An Algorithm for Forensic Toolmark Comparisons

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2022-08-08
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Cuellar, Maria
Hofmann, Heike
Jensen, Shane
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Center for Statistics and Applications in Forensic Evidence
The Center for Statistics and Applications in Forensic Evidence (CSAFE) carries out research on the scientific foundations of forensic methods, develops novel statistical methods and transfers knowledge and technological innovations to the forensic science community. We collaborate with more than 80 researchers and across six universities to drive solutions to support our forensic community partners with accessible tools, open-source databases and educational opportunities.
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Statistics
As leaders in statistical research, collaboration, and education, the Department of Statistics at Iowa State University offers students an education like no other. We are committed to our mission of developing and applying statistical methods, and proud of our award-winning students and faculty.
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Abstract
Forensic handheld toolmark examiners currently compare toolmarks (e.g. scratch marks on wire found as part of an explosive device, or on a door frame after someone broke into a house) by observing 2D images and subjectively deciding whether two marks were produced by the same source. Researchers have suggested that using algorithms could reduce the amount of human error. we present a clustering algorithm that performs very well on a dataset of 3D screwdriver marks generated in our lab under controlled conditions and a factorial design. The data allows us to cluster by screwdriver based on what force, angle, and side was used to make the mark. We hope our algorithm can be used by laboratories and help strengthen forensic science.
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This presentation is from the 2022 Joint Statistical Meetings (JSM), Washington, D.C., August 6-11, 2022. Posted with permission of CSAFE.
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