Abstract
AbstractWe 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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Statistical Analysis and Data Mining: The ASA Data Science Journal
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
