A Bayesian approach for interpreting shoemark evidence in forensic casework: accounting for wear features

Shoemark evidence remains a cornerstone of forensic crime investigation. Shoemarks can be used at a crime scene to reconstruct the course of events; they can be used as forensic intelligence tool to establish links between crime scenes; and when control material is available, used to help infer the participation of given individuals to the commission of a crime. Nevertheless, as for most other impression evidence, the current process used to evaluate and report the weight of shoemark evidence is under extreme scrutiny. Building on previous research, this paper proposes a model to evaluate shoemark evidence in a more transparent manner. The model is currently limited to sole pattern and wear characteristics. It does not account formally for cuts and other accidental damages. Furthermore, it requires the acquisition of relevant shoemark datasets and the development of automated comparison algorithms to deploy its full benefits. These are not currently available. Instead, we demonstrate, using casework examples, that a pragmatic consideration of the various variables of the model allows us to already evaluate shoemark evidence in a more transparent way and therefore begin to address the current scientific and legal concerns.