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Face completion is an important topic in the field of computer vision and image processing. Its core task is to restore image information, so that the generated completion results are as consistent as possible with the ground-truth results. In existing methods, there is no strong constraint on the consistency between the completion result and the true value, and the symmetry characteristics of the face are ignored, which makes it impossible to generate a natural and consistent completion result for any position and symmetrical position of the face. In response to these problems, we propose a novel method called face completion generative adversarial network (FC GAN). Our generator uses a u-net-like structure, and the discriminator uses a combination of global discriminator and local discriminator. We use a new perceptual loss based on VGG-19 to constrain the consistency of the completion result and the true value. We use symmetry awareness in our method and it takes full advantage of face symmetry features to optimize face completion. For irregular mask image completion, our method produces visually realistic and semantically correct results. We evaluate our model on the CelebA dataset and use FID and SSIM as the indicators. Compared with existing methods, the face completion method in this paper has a certain improvement in visual effects and evaluation indicators. [ABSTRACT FROM AUTHOR]