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

Imad Rida, Larbi Boubchir, Noor Al-Maadeed, Somaya Al-Maadeed, Ahmed Bouridane

Research output: Chapter in Book/Report/Conference proceedingChapter

11 Citations (Scopus)

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.
Original languageEnglish
Title of host publication2016 39th International Conference on Telecommunications and Signal Processing (TSP)
Place of PublicationPiscataway
PublisherIEEE
Pages652-655
ISBN (Print)978-1-5090-1289-3
DOIs
Publication statusE-pub ahead of print - 1 Dec 2016

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