The use of machine learning for the determination of a type/model of firearms by the characteristics on cartridge cases

• Published in: Forensic science international Vol. 358; p. 112021
• Main Authors: Giverts, Pavel, Sorokina, Ksenia, Barash, Mark, Fedorenko, Vladimir
• Format: Journal Article
• Language: English
• Published: Ireland Elsevier B.V 01.05.2024; Elsevier Limited
• Subjects: Accuracy; Artificial neural networks; Cartridge case; Cartridges; Class characteristics; Classification; Convolutional neural networks (CNN); Firearms; Forensic ballistics; Forensic firearm identification; Forensic science; Learning algorithms; Machine learning; Manufacturers; Neural networks; Small arms; Cartridge case; Class characteristics; Convolutional neural networks (CNN); Forensic firearm identification; Machine learning
• ISSN: 0379-0738; 1872-6283
• DOI: 10.1016/j.forsciint.2024.112021

Cartridge cases are commonly collected at crime scenes involving firearms. One of the stages in forensic examination is the determination of the type and model of firearms based on the class characteristics of these cartridge cases. A firearm examiner evaluates the class characteristics on the basis of their knowledge and experience, and by referring to collections of cartridge cases representing class characteristics of different firearms, special databases and reference books. However, this process is highly subjective. The novelty of this research is in developing objective methods of firearms determination by applying a machine learning approach. In this study, several Convolutional Neural Networks from Keras programming package were trained to determine the type/model of a firearm based on the class characteristics observed on cartridge cases from seven different categories of firearms. The prediction accuracies received by this method range from 71 to 81 percent for models based on different Convolutional Neural Networks, while using an ensemble of the machine learning models increased the accuracy to 88 %. The research demonstrates the efficacy of machine learning in enhancing accuracy and reducing subjectivity in firearm identification, highlighting its significant potential in forensic science applications. •The novel application of machine learning for forensic firearm examinations.•The development of CNN for the determination of the type/model of a firearm.•The developed CNN demonstrated accuracy of 88 % in cartridge cases examination.