A number of studies demonstrate that illumination is an important factor impacting the performance of computer vision tasks and illumination normalization can improve the performance of other visual analysis algorithms. At present, there are few methods aiming to illumination normalization of color face with deep learning. For this reason, we put forward a novel and practical deep fully convolutional neural network architecture for illumination normalization of color face. Comparing with the current methods based on deep learning, our approach does not need to input identity and illumination label. We preserve the identity by a well-designed generator and content loss. Experimental results show that the proposed method achieves favorable illumination normalization effect under various lighting variances and preserves identity effectively.