Learning Wear Patterns on Footwear Outsoles Using Convolutional Neural Networks
This work addresses the need for automated analysis of shoeprint wear patterns in forensic science, but it appears incremental as it applies existing CNN methods to a new dataset without claiming broad advancements.
The authors tackled the problem of analyzing unique wear patterns on footwear outsoles for forensic science by developing a convolutional neural network model to predict wear patterns and reconstruct original states, achieving empirical evaluations of performance.
Footwear outsoles acquire characteristics unique to the individual wearing them over time. Forensic scientists largely rely on their skills and knowledge, gained through years of experience, to analyse such characteristics on a shoeprint. In this work, we present a convolutional neural network model that can predict the wear pattern on a unique dataset of shoeprints that captures the life and wear of a pair of shoes. We present an additional architecture able to reconstruct the outsole back to its original state on a given week, and provide empirical evaluations of the performance of both models.