A Comprehensive Analysis of GAN-Based Data Augmentation and Transfer Learning for Finger Vein Biometrics

Background Finger vein biometrics has evolved into a reliable biometric modality because of its inherent resistance to spoofing and robustness to external attacks. In recent years, advances in deep learning-based person identification using finger vein trait have further enhanced its performance and effectiveness. However, convolutional neural networks (CNNs) need large volumes of labeled data to perform well, and finger vein datasets are typically small, making this shortage of training samples one of the major obstacles to building reliable, deployable systems. Method This paper addresses that gap by combining transfer learning with Conditional GAN-based data augmentation to build a more robust finger vein recognition framework. Rather than relying on conventional augmentation alone, synthetic, class-conditioned finger vein images are generated using two GAN variants, Conditional DCGAN and Conditional WGAN-GP, which incorporate class-conditional information to enable stable, controllable adversarial training and improve intra-class compactness and inter-class discrimination while enriching the training data. To evaluate the generalization capability of the proposed approach, extensive experiments are conducted on two widely used benchmark databases, THU-FVFDT2 and FV-USM, under various data-split schemes. For classification, four established CNN architectures such as VGG19, MobileNetV2, InceptionV3, and EfficientNetV2-M were fine-tuned and evaluated using standard classification metrics for all partitioning schemes. Results The experimental results were consistently strong across both datasets and confirming that the framework generalizes well with consistently high classification accuracy across both datasets. As observed, MobileNetV2 reached 99.74% identification accuracy on THU-FVFDT2 dataset, while EfficientNetV2-M achieved the best overall accuracy 99.85% on FV-USM dataset using the Conditional DCGAN augmentation technique. Conclusion These findings suggest that pairing transfer learning with GAN-based augmentation is a practical, effective way to overcome data scarcity in finger vein recognition, offering a promising direction for dependable biometric systems.