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 | |
| 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 |
Other
| Other | 27th Image and Vision Computing New Zealand Conference, IVCNZ 2012 |
|---|---|
| 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
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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.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Grassmannian locality preserving discriminant analysis to view invariant gait recognition with image sets
AU - Connie, Tee
AU - Michael, Goh Kah Ong
AU - Jin, Andrew Teoh Beng
PY - 2012/12/1
Y1 - 2012/12/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84873331864&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84873331864&partnerID=8YFLogxK
U2 - 10.1145/2425836.2425913
DO - 10.1145/2425836.2425913
M3 - Conference contribution
AN - SCOPUS:84873331864
SN - 9781450314732
SP - 400
EP - 405
BT - Proceedings of IVCNZ 2012 - The 27th Image and Vision Computing New Zealand Conference
ER -