# Learning Wear Patterns on Footwear Outsoles Using Convolutional Neural Networks

## TL;DR

This paper introduces a convolutional neural network model to analyze and predict wear patterns on footwear outsoles, aiding forensic analysis by automating the recognition of individual shoe characteristics.

## Contribution

The work presents a novel CNN-based approach for predicting wear patterns and reconstructing original outsole states, with empirical evaluation on a unique dataset.

## Key findings

- CNN accurately predicts wear patterns
- Reconstruction model restores outsole to original state
- Models outperform baseline methods

## Abstract

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.