Open Access
December 2017 Automatic matching of bullet land impressions
Eric Hare, Heike Hofmann, Alicia Carriquiry
Ann. Appl. Stat. 11(4): 2332-2356 (December 2017). DOI: 10.1214/17-AOAS1080

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 (Hamby, Brundage and Thorpe [AFTE Journal 41 (2009) 99–110]).

Citation

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Eric Hare. Heike Hofmann. Alicia Carriquiry. "Automatic matching of bullet land impressions." Ann. Appl. Stat. 11 (4) 2332 - 2356, December 2017. https://doi.org/10.1214/17-AOAS1080

Information

Received: 1 February 2017; Revised: 1 June 2017; Published: December 2017
First available in Project Euclid: 28 December 2017

zbMATH: 1383.62362
MathSciNet: MR3743299
Digital Object Identifier: 10.1214/17-AOAS1080

Keywords: 3D topological surface measurement , cross-correlation function , data visualization , feature importance , machine learning

Rights: Copyright © 2017 Institute of Mathematical Statistics

Vol.11 • No. 4 • December 2017
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