Dual Reweighting Domain Generalization for Face Presentation Attack Detection

Understand

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.