  Face Anti-Spoofing (FAS) is recently studied under the continual learning
setting, where the FAS models are expected to evolve after encountering the
data from new domains. However, existing methods need extra replay buffers to
store previous data for rehearsal, which becomes infeasible when previous data
is unavailable because of privacy issues. In this paper, we propose the first
rehearsal-free method for Domain Continual Learning (DCL) of FAS, which deals
with catastrophic forgetting and unseen domain generalization problems
simultaneously. For better generalization to unseen domains, we design the
Dynamic Central Difference Convolutional Adapter (DCDCA) to adapt Vision
Transformer (ViT) models during the continual learning sessions. To alleviate
the forgetting of previous domains without using previous data, we propose the
Proxy Prototype Contrastive Regularization (PPCR) to constrain the continual
learning with previous domain knowledge from the proxy prototypes. Simulate
practical DCL scenarios, we devise two new protocols which evaluate both
generalization and anti-forgetting performance. Extensive experimental results
show that our proposed method can improve the generalization performance in
unseen domains and alleviate the catastrophic forgetting of the previous
knowledge. The codes and protocols will be released soon.
