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Gait Recognition Based on Temporal Gait Information Enhancing

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MultiMedia Modeling (MMM 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14555))

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Abstract

Gait recognition is a long range biometric technology that identifies individuals by their walking patterns. Currently, gait recognition primarily extracts gait features using convolutional neural networks, which are based on either the global appearance or local human body regions. However, the global feature methods are lack of long range interactions in different local regions and lose temporal features by some extent, and the local feature method segmenting gait silhouettes into blocks limits the ability to characterize local feature weights. In this paper, we propose a gait recognition method that enhances interactions between local regions. To implement this method, we construct a new feature enhancement module, which is a global and local feature extractor based on SENet (GLFES), to enhance the recognition of local features using the attention mechanism. Extensive experiments based on our proposed method have been conducted on the public datasets CASIA-B and OUMVLP to achieve state-of-the-art performances.

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Acknowledgment

This work was supported by National Natural Science Foundation of China (Grant Nos. 61906074, 32371984), Guangdong Basic and Applied Basic Research Foundation (Grant No. 2019A1515011276), Guangzhou Basic and Applied Basic Research Foundation (Grant No. 2023A04J1669), Key-Area Research and Development Program of Guangdong Province (Grant No. 2019B020214003, 2023B0202090001), China Agriculture Research System (CARS-15-23).

Author information

Authors and Affiliations

  1. College of Electronic Engineering, College of Artificial Intelligence, South China Agricultural University, Guangzhou, 510642, China

    Qizhen Chen, Xin Chen, Xiaoling Deng & Yubin Lan

  2. National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, Guangzhou, 510642, China

    Qizhen Chen, Xin Chen, Xiaoling Deng & Yubin Lan

  3. Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou, 510642, China

    Qizhen Chen, Xin Chen, Xiaoling Deng & Yubin Lan

  4. Guangdong Engineering Technology Research Center of Smart Agriculture, Guangzhou, 510642, China

    Qizhen Chen, Xin Chen, Xiaoling Deng & Yubin Lan

Authors
  1. Qizhen Chen
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  2. Xin Chen
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  3. Xiaoling Deng
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  4. Yubin Lan
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Corresponding author

Correspondence to Xiaoling Deng .

Editor information

Editors and Affiliations

  1. University of Amsterdam, Amsterdam, The Netherlands

    Stevan Rudinac

  2. Delft University of Technology, Delft, The Netherlands

    Alan Hanjalic

  3. Delft University of Technology, Delft, The Netherlands

    Cynthia Liem

  4. University of Amsterdam, Amsterdam, The Netherlands

    Marcel Worring

  5. Reykjavik University, Reykjavik, Iceland

    Björn Þór Jónsson

  6. Microsoft Research Lab – Asia, Beijing, China

    Bei Liu

  7. The University of Tokyo, Tokyo, Japan

    Yoko Yamakata

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© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Chen, Q., Chen, X., Deng, X., Lan, Y. (2024). Gait Recognition Based on Temporal Gait Information Enhancing. In: Rudinac, S., et al. MultiMedia Modeling. MMM 2024. Lecture Notes in Computer Science, vol 14555. Springer, Cham. https://doi.org/10.1007/978-3-031-53308-2_33

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  • DOI: https://doi.org/10.1007/978-3-031-53308-2_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-53307-5

  • Online ISBN: 978-3-031-53308-2

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