Quantifying randomly acquired characteristics on outsoles in terms of shape and position

Footwear evidence has tremendous forensic value; it can focus a criminal investigation, link suspects to scenes, help reconstruct a series of events, or otherwise provide information vital to the successful resolution of a case. When considering the specific utility of a linkage, the strength of the connection between source footwear and an impression left at the scene of a crime varies with the known rarity of the shoeprint itself, which is a function of the class characteristics, as well as the complexity, clarity, and quality of randomly acquired characteristics (RACs) available for analysis. To help elucidate the discrimination potential of footwear as a source of forensic evidence, the aim of this research is to further characterize the chance association in position, shape, and geometry of RACs on a semi-random selection of footwear. To accomplish this goal in an efficient manner, a partially automated image processing chain was required, including steps for automated feature characterization. This technical note details the methods, procedures, and type of results available for subsequent statistical analysis after processing a collection of more than 1000 shoes and 57,426 randomly acquired characteristics.