Document Type

Article

Publication Version

Published Version

Publication Date

3-2020

Journal or Book Title

Forensic Science International

Volume

308

First Page

110167

DOI

10.1016/j.forsciint.2020.110167

Abstract

Recent advances in microscopy have made it possible to collect 3D topographic data, enabling more precise virtual comparisons based on the collected 3D data as a supplement to traditional comparison microscopy and 2D photography. Automatic comparison algorithms have been introduced for various scenarios, such as matching cartridge cases[1],[2] or matching bullet striae[3],[4],[5]. One key aspect of validating these automatic comparison algorithms is to evaluate the performance of the algorithm on external tests, that is, using data which were not used to train the algorithm. Here, we present a discussion of the performance of the matching algorithm[6] in three studies conducted using different Ruger weapons. We consider the performance of three scoring measures: random forest score, cross correlation, and consecutive matching striae (CMS) at the land-to-land level and, using Sequential Average Maxima scores, also at the bullet-to bullet level. Cross correlation and random forest scores both result in perfect discrimination of same-source and different-source bullets. At the land-to-land level, discrimination for both cross correlation and random forest scores (based on area under the curve, AUC) is excellent (≥0.90).

Comments

This article is published as Vanderplas, Susan, Melissa Nally, Tylor Klep, Cristina Cadevall, and Heike Hofmann. "Comparison of three similarity scores for bullet LEA matching." Forensic Science International (2020): 110167. Posted with permission of CSAFE.

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Copyright Owner

The Authors

Language

en

File Format

application/pdf

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