  Vein recognition has received increasing attention due to its high security
and privacy. Recently, deep neural networks such as Convolutional neural
networks (CNN) and Transformers have been introduced for vein recognition and
achieved state-of-the-art performance. Despite the recent advances, however,
existing solutions for finger-vein feature extraction are still not optimal due
to scarce training image samples. To overcome this problem, in this paper, we
propose an adversarial masking contrastive learning (AMCL) approach, that
generates challenging samples to train a more robust contrastive learning model
for the downstream palm-vein recognition task, by alternatively optimizing the
encoder in the contrastive learning model and a set of latent variables. First,
a huge number of masks are generated to train a robust generative adversarial
network (GAN). The trained generator transforms a latent variable from the
latent variable space into a mask space. Then, we combine the trained generator
with a contrastive learning model to obtain our AMCL, where the generator
produces challenging masking images to increase the contrastive loss and the
contrastive learning model is trained based on the harder images to learn a
more robust feature representation. After training, the trained encoder in the
contrastive learning model is combined with a classification layer to build a
classifier, which is further fine-tuned on labeled training data for vein
recognition. The experimental results on three databases demonstrate that our
approach outperforms existing contrastive learning approaches in terms of
improving identification accuracy of vein classifiers and achieves
state-of-the-art recognition results.
