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Footwear outsoles acquire characteristics unique to the individual wearingthem over time. Forensic scientists largely rely on their skills and knowledge,gained through years of experience, to analyse such characteristics on ashoeprint. In this work, we present a convolutional neural network model thatcan predict the wear pattern on a unique dataset of shoeprints that capturesthe life and wear of a pair of shoes. We present an additional architectureable to reconstruct the outsole back to its original state on a given week, andprovide empirical evaluations of the performance of both models.

Learning Wear Patterns on Footwear Outsoles Using Convolutional Neural Networks