Revolutionizing Forensic Toolmark Analysis: An Objective and Transparent
  Comparison Algorithm
Maria Cuellar Sheng Gao Heike Hofmann
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.