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Spoof-Guided Image Decomposition for Face Anti-spoofing

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Pattern Recognition and Computer Vision (PRCV 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14429))

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Abstract

Face spoofing attacks have become an increasingly critical concern when face recognition is widely applied. However, attacking materials have been made visually similar to real human faces, making spoof clues hard to be reliably detected. Previous methods have shown that auxiliary information extracted from the raw RGB data, including depth map, rPPG signal, HSV color space, etc., are promising ways to highlight the hidden spoofing details. In this paper, we consider extracting novel auxiliary information to expose hidden spoofing clues and remove scenarios specific, so as to help the neural network improve the generalization and interpretability of the model’s decision. Considering that presenting faces from spoof mediums will introduce 3D geometry and texture differences, we propose a spoof-guided face decomposition network to disentangle a face image into the components of normal, albedo, light, and shading, respectively. Besides, we design a multi-stream fusion network, which effectively extracts features from the inherent imaging components and captures the complementarity and discrepancy between them. We evaluate the proposed method on various databases, i.e. CASIA-MFSD, Replay-Attack, MSU-MFSD, and OULU-NPU. The results show that our proposed method achieves competitive performance in both intra-dataset and inter-dataset evaluation protocols.

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Acknowledgement

This work was supported in part by Chinese National Natural Science Foundation Projects #62276254, #62176256, #62106264, #62206280, #U2003111), Beijing Natural Science Foundation under no. L221013, the Defense Industrial Technology Development Program (#JCKY2021906A001), Shandong Provincial Natural Science Foundation under Project ZR2021MF066 and the InnoHK program.

Author information

Authors and Affiliations

  1. Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China

    Bin Zhang & Xiaoyu Zhang

  2. School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China

    Bin Zhang & Xiaoyu Zhang

  3. State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Xiangyu Zhu & Zhen Lei

  4. School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China

    Xiangyu Zhu & Zhen Lei

  5. Centre for Artificial Intelligence and Robotics, Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences, Hong Kong, China

    Zhen Lei

  6. ZKTeco Co., Ltd., Dongguan, China

    Shukai Chen

  7. China University of Petroleum (East China), Dongying, China

    Peng Li

Authors
  1. Bin Zhang
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  2. Xiangyu Zhu
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  3. Xiaoyu Zhang
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  4. Shukai Chen
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  5. Peng Li
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  6. Zhen Lei
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Corresponding author

Correspondence to Zhen Lei .

Editor information

Editors and Affiliations

  1. Nanjing University of Information Science and Technology, Nanjing, China

    Qingshan Liu

  2. Xiamen University, Xiamen, China

    Hanzi Wang

  3. Beijing University of Posts and Telecommunications, Beijing, China

    Zhanyu Ma

  4. Sun Yat-sen University, Guangzhou, China

    Weishi Zheng

  5. Peking University, Beijing, China

    Hongbin Zha

  6. Chinese Academy of Sciences, Beijing, China

    Xilin Chen

  7. Chinese Academy of Sciences, Beijing, China

    Liang Wang

  8. Xiamen University, Xiamen, China

    Rongrong Ji

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Zhang, B., Zhu, X., Zhang, X., Chen, S., Li, P., Lei, Z. (2024). Spoof-Guided Image Decomposition for Face Anti-spoofing. In: Liu, Q., et al. Pattern Recognition and Computer Vision. PRCV 2023. Lecture Notes in Computer Science, vol 14429. Springer, Singapore. https://doi.org/10.1007/978-981-99-8469-5_1

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  • DOI: https://doi.org/10.1007/978-981-99-8469-5_1

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-8468-8

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