Sala Grande - 19th IAFS World Meeting - 9th WPMO Triennial Meeting

15
Author(s): Guyomarch P 1 ; Martin J 2 ; Charton J 2 ; Santos F 1 ; Dutailly B 1
19 th IAFS WORLD MEETING
9 th WPMO TRIENNIAL MEETING
5 th MAFS MEETING
STATISTICAL FACIAL APPROXIMATION IN 3D WITH TIVMI USING GEOMETRIC
MORPHOMETRICS
Institution(s): 1 UNIVERSITÉ BORDEAUX 1, UMR, ANTHROPOLOGIE DES POPULATIONS PASSÉES ET PRÉSENTES; 2 UNIVERSITÉ BORDEAUX,
UMR, LABORATOIRE BORDELAIS DE RECHERCHE EN INFORMATIQUE
Abstract: Forensic sciences are required to provide identification methods reliable and reproducible enough to be valid in the legal
system. Facial approximation, or "estimation of the antemortem facial appearance of an individual from unknown skeletal remains" (as
defined by the SWGANTH), should also tend to fulfil those criteria.
We propose an objective approach for the prediction of facial features based on traditional and geometric morphometrics. A warping
algorithm is used to distort a neutral face in 3D until it fits the estimated landmarks. Texturing of the facial approximation is then
available to customise the subject. The method was implemented in the TIVMI software (Treatment and Increased Vision for Medical
Imaging). This computer program has been designed according to the needs of anthropologists increasingly using 3D imaging. DICOM
analysis, HMH-based surface reconstruction and geometric operations (landmarks, planes, segments and outlines) are available in 3D.
The first step of the facial approximation technique is to collect the coordinates (x, y, z) of a precise list of bony landmarks (˜ 50). This
can be made in TIVMI after reconstructing a skull from a CT-scan, or after importing a laser scanned surface. The coordinates may also
be recorded directly on the skull using a digitiser and imported in TIVMI. A study of a 500 CT-scan head sample of adult French (18 - 96
years, sex ratio 1.13:1) allowed for the elaboration of an automatic prediction of the facial landmarks. Principal component analysis is
used to define the craniofacial shape and multivariate regressions are ran, combining PC scores and biological factors (sex, age,
corpulence), to estimate the 3D cutaneous coordinates. Soft tissue depths are independently predicted using both craniometrics and
biological factors, which help the individualisation of the approximation.
This second step is automated in the software and produces the list of the most probable position of the facial landmarks.
The third step is to apply the warping algorithm to a neutral face. For this operation, two groups of landmarks are distinguished,
anatomical and STD points. The anatomical landmarks are strictly homologous and define the shape of the facial organs (eyes, nose,
mouth and ears), while STD points are non-homologous (those are projected from bony landmarks on the soft tissues) and render the
general shape of the face. The warping algorithm is programmed to give an optimal precision in fitting the anatomical landmarks and to
let a higher degree of freedom at the STD points. This allows for a smoother approximation more flexible to the facial variability. Finally,
the fourth step is to add textures and colours to the model in order to enhance its realistic aspect. Skin, eyes and hair colour, hairstyle
and suggestion of facial hair are available. Ageing of the model can also be performed through the modification of the skin texture.
Different facial approximations from the same skull and from different subjects are presented in order to visually evaluate the variability
of the results that can be obtained from the single neutral face model. If the biological factors are not available, it is also possible to
propose several approximations including the morphological changes between male and female, young and old, normal corpulence and
overweight. Even if significantly different, the changes related to these groups may be subtle and visual evaluation of those differences
on the approximated faces will be used to discuss the relevance of the use of such factors. The face created may also be exported in
independent graphic software to add elements and make modifications specific to the forensic case investigated.
Further research will be led to test the accuracy of this statistical facial approximation technique through the comparison of predicted
faces with photographs.
Keywords: Facial Reconstruction; Facial Reproduction; Computed Tomography; Software; Imaging