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


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.

Publication DOI:
Divisions: College of Business and Social Sciences > Aston Institute for Forensic Linguistics
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College of Engineering & Physical Sciences
Additional Information: © 2022 Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license 4.0
Uncontrolled Keywords: Calibration,Cartridge case,Feature,Firearm,Likelihood ratio,Validation,Pathology and Forensic Medicine,Law
Publication ISSN: 2589-871X
Last Modified: 23 May 2024 07:16
Date Deposited: 06 Jun 2022 11:26
Full Text Link:
Related URLs: https://linking ... 589871X22000572 (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2022-05
Published Online Date: 2022-05-27
Accepted Date: 2022-05-24
Authors: Basu, Nabanita
Bolton-King, Rachel S.
Morrison, Geoffrey Stewart (ORCID Profile 0000-0001-8608-8207)

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