Investigation of Gait Representations in Lower Knee Gait Recognition

Chirawat Wattanapanich, Hong Wei

2016

Abstract

This paper investigates the effect of lower knee gait representations on gait recognition. After reviewing three emerging gait representations, i.e. Gait Energy Image (GEI), Gait Entropy Image (GEnI), and Gait Gaussian Image (GGI), a new gait representation, Gait Gaussian Entropy Image (GGEnI), is proposed to combine advantages of entropy and Gaussian in improving the robustness to noises and appearance changes. Experimental results have shown that lower knee gait representations can successfully detect camera view angles in CASIA Gait Dataset B, and they are better than full body representations in gait recognition under the condition of wearing coat. The gait representations involving the Gaussian technique have shown robustness to noises, whilst the representations involving entropy provide a better robustness to appearance changes.

Paper Citation

in Harvard Style

Wattanapanich C. and Wei H. (2016). Investigation of Gait Representations in Lower Knee Gait Recognition . In Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-173-1, pages 678-683. DOI: 10.5220/0005817006780683

in Bibtex Style

@conference{icpram16,
author={Chirawat Wattanapanich and Hong Wei},
title={Investigation of Gait Representations in Lower Knee Gait Recognition},
booktitle={Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2016},
pages={678-683},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005817006780683},
isbn={978-989-758-173-1},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Investigation of Gait Representations in Lower Knee Gait Recognition
SN - 978-989-758-173-1
AU - Wattanapanich C.
AU - Wei H.
PY - 2016
SP - 678
EP - 683
DO - 10.5220/0005817006780683