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

A Pipeline for Cartridge Case Comparisons

A Pipeline for Cartridge Case Comparisons

We introduce novel sub-procedures in the pre-processing, comparison and similarity feature steps. The sub-procedures result in an improved error rate compared to our implementation of the CMC method while also…
Affect of Manufacturer Finishing Methods on Subclass Characteristics Production on Breech Faces

Affect of Manufacturer Finishing Methods on Subclass Characteristics Production on Breech Faces

The following is a presentation given at Association for Firearms and Toolmarks (AFTE) (2022).
An Algorithm for Forensic Toolmark Comparisons

An Algorithm for Forensic Toolmark Comparisons

Forensic handheld toolmark examiners currently compare toolmarks (e.g. scratch marks on wire found as part of an explosive device, or on a door frame after someone broke into a house)…
Exploration of Subclass Characteristics Manufacturing

Exploration of Subclass Characteristics Manufacturing

A webinar presented during the Forensic Technology Center of Excellence (FTCoE0 Firearm and Toolmark webinar series on the progress of our CSAFE project. This study assesses the production of subclass…