Computer-aided craniofacial superimposition validation study: the identification of the leaders and participants of the Polish-Lithuanian January Uprising (1863–1864)

Abstract

In 2017, a series of human remains corresponding to the executed leaders of the “January Uprising” of 1863–1864 were uncovered at the Upper Castle of Vilnius (Lithuania). During the archeological excavations, 14 inhumation pits with the human remains of 21 individuals were found at the site. The subsequent identification process was carried out, including the analysis and cross-comparison of post-mortem data obtained in situ and in the lab with ante-mortem data obtained from historical archives. In parallel, three anthropologists with diverse backgrounds in craniofacial identification and two students without previous experience attempted to identify 11 of these 21 individuals using the craniofacial superimposition technique. To do this, the five participants had access to 18 3D scanned skulls and 14 photographs of 11 different candidates. The participants faced a cross-comparison problem involving 252 skull-face overlay scenarios. The methodology follows the main agreements of the European project MEPROCS and uses the software Skeleton-ID™. Based on MEPROCS standard, a final decision was provided within a scale, assigning a value in terms of strong, moderate, or limited support to the claim that the skull and the facial image belonged (or not) to the same person for each case. The problem of binary classification, positive/negative, with an identification rate for each participant was revealed. The results obtained in this study make the authors think that both the quality of the materials used and the previous experience of the analyst play a fundamental role when reaching conclusions using the CFS technique.

The Critical Photographic Variables Contributing to Skull-Face Superimposition Methods to Assist Forensic Identification of Skeletons: A Review

“…Specifically in the VS domain, Perspective-n-Point (PnP) methods have been incorporated into the latest fully computerized VS methodology [65], however, solutions hold limits and challenges remain because the antemortem face is not accompanied by a real-life 3D scan of the person, the skull is a different structure to the face, and any errors in camera parameters used during the PnP process lead to errors in the end estimation result [67,83]. Ultimately, for the VS context where a single 2D antemortem facial photograph forms the sole, limited, reference material, and is specific to idiosyncrasies of just one individual in a single forensic case, a manually conducted camera vantage point estimation may provide the best casework solution.…”

Artificial Intelligence and Forensic Genetics: Current Applications and Future Perspectives

“…Other experimental papers have explored the challenging field of definition of the cause of death: in rare cases, it can be difficult to define the manner of death and AI could be helpful. For example, in cases of drowning, several research papers have focused on the application of AI in automating diatom testing, with the aim of achieving higher levels of efficiency and accuracy in the definition of the exact cause of death [11,[28][29][30][31][32][33]. Other authors have tried to apply AI in the evaluation of firearm lesions, demonstrating that digitizing and analyzing the fired projectile specimens could be used for firearm identification [34].…”