LAUSR

Forensic footwear evidence can prove invaluable to the resolution of a criminal investigation. Naturally, the value of a comparison varies with the rarity of the evidence, which is a function of both manufactured as well as randomly acquired characteristics (RACs). When focused specifically on the latter of these two types of features, empirical evidence demonstrates high discriminating power for the differentiation of known match and known non-match samples when presented with exemplars of high quality and exhibiting a sufficient number of clear and complex RACs. However, given the dynamic and unpredictable nature of the media, substrate, and deposition process encountered during the commission of a crime, RACs on crime scene prints are expected to exhibit a large range of variability in terms of reproducibility, clarity, and quality. Although the pattern recognition skill of the expert examiner is adept at recognizing and evaluating this type of natural variation, there is little research to suggest that objective and numerical metrics can globally process this variation when presented with RACs from degraded crime scene quality prints. As such, the goal of this study was to mathematically compare the loss and similarity of RACs in high quality exemplars versus crime-scene-like quality impressions as a function of RAC shape, perimeter, area, and common source. Results indicate that the unpredictable conditions associated with crime scene print production promotes RAC loss that varies between 33% and 100% with an average of 85%, and that when the entire outsole is taken as a constellation of features (or a RAC map), 64% of the crime-scene-like impressions exhibited 10 or fewer RACs, resulting in a 0.72 probability of stochastic dominance. Given this, individual RAC description and correspondence were further explored using five simple, but objective, numerical metrics of similarity. Statistically significant differences in similarity scores for RAC shape and size were consistently detected for three of the five metrics (modified phase only correlation, Euclidean distance, and Hausdorff distance). Conversely, a single metric (the matched filter) expressed the least dependence between score and both shape and size. Moreover, for all crime-scene-like RACs with coincidental association in position, the matched filter produced the greatest discrimination potential in sorting known matches and known non-matches. Despite this demonstrated success, numerical metrics of similarity are not without limitations, and the remainder of this work provides commentary on the difficulties associated with using objective metrics when faced with segmentation, incomplete information, and low signal-to-noise ratios.