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View-invariant gait recognition based on kinect skeleton feature

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Abstract

Gait recognition is a popular remote biometric identification technology. Its robustness against view variation is one of the challenges in the field of gait recognition. In this paper, the second-generation Kinect (2G–Kinect) is used as a tool to build a 3D–skeleton-based gait dataset, which includes both 2D silhouette images captured by 2G–Kinect and their corresponding 3D coordinates of skeleton joints. Given this dataset, a human walking model is constructed. Referring to the walking model, the length of some specific skeletons is selected as the static features, and the angles of swing limbs as the dynamic features, which are verified to be view-invariant. In addition, the gait recognition abilities of the static and dynamic features are investigated respectively. Given the investigation, a view-invariant gait recognition scheme is proposed based on the matching-level-fusion of the static and dynamic features, and the nearest neighbor (NN) method is used for recognition. Comparison between the existing Kinect-based gait recognition method and the proposed one on different datasets show that the proposed one has better recognition performance.

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Acknowledgements

This work is supported by Natural Science Foundation for Distinguished Young Scholars of Shandong Province (JQ201718), National Science Foundation of China (61572298), and Key Research and Development Foundation of Shandong Province (2016GGX101009). The contact author is Jiande Sun (jiandesun@hotmail.com).

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Authors and Affiliations

  1. School of Information Science and Engineering, Shandong Normal University, Jinan, China

    Jiande Sun, Jing Li, Wenbo Wan & Huaxiang Zhang

  2. Institute of Data Science and Technology, Shandong Normal University, Jinan, China

    Jiande Sun, Wenbo Wan & Huaxiang Zhang

  3. School of Information Science and Engineering, Shandong University, Jinan, China

    Yufei Wang

  4. School of Mechanical and Electrical Engineering, Shandong Management University, Jinan, China

    Jing Li

  5. Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University, Xi’an, China

    De Cheng

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  1. Jiande Sun
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Correspondence to Jiande Sun.

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Sun, J., Wang, Y., Li, J. et al. View-invariant gait recognition based on kinect skeleton feature. Multimed Tools Appl 77, 24909–24935 (2018). https://doi.org/10.1007/s11042-018-5722-1

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