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Face alignment and head pose estimation has become a thriving research field with various applications for the past decade. Several approaches process on 2D texture image but most of them perform decently only with small pose variation. Recently, many approaches apply depth information to align objects. However, applications are restricted because depth cameras are more expensive than common cameras, and many original image resources contain no depth information. Therefore, we propose a 3D face alignment algorithm in 2D image based on Active Shape Model, and use Speeded-Up Robust Features (SURF) descriptors as local texture model. We train a 3D shape model with different view-based local texture models from a 3D database, and then fit a face in a 2D image by these models. We also improve the performance by two-stage search strategy. Furthermore, the head pose can be estimated by the alignment result of the proposed 3D model. Finally, we demonstrate some applications applied by our method.