  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.
