Head-Pose Estimation In-the-Wild Using a Random Forest

Human head-pose estimation has attracted a lot of interest because it is the first step of most face analysis tasks. However, many of the existing approaches address this problem in laboratory conditions. In this paper, we present a real-time algorithm that estimates the head-pose from unrestricted 2D gray-scale images. We propose a classification scheme, based on a Random Forest, where patches extracted randomly from the image cast votes for the corresponding discrete head-pose angle. In the experiments, the algorithm performs similar and better than the state-of-the-art in controlled and in-the-wild databases respectively.