Abstract: In order to solve the problems of low image quality,long training process and slow generation speed of face expression transfer,this paper proposed a facial expression transfermethod based on generative adversarial network to make expression transfer faster and more natural.Firstly,the facial features are extracted by using convolutional neural network,and the images are mapped from high-dimensional space to shallow space.In the shallow space,the facial expression features are discriminated by using the Generative Adversarial Networks.Then the nearest neighbors up-sampling and convolutional neural networks are used to mapthe image from the shallow space to the high-dimensional space,and in this process,the face expression is changed by adding the facial expression feature maps into neural networks.Compared with Fader Networks,the network model parameter amount of the proposed method is reduced by 43.7% and training time is reduced by 36%.The experimental results show that the proposed method can effectively improve the quality and the speed of generated images.

Key words: Computer vision, Deep learning, Face expression transfer, Generative adversarial networks