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Article
Optimization of a Statistical Algorithm for Objective Comparison of Toolmarks
Journal of Forensic Sciences
  • Ryan Spotts, Ames Laboratory
  • L. Scott Chumbley, Iowa State University and Ames Laboratory
  • Laura Ekstrand, Ames Laboratory
  • Song Zhang, Ames Laboratory
  • James Kreiser, Illinois State Police
Document Type
Article
Publication Version
Accepted Manuscript
Publication Date
3-1-2015
DOI
10.1111/1556-4029.12642
Abstract

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 fifty sequentially manufactured pliers. 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.

Comments

This is the peer-reviewed version of the following article: Spotts, Ryan, L. Scott Chumbley, Laura Ekstrand, Song Zhang, and James Kreiser. "Optimization of a statistical algorithm for objective comparison of toolmarks." Journal of Forensic Sciences 60, no. 2 (2015): 303-314, which has been published in final form at DOI: 10.1111/1556-4029.12642 This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving. Posted with permission.

Copyright Owner
American Academy of Forensic Sciences
Language
en
File Format
application/pdf
Citation Information
Ryan Spotts, L. Scott Chumbley, Laura Ekstrand, Song Zhang, et al.. "Optimization of a Statistical Algorithm for Objective Comparison of Toolmarks" Journal of Forensic Sciences Vol. 60 Iss. 2 (2015) p. 303 - 314
Available at: http://works.bepress.com/l_chumbley/58/