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.
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