Review Article Open Access

Review on Vision-Based Gait Recognition: Representations, Classification Schemes and Datasets

Chin Poo Lee1, Alan Wee Chiat Tan1 and Kian Ming Lim1
  • 1 Multimedia University, Malaysia
American Journal of Applied Sciences
Volume 14 No. 2, 2017, 252-266

DOI: https://doi.org/10.3844/ajassp.2017.252.266

Submitted On: 29 September 2016 Published On: 6 February 2017

How to Cite: Lee, C. P., Tan, A. W. C. & Lim, K. M. (2017). Review on Vision-Based Gait Recognition: Representations, Classification Schemes and Datasets. American Journal of Applied Sciences, 14(2), 252-266. https://doi.org/10.3844/ajassp.2017.252.266

Abstract

Gait has unique advantage at a distance when other biometrics cannot be used since they are at too low resolution or obscured, as commonly observed in visual surveillance systems. This paper provides a survey of the technical advancements in vision-based gait recognition. A wide range of publications are discussed in this survey embracing different perspectives of the research in this area, including gait feature extraction, classification schemes and standard gait databases. There are two major groups of the state-of-the-art techniques in characterizing gait: Model-based and motion-free. The model-based approach obtains a set of body or motion parameters via human body or motion modeling. The model-free approach, on the other hand, derives a description of the motion without assuming any model. Each major category is further organized into several subcategories based on the nature of gait representation. In addition, some widely used classification schemes and benchmark databases for evaluating performance are also discussed.

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Keywords

  • Gait
  • Gait Recognition
  • Gait Datasets