Facial expression manipulation plays an increasingly important role in the field of computer graphics and has been widely used in generating facial animations. However, it is still a very challenging task as it needs full understanding of the input face and very depending on the facial appearance. In this paper, we present an end-to-end generative adversarial network for facial expression synthesis. Given the facial landmarks and the expression label of a target image, our method automatically generates a corresponding expression facial image with the identity information and facial details well preserved. Both qualitative and quantitative experiments are conducted on the CK+ and Oulu-CASIA datasets. Experimental results show that our method has the compelling perceptual results even there exist large differences in facial shapes for unseen subjects.