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A Survey of Behavioral Biometric Gait Recognition: Current Success and Future Perspectives

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Abstract

In today digital society, vulnerability to person authentication is a serious issue in real time scenarios like (airport, hospital, metro stations, etc.). This issue has increased the growth of video surveillance security systems. In recent decades behavioral biometric trait gait has emerged as a potential surveillance monitoring system because of its inconspicuous and unperceivable nature. Even more human gait has a benefit that it can be tracked at a distance and under low resolution videos. Finally, it is difficult to impersonate gait features. In this article, we comprehensively investigate the past and current research development in vision-based (VB) gait recognition. We give a brief description of feature selection and classification techniques used in gait recognition. The article extensively investigates feature representation techniques, classified into model-based and model-free. The article also provides a detail description of databases that are available for research purposes classified into two categories: VB and sensor-based. We extensively examine factors that affect gait recognition, and current research was done to cope with these factors. Moreover, this article proposes future perspectives after investigating state-of-art literature that can be more helpful to experts and new comers in gait recognition. In last, we also give a brief description of the proposed workflow.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Shri Mata Vaishno Devi University, Katra, 182320, India

    Jasvinder Pal Singh, Sanjeev Jain & Sakshi Arora

  2. School of Mathematics, Shri Mata Vaishno Devi University, Katra, 182320, India

    Uday Pratap Singh

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  1. Jasvinder Pal Singh
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  2. Sanjeev Jain
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  4. Uday Pratap Singh
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Correspondence to Sakshi Arora.

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Singh, J.P., Jain, S., Arora, S. et al. A Survey of Behavioral Biometric Gait Recognition: Current Success and Future Perspectives. Arch Computat Methods Eng 28, 107–148 (2021). https://doi.org/10.1007/s11831-019-09375-3

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