Sexing white 2D footprints using convolutional neural networks.

Footprints are left, or obtained, in a variety of scenarios from crime scenes to anthropological investigations. Determining the sex of a footprint can be useful in screening such impressions and attempts have been made to do so using single or multi landmark distances, shape analyses and via the de...

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Autores principales:	Marcin Budka, Matthew R Bennett, Sally C Reynolds, Shelby Barefoot, Sarah Reel, Selina Reidy, Jeremy Walker
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/b9eeca9929ad4e79b38ee8721fea0841
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id	oai:doaj.org-article:b9eeca9929ad4e79b38ee8721fea0841
record_format	dspace
spelling	oai:doaj.org-article:b9eeca9929ad4e79b38ee8721fea08412021-12-02T20:17:52ZSexing white 2D footprints using convolutional neural networks.1932-620310.1371/journal.pone.0255630https://doaj.org/article/b9eeca9929ad4e79b38ee8721fea08412021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0255630https://doaj.org/toc/1932-6203Footprints are left, or obtained, in a variety of scenarios from crime scenes to anthropological investigations. Determining the sex of a footprint can be useful in screening such impressions and attempts have been made to do so using single or multi landmark distances, shape analyses and via the density of friction ridges. Here we explore the relative importance of different components in sexing two-dimensional foot impressions namely, size, shape and texture. We use a machine learning approach and compare this to more traditional methods of discrimination. Two datasets are used, a pilot data set collected from students at Bournemouth University (N = 196) and a larger data set collected by podiatrists at Sheffield NHS Teaching Hospital (N = 2677). Our convolutional neural network can sex a footprint with accuracy of around 90% on a test set of N = 267 footprint images using all image components, which is better than an expert can achieve. However, the quality of the impressions impacts on this success rate, but the results are promising and in time it may be possible to create an automated screening algorithm in which practitioners of whatever sort (medical or forensic) can obtain a first order sexing of a two-dimensional footprint.Marcin BudkaMatthew R BennettSally C ReynoldsShelby BarefootSarah ReelSelina ReidyJeremy WalkerPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 8, p e0255630 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Marcin Budka Matthew R Bennett Sally C Reynolds Shelby Barefoot Sarah Reel Selina Reidy Jeremy Walker Sexing white 2D footprints using convolutional neural networks.
description	Footprints are left, or obtained, in a variety of scenarios from crime scenes to anthropological investigations. Determining the sex of a footprint can be useful in screening such impressions and attempts have been made to do so using single or multi landmark distances, shape analyses and via the density of friction ridges. Here we explore the relative importance of different components in sexing two-dimensional foot impressions namely, size, shape and texture. We use a machine learning approach and compare this to more traditional methods of discrimination. Two datasets are used, a pilot data set collected from students at Bournemouth University (N = 196) and a larger data set collected by podiatrists at Sheffield NHS Teaching Hospital (N = 2677). Our convolutional neural network can sex a footprint with accuracy of around 90% on a test set of N = 267 footprint images using all image components, which is better than an expert can achieve. However, the quality of the impressions impacts on this success rate, but the results are promising and in time it may be possible to create an automated screening algorithm in which practitioners of whatever sort (medical or forensic) can obtain a first order sexing of a two-dimensional footprint.
format	article
author	Marcin Budka Matthew R Bennett Sally C Reynolds Shelby Barefoot Sarah Reel Selina Reidy Jeremy Walker
author_facet	Marcin Budka Matthew R Bennett Sally C Reynolds Shelby Barefoot Sarah Reel Selina Reidy Jeremy Walker
author_sort	Marcin Budka
title	Sexing white 2D footprints using convolutional neural networks.
title_short	Sexing white 2D footprints using convolutional neural networks.
title_full	Sexing white 2D footprints using convolutional neural networks.
title_fullStr	Sexing white 2D footprints using convolutional neural networks.
title_full_unstemmed	Sexing white 2D footprints using convolutional neural networks.
title_sort	sexing white 2d footprints using convolutional neural networks.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/b9eeca9929ad4e79b38ee8721fea0841
work_keys_str_mv	AT marcinbudka sexingwhite2dfootprintsusingconvolutionalneuralnetworks AT matthewrbennett sexingwhite2dfootprintsusingconvolutionalneuralnetworks AT sallycreynolds sexingwhite2dfootprintsusingconvolutionalneuralnetworks AT shelbybarefoot sexingwhite2dfootprintsusingconvolutionalneuralnetworks AT sarahreel sexingwhite2dfootprintsusingconvolutionalneuralnetworks AT selinareidy sexingwhite2dfootprintsusingconvolutionalneuralnetworks AT jeremywalker sexingwhite2dfootprintsusingconvolutionalneuralnetworks
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Sexing white 2D footprints using convolutional neural networks.

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