A statistical sampling strategy for iris recognition

Autores
Garza Castañon, Luis E.; Morales Menéndez, Rubén; Montes de Oca, Saúl
Año de publicación
2006
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
We present a new approach for iris recognition based on a random sampling strategy. Iris recognition is a method to identify individuals, based on the analysis of the eye iris. This technique has received a great deal of attention lately, mainly due to iris unique characterics: highly randomized appearance and impossibility to alter its features. A typical iris recognition system is composed of four phases: image acquisition and preprocessing, iris localization and extraction, iris features characterization, and comparison and matching. Our work uses standard integrodifferential operators to locate the iris. Then, we process iris image with histogram equalization to compensate for illumination variations.The characterization of iris features is performed by using accumulated histograms. These histograms are built from randomly selected subimages of iris. After that, a comparison is made between accumulated histograms of couples of iris samples, and a decision is taken based on their differences and on a threshold calculated experimentally. We ran experiments with a database of 210 iris, extracted from 70 individuals, and found a rate of succesful identifications in the order of 97 %.
Applications in Artificial Intelligence - Applications
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
Identificación Biométrica
iris recognition systems
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/24248
Acceder
id SEDICI_ad716567b905994d396a056e7a1882f2
oai_identifier_str oai:sedici.unlp.edu.ar:10915/24248
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling A statistical sampling strategy for iris recognitionGarza Castañon, Luis E.Morales Menéndez, RubénMontes de Oca, SaúlCiencias InformáticasIdentificación Biométricairis recognition systemsWe present a new approach for iris recognition based on a random sampling strategy. Iris recognition is a method to identify individuals, based on the analysis of the eye iris. This technique has received a great deal of attention lately, mainly due to iris unique characterics: highly randomized appearance and impossibility to alter its features. A typical iris recognition system is composed of four phases: image acquisition and preprocessing, iris localization and extraction, iris features characterization, and comparison and matching. Our work uses standard integrodifferential operators to locate the iris. Then, we process iris image with histogram equalization to compensate for illumination variations.The characterization of iris features is performed by using accumulated histograms. These histograms are built from randomly selected subimages of iris. After that, a comparison is made between accumulated histograms of couples of iris samples, and a decision is taken based on their differences and on a threshold calculated experimentally. We ran experiments with a database of 210 iris, extracted from 70 individuals, and found a rate of succesful identifications in the order of 97 %.Applications in Artificial Intelligence - ApplicationsRed de Universidades con Carreras en Informática (RedUNCI)2006-08info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/24248enginfo:eu-repo/semantics/altIdentifier/isbn/0-387-34655-4info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/2.5/ar/Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T10:55:46Zoai:sedici.unlp.edu.ar:10915/24248Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 10:55:46.384SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv A statistical sampling strategy for iris recognition
title A statistical sampling strategy for iris recognition
spellingShingle A statistical sampling strategy for iris recognition
Garza Castañon, Luis E.
Ciencias Informáticas
Identificación Biométrica
iris recognition systems
title_short A statistical sampling strategy for iris recognition
title_full A statistical sampling strategy for iris recognition
title_fullStr A statistical sampling strategy for iris recognition
title_full_unstemmed A statistical sampling strategy for iris recognition
title_sort A statistical sampling strategy for iris recognition
dc.creator.none.fl_str_mv Garza Castañon, Luis E.
Morales Menéndez, Rubén
Montes de Oca, Saúl
author Garza Castañon, Luis E.
author_facet Garza Castañon, Luis E.
Morales Menéndez, Rubén
Montes de Oca, Saúl
author_role author
author2 Morales Menéndez, Rubén
Montes de Oca, Saúl
author2_role author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Identificación Biométrica
iris recognition systems
topic Ciencias Informáticas
Identificación Biométrica
iris recognition systems
dc.description.none.fl_txt_mv We present a new approach for iris recognition based on a random sampling strategy. Iris recognition is a method to identify individuals, based on the analysis of the eye iris. This technique has received a great deal of attention lately, mainly due to iris unique characterics: highly randomized appearance and impossibility to alter its features. A typical iris recognition system is composed of four phases: image acquisition and preprocessing, iris localization and extraction, iris features characterization, and comparison and matching. Our work uses standard integrodifferential operators to locate the iris. Then, we process iris image with histogram equalization to compensate for illumination variations.The characterization of iris features is performed by using accumulated histograms. These histograms are built from randomly selected subimages of iris. After that, a comparison is made between accumulated histograms of couples of iris samples, and a decision is taken based on their differences and on a threshold calculated experimentally. We ran experiments with a database of 210 iris, extracted from 70 individuals, and found a rate of succesful identifications in the order of 97 %.
Applications in Artificial Intelligence - Applications
Red de Universidades con Carreras en Informática (RedUNCI)
description We present a new approach for iris recognition based on a random sampling strategy. Iris recognition is a method to identify individuals, based on the analysis of the eye iris. This technique has received a great deal of attention lately, mainly due to iris unique characterics: highly randomized appearance and impossibility to alter its features. A typical iris recognition system is composed of four phases: image acquisition and preprocessing, iris localization and extraction, iris features characterization, and comparison and matching. Our work uses standard integrodifferential operators to locate the iris. Then, we process iris image with histogram equalization to compensate for illumination variations.The characterization of iris features is performed by using accumulated histograms. These histograms are built from randomly selected subimages of iris. After that, a comparison is made between accumulated histograms of couples of iris samples, and a decision is taken based on their differences and on a threshold calculated experimentally. We ran experiments with a database of 210 iris, extracted from 70 individuals, and found a rate of succesful identifications in the order of 97 %.
publishDate 2006
dc.date.none.fl_str_mv 2006-08
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
Objeto de conferencia
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/24248
url http://sedici.unlp.edu.ar/handle/10915/24248
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/isbn/0-387-34655-4
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
_version_ 1844615817541451776
score 13.069144

Publicaciones similares