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

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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Official URL: http://dx.doi.org/10.1145/2425836.2425913

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
URI: http://shdl.mmu.edu.my/id/eprint/4743

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