Xiaofeng Shao

In this paper, we focus on the facial completion task based on the parsing feature map. In recent years, methods based on deep learning have achieved remarkable results in face image completion. However, since many methods do not consider the semantic structure information of face images, these methods may lead to unreasonable or discontinuous cases in the return results. To solve this problem, we propose a face image completion network based on face parsing features. The network includes two sub-network: face parsing map prediction network and face image completion network. The face parsing map prediction network predicts the parsing features of the missing regions, and the face image completion network uses these features to guide the face image completion network to fill the missing areas. On the open dataset, CelebA-HQ and Flickr Faces High Quality(FFHQ), the validity of this method is verified by qualitative and quantitative analysis.

Face images are often used in social and entertainment activities to interact with information. However, during the transmission of digital images, there are factors that may destroy or obscure the key elements of the image, which may hinder the understanding of the image’s content. Therefore, the study of image completion of human faces has become an important research branch in the field of computer image processing. Compared with traditional image inpainting methods, deep-learning-based inpainting methods have significantly improved the results on face images, but in the case of complex semantic information and large missing areas, the completion results are still blurred, and the color of the boundary is inconsistent and does not match human visual perception. To solve this problem, this paper proposes a face completion method based on GAN priori to guide the network to complete face images by directly using the rich and diverse a priori information in the pre-trained GAN. The network model is a coarse-to-fine structure, where the damaged face images and the corresponding masks are first input to the coarse network to obtain the coarse results, and then the coarse results are input to the fine network with multi-resolution skip connections. The fine network uses the a priori information from the pre-trained GAN to guide the network to generate the face images, and finally uses the SN-PatchGAN discriminator to evaluate the completion results. The experiment is performed on the CelebA-HQ dataset. Compared with the latest three completion methods, the qualitative and quantitative experimental analysis shows that our method has obvious improvement in texture and fidelity.