  Face anti-spoofing approach based on domain generalization(DG) has drawn
growing attention due to its robustness forunseen scenarios. Existing DG
methods assume that the do-main label is known.However, in real-world
applications, thecollected dataset always contains mixture domains, where
thedomain label is unknown. In this case, most of existing meth-ods may not
work. Further, even if we can obtain the domainlabel as existing methods, we
think this is just a sub-optimalpartition. To overcome the limitation, we
propose domain dy-namic adjustment meta-learning (D2AM) without using do-main
labels, which iteratively divides mixture domains viadiscriminative domain
representation and trains a generaliz-able face anti-spoofing with
meta-learning. Specifically, wedesign a domain feature based on Instance
Normalization(IN) and propose a domain representation learning module(DRLM) to
extract discriminative domain features for cluster-ing. Moreover, to reduce the
side effect of outliers on cluster-ing performance, we additionally utilize
maximum mean dis-crepancy (MMD) to align the distribution of sample featuresto
a prior distribution, which improves the reliability of clus tering. Extensive
experiments show that the proposed methodoutperforms conventional DG-based face
anti-spoofing meth-ods, including those utilizing domain labels. Furthermore,
weenhance the interpretability through visualizatio
