  Although current face anti-spoofing methods achieve promising results under
intra-dataset testing, they suffer from poor generalization to unseen attacks.
Most existing works adopt domain adaptation (DA) or domain generalization (DG)
techniques to address this problem. However, the target domain is often unknown
during training which limits the utilization of DA methods. DG methods can
conquer this by learning domain invariant features without seeing any target
data. However, they fail in utilizing the information of target data. In this
paper, we propose a self-domain adaptation framework to leverage the unlabeled
test domain data at inference. Specifically, a domain adaptor is designed to
adapt the model for test domain. In order to learn a better adaptor, a
meta-learning based adaptor learning algorithm is proposed using the data of
multiple source domains at the training step. At test time, the adaptor is
updated using only the test domain data according to the proposed unsupervised
adaptor loss to further improve the performance. Extensive experiments on four
public datasets validate the effectiveness of the proposed method.
