Given a person's neutral face image, we can predict his/her expressive face images by machine learning techniques. Different from the prior expression cloning or image analogy approaches, we try to hallucinate the person's plausible facial expression with the help of a face expression database. In the first step, nonlinear manifold learning technique is used to obtain a smooth estimation for the expressive face image. In the second step, Markov network is adopted to learn the low-level local facial feature's relationship between the residual neutral and the expressional face image's patches in the training set, then belief propagation is employed to infer the expressional residual face image for that person. By integrating the two approaches, we obtain the final result. The experimental results show that the hallucinated facial expression is not only expressive but also close to the ground truth.