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Model-Based Human Gait Recognition Via Deterministic Learning

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Abstract

In this paper, we present a new model-based approach for human gait recognition in the sagittal plane via deterministic learning (DL) theory. Side silhouette lower limb joint angles characterize the gait system dynamics and are selected as the gait feature. Locally accurate identification of the gait system dynamics is achieved by using radial basis function (RBF) neural networks through DL. The obtained knowledge of the approximated gait system dynamics is stored in constant RBF networks. A gait signature is then derived from the extracted gait system dynamics along the phase portrait of joint angles. A bank of estimators is constructed using constant RBF networks to represent the training gait patterns. By comparing the set of estimators with a test gait pattern, a set of recognition errors are generated, and the average L 1 norms of the errors are taken as the similarity measure between the dynamics of the training gait patterns and the dynamics of the test gait pattern. Therefore, the test gait pattern can be rapidly recognized according to the smallest error principle. In contrast to other existing approaches, the main focus of this paper is on obtaining and reusing the knowledge of the gait system dynamics. Finally, experiments are carried out on the CASIA-A and CASIA-B gait databases to benchmark the effectiveness of the proposed approach.

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Acknowledgments

This work was supported by the National Science Fund for Distinguished Young Scholars (Grant No. 61225014), by the National Natural Science Foundation of China (Grant No. 60934001) and by China Postdoctoral Science Foundation (Grant No. 2013M531851). The authors would like to thank CBSR for providing access to the CASIA-A and CASIA-B gait databases and thank the anonymous reviewers for their constructive comments.

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Authors and Affiliations

  1. School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, 510640, People’s Republic of China

    Wei Zeng

  2. School of Physics and Mechanical and Electrical Engineering, Longyan University, Longyan, 364000, People’s Republic of China

    Wei Zeng

  3. School of Automation Science and Engineering, South China University of Technology, Guangzhou, 510640, People’s Republic of China

    Cong Wang & Yuanqing Li

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  1. Wei Zeng
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  2. Cong Wang
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  3. Yuanqing Li
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Correspondence to Cong Wang.

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Zeng, W., Wang, C. & Li, Y. Model-Based Human Gait Recognition Via Deterministic Learning. Cogn Comput 6, 218–229 (2014). https://doi.org/10.1007/s12559-013-9221-4

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  • DOI: https://doi.org/10.1007/s12559-013-9221-4

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