Learning Meta Pattern for Face Anti-Spoofing

Rizhao Cai, Zhi Li, Renjie Wan, Haoliang Li*, Yongjian Hu, Alex C. Kot

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

38 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)1201-1213
Number of pages13
JournalIEEE Transactions on Information Forensics and Security
Publication statusPublished - 10 Mar 2022

Scopus Subject Areas

  • Safety, Risk, Reliability and Quality
  • Computer Networks and Communications

User-Defined Keywords

  • domain generalization
  • Face Anti-Spoofing (FAS)
  • Face Presentation Attack Detection (Face PAD)
  • Meta Pattern (MP)


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