Skip to content

A finely tuned deep transfer learning algorithm to compare outsole images

Journal: Statistical Analysis and Data Mining
Published: 2023
Primary Author: Moonsoo Jang
Secondary Authors: Soyoung Park, Alicia Carriquiry
Research Area: Footwear

In forensic practice, evaluating shoeprint evidence is challenging because the differences between images of two different outsoles can be subtle. In this paper, we propose a deep transfer learning-based matching algorithm called the Shoe-MS algorithm that quantifies the similarity between two outsole images. The Shoe-MS algorithm consists of a Siamese neural network for two input images followed by a transfer learning component to extract features from outsole impression images. The added layers are finely tuned using images of shoe soles. To test the performance of the method we propose, we use a study dataset that is both realistic and challenging. The pairs of images for which we know ground truth include (1) close non-matches and (2) mock-crime scene pairs. The Shoe-MS algorithm performedwell in terms of prediction accuracy andwas able to determine the source of pairs of outsole images, even when comparisonswere challenging. When using a score-based likelihood ratio, the algorithm made the correct decision with high probability in a test of the hypothesis that images had a common source. An important advantage of the proposed approach is that pairs of images can be compared without alignment. In initial tests, Shoe-MS exhibited better-discriminating power than existing methods.

Related Resources

Graph-Theoretic Techniques for Forensic Image Comparisons

Graph-Theoretic Techniques for Forensic Image Comparisons

This presentation is from the 76th Annual Conference of the American Academy of Forensic Sciences (AAFS), Denver, Colorado, February 19-24, 2024.
ShoeCase: A data set of mock crime scene footwear impressions

ShoeCase: A data set of mock crime scene footwear impressions

This project’s main objective is to create an open-source database containing a sizeable number of high-quality images of shoe impressions. The Center for Statistics and Applications in Forensic Evidence (CSAFE)…
An automated alignment algorithm for identification of the source of footwear impressions with common class characteristics

An automated alignment algorithm for identification of the source of footwear impressions with common class characteristics

We 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…
A Semi-Automatic Tool for Footwear Impression Alignment

A Semi-Automatic Tool for Footwear Impression Alignment

We introduce a semi-automatic alignment tool tailored for two similar footwear impressions. The term “semi-automatic” is used because the alignment process is primarily automated, yet users have the flexibility to…