Minutiae Detection: An Image Exploring Agent-Based Model
- Autores
- Unanue, Ariel; Zapico, Adriana
- Año de publicación
- 2002
- Idioma
- inglés
- Tipo de recurso
- documento de conferencia
- Estado
- versión publicada
- Descripción
- Identification through fingerprints is one of the most precise methods to determine a person’s identity. For years, this task has been carried out manually, but, due to the great number of fingerprints involved in the comparison, the implementation of an Automated Fingerprint Identification System (A.F.I.S.) emerged as a necessity. A critical process in automated fingerprint identification is minutiae extraction, process in which the presence of image noise or filth will generate undesired spikes and breaks that will lead to a considerable number of spurious minutiae. To overcome this problem, before minutiae detection, a usual process applied is a post-processing of the thinned image or pruning step, being the latter not always successful. In this paper, we introduce a new minutiae detection method, without the need of a pruning step, based on image exploring agents. First, reactive agents are used over the thinned fingerprint image, that detect locations of interest that could be minutiae using an efficient coefficient, which we propose, and to our knowledge, has not been applied before for minutiae detection. Then, several agents run through the image, starting at the locations of interest, to determine whether they are real minutiae or not. Having a more reliable method for minutiae detection would lead into a better performance of the whole recognition system.
Sociedad Argentina de Informática e Investigación Operativa - Materia
-
Ciencias Informáticas
minutiae detection
image exploring agents - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- http://creativecommons.org/licenses/by-nc-sa/4.0/
- Repositorio
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/183076
Ver los metadatos del registro completo
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Minutiae Detection: An Image Exploring Agent-Based ModelUnanue, ArielZapico, AdrianaCiencias Informáticasminutiae detectionimage exploring agentsIdentification through fingerprints is one of the most precise methods to determine a person’s identity. For years, this task has been carried out manually, but, due to the great number of fingerprints involved in the comparison, the implementation of an Automated Fingerprint Identification System (A.F.I.S.) emerged as a necessity. A critical process in automated fingerprint identification is minutiae extraction, process in which the presence of image noise or filth will generate undesired spikes and breaks that will lead to a considerable number of spurious minutiae. To overcome this problem, before minutiae detection, a usual process applied is a post-processing of the thinned image or pruning step, being the latter not always successful. In this paper, we introduce a new minutiae detection method, without the need of a pruning step, based on image exploring agents. First, reactive agents are used over the thinned fingerprint image, that detect locations of interest that could be minutiae using an efficient coefficient, which we propose, and to our knowledge, has not been applied before for minutiae detection. Then, several agents run through the image, starting at the locations of interest, to determine whether they are real minutiae or not. Having a more reliable method for minutiae detection would lead into a better performance of the whole recognition system.Sociedad Argentina de Informática e Investigación Operativa2002info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf37-49http://sedici.unlp.edu.ar/handle/10915/183076enginfo:eu-repo/semantics/altIdentifier/issn/1660-1079info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T11:50:02Zoai:sedici.unlp.edu.ar:10915/183076Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:50:03.03SEDICI (UNLP) - Universidad Nacional de La Platafalse |
dc.title.none.fl_str_mv |
Minutiae Detection: An Image Exploring Agent-Based Model |
title |
Minutiae Detection: An Image Exploring Agent-Based Model |
spellingShingle |
Minutiae Detection: An Image Exploring Agent-Based Model Unanue, Ariel Ciencias Informáticas minutiae detection image exploring agents |
title_short |
Minutiae Detection: An Image Exploring Agent-Based Model |
title_full |
Minutiae Detection: An Image Exploring Agent-Based Model |
title_fullStr |
Minutiae Detection: An Image Exploring Agent-Based Model |
title_full_unstemmed |
Minutiae Detection: An Image Exploring Agent-Based Model |
title_sort |
Minutiae Detection: An Image Exploring Agent-Based Model |
dc.creator.none.fl_str_mv |
Unanue, Ariel Zapico, Adriana |
author |
Unanue, Ariel |
author_facet |
Unanue, Ariel Zapico, Adriana |
author_role |
author |
author2 |
Zapico, Adriana |
author2_role |
author |
dc.subject.none.fl_str_mv |
Ciencias Informáticas minutiae detection image exploring agents |
topic |
Ciencias Informáticas minutiae detection image exploring agents |
dc.description.none.fl_txt_mv |
Identification through fingerprints is one of the most precise methods to determine a person’s identity. For years, this task has been carried out manually, but, due to the great number of fingerprints involved in the comparison, the implementation of an Automated Fingerprint Identification System (A.F.I.S.) emerged as a necessity. A critical process in automated fingerprint identification is minutiae extraction, process in which the presence of image noise or filth will generate undesired spikes and breaks that will lead to a considerable number of spurious minutiae. To overcome this problem, before minutiae detection, a usual process applied is a post-processing of the thinned image or pruning step, being the latter not always successful. In this paper, we introduce a new minutiae detection method, without the need of a pruning step, based on image exploring agents. First, reactive agents are used over the thinned fingerprint image, that detect locations of interest that could be minutiae using an efficient coefficient, which we propose, and to our knowledge, has not been applied before for minutiae detection. Then, several agents run through the image, starting at the locations of interest, to determine whether they are real minutiae or not. Having a more reliable method for minutiae detection would lead into a better performance of the whole recognition system. Sociedad Argentina de Informática e Investigación Operativa |
description |
Identification through fingerprints is one of the most precise methods to determine a person’s identity. For years, this task has been carried out manually, but, due to the great number of fingerprints involved in the comparison, the implementation of an Automated Fingerprint Identification System (A.F.I.S.) emerged as a necessity. A critical process in automated fingerprint identification is minutiae extraction, process in which the presence of image noise or filth will generate undesired spikes and breaks that will lead to a considerable number of spurious minutiae. To overcome this problem, before minutiae detection, a usual process applied is a post-processing of the thinned image or pruning step, being the latter not always successful. In this paper, we introduce a new minutiae detection method, without the need of a pruning step, based on image exploring agents. First, reactive agents are used over the thinned fingerprint image, that detect locations of interest that could be minutiae using an efficient coefficient, which we propose, and to our knowledge, has not been applied before for minutiae detection. Then, several agents run through the image, starting at the locations of interest, to determine whether they are real minutiae or not. Having a more reliable method for minutiae detection would lead into a better performance of the whole recognition system. |
publishDate |
2002 |
dc.date.none.fl_str_mv |
2002 |
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/10915/183076 |
url |
http://sedici.unlp.edu.ar/handle/10915/183076 |
dc.language.none.fl_str_mv |
eng |
language |
eng |
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info:eu-repo/semantics/altIdentifier/issn/1660-1079 |
dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) |
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openAccess |
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http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) |
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application/pdf 37-49 |
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SEDICI (UNLP) - Universidad Nacional de La Plata |
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alira@sedici.unlp.edu.ar |
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13.070432 |