Head pose estimation based on random forests with binary pattern run length matrix

Abstract

In this paper, a novel approach for head pose estimation in gray-level images is presented. In the proposed algorithm, there were two techniques employed. In order to deal with the large set of training data, the method of Random Forests was employed; this is a state-of-the-art classification algorithm in the field of computer vision. In order to make this system robust in terms of illumination, a Binary Pattern Run Length matrix was employed; this matrix combined a Local Binary Pattern and a Run Length matrix. Experimental results show that our algorithm is robust against illumination change. © 2014 Springer-Verlag Berlin Heidelberg.