Affiliation:
1. Research Department, Ultra Electronics Forensic Technology, 5757 Cavendish Blvd., Suite 200, Montréal, Québec, H4W 2W8, Canada
Abstract
In firearm identification, a firearm examiner looks at a pair of fired bullets or cartridge cases using a comparison microscope and determines from this visual analysis if they were both fired from the same firearm. In the particular case of fired bullets, the individual firearm signature takes the form of a striated pattern. Over the time, the firearm examiner’s community developed two distinct approaches for bullet identification: pattern matching and line counting. More recently, the emergence of technology enabling the capture of surface topographies down to a submicron depth resolution has been a catalyst for the field of computerized objective ballistic identification. Objectiveness is achieved through the statistical analysis of various scores of known matches and known nonmatches exhibit pair comparison, which in turn implies the capture of large quantities of bullets and cartridge cases topographies. The main goal of this study was to develop an objective identification method for bullets fired from conventionally rifled barrels, and to test this method on public and proprietary bullet 3D image datasets captured at different lateral resolutions. Two newly developed bullet identification scores, the Line Counting Score (LCS) and the Pattern Matching Score, computed on 3D topographies yielded perfect match versus nonmatch separation for three different sets used in the standard Hamby–Brundage Test. A similar analysis performed using a larger, more-realistic set, enabled us to define a discriminative line at a false match rate of 1/10[Formula: see text]000 on a 2D plot that shows both identification scores for matches and nonmatches. The LCS is shown to produce a better sensitivity than the standard consecutive matching striae criteria for the more-realistic dataset. A likelihood function was also computed from a linear combination of both scores, and a conservative approach based on extreme value theory is proposed to extrapolate this function in the score domain where nonmatch data are not available. This study also provides a better understanding of the limitations of studies that involve very few firearms.
Funder
Ultra Electronics Forensic Technology
Publisher
World Scientific Pub Co Pte Lt
Subject
Artificial Intelligence,Computer Vision and Pattern Recognition,Software
Cited by
4 articles.
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