Abstract
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.
| Original language | English |
|---|---|
| Title of host publication | 2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 6245-6249 |
| Number of pages | 5 |
| ISBN (Print) | 9781479928927 |
| DOIs | |
| Publication status | Published - 2014 Jan 1 |
| Event | 2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014 - Florence, Italy Duration: 2014 May 4 → 2014 May 9 |
Publication series
| Name | ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings |
|---|---|
| ISSN (Print) | 1520-6149 |
Other
| Other | 2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014 |
|---|---|
| Country | Italy |
| City | Florence |
| Period | 14/5/4 → 14/5/9 |
Fingerprint
All Science Journal Classification (ASJC) codes
- Software
- Signal Processing
- Electrical and Electronic Engineering
Cite this
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Joint kernel collaborative representation on Tensor manifold for face recognition. / Lee, Yeong Khang; Teoh, Beng Jin; Toh, Kar Ann.
2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014. Institute of Electrical and Electronics Engineers Inc., 2014. p. 6245-6249 6854805 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Joint kernel collaborative representation on Tensor manifold for face recognition
AU - Lee, Yeong Khang
AU - Teoh, Beng Jin
AU - Toh, Kar Ann
PY - 2014/1/1
Y1 - 2014/1/1
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
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.
ER -