Automated segmentation of the breech and firing pin faces of fired cartridge case images

Roberto Puch-Solis

Abstract: Firearm identification plays a crucial role in criminal justice globally. The capability to link firearms to specific crimes is invaluable for investigations and court cases. Each firearm leaves distinctive markings on bullets and cartridge cases, creating a ‘‘mechanical fingerprint’’ that can be used for the comparison of bullets and cartridge cases and underpins this area of forensic science. Cartridge cases fired from the same firearm exhibit similar markings on their bases. These traces can be used for investigation purposes as a means to potentially provide a link between more than one scene where cartridge cases have been recovered, or to provide a potential evidential link between a firearm and a cartridge case. These applications involve comparing the markings on the base of two or more cartridge cases, consisting of the headstamp, breech face and firing pin areas. The headstamp area usually contains information about the manufacturer and the calibre. Once this is considered, the remaining task is to compare the breech and firing pin areas of the two cartridges. Currently, some automated methods exist for this comparison, all of which involve the removal of the headstamp area to minimize bias. Some semi-automated methods for headstamp removal are available, and recently, an automated deep learning method that can be applied to 256 × 256 pixel resolution images has been introduced. In this article, we also propose a deep learning method addressing a more computationally demanding task of removing the head stamp area in higher-resolution images, 512 × 512 and 2592 × 1944 pixels, which will permit the automated extraction of finer features at a higher resolution. We also (a) introduce a post-processing method that improves the performance of our method, (b) provide the labelled data that we have produced so it can be used, together with the NIST database of cartridge case images, as a benchmark for future research, and (c) provide the estimated weights and models of the convolutional neural networks that can either be used directly or as initial values for further research. This article contributes to the emerging body of research on deep learning applications in forensic science.