  Three-dimensional models of craniofacial variation over the general
population are useful for assessing pre- and post-operative head shape when
treating various craniofacial conditions, such as craniosynostosis. We present
a new method of automatically building both sagittal profile models and full 3D
surface models of the human head using a range of techniques in 3D surface
image analysis; in particular, automatic facial landmarking using supervised
machine learning, global and local symmetry plane detection using a variant of
trimmed iterative closest points, locally-affine template warping (for full 3D
models) and a novel pose normalisation using robust iterative ellipse fitting.
The PCA-based models built using the new pose normalisation are more compact
than those using Generalised Procrustes Analysis and we demonstrate their
utility in a clinical case study.
