Precise 3D Pose Estimation of Human Faces

Robust human face recognition is one of the most important open tasks in computer vision. This study deals with a challenging subproblem of face recognition: the aim of the paper is to give a precise estimation for the 3D head pose. The main contribution of this study is a novel non-rigid Structure from Motion (SfM) algorithm which utilizes the fact that the human face is quasi-symmetric. The input of the proposed algorithm is a set of tracked feature points of the face. In order to increase the precision of the head pose estimation, we improved one of the best eye corner detectors and fused the results with the input set of feature points. The proposed methods were evaluated on real and synthetic face sequences. The real sequences were captured using regular (low-cost) web-cams.