  In this paper, we present a method for automated estimation of a human face
given a skull remain. The proposed method is based on three statistical models.
A volumetric (tetrahedral) skull model encoding the variations of different
skulls, a surface head model encoding the head variations, and a dense
statistic of facial soft tissue thickness (FSTT). All data are automatically
derived from computed tomography (CT) head scans and optical face scans. In
order to obtain a proper dense FSTT statistic, we register a skull model to
each skull extracted from a CT scan and determine the FSTT value for each
vertex of the skull model towards the associated extracted skin surface. The
FSTT values at predefined landmarks from our statistic are well in agreement
with data from the literature.
  To recover a face from a skull remain, we first fit our skull model to the
given skull. Next, we generate spheres with radius of the respective FSTT value
obtained from our statistic at each vertex of the registered skull. Finally, we
fit a head model to the union of all spheres. The proposed automated method
enables a probabilistic face-estimation that facilitates forensic recovery even
from incomplete skull remains. The FSTT statistic allows the generation of
plausible head variants, which can be adjusted intuitively using principal
component analysis. We validate our face recovery process using an anonymized
head CT scan. The estimation generated from the given skull visually compares
well with the skin surface extracted from the CT scan itself.
