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

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

doi:10.1145/2425836.2425913

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

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

Department of Electrical and Electronic Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English
Title of host publication	Proceedings of IVCNZ 2012 - The 27th Image and Vision Computing New Zealand Conference
Pages	400-405
Number of pages	6
DOIs	https://doi.org/10.1145/2425836.2425913
Publication status	Published - 2012 Dec 1
Event	27th Image and Vision Computing New Zealand Conference, IVCNZ 2012 - Dunedin, New Zealand Duration: 2012 Nov 26 → 2012 Nov 28

Publication series

Name	ACM International Conference Proceeding Series

Other

Other	27th Image and Vision Computing New Zealand Conference, IVCNZ 2012
Country	New Zealand
City	Dunedin
Period	12/11/26 → 12/11/28

All Science Journal Classification (ASJC) codes

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

Access to Document

10.1145/2425836.2425913

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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

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title = "Grassmannian locality preserving discriminant analysis to view invariant gait recognition with image sets",

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.",

author = "Tee Connie and Michael, {Goh Kah Ong} and Jin, {Andrew Teoh Beng}",

year = "2012",

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doi = "10.1145/2425836.2425913",

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Connie, T, Michael, GKO & Jin, ATB 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. ACM International Conference Proceeding Series, pp. 400-405, 27th Image and Vision Computing New Zealand Conference, IVCNZ 2012, Dunedin, New Zealand, 12/11/26. https://doi.org/10.1145/2425836.2425913

Grassmannian locality preserving discriminant analysis to view invariant gait recognition with image sets. / Connie, Tee; Michael, Goh Kah Ong; Jin, Andrew Teoh Beng.

Proceedings of IVCNZ 2012 - The 27th Image and Vision Computing New Zealand Conference. 2012. p. 400-405 (ACM International Conference Proceeding Series).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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