Chumbley L Scott, Morris Max D, Kreiser M James, Fisher Charles, Craft Jeremy, Genalo Lawrence J, Davis Stephen, Faden David, Kidd Julie
Ames Laboratory, Iowa State University, 2220 Hoover, Ames, IA 50011, USA.
J Forensic Sci. 2010 Jul;55(4):953-61. doi: 10.1111/j.1556-4029.2010.01424.x. Epub 2010 May 10.
A statistical analysis and computational algorithm for comparing pairs of tool marks via profilometry data is described. Empirical validation of the method is established through experiments based on tool marks made at selected fixed angles from 50 sequentially manufactured screwdriver tips. Results obtained from three different comparison scenarios are presented and are in agreement with experiential knowledge possessed by practicing examiners. Further comparisons between scores produced by the algorithm and visual assessments of the same tool mark pairs by professional tool mark examiners in a blind study in general show good agreement between the algorithm and human experts. In specific instances where the algorithm had difficulty in assessing a particular comparison pair, results obtained during the collaborative study with professional examiners suggest ways in which algorithm performance may be improved. It is concluded that the addition of contextual information when inputting data into the algorithm should result in better performance.
本文描述了一种通过轮廓测量数据比较工具痕迹对的统计分析和计算算法。通过基于从50个连续制造的螺丝刀尖端以选定固定角度制作的工具痕迹进行的实验，建立了该方法的实证验证。给出了从三种不同比较场景获得的结果，这些结果与实际检验人员所具备的经验知识一致。在一项盲法研究中，算法产生的分数与专业工具痕迹检验人员对相同工具痕迹对的视觉评估之间的进一步比较总体上表明，算法与人类专家之间具有良好的一致性。在算法难以评估特定比较对的具体情况下，与专业检验人员合作研究期间获得的结果提出了改进算法性能的方法。得出的结论是，在将数据输入算法时添加上下文信息应会带来更好的性能。
