Generic 3D face pose estimation from a single 2D facial image is an extremely crucial requirement for face-related research areas. To meet with the remaining challenges for face pose estimation, suggested Murphy-Chutorian et al. [13], we believe that the first step is to create a large corpus of a 3D facial shape database in which the statistical relationship between projected 2D shapes and corresponding pose parameters can be easily observed. Because facial geometry provides the most essential information for facial pose, understanding the effect of pose parameters in 2D facial shapes is a key step toward solving the remaining challenges. In this paper, we present necessary tasks to reconstruct 3D facial shapes from multiple 2D images and then explain how to generate 2D projected shapes at any rotation interval. To deal with self occlusions, a novel hidden points removal (HPR) algorithm is also proposed. By flexibly changing the number of points in 2D shapes, we evaluate the performance of two different approaches for achieving generic 3D pose estimation in both coarse and fine levels and analyze the importance of facial shapes toward generic 3D pose estimation.
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