Bullet matching is a process used to determine whether two bullets may have been fired from the same gun barrel. Historically, this has been a manual process performed by trained forensic examiners. Recent work, however, has shown that it is possible to add statistical validity and objectivity to the procedure. In this article, we build upon the algorithms explored in Automatic Matching of Bullet Lands (Hare, Hofmann & Carriquiry (2017), Automatic matching of bullet lands. ArXiv E-Prints) by formalizing and defining a set of features, computed on pairs of bullet lands, which can be used in machine learning models to assess the probability of a match. We then use these features to perform an analysis of the two Hamby (Hamby, Brundage & Thorpe (2009), The identification of bullets fired from 10 consecutively rifled 9 mm Ruger pistol barrels: a research project involving 507 participants from 20 countries. AFTE J., 41, 99–110) bullet sets (Set 252 and Set 44), to assess the presence of microscope operator effects in scanning. We also take some first steps to address the issue of degraded bullet lands and provide a range of degradation at which the matching algorithm still performs well. Finally, we discuss generalizing land-to-land comparisons to full bullet comparisons as would be used for this procedure in a criminal justice situation

中文翻译：

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匹配退化土地印象的算法方法

子弹匹配是用于确定两颗子弹是否可能是从同一枪管中发射的过程。从历史上看，这是由训练有素的法医检查员执行的手动过程。然而，最近的工作表明，可以为该程序增加统计有效性和客观性。在本文中，我们建立在子弹土地自动匹配（Hare、Hofmann 和 Carriquiry（2017）、子弹土地的自动匹配。ArXiv E-Prints）中探索的算法的基础上，通过形式化和定义一组特征，在对子弹地，可用于机器学习模型以评估匹配的概率。然后我们使用这些特征对两个 Hamby (Hamby, Brundage & Thorpe (2009)，从 10 个连续膛线的 9 毫米鲁格手枪枪管发射的子弹的识别：一个涉及来自 20 个国家的 507 名参与者的研究项目。AFTE J., 41, 99–110) 子弹组（第 252 组和第 44 组），以评估显微镜操作员在扫描中的影响。我们还采取了一些第一步来解决退化的子弹着陆问题，并提供匹配算法仍然表现良好的退化范围。最后，我们讨论将陆对地比较概括为完整的子弹比较，这将在刑事司法情况下用于此程序