Palmprint anti-spoofing based on domain-adversarial training and online triplet mining

Palmprint recognition has gained increased attention as a novel biometric technology. Nonetheless, it faces a challenge in security as individuals may be able to forge palmprints for malicious purposes. To address this, it is essential to conduct palmprint anti-spoofing detection. Currently, there is a lack of datasets and algorithms in this field. In this paper, we construct a novel, large-scale palmprint attack dataset. Furthermore, we introduce domain generalization into the palmprint anti-spoofing realm. Domain-adversarial training and online triplet mining methods are proposed to enhance generalizability performance for unseen target domains. Experimental results show that compared to baseline, our method achieves superior results on the dataset.