Head pose estimation based on nonlinear interpolative mapping

The performance of face recognition systems depends on conditions being consistent, including lighting, pose and facial expression. To solve the problem produced by pose variation it is suggested to pre-estimate the pose orientation of the given head image before it is recognized. In this paper, we propose a head pose estimation method that is an improvement on the one proposed by N. Hu et al. The proposed method trains in a supervised manner a nonlinear interpolative mapping function that maps input images to predicted pose angles. This mapping function is a linear combination of some Radial Basis Functions (RBF). The experimental results show that our proposed method has a better performance than the method proposed by Nan Hu et al. in terms of both time efficiency and estimation accuracy.