Contactless Palmprint Image Recognition Across Smartphones With Self-Paced CycleGAN

Contactless palmprint recognition, an emerging biometric technology, has attracted increasing attention due to its noninvasive and high practicability characteristics. Although it is naturally suitable for mobile application scenarios, the following two challenges severely limit its recognition performance: 1) the inconsistency in acquisition devices used in training and testing, and 2) many subjects are unable to be imaged on each device, resulting in incomplete data problems. To address these issues, we propose a self-paced CycleGAN with self-attention modules, which simultaneously synthesizes missing data and alleviates the influence of different imaging devices. Specifically, we develop CycleGAN with self-attention modules to generate missing training data by effectively mining the structural correlation among samples while capturing the cross-domain features. Furthermore, a self-paced learning strategy, which is a human cognitive-driven learning mechanism, is used to guide learning the robust cross-domain feature representation and recognition model, by which the relatively easy learning samples are gradually involved in the training process. To verify the effectiveness of the proposed method, we conduct experiments on contactless palmprint datasets collected using different smartphones. The results show that our approach outperforms state-of-the-art methods in classifying contactless palmprint images.