Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/162989
Title: Learning meta pattern for face anti-spoofing
Authors: Cai, Rizhao
Li, Zhi
Wan, Renjie
Li, Haoliang
Hu, Yongjian
Kot, Alex Chichung
Keywords: Engineering::Computer science and engineering
Engineering::Electrical and electronic engineering
Issue Date: 2022
Source: Cai, R., Li, Z., Wan, R., Li, H., Hu, Y. & Kot, A. C. (2022). Learning meta pattern for face anti-spoofing. IEEE Transactions On Information Forensics and Security, 17, 1201-1213. https://dx.doi.org/10.1109/TIFS.2022.3158551
Journal: IEEE Transactions on Information Forensics and Security 
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.
URI: https://hdl.handle.net/10356/162989
ISSN: 1556-6013
DOI: 10.1109/TIFS.2022.3158551
Rights: © 2022 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
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