This thesis reports on research that had main goal of providing the criminal justice system with an objective and methodical assessment of the evidential value of physical fit comparisons of three materials: textiles, paper, and postage stamps.
The author describes methods for comparative analysis of fractured materials and provides an empirical analysis of physical fit comparisons for textiles, paper, and postage stamps that is lacking from current literature on the topic. The first goal of this research involved the development of an objective and systematic method for quantifying the similarity between fractured textile samples. The second goal of this study involved establishing the scientific foundations of individuality, concerning the orientation of microfibers in fractured paper edges. The third goal built upon the second, shifting from paper to postage stamps, noting several key differences that affect stamps’ ability to undergo physical fit analysis. The author’s goal is to advance the implementation and validation of the edge similarity score (ESS) metric in the physical fit discipline. The ESS is a quantitative metric used to measure the similarity of the edges of a fractured material, and defines the quality of alignment, or physical fit, between two materials as the ratio between the comparison areas in agreement and the total number of comparison areas. The resultant value is reported as a percentage, and allows an examiner to give a numerical weight to their conclusions in reports as well as in the courtroom. Research conclusions state that the development of a quantitative, systematic approach for assessing physical fits of textiles, as well as of paper, was successful. The microfiber alignment was also observed on the edges of true non-fitting postage stamps; it was challenging due to the lack of other distinguishing features along the comparison edge that could be used to classify a pair as either a fit or a non-fit, however, due to the difference of the number of aligning microfibers observed in true fits and true non-fits, the fits and non-fits were able to be accurately classified.
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