Head pose is an important indicator of a person's focus of attention. Also, head pose estimation can be used as the front-end analysis for multi-view face analysis. For example, face recognition and identification algorithms are usually view dependent. Pose classification can help such face recognition systems to select the best view model. Subspace analysis has been widely used for head pose estimation. However, such techniques are usually sensitive to data alignment and background noise. In this paper a two-stage approach is proposed to address this issue by combining the subspace analysis together with the topography method. The first stage is based on the subspace analysis of Gabor wavelets responses. Different subspace techniques were compared for better exploring the underlying data structure. Nearest prototype matching with Euclidean distance was used to get the pose estimate. The single pose estimate was relaxed to a subset of poses around it to incorporate certain tolerance to data alignment and background noise. In the second stage, the pose estimate is refined by analyzing finer geometrical structure details captured by bunch graphs. This coarse-to-fine framework was evaluated with a large data set. We examined 86 poses, with the pan angle spanning from -90^@? to 90^@? and the tilt angle spanning from -60^@? to 45^@?. The experimental results indicate that the integrated approach has a remarkably better performance than using subspace analysis alone.
['Junwen Wu', 'Mohan M. Trivedi']
A two-stage head pose estimation framework and evaluation