Few-Shot One-Class Domain Adaptation Based On Frequency For Iris Presentation Attack Detection

Abstract

Iris presentation attack detection (PAD) has achieved remarkable success to ensure the reliability and security of iris recognition systems. Most existing methods exploit discriminative features in the spatial domain and report outstanding performance under intra-dataset settings. However, the degradation of performance is inevitable under cross-dataset settings, suffering from domain shift. In consideration of real-world applications, a small number of bonafide samples are easily accessible. We thus define a new domain adaptation setting called Few-shot One-class Domain Adaptation (FODA), where adaptation only relies on a limited number of target bonafide samples. To address this problem, we propose a novel FODA framework based on the expressive power of frequency information. Specifically, our method integrates frequency-related information through two proposed modules. Frequency-based Attention Module (FAM) aggregates frequency information into spatial attention and explicitly emphasizes high-frequency fine-grained features. Frequency Mixing Module (FMM) mixes certain frequency components to generate large-scale target-style samples for adaptation with limited target bonafide samples. Extensive experiments on LivDet-Iris 2017 dataset demonstrate the proposed method achieves state-of-the-art or competitive performance under both cross-dataset and intra-dataset settings.