  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.
