American Journal of Applied Sciences

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

Chin Poo Lee, Alan Wee Chiat Tan and Kian Ming Lim

DOI : 10.3844/ajassp.2017.252.266

American Journal of Applied Sciences

Volume 14, Issue 2

Pages 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.

Copyright

© 2017 Chin Poo Lee, Alan Wee Chiat Tan and Kian Ming Lim. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.