Skip to content

Algorithmic approaches to match degraded land impressions

Journal: Law, Probability and Risk
Published: 2017
Primary Author: Eric Hare
Secondary Authors: Heike Hofmann, Alicia Carriquiry
Research Area: Firearms and Toolmarks

Bullet matching is a process used to determine whether two bullets may have been fired from the same gun barrel. Historically, this has been a manual process performed by trained forensic examiners. Recent work, however, has shown that it is possible to add statistical validity and objectivity to the procedure. In this article, we build upon the algorithms explored in Automatic Matching of Bullet Lands (Hare, Hofmann & Carriquiry (2017), Automatic matching of bullet lands. ArXiv E-Prints) by formalizing and defining a set of features, computed on pairs of bullet lands, which can be used in machine learning models to assess the probability of a match. We then use these features to perform an analysis of the two Hamby (Hamby, Brundage & Thorpe (2009), The identification of bullets fired from 10 consecutively rifled 9 mm Ruger pistol barrels: a research project involving 507 participants from 20 countries. AFTE J., 41, 99–110) bullet sets (Set 252 and Set 44), to assess the presence of microscope operator effects in scanning. We also take some first steps to address the issue of degraded bullet lands and provide a range of degradation at which the matching algorithm still performs well. Finally, we discuss generalizing land-to-land comparisons to full bullet comparisons as would be used for this procedure in a criminal justice situation.

Related Resources

Treatment of inconclusives in the AFTE range of conclusions

Treatment of inconclusives in the AFTE range of conclusions

In the past decade, and in response to the recommendations set forth by the National Research Council Committee on Identifying the Needs of the Forensic Sciences Community (2009), scientists have…
Using Machine Learning Methods to Predict Similarity of Striations on Bullet Lands

Using Machine Learning Methods to Predict Similarity of Striations on Bullet Lands

Recent advances in microscopy have made it possible to collect 3D topographic data, enabling virtual comparisons based on the collected 3D data next to traditional comparison microscopy. Automatic matching algorithms…
CSAFE 2021 Field Update

CSAFE 2021 Field Update

The 2021 Field Update was held June 14, 2021, and served as the closing to the first year of CSAFE 2.0. CSAFE brought together researchers, forensic science partners and interested…
Treatment of Inconclusive Results in Error Rates of Firearm Studies

Treatment of Inconclusive Results in Error Rates of Firearm Studies

This CSAFE webinar was held on February 10, 2021. Presenters: Heike Hofmann Professor and Kingland Faculty Fellow, Iowa State University Susan VanderPlas Research Assistant Professor, University of Nebraska, Lincoln Alicia…