An automated alignment algorithm for identification of the source of footwear impressions with common class characteristics

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2024-02
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Lee, Hana
Park, Soyoung
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Wiley Periodicals LLC.
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Carriquiry, Alicia
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Statistics

The Department of Statistics seeks to teach students in the theory and methodology of statistics and statistical analysis, preparing its students for entry-level work in business, industry, commerce, government, or academia.

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The Department of Statistics was formed in 1948, emerging from the functions performed at the Statistics Laboratory. Originally included in the College of Sciences and Humanities, in 1971 it became co-directed with the College of Agriculture.

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1948-present

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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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Abstract
We introduce an algorithmic approach designed to compare similar shoeprint images, with automated alignment. Our method employs the Iterative Closest Points (ICP) algorithm to attain optimal alignment, further enhancing precision through phase-only correlation. Utilizing diverse metrics to quantify similarity, we train a random forest model to predict the empirical probability that two impressions originate from the same shoe. Experimental evaluations using high-quality two-dimensional shoeprints showcase our proposed algorithm's robustness in managing dissimilarities between impressions from the same shoe, outperforming existing approaches.
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This article is published as Lee, Hana, Alicia Carriquiry, and Soyoung Park. "An automated alignment algorithm for identification of the source of footwear impressions with common class characteristics." Statistical Analysis and Data Mining: The ASA Data Science Journal 17, no. 1 (2024): e11659. https://doi.org/10.1002/sam.11659. © 2024 The Authors. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
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