@article{ref1,
title="Classification of firing pin impressions using HOG-SVM",
journal="Journal of forensic sciences",
year="2023",
author="Wen, Zhijian and Curran, James M. and Harbison, SallyAnn and Wevers, Gerhard E.",
volume="ePub",
number="ePub",
pages="ePub-ePub",
abstract="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.<p /> <p>Language: en</p>",
language="en",
issn="0022-1198",
doi="10.1111/1556-4029.15377",
url="http://dx.doi.org/10.1111/1556-4029.15377"
}