Abstract
One of the greatest challenges for gait recognition is identification across appearance change. In this paper, we present a gait recognition method called Sparse Grassmannian Locality Preserving Discriminant Analysis. The proposed method learns a compact and rich representation of the gait images through sparse representation. The use of Grassmannian locality preserving discriminant analysis further optimizes the performance by preserving both global discriminant and local geometrical structure of the gait data. Experiments demonstrate that the proposed method can tolerate variation in appearance for gait identification effectively.
| Original language | English |
|---|---|
| Title of host publication | 2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings |
| Pages | 2989-2993 |
| Number of pages | 5 |
| DOIs | |
| Publication status | Published - 2013 Oct 18 |
| Event | 2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Vancouver, BC, Canada Duration: 2013 May 26 → 2013 May 31 |
Other
| Other | 2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 |
|---|---|
| Country | Canada |
| City | Vancouver, BC |
| Period | 13/5/26 → 13/5/31 |
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All Science Journal Classification (ASJC) codes
- Software
- Signal Processing
- Electrical and Electronic Engineering
Cite this
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Gait recognition using Sparse Grassmannian Locality Preserving Discriminant Analysis. / Connie, Tee; Goh, Michael Kah Ong; Teoh, Beng Jin.
2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings. 2013. p. 2989-2993 6638206.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Gait recognition using Sparse Grassmannian Locality Preserving Discriminant Analysis
AU - Connie, Tee
AU - Goh, Michael Kah Ong
AU - Teoh, Beng Jin
PY - 2013/10/18
Y1 - 2013/10/18
N2 - One of the greatest challenges for gait recognition is identification across appearance change. In this paper, we present a gait recognition method called Sparse Grassmannian Locality Preserving Discriminant Analysis. The proposed method learns a compact and rich representation of the gait images through sparse representation. The use of Grassmannian locality preserving discriminant analysis further optimizes the performance by preserving both global discriminant and local geometrical structure of the gait data. Experiments demonstrate that the proposed method can tolerate variation in appearance for gait identification effectively.
AB - One of the greatest challenges for gait recognition is identification across appearance change. In this paper, we present a gait recognition method called Sparse Grassmannian Locality Preserving Discriminant Analysis. The proposed method learns a compact and rich representation of the gait images through sparse representation. The use of Grassmannian locality preserving discriminant analysis further optimizes the performance by preserving both global discriminant and local geometrical structure of the gait data. Experiments demonstrate that the proposed method can tolerate variation in appearance for gait identification effectively.
UR - http://www.scopus.com/inward/record.url?scp=84890519018&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84890519018&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2013.6638206
DO - 10.1109/ICASSP.2013.6638206
M3 - Conference contribution
AN - SCOPUS:84890519018
SN - 9781479903566
SP - 2989
EP - 2993
BT - 2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings
ER -