Feature-based Head Pose Estimation from Images
- Authors: Teodora Vatahska, Maren Bennewitz, and
Sven Behnke
- In Proceedings of IEEE-RAS 7th International
Conference on Humanoid Robots (Humanoids), Pittsburgh, USA, December
2007.
- Abstract:
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.
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