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

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

1,* , 1
, 1
 and 2
Received: 6 May 2012; in revised form: 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 which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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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.

AMA Style

Qin H, Qin L, Xue L, Li Y. A Kernel Gabor-Based Weighted Region Covariance Matrix for Face Recognition. Sensors. 2012; 12(6):7410-7422.

Chicago/Turabian Style

Qin, Huafeng; Qin, Lan; Xue, Lian; Li, Yantao. 2012. "A Kernel Gabor-Based Weighted Region Covariance Matrix for Face Recognition." Sensors 12, no. 6: 7410-7422.



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