFDF)network, which leverages cross-attention and weighted loss functions to improve the discrimination ofsubtle inter-class vein variations while reducing misclassification. Preprocessing with CLAHE,histogram equalization, bilateral filtering, and sharpening further improves image clarity. Experimentalevaluations on two public datasets (MMCBNU_6000 and FV) demonstrate the superiority of theproposed system, achieving accuracies of 97.5% and 98.5%, respectively, while reducing computationtime by more than 50% compared to existing baselines. Performance is validated through precision,recall, F1-score, Dice coefficient, and Jaccard index, showing consistent improvements over VGG16,DenseNet, Vision Transformer, and EfficientNet. The results highlight that the combination of Atten-D3Net and S-CFDF enables robust, accurate, and efficient finger vein recognition, offering strongpotential for deployment in secure biometric authentication systems