RESUMEN
A physical fit is an important observation that can result from the forensic analysis of trace evidence as it conveys a high degree of association between two items. However, physical fit examinations can be time-consuming, and potential bias from analysts may affect judgment. To overcome these shortcomings, a data analysis algorithm using mutual information and a decision tree has been developed to support practitioners in interpreting the evidence. We created these tools using data obtained from physical fit examinations of duct tape and textiles analyzed in previous studies, along with the reasoning behind the analysts' decisions. The relative feature importance is described by material type, enhancing the knowledge base in this field. Compared with the human analysis, the algorithms provided accuracies above 90%, with an improved rate of true positives for most duct tape subsets. Conversely, false positives were observed in high-quality scissor cut (HQ-HT-S) duct tape and textiles. As such, it is advised to use these algorithms in tandem with human analysis. Furthermore, the study evaluated the accuracy of physical fits when only partial sample lengths are available. The results of this investigation indicated that acceptable accuracies for correctly identifying true fits and non-fits occurred when at least 35% of a sample length was present. However, lower accuracies were observed for samples prone to stretching or distortion. Therefore, the models described here can provide a valuable supplementary tool but should not be the sole means of evaluating samples.
RESUMEN
During the examination of trace evidence, often a realignment along the edges of known and questioned items are made to determine if a physical fit is present and if those objects were once one continuous piece or object. Duct tape is an evidence type in which the evaluation of physical fits is often conducted and is regarded as conclusive evidence of an association between the items. The examination and conclusion of a physical fit between edges relies heavily on examiner discretion to identify distinctive features across the edges since there are no statistical approaches or objective methodologies for the comparison. This study developed an automated image processing and comparison method to quantify tape end matches using cross-correlation scores and an empirical approach to the assessment. Characterization of 150 hand torn duct tape end pair physical fits were also conducted where matching and non-matching sample distributions were created. This study also evaluated partial duct tape edges and the influence this has on a comparison. Given the strength associated with a physical fit and the presence of stretching or deformation along the fractured edge, an understanding of the value these samples have is paramount. Furthermore, random match probabilities were calculated based on the correlation scores from the inter-comparisons to model the weight of evidence or strength of association between the edges. Finally, the study demonstrated that not every true match holds the same association strength through score distributions, but the approach is able to distinguish matching and non-matching samples at edge widths greater than 27 %.