Learning Meta Pattern for Face Anti-Spoofing

TL;DR

This paper introduces a learnable Meta Pattern extraction method within a learning-to-learn framework to enhance the generalization of face anti-spoofing models across different domains, outperforming handcrafted features.

Contribution

It proposes a novel learnable Meta Pattern extraction approach and a hierarchical fusion network to improve cross-domain face anti-spoofing performance.

Findings

Meta Pattern outperforms handcrafted features

Achieves state-of-the-art results on domain generalization benchmarks

Hierarchical fusion enhances feature integration

Abstract

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.