Pilot Study of Automated Bullet Signature Identification Based on Topography Measurements and Correlations*^†

Abstract

A procedure for automated bullet signature identification is described based on topography measurements using confocal microscopy and correlation calculation. Automated search and retrieval systems are widely used for comparison of firearms evidence. In this study, 48 bullets fired from six different barrel manufacturers are classified into different groups based on the width class characteristic for each land engraved area of the bullets. Then the cross-correlation function is applied both for automatic selection of the effective correlation area, and for the extraction of a 2D bullet profile signature. Based on the cross-correlation maximum values, a list of top ranking candidates against a ballistics signature database of bullets fired from the same model firearm is developed. The correlation results show a 9.3% higher accuracy rate compared with a currently used commercial system based on optical reflection. This suggests that correlation results can be improved using the sequence of methods described here.

Automatic identification of bullet signatures based on consecutive matching striae (CMS) criteria

“…1(b). Generally, automatic identification systems for bullets create a feature profile by averaging multiple cross section profiles [5,7,10,11] instead of using a single section, thus attenuating the effects of random errors. Optimal feature profiles and higher bullet identification accuracy should also be achieved if those areas that do not contain valid striation information are excluded through certain processing before the averaging is calculated.…”

“…previous acquisition and measurement experience [11]. The band pass filter is a standard Gaussian filter [20] with limits of 250 and 15 mm.…”

“…With previous experience in automated bullet identification research using acquisition of topography images and the cross correlation score as a similarity metric [11], we try to address the aforementioned questions about statistics and subjectivity, and to provide a method for automation of CMS as the similarity metric. A computerized identification method based on 3D topography measurement and CMS criteria is described in this report.…”

“…[ [5][6][7][8][9][10][11] and commercial product manufacturers [12-16] refer to optical photographic images as 2D where the value at each pixel point represents the intensity of reflected light at the point, and topographic images as 3D where the value at each pixel point represents its topographic height at the point.…”

Optimal compression and binarization of signature profiles for automated bullet identification systems

“…As a well known similarity metric of two random signals, the cross correlation function (CCF) is used by some automated firearm identification systems [3][4][5][6][7] . In an ideal situation, the barrel will engrave very consistent striation marks along the entire land impression of the bullet, and any two horizontal section profiles at different axial positions would be highly correlated.…”