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Frame Correlation Knowledge Distillation for Gait Recognition in the Wild

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Biometric Recognition (CCBR 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14463))

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Abstract

Recently, large deep models have achieved significant progress on gait recognition in the wild. However, such models come with a high cost of runtime and computational resource consumption. In this paper, we investigate knowledge distillation (KD) for gait recognition, which trains compact student networks by using a cumbersome teacher network. We propose a novel scheme, named Frame Correlation KD (FCKD), to transfer the frame correlation map (FCM) from the teacher network to the student network. Since the teacher network usually learns more frame correlations, transferring such FCM from teacher to student makes the student more informative and mimic the teacher better, thus improving the recognition accuracy. Extensive experiments demonstrate the effectiveness of our approach in improving the performance of compact networks.

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Notes

  1. 1.

    DG2D represents DeepGaitV2-2D and DG3D represents DeepGaitV2-3D. (16) represents that the number of first-stage channels is 16.

  2. 2.

    We only consider the backbone for all experiments.

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Acknowledgement

This work was supported by the Key Program of National Natural Science Foundation of China (Grant No. U20B2069).

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Authors and Affiliations

  1. State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and Engineering, Beihang University, Beijing, 100191, China

    Guozhen Peng, Shaoxiong Zhang, Yuwei Zhao, Annan Li & Yunhong Wang

Authors
  1. Guozhen Peng
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  2. Shaoxiong Zhang
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  3. Yuwei Zhao
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  4. Annan Li
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  5. Yunhong Wang
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Corresponding author

Correspondence to Annan Li .

Editor information

Editors and Affiliations

  1. Hefei University of Technology, Hefei, China

    Wei Jia

  2. South China University of Technology, Guangzhou, China

    Wenxiong Kang

  3. China University of Mining and Technology, Xuzhou, China

    Zaiyu Pan

  4. Shandong University, Jinan, China

    Xianye Ben

  5. China University of Mining and Technology, Xuzhou, China

    Zhengfu Bian

  6. Southern University of Science and Technology, Shenzhen, China

    Shiqi Yu

  7. Chinese Academy of Sciences, Beijing, China

    Zhaofeng He

  8. China University of Mining and Technology, Xuzhou, China

    Jun Wang

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Peng, G., Zhang, S., Zhao, Y., Li, A., Wang, Y. (2023). Frame Correlation Knowledge Distillation for Gait Recognition in the Wild. In: Jia, W., et al. Biometric Recognition. CCBR 2023. Lecture Notes in Computer Science, vol 14463. Springer, Singapore. https://doi.org/10.1007/978-981-99-8565-4_27

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  • DOI: https://doi.org/10.1007/978-981-99-8565-4_27

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-8564-7

  • Online ISBN: 978-981-99-8565-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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