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Gait recognition with covariate separation based on adversarial learning

Journal of physics. Conference series, 2023-08, Vol.2577 (1), p.12008 [Peer Reviewed Journal]

Published under licence by IOP Publishing Ltd ;Published under licence by IOP Publishing Ltd. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1742-6588 ;EISSN: 1742-6596 ;DOI: 10.1088/1742-6596/2577/1/012008

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  • Title:
    Gait recognition with covariate separation based on adversarial learning
  • Author: Zhu, Yongsheng ; Hu, Weijun ; Zhou, Hongming
  • Subjects: Classifiers ; Feature extraction ; Gait recognition ; Physics
  • Is Part Of: Journal of physics. Conference series, 2023-08, Vol.2577 (1), p.12008
  • Description: Abstract In response to the poor recognition accuracy caused by viewpoint changes and wearing changes in gait recognition problems, a viewpoint classifier and a wearing classifier are proposed to be connected after the feature extractor. The parameters of the feature extractor are updated using the gradient reversal mechanism so that its function is opposite to that of the two classifiers, thereby separating the viewpoint information, wearing information at the feature extraction level, and reducing the impact of these two most common factors on gait recognition. Comparative experiments on the CASIA-B dataset show that under BG and CL conditions, our model has improved by an average of 2.1% and 1.9% in cross-viewpoint accuracy compared to the baseline model. Our model is more robust under complex walking conditions.
  • Publisher: Bristol: IOP Publishing
  • Language: English
  • Identifier: ISSN: 1742-6588
    EISSN: 1742-6596
    DOI: 10.1088/1742-6596/2577/1/012008
  • Source: Geneva Foundation Free Medical Journals at publisher websites
    IOPscience (Open Access)
    Institute of Physics Open Access Journal Titles
    ProQuest Central
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