Campus Units

Statistics

Document Type

Article

Publication Version

Published Version

Publication Date

2-21-2020

Journal or Book Title

Statistical Analysis and Data Mining: The ASA Data Science Journal

Volume

13

Issue

2

DOI

10.1002/sam.11449

Abstract

Footwear examiners are tasked with comparing an outsole impression (Q) left at a crime scene with an impression (K) from a database or from the suspect's shoe. We propose a method for comparing two shoe outsole impressions that relies on robust features (speeded‐up robust feature; SURF) on each impression and aligns them using a maximum clique (MC). After alignment, an algorithm we denote MC‐COMP is used to extract additional features that are then combined into a univariate similarity score using a random forest (RF). We use a database of shoe outsole impressions that includes images from two models of athletic shoes that were purchased new and then worn by study participants for about 6 months. The shoes share class characteristics such as outsole pattern and size, and thus the comparison is challenging. We find that the RF implemented on SURF outperforms other methods recently proposed in the literature in terms of classification precision. In more realistic scenarios where crime scene impressions may be degraded and smudged, the algorithm we propose—denoted MC‐COMP‐SURF—shows the best classification performance by detecting unique features better than other methods. The algorithm can be implemented with the R‐package shoeprintr.

Comments

This article is published as Park, Soyoung, and Alicia Carriquiry. "An algorithm to compare two‐dimensional footwear outsole images using maximum cliques and speeded‐up robust feature." Statistical Analysis and Data Mining: The ASA Data Science Journal (2020). Posted with permission of CSAFE.

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

Copyright Owner

The Authors

Language

en

File Format

application/pdf

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