  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.

Computer Vision and Pattern Recognition