Optimization of a Statistical Algorithm for Objective Comparison of Toolmarks

• NCJ Number: 248845
• Journal: Journal of Forensic Sciences Volume: 60 Issue: 2 Dated: March 2015 Pages: 303-314
• Author(s): Ryan Spotts; L. S. Chumbley; Laura Ekstrand; Song Zhang; James Kreiser
• Date Published: March 2015
• Length: 12 pages
• Grant Number(s): 2009-DNR-119
• Agencies: NIJ-Sponsored
• Publication Type: Research (Applied/Empirical); Report (Study/Research); Report (Grant Sponsored)

Annotation

Due to historical legal challenges, there is a driving force for the development of objective methods of forensic toolmark identification. This study utilizes an algorithm to separate matching and nonmatching shear cut toolmarks created using 50 sequentially manufactured pliers.

Abstract

Unlike previously analyzed striated screwdriver marks, shear cut marks contain discontinuous groups of striations, posing a more difficult test of algorithm applicability. The algorithm compares correlation between optical 3D toolmark topography data, producing a Wilcoxon rank sum test statistic. Relative magnitude of this metric separates the matching and nonmatching toolmarks. Results show a high degree of statistical separation between matching and nonmatching distributions. Further separation is achieved with optimized input parameters and implementation of a "leash" preventing a previous source of outliers; however, complete statistical separation was not achieved. This paper represents further development of objective methods of toolmark identification and further validation of the assumption that toolmarks are identifiably unique. (Publisher abstract modified)