TY - GEN
T1 - Improved OMP selecting sparse representation used with face recognition
AU - Zhang, Jian
AU - Yan, Ke
AU - He, Zhenyu
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/10/21
Y1 - 2014/10/21
N2 - With the worldwide strengthening of anti-terrorism and other identity verification, the products based on face recognition are used in real life more and more. The recognition as an important ways has become the focus of academic research in the world. Face recognition accuracy can be improved by increasing the number of training samples, but increasing number will result in a large computing complexity. In recent years, the sparse representation becomes hot in face recognition. In this paper, we propose an energy constraint orthogonal matching pursuit (ECOMP) algorithm for sparse representation in face recognition. It selects a few training samples and hierarchical structure for face recognition. In this method, we re-select training samples by ECOMP, calculate the weight of all the selected training samples and find the sparse training samples which can recover the test sample. While the AR and the ORL database experimental results show that this method has better performance than other identification methods.
AB - With the worldwide strengthening of anti-terrorism and other identity verification, the products based on face recognition are used in real life more and more. The recognition as an important ways has become the focus of academic research in the world. Face recognition accuracy can be improved by increasing the number of training samples, but increasing number will result in a large computing complexity. In recent years, the sparse representation becomes hot in face recognition. In this paper, we propose an energy constraint orthogonal matching pursuit (ECOMP) algorithm for sparse representation in face recognition. It selects a few training samples and hierarchical structure for face recognition. In this method, we re-select training samples by ECOMP, calculate the weight of all the selected training samples and find the sparse training samples which can recover the test sample. While the AR and the ORL database experimental results show that this method has better performance than other identification methods.
KW - image classification
KW - orthogonal matching pursuit
KW - sparse representation
UR - http://www.scopus.com/inward/record.url?scp=84910093556&partnerID=8YFLogxK
U2 - 10.1109/ICSESS.2014.6933637
DO - 10.1109/ICSESS.2014.6933637
M3 - Conference contribution
AN - SCOPUS:84910093556
T3 - Proceedings of the IEEE International Conference on Software Engineering and Service Sciences, ICSESS
SP - 589
EP - 592
BT - Proceedings of the IEEE International Conference on Software Engineering and Service Sciences, ICSESS
A2 - Prasad Babu, M. Surendra
A2 - Wenzheng, Li
A2 - Tsui, Eric
PB - IEEE Computer Society
T2 - 2014 5th IEEE International Conference on Software Engineering and Service Science, ICSESS 2014
Y2 - 27 June 2014 through 29 June 2014
ER -
