Revolutionizing Forensic Toolmark Analysis: An Objective and Transparent Comparison Algorithm

Forensic toolmark comparisons are currently performed subjectively by humans, which leads to a lack of consistency and accuracy. There is little evidence that examiners can determine whether pairs of marks were made by the same tool or different tools. There is also little evidence that they can make this classification when marks are made under different conditions, such as different angles of attack or direction of mark generation. We generate original toolmark data in 3D, extract the signal from each toolmarks, and train an algorithm to compare toolmark signals objectively. We find that toolmark signals cluster by tool, and not by angle or direction. That is, the variability within tool, regardless of angle/direction, is smaller than the variability between tools. The known-match and known-non-match densities of the similarities of pairs of marks have a small overlap, even when accounting for dependencies in the data, making them a useful instrument for determining whether a new pair of marks was made by the same tool. We provide a likelihood ratio approach as a formal method for comparing toolmark signals with a measure of uncertainty. This empirically trained, open-source method can be used by forensic examiners to compare toolmarks objectively and thus improve the reliability of toolmark comparisons. This can, in turn, reduce miscarriages of justice in the criminal justice system.