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Simple and efficient pose-based gait recognition method for challenging environments

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A Correction to this article was published on 11 December 2020

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Abstract

Gait is a biometry characterized by the identification of individuals by the way they walk. It is recently gaining evidence because it can be collected at distance and does not require subject cooperation, which is desirable on surveillance scenarios. Despite these advantages, the literature reports challenging situations where gait recognition is not accurate and although exist works that try to address these problems, most of them uses silhoettes, which carry appearance information that confounds with gait. Because of this limitation, a pose estimation method that use information of frames is employed for gait recognition and a multilayer perception, called PoseFrame, is created. As the focus of gait is the classification of a whole walking sequence, the results based on the frames are temporally aggregated for final classification. The method is tested on CASIA Dataset A, having accuracy above other pose-based works; and on CASIA Dataset B, achieving the best results in some situations. An ablation study is also performed, finding that the arms and feet are the most important body parts for gait recognition.

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Acknowledgements

The authors would like to thank the National Council for Scientific and Technological Development—CNPq (Grants~438629/2018-3 and ~309953/2019-7), the Minas Gerais Research Foundation—FAPEMIG (Grants~APQ-00567-14 and ~PPM-00540-17), the Coordination for the Improvement of Higher Education Personnel—CAPES (DeepEyes Project) and Petrobras (Grant~2017/00643-0).

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  1. Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, 31270-901, Brazil

    Vítor C. de Lima, Victor H. C. Melo & William R. Schwartz

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  1. Vítor C. de Lima
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  2. Victor H. C. Melo
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  3. William R. Schwartz
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Correspondence to Vítor C. de Lima.

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Lima, V.C.d., Melo, V.H.C. & Schwartz, W.R. Simple and efficient pose-based gait recognition method for challenging environments. Pattern Anal Applic 24, 497–507 (2021). https://doi.org/10.1007/s10044-020-00935-z

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