| Issue |
MATEC Web Conf.
Volume 277, 2019
2018 International Joint Conference on Metallurgical and Materials Engineering (JCMME 2018)
|
|
|---|---|---|
| Article Number | 03005 | |
| Number of page(s) | 6 | |
| Section | Material and Mechanics | |
| DOI | https://doi.org/10.1051/matecconf/201927703005 | |
| Published online | 02 April 2019 | |
Skeleton based gait recognition for long and baggy clothes
Graduate School of Engineering and Science, Shibaura Institute of Technology, Tokyo, Japan
* Corresponding author: mg17002@shibaura-it.ac.jp
Human gait is a significant biometric feature used for the identification of people by their style of walking. Gait offers recognition from a distance at low resolution while requiring no user interaction. On the other hand, other biometrics are likely to require a certain level of interaction. In this paper, a human gait recognition method is presented to identify people who are wearing long baggy clothes like Thobe and Abaya. Microsoft Kinect sensor is used as a tool to establish a skeleton based gait database. The skeleton joint positions are obtained and used to create five different datasets. Each dataset contained different combination of joints to explore their effectiveness. An evaluation experiment was carried out with 20 walking subjects, each having 25 walking sequences in total. The results achieved good recognition rates up to 97%.
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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