Facial image inpainting for big data using an effective attention mechanism and a convolutional neural network

Big data facial image is an important identity information for people. However, facial image inpainting using existing deep learning methods has some problems such as insufficient feature mining and incomplete semantic expression, leading to output image artifacts or fuzzy textures. Therefore, it is of practical significance to study how to effectively restore an incomplete facial image. In this study, we proposed a facial image inpainting method using a multistage generative adversarial network (GAN) and the global attention mechanism (GAM). For the overall network structure, we used the GAN as the main body, then we established skip connections to optimize the network structure, and used the encoder-decoder structure to better capture the semantic information of the missing part of a facial image. A local refinement network has been proposed to enhance the local restoration effect and to weaken the influence of unsatisfactory results. Moreover, GAM is added to the network to magnify the interactive features of the global dimension while reducing information dispersion, which is more suitable for restoring human facial information. Comparative experiments on CelebA and CelebA-HQ big datasets show that the proposed method generates realistic inpainting results in both regular and irregular masks and achieves peak signal-to-noise ratio (PSNR) and structural similarity (SSIM), as well as other evaluation indicators that illustrate the performance and efficiency of the proposed model.