Volume 55, Issue 4
PAPER
CRIMINALISTICS

Validation of Tool Mark Comparisons Obtained Using a Quantitative, Comparative, Statistical Algorithm

L. Scott Chumbley Ph.D.

Ames Laboratory, Iowa State University, 2220 Hoover, Ames, IA 50011.

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Max D. Morris Ph.D.

Ames Laboratory, Iowa State University, 2220 Hoover, Ames, IA 50011.

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M. James Kreiser B.S.

Illinois State Police, Retired, 3112 Sequoia Dr., Springfield, IL 62712.

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Charles Fisher B.S.

Ames Laboratory, Iowa State University, 2220 Hoover, Ames, IA 50011.

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Jeremy Craft M.S.

Ames Laboratory, Iowa State University, 2220 Hoover, Ames, IA 50011.

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Lawrence J. Genalo Ph.D.

Ames Laboratory, Iowa State University, 2220 Hoover, Ames, IA 50011.

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Stephen Davis B.S.

Ames Laboratory, Iowa State University, 2220 Hoover, Ames, IA 50011.

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David Faden B.S.

Ames Laboratory, Iowa State University, 2220 Hoover, Ames, IA 50011.

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Julie Kidd M.S.

Ames Laboratory, Iowa State University, 2220 Hoover, Ames, IA 50011.

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First published: 01 July 2010
Citations: 30
Additional information and reprint requests:
L. Scott Chumbley, Ph.D.
Materials Science and Engineering Department
Iowa State University
2220 Hoover
Ames, IA 50011
E‐mail: chumbley@iastate.edu

Abstract

Abstract: 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.

Number of times cited according to CrossRef: 30

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