Learning symmetrical model for head pose estimation

This paper tackles the problem of head pose estimation which has been considered an important research task for decades. The proposed approach selects a set of features from the symmetrical parts of the face. The size of bilateral symmetrical area of the face is a good indicator of the Yaw head pose. We train a Decision Tree model in order to recognize head pose with regard to the areas of symmetry. The approach does not need the location of interest points on face and is robust to partial occlusion. Tests were performed on a different dataset from that used for training the model and the results demonstrate that the change in the size of the regions that contain a bilateral symmetry provides accurate pose estimation.