Ear Detection and Localization with Convolutional Neural Networks in Natural Images and Videos.

[EN]The difficulty in precisely detecting and locating an ear within an image is the first step to tackle in an ear-based biometric recognition system, a challenge which increases in difficulty when working with variable photographic conditions. This is in part due to the irregular shapes of human e...

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Detalles Bibliográficos
Autores: Raveane, William, Galdámez, Pedro Luis, González Arrieta, María Angélica
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2019
País:España
Institución:Universidad de Salamanca (USAL)
Repositorio:GREDOS. Repositorio Institucional de la Universidad de Salamanca
OAI Identifier:oai:gredos.usal.es:10366/157155
Acceso en línea:http://hdl.handle.net/10366/157155
Access Level:acceso abierto
Palabra clave:Ear detection
Computer vision
Convolutional neural network
Image recognition
Video analysis
1203.17 Informática
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repository_id_str
spelling Ear Detection and Localization with Convolutional Neural Networks in Natural Images and Videos.Raveane, WilliamGaldámez, Pedro LuisGonzález Arrieta, María AngélicaEar detectionComputer visionConvolutional neural networkImage recognitionVideo analysis1203.17 Informática[EN]The difficulty in precisely detecting and locating an ear within an image is the first step to tackle in an ear-based biometric recognition system, a challenge which increases in difficulty when working with variable photographic conditions. This is in part due to the irregular shapes of human ears, but also because of variable lighting conditions and the ever changing profile shape of an ear’s projection when photographed. An ear detection system involving multiple convolutional neural networks and a detection grouping algorithm is proposed to identify the presence and location of an ear in a given input image. The proposed method atches the performance of other methods when analyzed against clean and purpose-shot photographs, reaching an accuracy of upwards of 98%, but clearly outperforms them with a rate of over 86% when the system is subjected to non-cooperative natural images where the subject appears in challenging orientations and photographic conditions.202420242019info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10366/157155reponame:GREDOS. Repositorio Institucional de la Universidad de Salamancainstname:Universidad de Salamanca (USAL)InglésCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccessoai:gredos.usal.es:10366/1571552026-06-07T06:28:51Z
dc.title.none.fl_str_mv Ear Detection and Localization with Convolutional Neural Networks in Natural Images and Videos.
title Ear Detection and Localization with Convolutional Neural Networks in Natural Images and Videos.
spellingShingle Ear Detection and Localization with Convolutional Neural Networks in Natural Images and Videos.
Raveane, William
Ear detection
Computer vision
Convolutional neural network
Image recognition
Video analysis
1203.17 Informática
title_short Ear Detection and Localization with Convolutional Neural Networks in Natural Images and Videos.
title_full Ear Detection and Localization with Convolutional Neural Networks in Natural Images and Videos.
title_fullStr Ear Detection and Localization with Convolutional Neural Networks in Natural Images and Videos.
title_full_unstemmed Ear Detection and Localization with Convolutional Neural Networks in Natural Images and Videos.
title_sort Ear Detection and Localization with Convolutional Neural Networks in Natural Images and Videos.
dc.creator.none.fl_str_mv Raveane, William
Galdámez, Pedro Luis
González Arrieta, María Angélica
author Raveane, William
author_facet Raveane, William
Galdámez, Pedro Luis
González Arrieta, María Angélica
author_role author
author2 Galdámez, Pedro Luis
González Arrieta, María Angélica
author2_role author
author
dc.subject.none.fl_str_mv Ear detection
Computer vision
Convolutional neural network
Image recognition
Video analysis
1203.17 Informática
topic Ear detection
Computer vision
Convolutional neural network
Image recognition
Video analysis
1203.17 Informática
description [EN]The difficulty in precisely detecting and locating an ear within an image is the first step to tackle in an ear-based biometric recognition system, a challenge which increases in difficulty when working with variable photographic conditions. This is in part due to the irregular shapes of human ears, but also because of variable lighting conditions and the ever changing profile shape of an ear’s projection when photographed. An ear detection system involving multiple convolutional neural networks and a detection grouping algorithm is proposed to identify the presence and location of an ear in a given input image. The proposed method atches the performance of other methods when analyzed against clean and purpose-shot photographs, reaching an accuracy of upwards of 98%, but clearly outperforms them with a rate of over 86% when the system is subjected to non-cooperative natural images where the subject appears in challenging orientations and photographic conditions.
publishDate 2019
dc.date.none.fl_str_mv 2019
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10366/157155
url http://hdl.handle.net/10366/157155
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv CC0 1.0 Universal
http://creativecommons.org/publicdomain/zero/1.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv CC0 1.0 Universal
http://creativecommons.org/publicdomain/zero/1.0/
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv reponame:GREDOS. Repositorio Institucional de la Universidad de Salamanca
instname:Universidad de Salamanca (USAL)
instname_str Universidad de Salamanca (USAL)
reponame_str GREDOS. Repositorio Institucional de la Universidad de Salamanca
collection GREDOS. Repositorio Institucional de la Universidad de Salamanca
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300719

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