Applying 3D measurements and computer matching algorithms to two firearm examination proficiency tests

Abstract

In order for a crime laboratory to assess a firearms examiner’s training, skills, experience, and aptitude, it is necessary for the examiner to participate in proficiency testing. As computer algorithms for comparisons of pattern evidence become more prevalent, it is of interest to test algorithm performance as well, using these same proficiency examinations. This article demonstrates the use of the Congruent Matching Cell (CMC) algorithm to compare 3D topography measurements of breech face impressions and firing pin impressions from a previously distributed firearms proficiency test. In addition, the algorithm is used to analyze the distribution of many comparisons from a collection of cartridge cases used to construct another recent set of proficiency tests. These results are provided along with visualizations that help to relate the features used in optical comparisons by examiners to the features used by computer comparison algorithms.

Firearm examination: Examiner judgments and computer‐based comparisons

“…This is clearly seen when looking at the total error rates, which decrease from 6.25% (breechface impressions) and 4.17% (firing pin impressions) to 2.22% (combined) for the computer‐based method and from 6.97% (breechface impressions) and 6.68% (firing pin impressions) to 5.06% for the examiners. These results are comparable to those of an earlier study [56] and suggest that when possible it is beneficial to combine the features of several firearm marks when judging the source of a cartridge case instead of basing an opinion on the features of just one mark (assuming that no firearm components have been replaced).…”

Convergence‐improved congruent matching cells (CMC) method for firing pin impression comparison

“…In the CMC validation experiments [4,9,17,18], since the true matching angle was unknown, the possible registration angle was always estimated in advance based on empirical observations of surface characteristics, and the rotation angle was set within a limited range such as 30° [9] with respect to the pre‐estimated angle, in order to reduce the otherwise large numbers of cross‐correlation calculations required. In this paper, however, the rotation correlation should be extended to the full 360° range so as to achieve complete automated ballistic comparison.…”

“…In the CMC validation experiments [4,9,17,18], since the true matching angle was unknown, the possible registration angle was always estimated in advance based on empirical observations of surface characteristics, and the rotation angle was set within a limited range angle. Certainly, if necessary, the search can be further refined in smaller increments to determine a more precise registration angle near the previous registration angle.…”

“…Ballistic examination traditionally relies mainly on highly skilled practitioners and meticulous side‐by‐side comparisons of toolmarks under a comparison microscope [3,4]. Since the early 1990 s, commercial automated ballistic identification systems such as Drugfire [5] and the Integrated Ballistic Identification System (IBIS) [6] have been developed, producing a revolution in the speed at which ballistic toolmark analysis can proceed.…”

Interpol review of forensic firearm examination 2016-2019

“…The resulting similarity maps from a comparison are shown to help relate the features used by examiners to those used by the algorithm. The results are perceived to be good, and can be improved by combining the information from both breechface and firing impressions [ 45 ].…”