Automatic Matching of Bullet Land Impressions

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2017-01-01
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Hare, Eric
Hofmann, Heike
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Carriquiry, Alicia
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Center for Statistics and Applications in Forensic Evidence
The Center for Statistics and Applications in Forensic Evidence (CSAFE) carries out research on the scientific foundations of forensic methods, develops novel statistical methods and transfers knowledge and technological innovations to the forensic science community. We collaborate with more than 80 researchers and across six universities to drive solutions to support our forensic community partners with accessible tools, open-source databases and educational opportunities.
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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]).

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This is a manuscript of an article published as Hare, Eric, Heike Hofmann, and Alicia Carriquiry. "Automatic matching of bullet land impressions." The Annals of Applied Statistics 11, no. 4 (2017): 2332-2356. Posted with permission from CSAFE.

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Sun Jan 01 00:00:00 UTC 2017
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