Learning Meta Pattern for Face Anti-Spoofing

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

5 Scopus Citations
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Author(s)

  • Rizhao Cai
  • Zhi Li
  • Renjie Wan
  • Yongjian Hu
  • Alex C. Kot

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)1201-1213
Journal / PublicationIEEE Transactions on Information Forensics and Security
Volume17
Online published10 Mar 2022
Publication statusPublished - 2022

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.

Research Area(s)

  • Data mining, Deep learning, Face recognition, Faces, Feature extraction, gfhgf, Neural networks, Optimization

Citation Format(s)

Learning Meta Pattern for Face Anti-Spoofing. / Cai, Rizhao; Li, Zhi; Wan, Renjie; Li, Haoliang; Hu, Yongjian; Kot, Alex C.

In: IEEE Transactions on Information Forensics and Security, Vol. 17, 2022, p. 1201-1213.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review