Feature-based Head Pose Estimation from Images

Estimating the head pose is an important capability of a robot when interacting with humans since the head pose usually indicates the focus of attention. In this paper, we present a novel approach to estimate the head pose from monocular images. Our approach proceeds in three stages. First, a face detector roughly classifies the pose as frontal, left, or right profile. Then, classifiers trained with AdaBoost using Haar-like features, detect distinctive facial features such as the nose tip and the eyes. Based on the positions of these features, a neural network finally estimates the three continuous rotation angles we use to model the head pose. Since we have a compact representation of the face using only few distinctive features, our approach is computationally highly efficient. As we show in experiments with standard databases as well as with real-time image data, our system locates the distinctive features with a high accuracy and provides robust estimates of the head pose.