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 because
finger vein images often suffer from low contrast, light scattering, and noise introduced by biological
tissues. This study proposes a novel deep learning-based framework that jointly enhances segmentation
and classification performance through two key innovations. First, introduce Atten-D3Net, an
enhanced U-Net variant that integrates dilated fused convolutional layers, depth-wise separable fused
convolutions, and a spatial attention mechanism to capture fine-grained vein patterns under
challenging 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 of
subtle inter-class vein variations while reducing misclassification. Preprocessing with CLAHE,
histogram equalization, bilateral filtering, and sharpening further improves image clarity. Experimental
evaluations on two public datasets (MMCBNU_6000 and FV) demonstrate the superiority of the
proposed system, achieving accuracies of 97.5% and 98.5%, respectively, while reducing computation
time 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 strong
potential for deployment in secure biometric authentication systems.