Classification of firing pin impressions using HOG-SVM.

Publisher: Blackwell Pub Country of Publication: United States NLM ID: 0375370 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1556-4029 (Electronic) Linking ISSN: 00221198 NLM ISO Abbreviation: J Forensic Sci Subsets: PubMed not MEDLINE; MEDLINE

Publication: 2006- : Malden, MA : Blackwell Pub.
Original Publication: [Chicago, Ill.] : Callaghan and Co., 1956-

Crimes, such as robbery and murder, often involve firearms. In order to assist with the investigation into the crime, firearm examiners are asked to determine whether cartridge cases found at a crime scene had been fired from a suspect's firearm. This examination is based on a comparison of the marks left on the surfaces of cartridge cases. Firing pin impressions can be one of the most commonly used of these marks. In this study, a total of nine Ruger model 10/22 semiautomatic rifles were used. Fifty cartridges were fired from each rifle. The cartridge cases were collected, and each firing pin impression was then cast and photographed using a comparison microscope. In this paper, we will describe how one may use a computer vision algorithm, the Histogram of Orientated Gradient (HOG), and a machine learning method, Support Vector Machines (SVMs), to classify images of firing pin impressions. Our method achieved a reasonably high accuracy at 93%. This can be used to associate a firearm with a cartridge case recovered from a scene. We also compared our method with other feature extraction algorithms. The comparison results showed that the HOG-SVM method had the highest performance in this classification task.
(© 2023 American Academy of Forensic Sciences.)

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Keywords: computer vision; firing pin impression; histogram of orientated gradient; image classification; support vector machine

Date Created: 20230911 Latest Revision: 20231026

20240628

10.1111/1556-4029.15377

37691406