GAN-Based Inter-Class Sample Generation for Contrastive Learning of Vein Image Representations

Biometric vein recognition using deep learning has shown promising performance recently. However, due to the lack of vein image training data, the performance is greatly limited. Although the use of traditional data augmentation can alleviate this problem to some extent, it can only expand the intra-class samples and is fundamentally limited to the intra-class space, resulting in limited performance improvement. In this paper, we tackle this deep learning data shortage problem by proposing a GAN-based framework that can generate arbitrary-pattern vein images, which can augment the training data with new vein classes. The proposed generation framework consists of three progressive synthetic steps, namely, generation of random vein patterns in the binary space, refinement of the binary vein patterns, and rendering them into grayscale vein images. Moreover, we use the synthetic dataset to pretrain the feature embedding network via unsupervised contrastive learning, which allows learning data augmentation-invariant and instance-separating representations. After that, we further fine-tune the embedding network on the real training data in a supervised manner. Our results show that high-fidelity and diverse vein image samples can be generated to alleviate the problem of data shortage and improve the learning of feature representations for biometric vein verification.