Robust model-free gait recognition by statistical dependency feature selection and Globality-Locality Preserving Projections

Rida, Imad, Boubchir, Larbi, Al-Maadeed, Noor, Al-Maadeed, Somaya and Bouridane, Ahmed (2016) Robust model-free gait recognition by statistical dependency feature selection and Globality-Locality Preserving Projections. In: 2016 39th International Conference on Telecommunications and Signal Processing (TSP). IEEE, Piscataway, pp. 652-655. ISBN 978-1-5090-1289-3

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Abstract

Gait recognition aims to identify people through the analysis of the way they walk. The challenge of model-free based gait recognition is to cope with various intra-class variations such as clothing variations and carrying conditions that adversely affect the recognition performances. This paper proposes a novel method which combines Statistical Dependency (SD) feature selection with Globality-Locality Preserving Projections (GLPP) to alleviate the impact of intra-class variations so as to improve the recognition performances. The proposed method has been evaluated using CASIA Gait database (Dataset B) under variations of clothing and carrying conditions. The experimental results demonstrate that the proposed method achieves a Correct Classification Rate (CCR) up to 86% when compared to existing state-of-the-art methods.

Item Type:	Book Section
Uncontrolled Keywords:	Globally-Locality Preserving Projections, Gait recognition, Model free, Feature selection, Statistical Dependency
Subjects:	G400 Computer Science
Department:	Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User:	Becky Skoyles
Date Deposited:	10 Jan 2017 13:58
Last Modified:	12 Oct 2019 22:27
URI:	http://nrl.northumbria.ac.uk/id/eprint/29054

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