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基于地形测量和相关性的自动子弹痕迹识别初步研究。

Pilot study of automated bullet signature identification based on topography measurements and correlations.

作者信息

Chu Wei, Song John, Vorburger Theodore, Yen James, Ballou Susan, Bachrach Benjamin

机构信息

National Institute of Standards and Technology, Gaithersburg, MD 20899, USA.

出版信息

J Forensic Sci. 2010 Mar 1;55(2):341-7. doi: 10.1111/j.1556-4029.2009.01276.x. Epub 2010 Jan 19.

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.

摘要

描述了一种基于共聚焦显微镜地形测量和相关性计算的自动子弹特征识别程序。自动搜索和检索系统广泛用于枪支证据的比较。在本研究中,从六个不同枪管制造商发射的48颗子弹根据子弹每个膛线刻痕区域的宽度类别特征被分为不同组。然后,互相关函数既用于自动选择有效相关区域,也用于提取二维子弹轮廓特征。基于互相关最大值,建立了一份针对从同一型号枪支发射的子弹弹道特征数据库的顶级候选列表。相关性结果显示,与目前使用的基于光反射的商业系统相比,准确率提高了9.3%。这表明使用此处描述的方法序列可以提高相关性结果。

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