D. J. Tan , N. Navab, F. Tombari Adaptive Learning-based Temporal Tracker for 3D Head Shape Models British Machine Vision Conference, London, UK, September 4-7, 2017 (bib)
	We propose a learning-based 3D temporal tracker that estimates the orientation and location of the head in the 3D scene. The algorithm is based on random forest that learns the 6D pose from a class of head models. A unique attribute of our approach is the capacity to adapt the learned tracker for a specific user, i.e., after learning, the tracker can deform the shape of the learned model to a specific instance of the class in order to match the user’s head shape. To find the user’s head shape model, we use a fast calibration method to personalize the model for a specific user. As a consequence, this technique enhances the accuracy of the head pose estimation as the personalized model becomes more detailed, and tracks at 1.4 ms per frame using a single CPU core.
	This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each authors copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.