A kernel Gabor-based weighted region covariance matrix for face recognition

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...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2012-06, Vol.12 (6), p.7410-7422
Main Authors: Qin, Huafeng, Qin, Lan, Xue, Lian, Li, Yantao
Format: Article
Language:English
Subjects:
Algorithms
Covariance matrix
Face recognition
Feature recognition
Gabor features
kernalization
Kernels
Preserves
Recognition
Sensors
weighted region covariance matrix
Weighting
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Summary: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).
ISSN:1424-8220
1424-8220
DOI:10.3390/s120607410