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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. [ABSTRACT FROM AUTHOR]

Accuracy is the most important factor supporting the reliability of forensic facial reconstruction ( FFR) comparing to the corresponding actual face. A number of methods have been employed to evaluate objective accuracy of FFR. Recently, it has been attempted that the degree of resemblance between computer-generated FFR and actual face is measured by geometric surface comparison method. In this study, three FFRs were produced employing live adult Korean subjects and three-dimensional computerized modeling software. The deviations of the facial surfaces between the FFR and the head scan CT of the corresponding subject were analyzed in reverse modeling software. The results were compared with those from a previous study which applied the same methodology as this study except average facial soft tissue depth dataset. Three FFRs of this study that applied updated dataset demonstrated lesser deviation errors between the facial surfaces of the FFR and corresponding subject than those from the previous study. The results proposed that appropriate average tissue depth data are important to increase quantitative accuracy of FFR. [ABSTRACT FROM AUTHOR]