Estimating Error Rates for Firearm Evidence Identifications in Forensic Science

Published: March 01, 2018

Author(s)

Jun-Feng Song, Theodore V. Vorburger, Wei Chu, James H. Yen, Johannes A. Soons, D Ott, Nien F. Zhang

Abstract

Estimating error rates for firearm evidence identification is a fundamental challenge in forensic science. This paper describes the recently developed Congruent Matching Cells (CMC) method for image comparisons, its application to firearm identification, and its initial tests for error rate estimation. The CMC method divides compared topography images into correlation cells. Three sets of identification parameters are derived for quantifying both the topography similarity of the correlated cell pairs and the pattern congruency of the cell distributions. An identification (declared match) requires a significant number of CMCs, i.e., cell pairs that meet all similarity and congruency requirements. Initial testing on breech face impressions of a set of 40 cartridge cases fired with consecutively manufactured pistol slides showed wide separation between the distributions of CMCs observed for known matching and known non-matching image pairs. Another test on 95 cartridge cases from a different population of slides also yielded widely separated distributions. The test results were used to develop two statistical models for the probability mass function of CMC comparison scores. The models were applied to develop a framework for estimating cumulative false positive and false negative error rates and individual error rates of identifications and exclusions for this population of breech face impressions. The CMC method can provide a statistical foundation for estimating error rates in firearm evidence identifications, thus emulating methods used for forensic identification of DNA evidence.
Citation: Forensic Science International
Volume: 284
Pub Type: Journals

Download Paper

Keywords

Forensics, ballistics identification, error rate, congruent matching cell, CMC
Created March 01, 2018, Updated November 10, 2018