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In this paper, we present a method for automated estimation of a human facegiven a skull remain. The proposed method is based on three statistical models.A volumetric (tetrahedral) skull model encoding the variations of differentskulls, a surface head model encoding the head variations, and a densestatistic of facial soft tissue thickness (FSTT). All data are automaticallyderived from computed tomography (CT) head scans and optical face scans. Inorder to obtain a proper dense FSTT statistic, we register a skull model toeach skull extracted from a CT scan and determine the FSTT value for eachvertex of the skull model towards the associated extracted skin surface. TheFSTT values at predefined landmarks from our statistic are well in agreementwith data from the literature.  To recover a face from a skull remain, we first fit our skull model to thegiven skull. Next, we generate spheres with radius of the respective FSTT valueobtained from our statistic at each vertex of the registered skull. Finally, wefit a head model to the union of all spheres. The proposed automated methodenables a probabilistic face-estimation that facilitates forensic recovery evenfrom incomplete skull remains. The FSTT statistic allows the generation ofplausible head variants, which can be adjusted intuitively using principalcomponent analysis. We validate our face recovery process using an anonymizedhead CT scan. The estimation generated from the given skull visually compareswell with the skin surface extracted from the CT scan itself.

A method for automatic forensic facial reconstruction based on dense statistics of soft tissue thickness