Gabor filter bank-based GEI features for human Gait recognition

Lishani, Ait, Boubchir, Larbi, Khalifa, Emad and Bouridane, Ahmed (2016) Gabor filter bank-based GEI features for human Gait recognition. In: Proceedings of the 39th International Conference on Telecommunications and Signal Processing (TSP). IEEE, pp. 648-651. ISBN 978-1-5090-1288-6

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Abstract

This paper proposes a supervised feature extraction approach which is capable to select distinctive features for the recognition of human gait under clothing and carrying conditions thus improving the recognition performances. The principle of the suggested approach is based on the use of feature texture descriptors extracted from Gait Energy Image (GEI). The proposed features are computed using the bank of Gabor filters and then selected using Spectral Regression Kernel Discriminant Analysis (SRKDA) reduction algorithm. The proposed method is evaluated on CASIA Gait database (dataset B) under variations of clothing and carrying conditions for different viewing angles; and the experimental results using one-against-all SVM classifier have given attractive results of up to 91% in terms of Correct Classification Rate (CCR) when compared to existing and similar state-of-the-art methods.

Item Type:	Book Section
Uncontrolled Keywords:	SRKDA, Gait recognition, feature extraction, GEI, Gabor filter
Subjects:	G400 Computer Science G600 Software Engineering
Department:	Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User:	Becky Skoyles
Date Deposited:	09 Jan 2017 14:28
Last Modified:	12 Oct 2019 22:27
URI:	http://nrl.northumbria.ac.uk/id/eprint/29053

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