Person-independent head pose estimation based on random forest regression

In this paper, a novel approach for person-independent head pose estimation in gray-level images is presented. There are two steps of the proposed method. In order to preserve similar patterns of faces under various poses, a novel multi-view face detector using tree-structured cascaded-Adaboost classifiers is applied. Furthermore, based on the cropped face images, randomized regression trees are learned and applied to estimate head pose precisely. Experiments show that our method achieves better pose estimation results in both horizontal and vertical orientations in comparison with the reported result with skin color information.