Contextual Feature Constrained Semantic Face Completion With Paired Discriminator

Abstract

Image semantic completion is to employ remaining image information to restore the damaged or missing areas. Face completion task is usually more challenging than other image inpainting problems as it requires stronger semantic consistency. We proposed a contextual feature constrained DCGAN with paired discriminator to inpaint damaged face images, which is capable of overcoming the DCGAN's shortages of insufficient feature learning capability and unstable training process. Our network is composed of an encoder-decoder generator and a local and global (paired) adversarial discriminator. Generator is used to produce missing parts, and the paired discriminators evaluate the authenticity of the local generated parts and the consistency of the global completed image respectively. In addition, the proposed network generates the image by optimizing the generator with three types of loss functions, i.e., image reconstruction loss, paired feature matching losses and paired feature reconstruction losses. The experiments on celebA and Stanford Cars dataset show that our model could generate reasonable repair results and that on some of the evaluation indicators were higher than baseline on most test datasets.