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In this paper, a novel example-based shape learning (ESL) strategy has been proposed for facial feature alignment. The method is motivated by an intuitive and experimental observation that there exists an approximate linearity relationship between the image difference and the shape difference, that is, similar face images imply similar face shapes. Therefore, given a learning set of face images with their corresponding face landmarks labeled, the shape of any novel face image can be learned by estimating its similarities to the training images in the learning set and applying these similarities to the shape reconstruction of a novel face image. Concretely, if the novel face image is expressed by an optimal linear combination of the training images, the same linear combination coefficients can be directly applied to the linear combination of the training shapes to construct the optimal shape for the novel face image. Our experiments have convincingly shown the effectiveness and efficiency of the proposed approach in both speed and accuracy performance compared with other methods.