This paper introduces a new approach to feature-based head tracking and pose estimation. Head tracking and pose estimation find their most important applications in motion analysis for model-based video coding. The proposed algorithm employs an underlying 3D head model, feature-based pose estimation, and texture mapping to produce accurate templates for the feature tracking. In this way, the set of templates used for the matching is constantly updated with the pose changes, allowing the algorithm to track the features over a large range of head motion without loss of precision and error accumulation. Given a rough estimate of the head scale, the initial feature identification is performed automatically and the tracking is successful over a large number of video frames. Computational complexity is also considered with the aim towards creating a real-time end-to-end model-based video coding system.