Citation

Tee, Connie and Goh, Michael Kah Ong and Teoh, Andrew Beng Jin (2012) Grassmannian locality preserving discriminant analysis to view invariant gait recognition with image sets. In: Proceedings of the 27th Conference on Image and Vision Computing New Zealand - IVCNZ '12. ACM Digital Library, pp. 400-405. ISBN 978-1-4503-1473-2

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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.

Item Type:	Book Section
Subjects:	T Technology > T Technology (General)
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	08 Jan 2014 03:35
Last Modified:	08 Jan 2014 03:35
URII:	http://shdl.mmu.edu.my/id/eprint/4743

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