An algorithm for forensic toolmark comparisons
Maria Cuellar, Sheng Gao, Heike Hofmann
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ABSTRACT
Forensic toolmark analysis traditionally relies on subjective human judgment, leading to inconsistencies and lack of transparency. The multitude of variables, including angles and directions of mark generation, further complicates comparisons. To address this, we first generate a dataset of 3D toolmarks from various angles and directions using consecutively manufactured slotted screwdrivers. By using PAM clustering, we find that there is clustering by tool rather than angle or direction. Using Known Match and Known Non-Match densities, we establish thresholds for classification. Fitting Beta distributions to the densities, we allow for the derivation of likelihood ratios for new toolmark pairs. With a cross-validated sensitivity of 98 % and specificity of 96 %, our approach enhances the reliability of toolmark analysis. This approach is applicable to slotted screwdrivers, and for screwdrivers that are made with a similar production method. With data collection of other tools and factors, it could be applied to compare toolmarks of other types. This empirically trained, open-source solution offers forensic examiners a standardized means to objectively compare toolmarks, potentially decreasing the number of miscarriages of justice in the legal system.
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© 2024 The Authors. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License which permits unrestricted noncommercial use, distribution, and reproduction, provided the original work is properly cited and not changed in any way.