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