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AdaptiveGait: adaptive feature fusion network for gait recognition

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Abstract

Gait recognition is a biometric approach used to identify people based on their walking patterns at long distances and low resolutions. Most advanced gait recognition methods based on silhouettes employ the focal convolution module. However, experiments have demonstrated that the horizontal segmentation method used in this module causes information loss at the feature map demarcation line. In this paper, we propose an adaptive feature fusion block (AFFB) for feature extraction that utilizes comprehensive global features to compensate for the lost local features, significantly reducing feature loss caused by local convolution. Additionally, we introduce a feature expansion module (FEM) to enrich the temporal information of gait features and adaptively balance the body detail information extracted by the model with the overall body information . We evaluated our model on CASIA-B and OU-MVLP datasets and compared it to other gait models using RANK-1 accuracy. The experimental results show that our model can represent gait features better than other models and achieved high accuracy in gait recognition across perspectives and various walking conditions.The source code will be available on https://github.com/Lentia/AdaptiveGait.

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Acknowledgements

This work was supported in part by the Key R &D Project of Shandong Province (2022CXGC010503), in part by 2022 Industry Leading Talents Support Program "Hai You Project" (Research and industrialization of management and control system for information technology service industry based on AI and edge computing), and in part by the National Natural Science Foundation of China (61876099).

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Author notes

  1. Tian Liang and Zhenxue Chen contributed equally to this work.

Authors and Affiliations

  1. School of Control Science and Engineering, Shandong University, 250061, Jinan, China

    Tian Liang, Zhenxue Chen, Chengyun Liu & Yuchen Hu

  2. Institute of Marine Science and Technology, Shandong University, 266237, Qingdao, China

    Jiyang Chen

  3. Shandong Zhengzhong Information Technology CO., LTD., 250098, Jinan, China

    Jiyang Chen

  4. Department of Electrical and Computer Engineering, University of Windsor, ON N9B 3P4, Windsor, Canada

    Q. M. Jonathan Wu

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  1. Tian Liang
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  2. Zhenxue Chen
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  3. Chengyun Liu
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  4. Jiyang Chen
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  5. Yuchen Hu
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Correspondence to Zhenxue Chen or Chengyun Liu.

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Liang, T., Chen, Z., Liu, C. et al. AdaptiveGait: adaptive feature fusion network for gait recognition. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-18692-0

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