Abstract: The texture feature based methods are widely used for face recognition. However, as the texture feature relies on the high frequency details of the image, the performance of a method that merely utilizes texture feature deteriorates drastically when the image is blurred. To overcome this defect of the texture feature, a method fusing color and texture features was proposed for blurred face recognition. This method extracted a type of facial color feature using an opponent color model which was in accordance with the human visual mechanism. This type of color feature and a certain type of texture feature were used for recognition separately, and then their similarity scores were fused to make final decision. The color feature is a description of the low frequency components of the image, which is robust to image blur and complementary to the texture feature. Experiments on the FERET and AR face databases show that the recognition performance for blurred face image is effectively improved by fusing the color and texture features.