Share Email Print

Optical Engineering

Maximum margin sparse representation discriminative mapping with application to face recognition
Author(s): Qiang Zhang; Yunze Cai; Xiaoming Xu
Format Member Price Non-Member Price
PDF $20.00 $25.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Sparse subspace learning has drawn more and more attention recently. We propose a novel sparse subspace learning algorithm called maximum margin sparse representation discriminative mapping (MSRDM), which adds the discriminative information into sparse neighborhood preservation. Based on combination of maximum margin discriminant criterion and sparse representation, MSRDM can preserve both local geometry structure and classification information. MSRDM can avoid the small sample size problem in face recognition naturally and the computation is efficient. To improve face recognition performance, we propose to integrate Gabor-like complex wavelet and natural image features by complex vectors as input features of MSRDM. Experimental results on ORL, UMIST, Yale, and PIE face databases demonstrate the effectiveness of the proposed face recognition method.

Paper Details

Date Published: 1 February 2013
PDF: 13 pages
Opt. Eng. 52(2) 027202 doi: 10.1117/1.OE.52.2.027202
Published in: Optical Engineering Volume 52, Issue 2
Show Author Affiliations
Qiang Zhang, Shanghai Jiao Tong Univ. (China)
Yunze Cai, Shanghai Jiao Tong Univ. (China)
Xiaoming Xu, Shanghai Jiao Tong Univ. (China)

© SPIE. Terms of Use
Back to Top