Automatic Matching of Bullet Land Impressions

TL;DR

This paper introduces an automated method for analyzing 3D bullet land impressions to improve firearm identification accuracy, addressing challenges of damage and large-scale matching in forensic firearm examination.

Contribution

It presents a novel automated framework that quantifies land impression similarities, enabling reliable matching even with damaged surfaces and large datasets.

Findings

Successfully identified all 10,384 land-to-land matches in a large dataset.

Effectively handled damaged land impressions unsuitable for manual comparison.

Provided a quantifiable measure of matchability for firearm barrels.

Abstract

In 2009, the National Academy of Sciences published a report questioning the scientific validity of many forensic methods including firearm examination. Firearm examination is a forensic tool used to help the court determine whether two bullets were fired from the same gun barrel. During the firing process, rifling, manufacturing defects, and impurities in the barrel create striation marks on the bullet. Identifying these striation markings in an attempt to match two bullets is one of the primary goals of firearm examination. We propose an automated framework for the analysis of the 3D surface measurements of bullet land impressions which transcribes the individual characteristics into a set of features that quantify their similarities. This makes identification of matches easier and allows for a quantification of both matches and matchability of barrels. The automatic matching routine we propose manages to (a) correctly identify land impressions (the surface between two bullet groove impressions) with too much damage to be suitable for comparison, and (b) correctly identify all 10,384 land-to-land matches of the James Hamby study.