TY - GEN
T1 - Gait recognition using Sparse Grassmannian Locality Preserving Discriminant Analysis
AU - Connie, Tee
AU - Goh, Michael Kah Ong
AU - Teoh, Andrew 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
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 2989
EP - 2993
BT - 2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings
T2 - 2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013
Y2 - 26 May 2013 through 31 May 2013
ER -