Face pose estimation and synthesis by 2D morphable model

In this paper, we present face pose estimate and multi-pose synthesis technique. Through combining composite principal component analysis (CPCA) of the shape feature and texture feature respectively in eigenspace, we can get new eigenvectors to represent the human face pose. Support vector machine (SVM) has the optimal hyperplane that the expected classification error for unseen test samples is minimized. We utilize CPCA-SVM technology to get face pose discrimination. As for pose synthesis, the face shape model and the texture model are established through statistical learning. Using these two models and Delaunay triangular, we can match a face image with parameter vectors, the shape model, and the texture model. The synthesized image contains much more personal details, which improve its reality. Accurate pose discrimination and multi-pose synthesis helps to get optimal face and improve recognition rate.