Volume 63, Issue 3
Technical Note

An Improved Version of a Tool Mark Comparison Algorithm

Jeremy R. Hadler Ph.D.

Corresponding Author

E-mail address: jrhadler@iastate.edu

Department of Statistics, Iowa State University, Ames, IA, 50010

Additional information and reprint requests:

Jeremy R. Hadler, Ph.D.

Department of Statistics

Iowa State University

Ames

IA 50010

E‐mail: jrhadler@iastate.edu

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Max D. Morris Ph.D.

Department of Statistics, Iowa State University, Ames, IA, 50010

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First published: 08 September 2017
Citations: 3

Abstract

Chumbley et al. (2010) described a statistically based algorithm for comparing pairs of tool marks. They presented empirical evidence that the algorithm produces well‐separated similarity score values for “matching” and “non‐matching” pairs of tool marks. However, the algorithm has two substantial weaknesses. First, it is “uncalibrated” in the sense that error rates can be determined only through empirical investigation. Second, it relies on a randomized test and can lead to different similarity scores when the algorithm is repeatedly applied to the same pair of tool marks. We present an improved version of the procedure, which eliminates the randomized scores and yields more consistent and predictable error rate control. This is accomplished by replacement of a random sampling step from the original algorithm with a deterministic process. We demonstrate the improved algorithm and compare its performance to the original by applying to known “matching” and “non‐matching” pairs of tool marks.

Number of times cited according to CrossRef: 3

  • The forensic examination of marks review: 2016 to 2018, Forensic Science International: Synergy, 10.1016/j.fsisyn.2020.01.016, (2020).
  • Sharp force trauma with two katana swords: identifying the murder weapon by comparing tool marks on the skull bone, International Journal of Legal Medicine, 10.1007/s00414-020-02372-3, (2020).
  • Adapting the Chumbley Score to Match Striae on Land Engraved Areas (LEAs) of Bullets, , Journal of Forensic Sciences, 10.1111/1556-4029.13950, 64, 3, (728-740), (2018).

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