Gait recognition using Sparse Grassmannian Locality Preserving Discriminant Analysis

Tee Connie, Michael Kah Ong Goh, Andrew Beng Jin Teoh

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

5 Citations (Scopus)

Abstract

One of the greatest challenges for gait recognition is identification across appearance change. In this paper, we present a gait recognition method called Sparse Grassmannian Locality Preserving Discriminant Analysis. The proposed method learns a compact and rich representation of the gait images through sparse representation. The use of Grassmannian locality preserving discriminant analysis further optimizes the performance by preserving both global discriminant and local geometrical structure of the gait data. Experiments demonstrate that the proposed method can tolerate variation in appearance for gait identification effectively.

Original languageEnglish
Title of host publication2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings
Pages2989-2993
Number of pages5
DOIs
Publication statusPublished - 2013 Oct 18
Event2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Vancouver, BC, Canada
Duration: 2013 May 262013 May 31

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013
CountryCanada
CityVancouver, BC
Period13/5/2613/5/31

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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    Connie, T., Goh, M. K. O., & Teoh, A. B. J. (2013). Gait recognition using Sparse Grassmannian Locality Preserving Discriminant Analysis. In 2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings (pp. 2989-2993). [6638206] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). https://doi.org/10.1109/ICASSP.2013.6638206