Experimental Investigation of Geometric Morphometrics for Weapon Identification from Three Dimensional Data within Skeletal Trauma Analysis

  • Rebecca Strong

Student thesis: Doctoral Thesis


Current standards on the analysis of sharp force skeletal trauma in forensic contexts have focussed on utilising morphological and metrical differences to deduce the type of weapon which most likely caused the injury. Research has demonstrated, however, that cut marks created by the same weapon types can produce cut marks that are both morphologically and metrically different. Thus, making the interpretation of weapon type from skeletal sharp force trauma a subjective process. Geometric morphometric analysis (GM) is a method of quantifying and documenting measurable shape changes using recognised landmarks through statistical analysis. GM, therefore, could be employed to interpret weapon type from cut mark shape, aiding in reducing subjectivity. With sharp instruments continually identified as the leading cause of homicide within the UK, an analytical and standardised methodological approach that can aid in the identification of potential weapon types from cut mark morphology would be beneficial. Previous research has demonstrated that GM can be used to infer weapon type from cut mark morphology however, this research has only focused on 2D representations of sharp force trauma rather than considering the trauma holistically as a 3D object. The primary aim of this research is to determine if GM can be used to identify weapon type from the 3D surface of the cut mark. A series of experiments were performed creating cut marks in both modelling clay (n=140) and skeletal porcine material (n=33). Five weapons, including four knives and a pair of scissors were used by two individuals to manually create incision cut marks at a 45-degree angle. Employing two different imaging techniques, structured light scanning, and photogrammetry, each cut mark was imaged to create a 3D model for GM. The results demonstrate that cut mark shape can be used to differentiate weapon types, with cross validated discriminant function analysis exhibiting an overall correct classification rate of 85% for identifying weapon type. In some cases when the weapons are considered individually the correct classification rate increased to 100%. While the analysis demonstrated the weapons of the same type can be differentiated by cut mark shape to provide a weapon identification, the results have highlighted some significant methodological points. The first, that the structured light scanner is not a suitable imaging method for sharp force injuries smaller than two centimetres. Furthermore, photogrammetry would be a beneficial alternative imaging modality for applying this method in forensic case work, as photography is the key method of recording forensic evidence. This allows for easy integration of this method of identifying weapon types into forensic case work. This method of analysing and interpreting skeletal sharp force trauma has provided the first steps in weapon identification research, has developed an analytical technique that reduces subjectivity and human error to provide statistically sound results, and known error rates can be used to corroborate existing evidence or testimonies collected during a criminal investigation.
Date of Award16 Feb 2024
Original languageEnglish
Awarding Institution
  • Teesside University
SupervisorMelanie Brown (Supervisor) & Timothy Thompson (Supervisor)

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