Key Laboratory of Optoelectronic Technology and Systems of Ministry of Education, College of Opto-Electronic Engineering, Chongqing University, Chongqing 400030, China.
Sensors (Basel). 2012;12(6):7410-22. doi: 10.3390/s120607410. Epub 2012 May 31.
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).
本文提出了一种新的图像区域描述符，用于人脸识别，命名为基于核的 Gabor 加权区域协方差矩阵 (KGWRCM)。由于不同部分对面部特征的描述和识别有不同的效果，我们通过计算人脸样本中每个像素的相似性来构建一个加权矩阵，以强调特征。然后，我们将这些加权矩阵合并到一个区域协方差矩阵中，命名为加权区域协方差矩阵 (WRCM)，以获得用于识别的面部有判别力的特征。最后，为了在更高维空间中进一步保留判别特征，我们开发了基于核的 Gabor 加权区域协方差矩阵 (KGWRCM)。实验结果表明，KGWRCM 优于包括基于核的 Gabor 区域协方差矩阵 (KGCRM)在内的其他算法。
