Single Domain Dynamic Generalization for Iris Presentation Attack Detection
This addresses the challenge of domain generalization in iris biometric security, but it is incremental as it builds on existing domain generalization methods.
The paper tackled the problem of iris presentation attack detection degrading on unseen domains when trained on a single domain, and proposed a framework that simultaneously exploits domain-invariant and domain-specific features, achieving state-of-the-art results on the LivDet-Iris 2017 dataset.
Iris presentation attack detection (PAD) has achieved great success under intra-domain settings but easily degrades on unseen domains. Conventional domain generalization methods mitigate the gap by learning domain-invariant features. However, they ignore the discriminative information in the domain-specific features. Moreover, we usually face a more realistic scenario with only one single domain available for training. To tackle the above issues, we propose a Single Domain Dynamic Generalization (SDDG) framework, which simultaneously exploits domain-invariant and domain-specific features on a per-sample basis and learns to generalize to various unseen domains with numerous natural images. Specifically, a dynamic block is designed to adaptively adjust the network with a dynamic adaptor. And an information maximization loss is further combined to increase diversity. The whole network is integrated into the meta-learning paradigm. We generate amplitude perturbed images and cover diverse domains with natural images. Therefore, the network can learn to generalize to the perturbed domains in the meta-test phase. Extensive experiments show the proposed method is effective and outperforms the state-of-the-art on LivDet-Iris 2017 dataset.