Real time head pose estimation from consumer depth cameras

Abstract

We present a system for estimating location and orientation of a person's head, from depth data acquired by a low quality device. Our approach is based on discriminative random regression forests: ensembles of random trees trained by splitting each node so as to simultaneously reduce the entropy of the class labels distribution and the variance of the head position and orientation. We evaluate three different approaches to jointly take classification and regression performance into account during training. For evaluation, we acquired a new dataset and propose a method for its automatic annotation. © 2011 Springer-Verlag.