Single Domain Dynamic Generalization for Iris Presentation Attack
  Detection
Yachun Li Jingjing Wang Yuhui Chen Di Xie Shiliang Pu
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.