Classification of firing pin impressions using HOG‐SVM

<p>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 HOGSVM method had the highest performance in this classification task. </p>