Quantitative matching of forensic evidence fragments using fracture surface topography and statistical learning

Geoffrey Z. Thompson, Bishoy Dawood, Tianyu Yu, Barbara K. Lograsso, John D. Vanderkolk, Ranjan Maitra, William Q. Meeker and Ashraf F. Bastawros ()
Additional contact information
Geoffrey Z. Thompson: Iowa State University
Bishoy Dawood: Iowa State University
Tianyu Yu: Iowa State University
Barbara K. Lograsso: Iowa State University
John D. Vanderkolk: Indiana State Police Laboratory
Ranjan Maitra: Iowa State University
William Q. Meeker: Iowa State University
Ashraf F. Bastawros: Iowa State University

Nature Communications, 2024, vol. 15, issue 1, 1-12

Abstract: Abstract The complex jagged trajectory of fractured surfaces of two pieces of forensic evidence is used to recognize a “match” by using comparative microscopy and tactile pattern analysis. The material intrinsic properties and microstructures, as well as the exposure history of external forces on a fragment of forensic evidence have the premise of uniqueness at a relevant microscopic length scale (about 2–3 grains for cleavage fracture), wherein the statistics of the fracture surface become non-self-affine. We utilize these unique features to quantitatively describe the microscopic aspects of fracture surfaces for forensic comparisons, employing spectral analysis of the topography mapped by three-dimensional microscopy. Multivariate statistical learning tools are used to classify articles and result in near-perfect identification of a “match” and “non-match” among candidate forensic specimens. The framework has the potential for forensic application across a broad range of fractured materials and toolmarks, of diverse texture and mechanical properties.

Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-024-51594-1 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-51594-1

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-024-51594-1

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().