提出了一种利用所提取的彩色Gabor特征来提高人脸识别系统性能的方法。首先利用四元数表示彩色信息，考虑到Gabor滤波器具有空间局部性和方向选择性的特点，将其扩展到四元数空间。然后通过人脸图像特征点与Gabor滤波器的卷积来提取特征，这样就将传统的灰度Gabor特征拓展为彩色Gabor特征。最后对于所提取的特征利用PCA降维后送入支持向量机中分类。实验采用彩色FERET人脸库并利用ROC曲线进行交叉检验，结果说明通过提取和利用这种彩色纹理信息能显著提高人脸识别系统性能。

In this paper a method which can improve the performance of face recognition system using color Gabor features is presented. First, quaternion is used to describe the color information, considering that Gabor filters have desirable characteristics of spatial locality and orientation selectivity, and they are extended to quaternion space. Then utilizing the convolution of the key points and the Gabor filters to extract features, by doing this the gray Gabor features are extended to the color ones. In the end, for the extracted features, we used PCA for dimension reduction and SVM for recognition. The experiment carried on Color FERET Database and the result utilizing ROC curve for cross-validation show that the use of color texture information can improve the efficiency of face recognition system markedly.