Automatic ear detection and feature extraction using Geometric Morphometrics and Convolutional Neural Networks

Autores
Cintas, Celia; Quinto Sanchez, Mirsha Emmanuel; Acuña,Victor; Paschetta, Carolina Andrea; de Azevedo, Soledad; Silva de Cerqueira, Caio Cesar; Ramallo, Virginia; Gallo, Carla; Poletti, Giovanni; Bortolini, Maria Catira; Canizales Quinteros, Samuel; Rothhammer, Francisco; Bedoya, Gabriel; Ruiz Linares, Andres; González José, Rolando; Delrieux, Claudio Augusto
Año de publicación
2017
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Accurate gathering of phenotypic information is a key aspect in several subject matters, including biometrics, biomedical analysis, forensics, and many other. Automatic identification of anatomical structures of biometric interest, such as fingerprints, iris patterns, or facial traits, are extensively used in applications like access control and anthropological research, all having in common the drawback of requiring intrusive means for acquiring the required information. In this regard, the ear structure has multiple advantages. Not only the ear´s biometric markers can be easily captured from the distance with non intrusive methods, but also they experiment almost no changes over time, and are not influenced by facial expressions. Here we present a new method based on Geometric Morphometrics and Deep Learning for automatic ear detection and feature extraction in the form of landmarks. A convolutional neural network was trained with a set of manually landmarked examples. The network is able to provide morphometric landmarks on ears´ images automatically, with a performance that matches human landmarking. The feasibility of using ear landmarks as feature vectors opens a novel spectrum of biometrics applications.
Fil: Cintas, Celia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico; Argentina
Fil: Quinto Sanchez, Mirsha Emmanuel. Universidad Nacional Autónoma de México; México. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Acuña,Victor. University College London; Estados Unidos
Fil: Paschetta, Carolina Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico; Argentina
Fil: de Azevedo, Soledad. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico; Argentina
Fil: Silva de Cerqueira, Caio Cesar. Estado de São Paulo. Superintendência da Polícia Técnico-Científica; Brasil
Fil: Ramallo, Virginia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico; Argentina
Fil: Gallo, Carla. Universidad Peruana Cayetano Heredia; Perú
Fil: Poletti, Giovanni. Universidad Peruana Cayetano Heredia; Perú
Fil: Bortolini, Maria Catira. Universidade Federal do Rio Grande do Sul; Brasil
Fil: Canizales Quinteros, Samuel. Universidad Nacional Autónoma de México; México
Fil: Rothhammer, Francisco. Universidad de Tarapacá; Chile
Fil: Bedoya, Gabriel. Universidad de Antioquia; Colombia
Fil: Ruiz Linares, Andres. Fudan University; China. Aix Marseille Université; Francia
Fil: González José, Rolando. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico; Argentina
Fil: Delrieux, Claudio Augusto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto Argentino de Oceanografía. Universidad Nacional del Sur. Instituto Argentino de Oceanografía; Argentina. Universidad Nacional del Sur; Argentina
Materia
Morfometria Geometrica
Deep Learning
Landmarks
Biometrics
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/39534
Acceder
id CONICETDig_5ca05781bf4869ac67ed73e7ecee943a
oai_identifier_str oai:ri.conicet.gov.ar:11336/39534
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Automatic ear detection and feature extraction using Geometric Morphometrics and Convolutional Neural NetworksCintas, CeliaQuinto Sanchez, Mirsha EmmanuelAcuña,VictorPaschetta, Carolina Andreade Azevedo, SoledadSilva de Cerqueira, Caio CesarRamallo, VirginiaGallo, CarlaPoletti, GiovanniBortolini, Maria CatiraCanizales Quinteros, SamuelRothhammer, FranciscoBedoya, GabrielRuiz Linares, AndresGonzález José, RolandoDelrieux, Claudio AugustoMorfometria GeometricaDeep LearningLandmarksBiometricshttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Accurate gathering of phenotypic information is a key aspect in several subject matters, including biometrics, biomedical analysis, forensics, and many other. Automatic identification of anatomical structures of biometric interest, such as fingerprints, iris patterns, or facial traits, are extensively used in applications like access control and anthropological research, all having in common the drawback of requiring intrusive means for acquiring the required information. In this regard, the ear structure has multiple advantages. Not only the ear´s biometric markers can be easily captured from the distance with non intrusive methods, but also they experiment almost no changes over time, and are not influenced by facial expressions. Here we present a new method based on Geometric Morphometrics and Deep Learning for automatic ear detection and feature extraction in the form of landmarks. A convolutional neural network was trained with a set of manually landmarked examples. The network is able to provide morphometric landmarks on ears´ images automatically, with a performance that matches human landmarking. The feasibility of using ear landmarks as feature vectors opens a novel spectrum of biometrics applications.Fil: Cintas, Celia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico; ArgentinaFil: Quinto Sanchez, Mirsha Emmanuel. Universidad Nacional Autónoma de México; México. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Acuña,Victor. University College London; Estados UnidosFil: Paschetta, Carolina Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico; ArgentinaFil: de Azevedo, Soledad. