In this paper, we focus on dealing with problems of large-pose face alignment. Recently proposed heatmap-based algorithms have made promising performance on this problem. However, the traditional heatmap is constructed based on Gaussian model with fixed variance, which is inconsistent with the local shape of faces. In this paper, we propose a shape-aware heatmap to efficiently solve the problems of large-pose face alignment. Specifically, we design a novel heatmap based on Gaussian mixture model, where positions of several adjacent landmarks are utilized to construct different components. Thus the probability distribution is modified to fit the shape of the local region. The experimental results on Menpo-3D and AFLW2000-3D databases show that the proposed method outperforms the state-of-the-art algorithms.