TY - GEN
T1 - Joint kernel collaborative representation on Tensor manifold for face recognition
AU - Lee, Yeong Khang
AU - Beng Jin Teoh, Andrew
AU - Toh, Kar Ann
N1 - Copyright:
Copyright 2014 Elsevier B.V., All rights reserved.
PY - 2014
Y1 - 2014
N2 - Gabor-based region covariance matrix (GRCM) is an emerging face feature descriptor, which has been shown promising for face recognition. The GRCM lies on Tensor manifold is inherently non-Euclidean, hence a disconnect exists between GRCM descriptor and vector-based classifiers, such as collaborative representation-based classifier (CRC). CRC is a strong alternative to sparse representation-based classifier yet enjoys high efficiency. In this paper, we bridge GRCM and CRC with kernel learning method. We investigate several geodesic distances on Tensor manifold that satisfy the Mercer's condition for kernel CRC construction as well as for speedy computation. Apart from that, we also devise two strategies to jointly combine the regionalized GRCMs with Tensor kernel CRC. Extensive experiments on the ORL and FERET datasets are conducted to verify the efficacy of the proposed method.
AB - Gabor-based region covariance matrix (GRCM) is an emerging face feature descriptor, which has been shown promising for face recognition. The GRCM lies on Tensor manifold is inherently non-Euclidean, hence a disconnect exists between GRCM descriptor and vector-based classifiers, such as collaborative representation-based classifier (CRC). CRC is a strong alternative to sparse representation-based classifier yet enjoys high efficiency. In this paper, we bridge GRCM and CRC with kernel learning method. We investigate several geodesic distances on Tensor manifold that satisfy the Mercer's condition for kernel CRC construction as well as for speedy computation. Apart from that, we also devise two strategies to jointly combine the regionalized GRCMs with Tensor kernel CRC. Extensive experiments on the ORL and FERET datasets are conducted to verify the efficacy of the proposed method.
UR - http://www.scopus.com/inward/record.url?scp=84905252080&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84905252080&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2014.6854805
DO - 10.1109/ICASSP.2014.6854805
M3 - Conference contribution
AN - SCOPUS:84905252080
SN - 9781479928927
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 6245
EP - 6249
BT - 2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
Y2 - 4 May 2014 through 9 May 2014
ER -