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Non-significant Information Enhancement Based Attention Network for Face Anti-spoofing

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Pattern Recognition and Computer Vision (PRCV 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 13021))

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Abstract

Face anti-spoofing is an essential step in ensuring the safety of face recognition systems. Recently, some deep convolutional neural networks (CNNs) based methods have been developed to improve the performance of face anti-spoofing. These methods mainly employ the attention information of the facial images to learn the embedding feature. However, the previous work ignores the potential benefits of non-significant information. To overcome this issue, this paper presents a novel Non-Significant information Enhancement based Attention module (NSEA). NSEA not only focuses on the significant region for feature learning but also preserves the non-significant region to enhance feature representation. Additionally, we also introduce the Multi-Scale Refinement Strategy (MSRS) to capture the fine-grained information and refine the coarse binary mask at different scales. A network build with NESA and MSRS, called the Non-Significant information Enhancement based Attention Network (NSEAN). Experiments are conducted on five benchmark databases, including OULU-NPU, SiW, CASIA-MSFD, MSU-MFSD, and Replay-attack. The quantitative results demonstrate the advantages of the proposed method over state-of-the-art methods.

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Authors and Affiliations

  1. PCA Lab, Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education, Nanjing University of Science and Technology, Nanjing, China

    Yangwei Dong, Jianjun Qian & Jian Yang

Authors
  1. Yangwei Dong
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  2. Jianjun Qian
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  3. Jian Yang
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Corresponding author

Correspondence to Jianjun Qian .

Editor information

Editors and Affiliations

  1. University of Science and Technology Beijing, Beijing, China

    Huimin Ma

  2. Chinese Academy of Sciences, Beijing, China

    Liang Wang

  3. Tsinghua University, Beijing, China

    Changshui Zhang

  4. Zhejiang University, Hangzhou, China

    Fei Wu

  5. Chinese Academy of Sciences, Beijing, China

    Tieniu Tan

  6. Hunan University, Changsha, China

    Yaonan Wang

  7. Sun Yat-Sen University, Guangzhou, Guangdong, China

    Jianhuang Lai

  8. Beijing Jiaotong University, Beijing, China

    Yao Zhao

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Dong, Y., Qian, J., Yang, J. (2021). Non-significant Information Enhancement Based Attention Network for Face Anti-spoofing. In: Ma, H., et al. Pattern Recognition and Computer Vision. PRCV 2021. Lecture Notes in Computer Science(), vol 13021. Springer, Cham. https://doi.org/10.1007/978-3-030-88010-1_36

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  • DOI: https://doi.org/10.1007/978-3-030-88010-1_36

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-88009-5

  • Online ISBN: 978-3-030-88010-1

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