  Face anti-spoofing approaches based on domain generalization (DG) have drawn
growing attention due to their robustness for unseen scenarios. Previous
methods treat each sample from multiple domains indiscriminately during the
training process, and endeavor to extract a common feature space to improve the
generalization. However, due to complex and biased data distribution, directly
treating them equally will corrupt the generalization ability. To settle the
issue, we propose a novel Dual Reweighting Domain Generalization (DRDG)
framework which iteratively reweights the relative importance between samples
to further improve the generalization. Concretely, Sample Reweighting Module is
first proposed to identify samples with relatively large domain bias, and
reduce their impact on the overall optimization. Afterwards, Feature
Reweighting Module is introduced to focus on these samples and extract more
domain-irrelevant features via a self-distilling mechanism. Combined with the
domain discriminator, the iteration of the two modules promotes the extraction
of generalized features. Extensive experiments and visualizations are presented
to demonstrate the effectiveness and interpretability of our method against the
state-of-the-art competitors.
