Contrastive Learning-Based Finger-Vein Recognition with Automatic Adversarial Augmentation

In finger-vein recognition tasks, obtaining large labeled datasets for supervised deep learning is often difficult. To address this challenge, self-supervised learning (SSL) provides a solution by first pre-training a neural network using unlabeled data and subsequently fine-tuning it for downstream tasks. Contrastive learning, a variant of SSL, enables effective learning of image-level representations. To address the issue of insufficient labeled data for vein feature extraction and classification, we propose CL3A-FV, a Contrastive Learning-based Finger-Vein image recognition approach with Automatic Adversarial Augmentation in this paper. Specifically, CL3A-FV consists of the dual-branch augmentation network, Siamese encoder, discriminator, and distributor. The training process involves two steps: 1) training the Siamese encoder by updating its parameters while keeping other components fixed; and 2) training the dual-branch augmentation network with a fixed Siamese encoder, integrating a discriminator to distinguish views generated by the two branches, and a distributor to constrain the distribution of the augmented data. Both networks are updated adversarially using the stochastic gradient descent. We conduct extensive experiments to evaluate CL3A-FV on three finger-vein datasets, and the experimental results show that the proposed CL3A-FV achieves significant improvements compared to traditional self-supervised learning techniques and supervised methods.