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico; ArgentinaFil: Silva de Cerqueira, Caio Cesar. Estado de São Paulo. Superintendência da Polícia Técnico-Científica; BrasilFil: Ramallo, Virginia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico; ArgentinaFil: Gallo, Carla. Universidad Peruana Cayetano Heredia; PerúFil: Poletti, Giovanni. Universidad Peruana Cayetano Heredia; PerúFil: Bortolini, Maria Catira. Universidade Federal do Rio Grande do Sul; BrasilFil: Canizales Quinteros, Samuel. Universidad Nacional Autónoma de México; MéxicoFil: Rothhammer, Francisco. Universidad de Tarapacá; ChileFil: Bedoya, Gabriel. Universidad de Antioquia; ColombiaFil: Ruiz Linares, Andres. Fudan University; China. Aix Marseille Université; FranciaFil: González José, Rolando. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico; ArgentinaFil: Delrieux, Claudio Augusto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto Argentino de Oceanografía. Universidad Nacional del Sur. Instituto Argentino de Oceanografía; Argentina. Universidad Nacional del Sur; ArgentinaThe Institution of Engineering and Technology2017-05info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/39534Cintas, Celia; Quinto Sanchez, Mirsha Emmanuel; Acuña,Victor; Paschetta, Carolina Andrea; de Azevedo, Soledad; et al.; Automatic ear detection and feature extraction using Geometric Morphometrics and Convolutional Neural Networks; The Institution of Engineering and Technology; IET Biometrics; 6; 3; 5-2017; 211-2232047-49382047-4946CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/ 10.1049/iet-bmt.2016.0002info:eu-repo/semantics/altIdentifier/url/http://ieeexplore.ieee.org/document/7898901/info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T14:37:48Zoai:ri.conicet.gov.ar:11336/39534instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-15 14:37:49.139CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Automatic ear detection and feature extraction using Geometric Morphometrics and Convolutional Neural Networks
title Automatic ear detection and feature extraction using Geometric Morphometrics and Convolutional Neural Networks
spellingShingle Automatic ear detection and feature extraction using Geometric Morphometrics and Convolutional Neural Networks
Cintas, Celia
Morfometria Geometrica
Deep Learning
Landmarks
Biometrics
title_short Automatic ear detection and feature extraction using Geometric Morphometrics and Convolutional Neural Networks
title_full Automatic ear detection and feature extraction using Geometric Morphometrics and Convolutional Neural Networks
title_fullStr Automatic ear detection and feature extraction using Geometric Morphometrics and Convolutional Neural Networks
title_full_unstemmed Automatic ear detection and feature extraction using Geometric Morphometrics and Convolutional Neural Networks
title_sort Automatic ear detection and feature extraction using Geometric Morphometrics and Convolutional Neural Networks
dc.creator.none.fl_str_mv Cintas, Celia
Quinto Sanchez, Mirsha Emmanuel
Acuña,Victor
Paschetta, Carolina Andrea
de Azevedo, Soledad
Silva de Cerqueira, Caio Cesar
Ramallo, Virginia
Gallo, Carla
Poletti, Giovanni
Bortolini, Maria Catira
Canizales Quinteros, Samuel
Rothhammer, Francisco
Bedoya, Gabriel
Ruiz Linares, Andres
González José, Rolando
Delrieux, Claudio Augusto
author Cintas, Celia
author_facet Cintas, Celia
Quinto Sanchez, Mirsha Emmanuel
Acuña,Victor
Paschetta, Carolina Andrea
de Azevedo, Soledad
Silva de Cerqueira, Caio Cesar
Ramallo, Virginia
Gallo, Carla
Poletti, Giovanni
Bortolini, Maria Catira
Canizales Quinteros, Samuel
Rothhammer, Francisco
Bedoya, Gabriel
Ruiz Linares, Andres
González José, Rolando
Delrieux, Claudio Augusto
author_role author
author2 Quinto Sanchez, Mirsha Emmanuel
Acuña,Victor
Paschetta, Carolina Andrea
de Azevedo, Soledad
Silva de Cerqueira, Caio Cesar
Ramallo, Virginia
Gallo, Carla
Poletti, Giovanni
Bortolini, Maria Catira
Canizales Quinteros, Samuel
Rothhammer, Francisco
Bedoya, Gabriel
Ruiz Linares, Andres
González José, Rolando
Delrieux, Claudio Augusto
author2_role author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Morfometria Geometrica
Deep Learning
Landmarks
Biometrics
topic Morfometria Geometrica
Deep Learning
Landmarks
Biometrics
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Accurate gathering of phenotypic information is a key aspect in several subject matters, including biometrics, biomedical analysis, forensics, and many other. Automatic identification of anatomical structures of biometric interest, such as fingerprints, iris patterns, or facial traits, are extensively used in applications like access control and anthropological research, all having in common the drawback of requiring intrusive means for acquiring the required information. In this regard, the ear structure has multiple advantages. Not only the ear´s biometric markers can be easily captured from the distance with non intrusive methods, but also they experiment almost no changes over time, and are not influenced by facial expressions. Here we present a new method based on Geometric Morphometrics and Deep Learning for automatic ear detection and feature extraction in the form of landmarks. A convolutional neural network was trained with a set of manually landmarked examples. The network is able to provide morphometric landmarks on ears´ images automatically, with a performance that matches human landmarking. The feasibility of using ear landmarks as feature vectors opens a novel spectrum of biometrics applications.
