Fast frontal view gait authentication based on the statistical registration and human gait modeling

Kosuke Okusa, Toshinari Kamakura

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

We study the problem of analyzing and classifying frontal view gait video data. In this study, we suppose that frontal view gait data as a mixing of scale changing, human movements and speed changing parameters. We estimate these parameters using the statistical registration and modeling on a video data. To demonstrate the effectiveness of our method, we conducted experiment, assessing the proposed method for frontal view human gait authentication. We apply K-nearestneighbor classifier, using the estimated parameters, to perform the human gait authentication, and present results from an experiment involving 120 subjects. As a result, our method shows high recognition rate and low calculation cost.

Original languageEnglish
Title of host publicationProceedings of the World Congress on Engineering 2013, WCE 2013
Pages274-279
Number of pages6
Publication statusPublished - Nov 25 2013
Externally publishedYes
Event2013 World Congress on Engineering, WCE 2013 - London, United Kingdom
Duration: Jul 3 2013Jul 5 2013

Publication series

NameLecture Notes in Engineering and Computer Science
Volume1 LNECS
ISSN (Print)2078-0958

Other

Other2013 World Congress on Engineering, WCE 2013
CountryUnited Kingdom
CityLondon
Period7/3/137/5/13

All Science Journal Classification (ASJC) codes

  • Computer Science (miscellaneous)

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