In this paper, we present a shape constrained Markov network for accurate face alignment. The global face shape is defined as a set of weighted shape samples which are integrated into the Markov network optimization. These weighted samples provide structural constraints to make the Markov network more robust to local image noise. We propose a hierarchical Condensation algorithm to draw the shape samples efficiently. Specifically, a proposal density incorporating the local face shape is designed to generate more samples close to the image features for accurate alignment, based on a local Markov network search. A constrained regularization algorithm is also developed to weigh favorably those points that are already accurately aligned. Extensive experiments demonstrate the accuracy and effectiveness of our proposed approach.