Statistical Analysis of Firearms/Toolmarks Interpretation of Cartridge Case Evidence Using IBIS and Bayesian Networks

Abstract

The IBIS system provides a means of correlating the images of two breech face or firing pin impressions. Cartridges fired by the same gun result in similar images and thus higher scores. The generated scores, together with related firearm and ammunition information were transformed into a Bayesian network. Bayesian networks allow for the assessment of evidence based upon two propositions (same gun ordifferent gun). This allows a forensic scientist to provide insight to courts and investigators as to the value of the evidence.The breech face (BF) and firing pin (FP) scores, and their product, were used to assess the ability of the system to classify an unknowncartridge case into a same-gun or different-gun category. The IBIS system does not provide for an easy means to use the combination of the BF and FP scores. Twenty sets of known and questioned cartridge cases, from a large collection which had been analyzed by operational firearms examiners, were examined and tested using the Bayesian networks. Out of the 20 comparisons, there were eight true positives, seven true negatives, five false negatives, and zero false positives. In all instances of eliminations, the support for the different-gun hypothesis was, at minimum, strong.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Oct 24, 2015
Accession Number
AD1028351

Entities

People

  • Elizabeth C. Dearth
  • Eric F. Law
  • Keith B. Morris
  • Roger L. Jefferys

Organizations

  • West Virginia University

Tags

Communities of Interest

  • Advanced Electronics
  • C4I
  • Energy and Power Technologies
  • Human Systems

DTIC Thesaurus Topics

  • Accuracy
  • Algorithms
  • Bayesian Networks
  • Computational Science
  • Data Analysis
  • Data Mining
  • Databases
  • Discriminant Analysis
  • Identification Systems
  • Information Processing
  • Information Science
  • Machine Learning
  • Neural Networks
  • Probability
  • Statistical Analysis
  • Students
  • Supervised Machine Learning

Readers

  • Computer Vision.
  • Educational Psychology
  • ballistics.

Technology Areas

  • AI & ML