Ibáñez Oscar, Valsecchi Andrea, Cavalli Fabio, Huete María Isabel, Campomanes-Alvarez Blanca Rosario, Campomanes-Alvarez Carmen, Vicente Ricardo, Navega David, Ross Ann, Wilkinson Caroline, Jankauskas Rimantas, Imaizumi Kazuhiko, Hardiman Rita, Jayaprakash Paul Thomas, Ruiz Elena, Molinero Francisco, Lestón Patricio, Veselovskaya Elizaveta, Abramov Alexey, Steyn Maryna, Cardoso Joao, Humpire Daniel, Lusnig Luca, Gibelli Daniele, Mazzarelli Debora, Gaudio Daniel, Collini Federica, Damas Sergio
Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain; European Centre for Soft Computing, Mieres, Spain.
European Centre for Soft Computing, Mieres, Spain.
Leg Med (Tokyo). 2016 Nov;23:59-70. doi: 10.1016/j.legalmed.2016.09.009. Epub 2016 Oct 3.
Craniofacial superimposition has the potential to be used as an identification method when other traditional biological techniques are not applicable due to insufficient quality or absence of ante-mortem and post-mortem data. Despite having been used in many countries as a method of inclusion and exclusion for over a century it lacks standards. Thus, the purpose of this research is to provide forensic practitioners with standard criteria for analysing skull-face relationships. Thirty-seven experts from 16 different institutions participated in this study, which consisted of evaluating 65 criteria for assessing skull-face anatomical consistency on a sample of 24 different skull-face superimpositions. An unbiased statistical analysis established the most objective and discriminative criteria. Results did not show strong associations, however, important insights to address lack of standards were provided. In addition, a novel methodology for understanding and standardizing identification methods based on the observation of morphological patterns has been proposed.
当由于生前和死后数据质量不足或缺失而无法使用其他传统生物学技术时，颅面叠加有潜力用作一种识别方法。尽管在许多国家，它作为一种纳入和排除的方法已使用了一个多世纪，但它缺乏标准。因此，本研究的目的是为法医从业者提供分析颅骨与面部关系的标准准则。来自16个不同机构的37位专家参与了本研究，该研究包括在24个不同的颅面叠加样本上评估65条评估颅骨与面部解剖一致性的标准。一项无偏差统计分析确定了最客观且具有区分性的标准。结果并未显示出强烈的关联，然而，提供了应对标准缺失问题的重要见解。此外，还提出了一种基于形态学模式观察来理解和规范识别方法的新方法。
