Back to Search Start Over

Forensic facial approximation assessment: can application of different average facial tissue depth data facilitate recognition and establish acceptable level of resemblance?

Authors :
Lara Maria Herrera
Rodolfo Francisco Haltenhoff Melani
Raíssa Ananda Paim Strapasson
Jorge Vicente Lopes da Silva
Universidade de São Paulo (USP)
Renato Archer Informat Technol Ctr
Universidade Estadual Paulista (Unesp)
Source :
Web of Science, Repositório Institucional da UNESP, Universidade Estadual Paulista (UNESP), instacron:UNESP
Publication Year :
2016
Publisher :
Elsevier B.V., 2016.

Abstract

Made available in DSpace on 2018-11-26T17:07:15Z (GMT). No. of bitstreams: 0 Previous issue date: 2016-09-01 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. Univ Sao Paulo, Sch Dent, Dept Community Dent, Ave Prof Lineu Prestes 2227, BR-05508000 Sao Paulo, SP, Brazil Renato Archer Informat Technol Ctr, Dimens Technol Div 3, Rodovia Dom Pedro 1,Km 143-6, BR-13069901 Campinas, SP, Brazil Sao Paulo State Univ, Araraquara Sch Dent, Dept Community Dent, Rua Humaita 1680, BR-14801903 Araraquara, SP, Brazil Sao Paulo State Univ, Araraquara Sch Dent, Dept Community Dent, Rua Humaita 1680, BR-14801903 Araraquara, SP, Brazil

Details

Language :
English
Database :
OpenAIRE
Journal :
Web of Science, Repositório Institucional da UNESP, Universidade Estadual Paulista (UNESP), instacron:UNESP
Accession number :
edsair.doi.dedup.....f12b67139e70845f0758d1ff594ae4fb