A Fully Automatic Method for Comparing Cartridge Case Images

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2017-07-10
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Tai, Xiao Hui
Stats, M.
Eddy, William
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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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Center for Statistics and Applications in Forensic Evidence
Abstract

When a gun is fired, it leaves marks on cartridge cases that are thought to be unique to the gun. In current practice, firearms examiners inspect cartridge cases for “sufficient agreement,” in which case they conclude that they come from the same gun, testifying in courts as such. A 2016 President's Council of Advisors on Science and Technology report questioned the scientific validity of such analysis (President's Committee of Advisors on Science and Technology, Washington, DC, Executive Office of the President). One recommendation was to convert firearms analysis to an objective method. We propose a fully automated, open‐source method for comparing breechface marks on cartridge cases using 2D optical images. We improve on existing methodology by automating the selection of marks, and removing the effects of circular symmetry. We propose an empirical computation of a “random match probability” given a known database, which can be used to quantify the weight of evidence. We demonstrate an improvement in accuracy on images from controlled test fires.

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This is a manuscript of an article published as Tai, Xiao Hui, and William F. Eddy. "A fully automatic method for comparing cartridge case images." Journal of forensic sciences 63, no. 2 (2018): 440-448. Posted with permission of CSAFE.

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Sun Jan 01 00:00:00 UTC 2017
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