Grassmannian locality preserving discriminant analysis to view invariant gait recognition with image sets

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

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

4 Citations (Scopus)

Abstract

In studies to date, gait recognition across appearance changes has been a challenging task. In this paper, we present a gait recognition method that models the gait image sets as subspaces on the Grassmannian manifold. This formulation provides a convenient way to represent the subspaces as points on the manifold. By using a suitable Grassmannian kernel, the non-linear manifold can be treated as if it were a Euclidean space. This implies that conventional data analysis tool like LDA can be used on this manifold. To this end, we apply a graph based locality preserving discriminant analysis method on the Grassmannian manifold. Experiment results suggest that the proposed method can tolerate variations in appearance for gait identification.

Original languageEnglish
Title of host publicationProceedings of IVCNZ 2012 - The 27th Image and Vision Computing New Zealand Conference
Pages400-405
Number of pages6
DOIs
Publication statusPublished - 2012 Dec 1
Event27th Image and Vision Computing New Zealand Conference, IVCNZ 2012 - Dunedin, New Zealand
Duration: 2012 Nov 262012 Nov 28

Publication series

NameACM International Conference Proceeding Series

Other

Other27th Image and Vision Computing New Zealand Conference, IVCNZ 2012
CountryNew Zealand
CityDunedin
Period12/11/2612/11/28

    Fingerprint

All Science Journal Classification (ASJC) codes

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

Cite this

Connie, T., Michael, G. K. O., & Jin, A. T. B. (2012). Grassmannian locality preserving discriminant analysis to view invariant gait recognition with image sets. In Proceedings of IVCNZ 2012 - The 27th Image and Vision Computing New Zealand Conference (pp. 400-405). (ACM International Conference Proceeding Series). https://doi.org/10.1145/2425836.2425913