PalmGAN for Cross-Domain Palmprint Recognition

Abstract

Nowadays, many efficient palmprint recognition algorithms have emerged. However, previous algorithms can only be used in a single domain. Furthermore, they also require a large amount of labeled data, which is difficult and costly to obtain. In order to solve these problems, we proposed PalmGAN for cross-domain palmprint recognition. Firstly, the labeled fake images were generated to reduce domain gaps, whose styles are similar to the target domain, and at the same time, the identity information remains unchanged. Based on these fake images, supervised Deep Hash Network (DHN) can be trained and directly used for unsupervised identification in the target domain. Moreover, we established semi-uncontrolled and uncontrolled databases, which were collected in uncontrolled environments. Experiments on several popular databases and self-built databases obtained satisfactory performances. PalmGAN can effectively achieve up to 5.08% improvement for cross-domain recognition, and Equal Error Rate (EER) can decrease to 0% for cross-domain recognition between Blue and Green databases.