Abstract: Face image inpainting is a hot topic of image processing research in recent years. This paper proposes a face image restoration method based on cascade generative adversarial network. In this method, the generator employs a cascading structure consisting of a coarse network and a refinement network and adopts dense connections to recover more details of the missing face area; the discriminator uses a dual discriminant model combining local and global features to improve the discriminant accuracy; the loss function consists of reconstruction loss and generative adversarial loss for better training performance. Experiments on CelebA dataset show that the proposed method can restore facial image with more than 50% missing area. The objective evaluation index PSNR and SSIM are 1.1 dB to 7.5 dB and 0.02 to 0.15 higher respectively compared with state of the arts. For subjective evaluation, the restored face images look more detailed and natural.