Ballistics Image Processing and Analysis for Firearm Identification

Firearm identification is an intensive and time-consuming process that requires physical interpretation of forensic ballistics evidence. Especially as the level of violent crime involving firearms escalates, the number of firearms to be identified accumulates dramatically. The demand for an automatic firearm identification system arises. This chapter proposes a new, analytic system for automatic firearm identification based on the cartridge and projectile specimens. Not only do we present an approach for capturing and storing the surface image of the spent projectiles at high resolution using line-scan imaging technique for the projectiles database, but we also present a novel and effective FFT-based analysis technique for analyzing and identifying the projectiles

Assessing Forensic Ballistics Three-Dimensionally through Graphical Reconstruction and Immersive VR Observation

© The Author(s) 2022. This article is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.A crime scene can provide valuable evidence critical to explain reason and modality of the occurred crime, and it can also lead to the arrest of criminals. The type of evidence collected by crime scene investigators or by law enforcement may accordingly effective involved cases. Bullets and cartridge cases examination is of paramount importance in forensic science because they may contain traces of microscopic striations, impressions and markings, which are unique and reproducible as “ballistic fingerprints”. The analysis of bullets and cartridge cases is a complicated and challenging process, typically based on optical comparison, leading to the identification of the employed firearm. New methods have recently been proposed for more accurate comparisons, which rely on three-dimensionally reconstructed data. This paper aims at further advancing recent methods by introducing a novel immersive technique for ballistics comparison by means of Virtual Reality. Users can three-dimensionally examine the cartridge cases shapes through intuitive natural gestures, from any vantage viewpoint (including internal iper-magnified views), while having at their disposal sets of visual aids which could not be easily implemented in desktop-based applications. A user study was conducted to assess viability and performance of our solution, which involved fourteen individuals acquainted with the standard procedures used by law enforcement agencies. Results clearly indicated that our approach lead to faster adaptation of users to the UI/UX and more accurate and explainable ballistics examination results.Peer reviewe

Firearm identification with hierarchical neural networks by analyzing the firing pin images retrieved from cartridge cases

When a gun is fired, characteristic markings on the cartridge and projectile of a bullet are produced. Over thirty different features can be distinguished from observing these marks, which in combination produce a fingerprint for identification of a firearm. ln this paper, through the use of hierarchial neural networks a firearm identification system based on cartridge case images is proposed. We focus on the cartridge case identification of rim-fire mechanism. Experiments show that the model proposed has high performance and robustness by integrating two levels Self- Organizing Feature Map (SOFM) neural networks and the decision-making strategy. This model will also make a significant contribution towards the further processing, such as the more efficient and precise identification of cartridge cases by combination with more characteristics on cartridge cases image

Automated bullet-identification system based on surface topography techniques

Every firearm has individual characteristics that are as unique to it as fingerprints are to human beings. When a firearm is fired, it transfers these characteristics – in the form of microscopic scratches and dents – to the fired bullets and cartridge casings. The rifling of the barrel of the firearm marks the bullets travelling through it, and the firearm's breech mechanism marks the ammunition's cartridge casing. Characterising these marks is the critical element in identifying firearms. Traditionally the comparison of ballistic evidence has been a tedious and time-consuming process requiring highly skilled examiners. In the past decade, engineers have created automated ballistics identification systems that meld traditional comparison microscopes with digital cameras, computers, huge databases, and image analysis techniques. This kind of system can help investigators to link crimes by automatically finding similarities among images of bullet but suffering significant drawbacks and minimal matching. More recently, approaches based on 3D digital representations of evidence surface topography have started to appear, both in research and industrial products. Potentially the introduction of 3D surface topography measurement can overcome the limitations of digital imaging systems by making the bullet surface measurement reproducible and reliable. A 3D quantitative approach for bullet identification is proposed in this paper. In this system the surface topography of the whole bullet can be acquired for analysis and identification. Primary researches have been done by applying advanced surface topography techniques for bullet marks’ characteristics extraction. A variety of 2D and 3D visualization graphics have also been provided to help firearm examiners to make final decisions

Gun model classification based on fired cartridge case head images with siamese networks

The identification of the firearm model that triggered the firing of a bullet is an important forensic information that, historically, has been done by trained examiners through visual inspection using microscopes. This is an extensive and very time-consuming process that requires the examiners to be trained to identify and compare the fired cartridges. This paper proposes an automated objective method for binary classifying pairs of fired cartridge head images as belonging to the same or different classes, using siamese neural networks (SNNs). With this technique, an accuracy of up to 70% was reached by using firing pin mark images as the input of the SNN. For the training and optimization of the network this paper also analyses and presents different image preprocessing approaches.info:eu-repo/semantics/acceptedVersio

Image matching of firearm fingerprints

A spent cartridge case exhibits characteristic markings (firearm fingerprint) that can be used to identify the type and possibly make of weapon in which the cartridge was fired. This report details research into the use of discriminant analysis for the purpose of matching spent rim-fire cartridge cases to specific make and model firearms. The discrimination and classification are based on several scalar shape parameters for the two-dimensional silhouette of the firing pin (FP) impression-- shape factor calculated from the second order moment of inertia, G factor calculated from the distance transform, and the P2A factor- as well as the distance between the centre of the cartridge case and the centroid of the FP impression, and the orientation of the principal centroidal axes associated with the FP impression. Classification results for two case studies are detailed: (i) 3 different make/model weapons producing different shaped FP impressions, and (ii) 5 different make/model weapons each producing a rectangular FP impression

Correlation of distance and damage in a ballistic setting

Forensic Investigation is a discipline which relies on various fields in order to be able to reconstruct an incident. Forensic Ballistics focuses upon the mechanics of projectile launch, flight and the effects of the projectile when impacting a target as well as firearms and ammunition. One of the most common evidence types in firearms related events is Gun Shot Residue (GSR), where typical analysis methods involves chemical confirmatory tests. Therefore, the fields traditionally associated with forensic ballistics are chemistry and physics, however there are various other scientific fields which could potentially further knowledge in this area such as radiography and computational science. Arguably one of the most important considerations within Forensic Ballistics is the ability to accurately reconstruct an incident. Currently there is limited literature aimed at understanding GSR spread at distances above 15 metres, which is a limitation for the criminal justice system (chapter 1). This work aims to further this knowledge by gaining an understanding of GSR spread at various distances, both short and long range (chapter 4), whilst combining this with Gun Shot Wound (GSW) damage using radiography (chapter 3). The data obtained will then be used for computational modelling with the aim of predicting shooter distance (chapter 5)

Firearm-mark Evidence: Looking Back and Looking Ahead

This article, written as a contribution to a festschrift for Paul Giannelli, surveys the development of the law on one type of feature-matching evidence that repeatedly attracted Professor Giannelli’s attention — “firearm-mark evidence.” By inspecting toolmarks on bullets or spent cartridge cases, firearms examiners can supply valuable information on whether a particular gun fired the ammunition in question. But the limits on this information have not always been respected in court, and a growing number of opinions have tried to address this fact. The article explains how the courts have moved from a position of skepticism of the ability of examiners to link bullets and other ammunition components to a particular gun to full-blown acceptance of identification “to the exclusion of all other firearms.” From that apogee, challenges to firearm-mark evidence over the past decade or so, have generated occasional restrictions on the degree of confidence that firearms experts can express in court, but they have not altered the paradigm of making source attributions and exclusions instead of statements about the degree to which the evidence supports these conclusions. After reviewing the stages in the judicial reception of firearm-mark evidence, the article concludes by describing a more scientific, quantitative, evidence-based form of testimony that should supplant or augment the current experience-based decisions of skilled witnesses