Forensic facial approximation assessment: can application of different average facial tissue depth data facilitate recognition and establish acceptable level of resemblance?
Herrera, Lara Maria [UNESP]
Paim Strapasson, Raissa Ananda
Lopes da Silva, Jorge Vicente
Haltenhoff Melani, Rodolfo Francisco
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Facial soft tissue thicknesses (FSTT) are important guidelines for modeling faces from skull. Amid so many FSTT data, Forensic artists have to make a subjective choice of a dataset that best meets their needs. This study investigated the performance of four FSTT datasets in the recognition and resemblance of Brazilian living individuals and the performance of assessors in recognizing people, according to sex and knowledge on Human Anatomy and Forensic Dentistry. Sixteen manual facial approximations (FAs) were constructed using three-dimensional (3D) prototypes of skulls (targets). The American method was chosen for the construction of the faces. One hundred and twenty participants evaluated all FAs by means of recognition and resemblance tests. This study showed higher proportions of recognition by FAs conducted with FSTT data from cadavers compared with those conducted with medical imaging data. Targets were also considered more similar to FAs conducted with FSTT data from cadavers. Nose and face shape, respectively, were considered the most similar regions to targets. The sex of assessors (male and female) and the knowledge on Human Anatomy and Forensic Dentistry did not play a determinant role to reach greater recognition rates. It was possible to conclude that FSTT data obtained from imaging may not facilitate recognition and establish acceptable level of resemblance. Grouping FSTT data by regions of the face, as proposed in this paper, may contribute to more accurate FAs. (C) 2016 Elsevier Ireland Ltd. All rights reserved.
Forensic science, Forensic dentistry, Facial approximation, Facial reconstruction, Facial tissue depths, Face recognition
Forensic Science International. Clare: Elsevier Ireland Ltd, v. 266, p. 311-319, 2016.