Tracking Head Yaw by Interpolation of Template Responses

Abstract

We propose an appearance based machine learning architecture that estimates and tracks in real time large range head yaw given a single non-calibrated monocular grayscale low resolution image sequence of the head. The architecture is composed of five parallel template detectors, a Radial Basis Function Network and two Kalman filters. The template detectors… (More)
DOI: 10.1109/CVPR.2004.468

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