Facial expression synthesis technology is designed to reconstruct face image with new expressions while retaining identity information. The development of deep learning provides a new solution for the synthesis of facial expressions. This paper introduces the development of facial expression synthesis technology from the aspects of feature extraction， expression synthesis of generated antagonistic networks and experimental evaluation. Firstly， extraction of facial features is introduced， which is the key technology in expression synthesis. Facial features can describe facial expressions objectively and comprehensively. Secondly， the state-of-the-art facial expression synthesis methods based on deep learning are analyzed， in which methods based on generative adversarial network （GAN） are mainly discussed. By research on facial expression datasets and evaluation methods， the widely used facial expression datasets and objective evaluation methods are given in this paper. Finally， future work is discussed according to the existing problems of facial expression synthesis methods.