Adaptive Mixture of Experts Learning for Generalizable Face Anti-Spoofing

• URL: http://arxiv.org/abs/2207.09868v1
• Date: Wed, 20 Jul 2022 13:02:51 GMT
• Title: Adaptive Mixture of Experts Learning for Generalizable Face Anti-Spoofing
• Authors: Qianyu Zhou, Ke-Yue Zhang, Taiping Yao, Ran Yi, Shouhong Ding, Lizhuang Ma
• Abstract summary: Face anti-spoofing approaches based on domain generalization (DG) have drawn growing attention. Existing DG-based FAS approaches always capture the domain-invariant features for generalizing on the various unseen domains. We propose an Adaptive Mixture of Experts Learning framework, which exploits the domain-specific information to adaptively establish the link among the seen source domains and unseen target domains.
• Score: 37.75738807247752
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With various face presentation attacks emerging continually, face anti-spoofing (FAS) approaches based on domain generalization (DG) have drawn growing attention. Existing DG-based FAS approaches always capture the domain-invariant features for generalizing on the various unseen domains. However, they neglect individual source domains' discriminative characteristics and diverse domain-specific information of the unseen domains, and the trained model is not sufficient to be adapted to various unseen domains. To address this issue, we propose an Adaptive Mixture of Experts Learning (AMEL) framework, which exploits the domain-specific information to adaptively establish the link among the seen source domains and unseen target domains to further improve the generalization. Concretely, Domain-Specific Experts (DSE) are designed to investigate discriminative and unique domain-specific features as a complement to common domain-invariant features. Moreover, Dynamic Expert Aggregation (DEA) is proposed to adaptively aggregate the complementary information of each source expert based on the domain relevance to the unseen target domain. And combined with meta-learning, these modules work collaboratively to adaptively aggregate meaningful domain-specific information for the various unseen target domains. Extensive experiments and visualizations demonstrate the effectiveness of our method against the state-of-the-art competitors.

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