Comparison of three similarity scores for bullet LEA matching

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dc.contributor.author Vanderplas, Susan
dc.contributor.author Nally, Melissa
dc.contributor.author Klep, Tylor
dc.contributor.author Cadevall, Cristina
dc.contributor.author Hofmann, Heike
dc.contributor.department Center for Statistics and Applications in Forensic Evidence
dc.date 2020-02-18T21:59:42.000
dc.date.accessioned 2020-06-30T01:58:06Z
dc.date.available 2020-06-30T01:58:06Z
dc.date.copyright Wed Jan 01 00:00:00 UTC 2020
dc.date.embargo 2020-02-17
dc.date.issued 2020-03-01
dc.description.abstract <p>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).</p>
dc.description.comments <p>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." <em>Forensic Science International</em> (2020): 110167. Posted with permission of CSAFE.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/csafe_pubs/1/
dc.identifier.articleid 1004
dc.identifier.contextkey 16566792
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath csafe_pubs/1
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/20355
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/csafe_pubs/1/1_s2.0_S0379073820300293_main.pdf|||Fri Jan 14 17:57:18 UTC 2022
dc.source.uri 10.1016/j.forsciint.2020.110167
dc.subject.disciplines Forensic Science and Technology
dc.subject.keywords forensic science
dc.subject.keywords toolmark
dc.subject.keywords cross correlation
dc.subject.keywords random forest
dc.subject.keywords 3D microscopy
dc.subject.keywords Land engraved areas (LEAs)
dc.title Comparison of three similarity scores for bullet LEA matching
dc.type article
dc.type.genre article
dspace.entity.type Publication
relation.isOrgUnitOfPublication d8a3c72b-850f-40f6-87c4-8812547080c7
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