Face inpainting aims to repair the missing regions in the input face and generate satisfactory high-quality results. However， it is difficult to directly repair the incomplete face when the missing region is large， and the global context awareness ability of the inpainting network determines the quality of the inpainting results. Therefore， a dual self-attention mechanism that combines soft attention and hard attention is proposed to improve the global context awareness of the inpainting network. This module obtains soft and hard attention features by calculating the global similarity and can adaptively fuse the attention features. Besides， a multi-scale generative adversarial network is proposed to promote the inpainting network to generate more high-quality inpainting results， by strengthening the supervision of inpainting results. Experimental results demonstrate that our method is superior to five state-of-the-art comparison methods from both quantitative and qualitative experiments.