  The rapid increase in the availability of accurate 3D scanning devices has
moved facial recognition and analysis into the 3D domain. 3D facial landmarks
are often used as a simple measure of anatomy and it is crucial to have
accurate algorithms for automatic landmark placement. The current
state-of-the-art approaches have yet to gain from the dramatic increase in
performance reported in human pose tracking and 2D facial landmark placement
due to the use of deep convolutional neural networks (CNN). Development of deep
learning approaches for 3D meshes has given rise to the new subfield called
geometric deep learning, where one topic is the adaptation of meshes for the
use of deep CNNs. In this work, we demonstrate how methods derived from
geometric deep learning, namely multi-view CNNs, can be combined with recent
advances in human pose tracking. The method finds 2D landmark estimates and
propagates this information to 3D space, where a consensus method determines
the accurate 3D face landmark position. We utilise the method on a standard 3D
face dataset and show that it outperforms current methods by a large margin.
Further, we demonstrate how models trained on 3D range scans can be used to
accurately place anatomical landmarks in magnetic resonance images.
