  Head pose estimation and tracking is useful in variety of medical
applications. With the advent of RGBD cameras like Kinect, it has become
feasible to do markerless tracking by estimating the head pose directly from
the point clouds. One specific medical application is robot assisted
transcranial magnetic stimulation (TMS) where any patient motion is compensated
with the help of a robot. For increased patient comfort, it is important to
track the head without markers. In this regard, we address the head pose
estimation problem using two different approaches. In the first approach, we
build upon the more traditional approach of model based head tracking, where a
head model is morphed according to the particular head to be tracked and the
morphed model is used to track the head in the point cloud streams. In the
second approach, we propose a new multi-scale convolutional neural network
architecture for more accurate pose regression. Additionally, we outline a
systematic data set acquisition strategy using a head phantom mounted on the
robot and ground-truth labels generated using a highly accurate tracking
system.
