An algorithm for forensic toolmark comparisons
This provides forensic examiners with a standardized, objective method for toolmark comparisons, potentially reducing miscarriages of justice, though it is currently limited to specific screwdriver types.
The paper tackled the problem of subjective and inconsistent forensic toolmark analysis by developing an algorithm that uses 3D toolmark data and statistical methods to classify matches, achieving a cross-validated sensitivity of 98% and specificity of 96%.
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.