Modelling wear patterns on footwear outsoles
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Authors
Francis, Xavier S.
Author ORCID Profiles (clickable)
Degree
Master of Computing
Grantor
Unitec Institute of Technology
Date
2019
Supervisors
Sharifzadeh, Hamid
Newton, Angus
Baghaei, Nilufar
Newton, Angus
Baghaei, Nilufar
Type
Masters Thesis
Ngā Upoko Tukutuku (Māori subject headings)
Keyword
forensic podiatry
outsoles
footwear outsoles
soles
criminal identification
crime suspects
shoeprints
computational forensics
modelling
outsoles
footwear outsoles
soles
criminal identification
crime suspects
shoeprints
computational forensics
modelling
ANZSRC Field of Research Code (2020)
Citation
Francis, X. S. (2019). Modelling wear patterns on footwear outsoles (Unpublished document submitted in partial fulfilment of the requirements for the degree of Master of Computing). Unitec Institute of Technology, Auckland, New Zealand. Retrieved from https://hdl.handle.net/10652/4632
Abstract
ABSTRACT:
The outsoles of footwear develop nicks, cuts, and tears via repeated exposure to the abrasive forces that occur between the outsole and the ground. These abrasions result in the formation of characteristics unique to the outsole and the individual wearing them; additionally resulting in the degradation of the outsole design imprinted by the manufacturer. The combination of these characteristics allow the forensic scientist to uniquely identify the individual to whom it belongs. Quite often a period of time can elapse between the discovery of a shoeprint at the crime scene and the identification of a suspect. In these instances, the forensic scientist must rely on their training and expertise—developed through years of experience and study—to determine if the crime scene shoeprint matches the out-sole of the suspect’s shoe.
This work introduces a computational framework capable of modelling wear patterns on the out-soles of footwear. This model is able to predict the evolution of the wear pattern after an arbitrary time period given in weeks. We introduce an additional model capable of reconstructing the outsole back to its original state on a given week. This framework—built on convolutional neural networks—provides an objective point of reference for forensic scientists in their evaluation of outsole wear patterns.
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