We propose a novel method to quantify the similarity between an impression (Q) from an unknown source and a test impression (K) from a known source. Using the property of geometrical congruence in the impressions, the degree of correspondence is quantified using ideas from graph theory and maximum clique (MC). The algorithm uses the x and y coordinates of the edges in the images as the data. We focus on local areas in Q and the corresponding regions in K and extract features for comparison. Using pairs of images with known origin, we train a random forest to classify pairs into mates and non-mates. We collected impressions from 60 pairs of shoes of the same brand and model, worn over six months. Using a different set of very similar shoes, we evaluated the performance of the algorithm in terms of the accuracy with which it correctly classified images into source classes. Using classification error rates and ROC curves, we compare the proposed method to other algorithms in the literature and show that for these data, our method shows good classification performance relative to other methods. The algorithm can be implemented with the R package shoeprintr.
Quantifying the similarity of 2D images using edge pixels: An application to the forensic comparison of footwear impressions

Journal: Journal of Applied Statistics
Published: 2020
Primary Author: Soyoung Park
Secondary Authors: Alicia Carriquiry
Type: Publication
Research Area: Footwear
Related Resources
A New Algorithm for Source Identification of Look-alike Footwear Impressions Based on Automatic Alignment
Presentation at the International Association for Identification
Center for Statistics and Application in Forensic Evidence Update
The information below highlights a sample of current research initiatives led by the CSAFE team. Additional accomplishments in other forensic science disciplines will be discussed in subsequent issues of Forensic…
Automatic Class Characteristic Recognition in Shoe Tread Images
One of the fundamental problems in footwear forensics is that the distribution of class characteristics in the local population is not currently knowable. Surveillance devices for gathering this data are…
Modeling And iNventory of Tread Impression System (MANTIS): The development, deployment and application of an active footwear data collection system
This CSAFE webinar was held on March 24, 2022. Presenters: Dr. Richard Stone Iowa State University Dr. Susan Vanderplas University of Nebraska, Lincoln Presentation Description: This webinar details the development,…