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

Sensors

Huafeng Qin¹  Lan Qin¹  Lian Xue¹ 

[1]Key Laboratory of Optoelectronic Technology and Systems of Ministry of Education, College of Opto-Electronic Engineering, Chongqing University, Chongqing 400030, China

[2] E-Mails:

关键词: face recognition;    Gabor features;    weighted region covariance matrix;    kernalization;   

DOI  :  10.3390/s120607410

来源: mdpi

【 摘 要 】

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).

【 授权许可】

CC BY   
© 2012 by the authors; licensee MDPI, Basel, Switzerland.