An integrated two-stage framework for robust head pose estimation

Authors:
Junwen Wu;Mohan M. Trivedi
Affiliations:
Computer Vision and Robotics Research Laboratory, University of California, San Diego, La Jolla, CA;Computer Vision and Robotics Research Laboratory, University of California, San Diego, La Jolla, CA
Venue:
AMFG'05 Proceedings of the Second international conference on Analysis and Modelling of Faces and Gestures
Year:
2005

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Abstract

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 using Euclidean distance was used to get the pose estimate. The single pose estimated was relaxed to a subset of poses around it to incorporate certain tolerance to data alignment and background noise. In the second stage, the uncertainty is eliminated 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 –90o to 90o and the tilt angle spanning from –60o to 45o. The experimental results indicate that the integrated approach has a remarkably better performance than using subspace analysis alone.