传统上,火器和工具标记审查员使用比较显微镜手动评估两颗子弹特征的相似性。显微镜的进步使收集3D地形数据成为可能,并引入了几种自动比较算法,用于使用这些数据比较子弹条纹。在这项研究中,互相关的开源方法,全等匹配的轮廓段,连续匹配的条纹,并对随机森林模型进行了评估。使用连续制造的枪支的四个数据集进行了这些自动化方法的统计表征,以提供具有挑战性的比较方案。每种自动化方法都以成对的方式应用于所有样品,并对分类性能进行了比较。基于这些发现,通过经验学习和构建贝叶斯网络,以利用每种方法的优势,对自动结果之间的关系进行建模,并将它们组合成给定比较的后验概率。该网络的评估类似于自动化方法,并对结果进行了比较。所开发的贝叶斯网络对99.6%的样本进行了正确分类,与单独使用时的自动化方法相比,所得概率分布的分离程度更高。
Traditionally, firearm and toolmark examiners manually evaluate the similarity of features on two bullets using comparison microscopy. Advances in microscopy have made it possible to collect 3D topographic data, and several automated comparison algorithms have been introduced for the comparison of bullet striae using these data. In this study, open-source approaches for cross-correlation, congruent matching profile segments, consecutive matching striations, and a random forest model were evaluated. A statistical characterization of these automated approaches was performed using four datasets of consecutively manufactured firearms to provide a challenging comparison scenario. Each automated approach was applied to all samples in a pairwise fashion, and classification performance was compared. Based on these findings, a Bayesian network was empirically learned and constructed to leverage the strengths of each individual approach, model the relationship between the automated results, and combine them into a posterior probability for the given comparison. The network was evaluated similarly to the automated approaches, and the results were compared. The developed Bayesian network classified 99.6% of the samples correctly, and the resultant probability distributions were significantly separated more so than the automated approaches when used in isolation.