  Face Anti-Spoofing (FAS) is essential to secure face recognition systems and
has been extensively studied in recent years. Although deep neural networks
(DNNs) for the FAS task have achieved promising results in intra-dataset
experiments with similar distributions of training and testing data, the DNNs'
generalization ability is limited under the cross-domain scenarios with
different distributions of training and testing data. To improve the
generalization ability, recent hybrid methods have been explored to extract
task-aware handcrafted features (e.g., Local Binary Pattern) as discriminative
information for the input of DNNs. However, the handcrafted feature extraction
relies on experts' domain knowledge, and how to choose appropriate handcrafted
features is underexplored. To this end, we propose a learnable network to
extract Meta Pattern (MP) in our learning-to-learn framework. By replacing
handcrafted features with the MP, the discriminative information from MP is
capable of learning a more generalized model. Moreover, we devise a two-stream
network to hierarchically fuse the input RGB image and the extracted MP by
using our proposed Hierarchical Fusion Module (HFM). We conduct comprehensive
experiments and show that our MP outperforms the compared handcrafted features.
Also, our proposed method with HFM and the MP can achieve state-of-the-art
performance on two different domain generalization evaluation benchmarks.
