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KDFAS: Multi-stage Knowledge Distillation Vision Transformer 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

With the commercial application of face recognition systems, face anti-spoofing has been studied extensively to enhance security in recent years. In this work, a lightweight network via knowledge distillation for face anti-spoofing is proposed. The main innovations of our approach are threefold: (1) In convolutional neural network based knowledge distillation, the local receptive field of teacher network may be inconsistent with that of student network, which results in misguiding. In our method, vision transformer architecture is leveraged because of its global modeling capabilities. (2) Beyond conventional decision-level knowledge transfer in the classification step via kullback-leibler loss, we present multi-stage feature-level knowledge distillation strategy to guide the feature learning of student network which can transfer richer knowledge from teacher to student network. (3) In contrast to traditional projection head learning, we construct a covariance matrix to solve the embedding dimensionality mismatching problem between teacher and student network in middle layers. Compared to teacher model of 1.28 GB, the memory of student model is only 330.8 MB, which effectively achieves a trade-off between memory and accuracy. Extensive experiments on three standard benchmarks demonstrate the superiority of our proposed method, which evidently corroborates the significance of multi-stage knowledge distillation for face anti-spoofing.

This work was supported by the National Natural Science Foundation of China (62101376) and Natural Science Foundation of Shanxi Province of China (201901D211078).

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Author information

Authors and Affiliations

  1. College of Data Science, Taiyuan University of Technology, Taiyuan, 030024, China

    Jun Zhang, Yunfei Zhang, Feixue Shao & Daoxiang Zhou

  2. School of Artificial Intelligence, Beijing Normal University, Beijing, 100875, China

    Xuetao Ma

Authors
  1. Jun Zhang
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  2. Yunfei Zhang
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  3. Feixue Shao
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  4. Xuetao Ma
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  5. Daoxiang Zhou
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Corresponding author

Correspondence to Daoxiang Zhou .

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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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Zhang, J., Zhang, Y., Shao, F., Ma, X., Zhou, D. (2024). KDFAS: Multi-stage Knowledge Distillation Vision Transformer 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_13

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  • DOI: https://doi.org/10.1007/978-981-99-8469-5_13

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  • Print ISBN: 978-981-99-8468-8

  • Online ISBN: 978-981-99-8469-5

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