Single Domain Dynamic Generalization for Iris Presentation Attack Detection

• URL: http://arxiv.org/abs/2305.12800v1
• Date: Mon, 22 May 2023 07:54:13 GMT
• Title: Single Domain Dynamic Generalization for Iris Presentation Attack Detection
• Authors: Yachun Li, Jingjing Wang, Yuhui Chen, Di Xie, Shiliang Pu
• Abstract summary: Iris presentation generalization has achieved great success under intra-domain settings but easily degrades on unseen domains. We propose a Single Domain Dynamic Generalization (SDDG) framework, which exploits domain-invariant and domain-specific features on a per-sample basis. The proposed method is effective and outperforms the state-of-the-art on LivDet-Iris 2017 dataset.
• Score: 41.126916126040655
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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