Automatic makeup refers to the editing and synthesis of face makeup through computer algorithms. It belongs to the field of face image analysis, and plays an important role in interactive entertainment applications, image and video editing, and face recognition. However, as a face editing problem, it is still difficult to ensure that the editing result of the image is natural and satisfies the editing requirements. Makeup still has some difficulties such as precisely controlling the editing area is hard, the image consistency before and after editing is poor, and the image quality is insufficient. In response to these difficulties, this study innovatively proposes a mask-controlled automatic makeup generative adversarial network. Through a masking method, this network can edit the makeup area with emphasis, restrict the area that does not require editing, and maintain the key information. At the same time, it can separately edit the eye shadow, lips, cheeks, and other local areas of the face to achieve makeup on specific areas and enrich the makeup function. In addition, this network can be trained jointly on multiple datasets. In addition to makeup dataset, it can also use other face datasets as an aid to enhance the model's generalization ability and get a more natural makeup result. Finally, based on a variety of evaluation methods, more comprehensive qualitative and quantitative experiments are carried out, the results are compared with the other methods, and the performance of the proposed method is comprehensively evaluated.