Improved human gait recognition

Imad Rida, Ahmed Bouridane, Gian Luca Marcialis, Pierluigi Tuveri

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

12 Citations (Scopus)

Abstract

Gait recognition is an emerging biometric technology which aims to identify people purely through the analysis of the way they walk. The technology has attracted interest as a method of identification because of its non-invasiveness, since it does not require the subject’s cooperation. However, "covariates" which include clothing, carrying conditions, and other intra-class variations affect the recognition performances. This paper proposes a feature selection mask which is able to select most relevant discriminative features for human recognition to alleviate the impact of covariates so as to improve the recognition performances. The proposed method has been evaluated using CASIA Gait Database (Dataset B) and the experimental results demonstrate that the proposed technique yields 77.38 % of correct recognition
Original languageEnglish
Title of host publicationImage Analysis and Processing
Place of PublicationLondon
PublisherSpringer
Pages119-129
Volume9280
ISBN (Print)978-3-319-23233-1
DOIs
Publication statusPublished - 21 Aug 2015

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
ISSN (Electronic)0302-9743

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