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Forensic comparison of fired cartridge cases: Feature-extraction methods for feature-based calculation of likelihood ratios

Basu, Nabanita, Bolton-King, Rachel S. and Morrison, Geoffrey Stewart (2022) Forensic comparison of fired cartridge cases: Feature-extraction methods for feature-based calculation of likelihood ratios. Forensic Science International: Synergy. p. 100272. ISSN 2589-871X

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Official URL: http://dx.doi.org/10.1016/j.fsisyn.2022.100272

Abstract or description

We describe and validate a feature-based system for calculation of likelihood ratios from 3D digital images of fired cartridge cases. The system includes a database of 3D digital images of the bases of 10 cartridges fired per firearm from approximately 300 firearms of the same class (semi-automatic pistols that fire 9 mm diameter centre-fire Luger-type ammunition, and that have hemispherical firing pins and parallel breech-face marks). The images were captured using Evofinder®, an imaging system that is commonly used by operational forensic laboratories. A key component of the research reported is the comparison of different feature-extraction methods. Feature sets compared include those previously proposed in the literature, plus Zernike-moment based features. Comparisons are also made of using feature sets extracted from the firing-pin impression, from the breech-face region, and from the whole region of interest (firing-pin impression + breech-face region + flowback if present). Likelihood ratios are calculated using a statistical modelling pipeline that is standard in forensic voice comparison. Validation is conducted and results are assessed using validation procedures and validation metrics and graphics that are standard in forensic voice comparison.

Item Type: Article
Uncontrolled Keywords: Firearm; Cartridge case; Likelihood ratio; Feature; Calibration; Validation
Faculty: School of Law, Policing and Forensics > Forensic Sciences and Policing
Depositing User: Rachel BOLTON-KING
Date Deposited: 06 Jun 2022 10:25
Last Modified: 27 May 2023 01:38
Related URLs:
URI: https://eprints.staffs.ac.uk/id/eprint/7348

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