Facial pose estimation via dense and sparse representation

Facial pose estimation is an important part for facial analysis such as face and facial expression recognition. In most existing methods, facial features are essential for facial pose estimation. However, occluded key features and uncontrolled illumination of face images make the facial feature detection vulnerable. In this paper, we propose methods for facial pose estimation via dense reconstruction and sparse representation but avoid localizing facial features. The Sparse Representation Classifier (SRC) method has achieved successful results in face recognition. In this paper, we explore SRC in pose estimation. Sparse representation learns a dictionary of base functions, so each input pose can be approximated by a linear combination of just a sparse subset of the bases. The experiment conducted on the CMU Multiple face database has shown the effectiveness of the proposed method.