Unconstrained Head Pose Estimation with Constrained Local Model and Memory Based Particle Filter by 3D Point Distribution Models

In this paper, a novel and efficient method was proposed for uncon-strained head pose estimation of the human face and robustness to changes of pose, position and facial expression. A Constrained Local Model (CLM) by a 3D Point Distribution Model (PDM) was proposed for locating 2D facial land-marks in optional poses of the human face. Also, a memory based Particle Filter (PF) was used to improve the manner of prior distribution in PF and reduce the number of particles. However, a fast search method was proposed from the trained memory of PF. In fact, instead of calculating the similarity distance be-tween each particle and the total templates in the memory, a small number of templates were utilized. The present method was tested on two available video databases to evaluate performance of proposed method. Promising results dis-played better performance than the current state-of-the-art approaches in head pose tracking with our extension of the 3D Constrained Local Model (CLM-Z).