An automated alignment algorithm for identification of the source of footwear impressions with common class characteristics
Date
2024-02
Authors
Major Professor
Advisor
Committee Member
Journal Title
Journal ISSN
Volume Title
Publisher
Wiley Periodicals LLC.
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.
Series Number
Journal Issue
Is Version Of
Versions
Series
Academic or Administrative Unit
Type
article
Comments
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.