A Multilinear Model for Bidirectional Craniofacial Reconstruction

Abstract

We present a bidirectional facial reconstruction method for estimating the skull given a scan of the skin surface and vice versa estimating the skin surface given the skull. Our approach is based on a multilinear model that describes the correlation between the skull and the facial soft tissue thickness (FSTT) on the one hand and the head/face surface geometry on the other hand. Training this model requires to densely sample the Cartesian product space of skull shape times FSTT variation, which cannot be obtained by measurements alone. We generate this data by enriching measured data-volumetric computed tomography scans and 3D surface scans of the head-by simulating statistically plausible FSTT variations. We demonstrate the versatility of our novel multilinear model by estimating faces from given skulls as well as skulls from given faces within just a couple of seconds. To foster further research in this direction, we will make our multilinear model publicly available.