Face Image Inpainting Using Attribute Guided

Face image inpainting is becoming more and more significant in the practical fields of criminal in-vestigation,cosmetic plastic surgery,and security protection.But most of traditional existing work is based on diffusion and texture methods,failing to effectively use the embedding attribute knowledge and semantic in-formation of face images.It is difficult to ensure the authenticity and preserve semantic consistency at the same time.To tackle this problem,a face image inpainting method based on attribute control is proposed.First,a modified generative adversarial network is proposed to restore face images and control facial features by add-ing additional face attribute information and a classification network.Then an attention module based on multi-scale feature fusion is proposed,through increasing the receptive field of the neural network to obtain more global features to improve face image inpainting quality without increasing the arithmetic complexity.Finally,combining the four evaluation metrics,MAE,PSNR,SSIM,and FID,the experiments on the CelebA dataset show that the proposed method can not only generate high-quality face image inpainting results steered by semantic information,but also generate corresponding facial features based on attribute control.