Learning domain-adaptive palmprint anti-spoofing feature from multi-source domains

Abstract

Palmprint anti-spoofing is essential for securing palmprint recognition systems. Although some anti-spoofing methods excel on closed datasets, their ability to generalize across unknown domains is often limited. This paper introduces the Domain-Adaptive Palmprint Anti-Spoofing Network (DAPANet), which leverages multiple known spoofing domains to extract domain-invariant spoofing clues from unlabeled domains. DAPANet tackles the domain adaptation challenge using three strategies: global domain alignment, subdomain alignment, and the separation of distinct subdomains. The framework consists of a public feature extraction module, a domain adaptation module, a domain classifier, and a fusion classifier. Initially, the public feature extraction module extracts palmprint features. Subsequently, the domain adaptation module aligns target domain features with source domain features to generate domain-specific outputs. The domain classifier provides initial classifiable features, which are then integrated by DAPANet, employing a unified fusion classifier for decision-making. Comprehensive experiments conducted on XJTU-PalmReplay database across various cross-domain scenarios confirm the efficacy of the proposed method.