Please use this identifier to cite or link to this item: http://doi.org/10.25358/openscience-180
Authors: Gietzen, Thomas
Brylka, Robert
Achenbach, Jascha
zum Hebel, Katja
Schömer, Elmar
Botsch, Mario
Schwanecke, Ulrich
Schulze, Ralf
Title: A method for automatic forensic facial reconstruction based on dense statistics of soft tissue tissue thickness
Online publication date: 11-Jul-2019
Language: english
Abstract: In this paper, we present a method for automated estimation of a human face given a skull remain remain. Our 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 extracted skin surface. The FSTT values at predefined landmarks from our statistic are well in agreement agreement with data from the literature. To recover a face from a skull remain, we first fit our skull skull model to the given skull. Next, we generate spheres with radius of the respective FSTT value obtained obtained from our statistic at each vertex of the registered skull. Finally, we fit a head model to the the union of all spheres. The proposed automated method enables a probabilistic face-estimation that that facilitates forensic recovery even from incomplete skull remains. The FSTT statistic allows the the generation of plausible head variants, which can be adjusted intuitively using principal component component analysis. We validate our face recovery process using an anonymized head CT scan. The estimation estimation generated from the given skull visually compares well with the skin surface extracted from from the CT scan itself.
DDC: 610 Medizin
610 Medical sciences
Institution: Johannes Gutenberg-Universität Mainz
Department: FB 08 Physik, Mathematik u. Informatik
FB 04 Medizin
Place: Mainz
DOI: http://doi.org/10.25358/openscience-180
Version: Published version
Publication type: Zeitschriftenaufsatz
License: CC-BY
Information on rights of use: https://creativecommons.org/licenses/by/4.0/
Journal: PLOS ONE
14
1
Pages or article number: e0210257
Publisher: PLOS
Publisher place: San Francisco, California, US
Issue date: 2019
ISSN: 1932-6203
Publisher's URL: http://dx.doi.org/10.1371/journal.pone.0210257
Appears in collections:JGU-Publikationen

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