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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Geoffrey Z. Thompson & Bishoy Dawood & Tianyu Yu & Barbara K. Lograsso & John D. Vanderkolk & Ranjan Maitra & William Q. Meeker & Ashraf F. Bastawros, 2024. "Quantitative matching of forensic evidence fragments using fracture surface topography and statistical learning," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-51594-1
    DOI: 10.1038/s41467-024-51594-1
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    References listed on IDEAS

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    1. Xiao, Yuanhui, 2017. "A fast algorithm for two-dimensional Kolmogorov–Smirnov two sample tests," Computational Statistics & Data Analysis, Elsevier, vol. 105(C), pages 53-58.
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