1 February 2013 Maximum margin sparse representation discriminative mapping with application to face recognition
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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.
© 2013 Society of Photo-Optical Instrumentation Engineers (SPIE)
Qiang Zhang, Qiang Zhang, Yunze Cai, Yunze Cai, Xiaoming Xu, Xiaoming Xu, } "Maximum margin sparse representation discriminative mapping with application to face recognition," Optical Engineering 52(2), 027202 (1 February 2013). https://doi.org/10.1117/1.OE.52.2.027202 . Submission:
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