Learning Meta Pattern for Face Anti-Spoofing

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.

Accepted by IEEE Transactions on Information Forensics and Security (https://ieeexplore.ieee.org.remotexs.ntu.edu.sg/document/9732458) Source code available in https://github.com/RizhaoCai/MetaPattern_FAS

References in corpus (7)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• Learn Convolutional Neural Network for Face Anti-Spoofing
• NAS-FAS: Static-Dynamic Central Difference Network Search for Face Anti-Spoofing
• DRL-FAS: A Novel Framework Based on Deep Reinforcement Learning for Face Anti-Spoofing
• Learning deep forest with multi-scale Local Binary Pattern features for face anti-spoofing
• A Compact Deep Learning Model for Face Spoofing Detection

Cited by in corpus (1)

• Domain-Generalized Face Anti-Spoofing with Unknown Attacks