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Sensors 2012, 12(6), 7410-7422; doi:10.3390/s120607410

A Kernel Gabor-Based Weighted Region Covariance Matrix for Face Recognition

1
Key Laboratory of Optoelectronic Technology and Systems of Ministry of Education, College of Opto-Electronic Engineering, Chongqing University, Chongqing 400030, China
2
College of Computer Science, Chongqing University, Chongqing 400030, China
*
Author to whom correspondence should be addressed.
Received: 6 May 2012 / Revised: 16 May 2012 / Accepted: 17 May 2012 / Published: 31 May 2012
(This article belongs to the Section Physical Sensors)
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Abstract

This paper proposes a novel image region descriptor for face recognition, named kernel Gabor-based weighted region covariance matrix (KGWRCM). As different parts are different effectual in characterizing and recognizing faces, we construct a weighting matrix by computing the similarity of each pixel within a face sample to emphasize features. We then incorporate the weighting matrices into a region covariance matrix, named weighted region covariance matrix (WRCM), to obtain the discriminative features of faces for recognition. Finally, to further preserve discriminative features in higher dimensional space, we develop the kernel Gabor-based weighted region covariance matrix (KGWRCM). Experimental results show that the KGWRCM outperforms other algorithms including the kernel Gabor-based region covariance matrix (KGCRM).
Keywords: face recognition; Gabor features; weighted region covariance matrix; kernalization face recognition; Gabor features; weighted region covariance matrix; kernalization
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Qin, H.; Qin, L.; Xue, L.; Li, Y. A Kernel Gabor-Based Weighted Region Covariance Matrix for Face Recognition. Sensors 2012, 12, 7410-7422.

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