Face Inpainting Algorithm Combining Edge Information with Gated Convolution

Abstract

For the face inpainting under arbitrary shape occlusion, the existing methods are easy to produce edge blur and distortion of the inpainting results. In this paper, an algorithm for face inpainting combining edge information with gated convolution is proposed. Firstly, the edge image of occluded area is generated by prior face knowledge to constrain the process of face inpainting. Secondly, the gated convolution holds the ability to extract accurate local feature in the absence of some pixels, and a gated convolution-based generative adversarial network (GAN) for image inpainting is designed. The model consists of two parts: edge connection GAN and image inpainting GAN. The edge connection network uses the binary occlusion image, the image to be repaired and its edge image for training, and realizes the automatic completion and connection of the missing edge image. The image inpainting GAN takes the completed edge image as the guidance information, and combines the occlusion image to repair the missing area. The experimental results show that the inpainting effect of this algorithm is better than that of other algorithms, and its evaluation indicators are better than those of the current image inpainting algorithms based on deep learning.

Keywords

• face inpainting
• canny edge
• gated convolution
• deep learning
• generative adversarial network (gan)