Automatic gait recognition using width vector mean

Sungjun Hong, Heesung Lee, Euntai Kim

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

6 Citations (Scopus)

Abstract

Gait recognition systems have recently attracted much interest from biometric researchers. In this work, we present an alternative gait representation of width vector profile. The proposed model-free gait representation, width vector mean, is defined by the arithmetic mean of width vector profiles obtained from a gait sequence. Different gait feature are extracted from the width vector mean such the downsampled width vector mean and the principal components of the width vector. To solve the classification problem, we use the Euclidean distance and a nearest neighbor (NN) approach. The Extensive experiments are carried out on the NLPR gait database to demonstrate the validity of the proposed gait representation.

Original languageEnglish
Title of host publication2009 4th IEEE Conference on Industrial Electronics and Applications, ICIEA 2009
Pages647-650
Number of pages4
DOIs
Publication statusPublished - 2009
Event2009 4th IEEE Conference on Industrial Electronics and Applications, ICIEA 2009 - Xi'an, China
Duration: 2009 May 252009 May 27

Publication series

Name2009 4th IEEE Conference on Industrial Electronics and Applications, ICIEA 2009

Other

Other2009 4th IEEE Conference on Industrial Electronics and Applications, ICIEA 2009
CountryChina
CityXi'an
Period09/5/2509/5/27

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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