3D head pose estimation using support vector machines and physics-based deformable surfaces
This paper presents a novel approach for estimating 3D head pose in single-view video sequences. Following initialization by a face detector, a tracking technique that utilizes a 3D deformable surface model to approximate the image intensity is used to track the face in the video sequence. Head pose estimation is performed by using a feature vector which is a by-product of the equations that govern the deformation of the surface model used in the tracking. The afore-mentioned vector is used for training support vector machines (SVM) in order to estimate the 3D head pose. The proposed method was applied to IDIAP head pose estimation database. The obtained results show that the proposed method can achieve an accuracy of 82% if angles are estimated in 10deg increments and 75% if angle are estimated in 5deg increments.