Skip to content

Comparison of three similarity scores for bullet LEA matching

Journal: Forensic Science International
Published: 2020
Primary Author: Susan VanderPlas
Secondary Authors: Melissa Nally, Tyler Klep, Cristina Cadevall, Heike Hofmann
Research Area: Firearms and Toolmarks

Recent advances in microscopy have made it possible to collect 3D topographic data, enabling more precise virtual comparisons based on the collected 3D data as a supplement to traditional comparison microscopy and 2D photography. Automatic comparison algorithms have been introduced for various scenarios, such as matching cartridge cases[1],[2] or matching bullet striae[3],[4],[5]. One key aspect of validating these automatic comparison algorithms is to evaluate the performance of the algorithm on external tests, that is, using data which were not used to train the algorithm. Here, we present a discussion of the performance of the matching algorithm[6] in three studies conducted using different Ruger weapons. We consider the performance of three scoring measures: random forest score, cross correlation, and consecutive matching striae (CMS) at the land-to-land level and, using Sequential Average Maxima scores, also at the bullet-to bullet level. Cross correlation and random forest scores both result in perfect discrimination of same-source and different-source bullets. At the land-to-land level, discrimination for both cross correlation and random forest scores (based on area under the curve, AUC) is excellent (≥0.90).

Related Resources

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…
CSAFE 2020 All Hands Meeting

CSAFE 2020 All Hands Meeting

The 2020 All Hands Meeting was held May 12 and 13, 2020 and served as the closing to the last 5 years of CSAFE research and focused on kicking off…
A Robust Approach to Automatically Locating Grooves in 3D Bullet Land Scans

A Robust Approach to Automatically Locating Grooves in 3D Bullet Land Scans

Land engraved areas (LEAs) provide evidence to address the same source–different source problem in forensic firearms examination. Collecting 3D images of bullet LEAs requires capturing portions of the neighboring groove…