Fil: Cintas, Celia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico; Argentina
Fil: Quinto Sanchez, Mirsha Emmanuel. Universidad Nacional Autónoma de México; México. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Acuña,Victor. University College London; Estados Unidos
Fil: Paschetta, Carolina Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico; Argentina
Fil: de Azevedo, Soledad. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico; Argentina
Fil: Silva de Cerqueira, Caio Cesar. Estado de São Paulo. Superintendência da Polícia Técnico-Científica; Brasil
Fil: Ramallo, Virginia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico; Argentina
Fil: Gallo, Carla. Universidad Peruana Cayetano Heredia; Perú
Fil: Poletti, Giovanni. Universidad Peruana Cayetano Heredia; Perú
Fil: Bortolini, Maria Catira. Universidade Federal do Rio Grande do Sul; Brasil
Fil: Canizales Quinteros, Samuel. Universidad Nacional Autónoma de México; México
Fil: Rothhammer, Francisco. Universidad de Tarapacá; Chile
Fil: Bedoya, Gabriel. Universidad de Antioquia; Colombia
Fil: Ruiz Linares, Andres. Fudan University; China. Aix Marseille Université; Francia
Fil: González José, Rolando. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico; Argentina
Fil: Delrieux, Claudio Augusto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto Argentino de Oceanografía. Universidad Nacional del Sur. Instituto Argentino de Oceanografía; Argentina. Universidad Nacional del Sur; Argentina
description Accurate gathering of phenotypic information is a key aspect in several subject matters, including biometrics, biomedical analysis, forensics, and many other. Automatic identification of anatomical structures of biometric interest, such as fingerprints, iris patterns, or facial traits, are extensively used in applications like access control and anthropological research, all having in common the drawback of requiring intrusive means for acquiring the required information. In this regard, the ear structure has multiple advantages. Not only the ear´s biometric markers can be easily captured from the distance with non intrusive methods, but also they experiment almost no changes over time, and are not influenced by facial expressions. Here we present a new method based on Geometric Morphometrics and Deep Learning for automatic ear detection and feature extraction in the form of landmarks. A convolutional neural network was trained with a set of manually landmarked examples. The network is able to provide morphometric landmarks on ears´ images automatically, with a performance that matches human landmarking. The feasibility of using ear landmarks as feature vectors opens a novel spectrum of biometrics applications.
publishDate 2017
dc.date.none.fl_str_mv 2017-05
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/39534
Cintas, Celia; Quinto Sanchez, Mirsha Emmanuel; Acuña,Victor; Paschetta, Carolina Andrea; de Azevedo, Soledad; et al.; Automatic ear detection and feature extraction using Geometric Morphometrics and Convolutional Neural Networks; The Institution of Engineering and Technology; IET Biometrics; 6; 3; 5-2017; 211-223
2047-4938
2047-4946
CONICET Digital
CONICET
url http://hdl.handle.net/11336/39534
identifier_str_mv Cintas, Celia; Quinto Sanchez, Mirsha Emmanuel; Acuña,Victor; Paschetta, Carolina Andrea; de Azevedo, Soledad; et al.; Automatic ear detection and feature extraction using Geometric Morphometrics and Convolutional Neural Networks; The Institution of Engineering and Technology; IET Biometrics; 6; 3; 5-2017; 211-223
2047-4938
2047-4946
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/ 10.1049/iet-bmt.2016.0002
info:eu-repo/semantics/altIdentifier/url/http://ieeexplore.ieee.org/document/7898901/
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv The Institution of Engineering and Technology
publisher.none.fl_str_mv The Institution of Engineering and Technology
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.22299

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