A-Survey: Identification and Classification of Fingerprints via the Extreme Learning Machine Algorithm

The fingerprint comes to be the most popular and utilized biometric for identifying persons owing to its bio-invariant characteristic, precision, as well as easy acquisition. A sub-system of an identification system is the classification stage in order to diminish the penetration rate and computati...

Full description

Bibliographic Details
Main Authors: David Zabala-Blanco, Diego Martinez-Pereira, Marco Flores-Calero, Jayanta Datta, Ali Dehghan Firoozabadi
Format: Article
Language:English
Published: Asociación Española para la Inteligencia Artificial 2023-04-01
Series:Inteligencia Artificial
Subjects:
Online Access:https://journal.iberamia.org/index.php/intartif/article/view/770
_version_ 1797672204747407360
author David Zabala-Blanco
Diego Martinez-Pereira
Marco Flores-Calero
Jayanta Datta
Ali Dehghan Firoozabadi
author_facet David Zabala-Blanco
Diego Martinez-Pereira
Marco Flores-Calero
Jayanta Datta
Ali Dehghan Firoozabadi
author_sort David Zabala-Blanco
collection DOAJ
description The fingerprint comes to be the most popular and utilized biometric for identifying persons owing to its bio-invariant characteristic, precision, as well as easy acquisition. A sub-system of an identification system is the classification stage in order to diminish the penetration rate and computational complexity. Actually, there are many formal investigations regarding techniques by exploiting convolutional neural networks  (CNN)  together with fingerprint images, which have superior performance metrics at the cost of large training times even employing high-performance computing, which is not feasible in the standard world. In our manuscript,  researches about identifying and classifying fingerprint databases by recurring to extreme learning machines (ELM) will be extensively reported and discussed for the first time. The diverse methodologies (ELM plus feature extractors) given by the authors will be studied and contrasted considering performance analysis.  Consequently,  academic papers with diverse versions of ELMs are developed to observe the pros and cons that they exhibit with each other and to probe how they may help for minimizing the penetration rate of fingerprint databases.  In fact,  this issue is very relevant because enhancing the penetration rate means shorting search times and computational complexity in fingerprints.
first_indexed 2024-03-11T21:26:38Z
format Article
id doaj.art-3096e207d0e940e6b68e4fdbb2566ec5
institution Directory Open Access Journal
issn 1137-3601
1988-3064
language English
last_indexed 2024-03-11T21:26:38Z
publishDate 2023-04-01
publisher Asociación Española para la Inteligencia Artificial
record_format Article
series Inteligencia Artificial
spelling doaj.art-3096e207d0e940e6b68e4fdbb2566ec52023-09-27T22:03:04ZengAsociación Española para la Inteligencia ArtificialInteligencia Artificial1137-36011988-30642023-04-01267110.4114/intartif.vol26iss71pp75-113A-Survey: Identification and Classification of Fingerprints via the Extreme Learning Machine AlgorithmDavid Zabala-Blanco0Diego Martinez-Pereira1Marco Flores-Calero2Jayanta Datta3Ali Dehghan Firoozabadi4Universidad Católica del Maule, Talca, ChileUniversidad Católica del Maule, Talca, ChileUniversidad de las Fuerzas Armadas-ESPE, Sangolquí, EcuadorUniversidad de Chile, Santiago de Chile, ChileUniversidad Tecnológica Metropolitana, Santiago de Chile, Chile The fingerprint comes to be the most popular and utilized biometric for identifying persons owing to its bio-invariant characteristic, precision, as well as easy acquisition. A sub-system of an identification system is the classification stage in order to diminish the penetration rate and computational complexity. Actually, there are many formal investigations regarding techniques by exploiting convolutional neural networks  (CNN)  together with fingerprint images, which have superior performance metrics at the cost of large training times even employing high-performance computing, which is not feasible in the standard world. In our manuscript,  researches about identifying and classifying fingerprint databases by recurring to extreme learning machines (ELM) will be extensively reported and discussed for the first time. The diverse methodologies (ELM plus feature extractors) given by the authors will be studied and contrasted considering performance analysis.  Consequently,  academic papers with diverse versions of ELMs are developed to observe the pros and cons that they exhibit with each other and to probe how they may help for minimizing the penetration rate of fingerprint databases.  In fact,  this issue is very relevant because enhancing the penetration rate means shorting search times and computational complexity in fingerprints. https://journal.iberamia.org/index.php/intartif/article/view/770Extreme learning machinesFingerprint databasesIdentification systemClassification subsystem
spellingShingle David Zabala-Blanco
Diego Martinez-Pereira
Marco Flores-Calero
Jayanta Datta
Ali Dehghan Firoozabadi
A-Survey: Identification and Classification of Fingerprints via the Extreme Learning Machine Algorithm
Inteligencia Artificial
Extreme learning machines
Fingerprint databases
Identification system
Classification subsystem
title A-Survey: Identification and Classification of Fingerprints via the Extreme Learning Machine Algorithm
title_full A-Survey: Identification and Classification of Fingerprints via the Extreme Learning Machine Algorithm
title_fullStr A-Survey: Identification and Classification of Fingerprints via the Extreme Learning Machine Algorithm
title_full_unstemmed A-Survey: Identification and Classification of Fingerprints via the Extreme Learning Machine Algorithm
title_short A-Survey: Identification and Classification of Fingerprints via the Extreme Learning Machine Algorithm
title_sort a survey identification and classification of fingerprints via the extreme learning machine algorithm
topic Extreme learning machines
Fingerprint databases
Identification system
Classification subsystem
url https://journal.iberamia.org/index.php/intartif/article/view/770
work_keys_str_mv AT davidzabalablanco asurveyidentificationandclassificationoffingerprintsviatheextremelearningmachinealgorithm
AT diegomartinezpereira asurveyidentificationandclassificationoffingerprintsviatheextremelearningmachinealgorithm
AT marcoflorescalero asurveyidentificationandclassificationoffingerprintsviatheextremelearningmachinealgorithm
AT jayantadatta asurveyidentificationandclassificationoffingerprintsviatheextremelearningmachinealgorithm
AT alidehghanfiroozabadi asurveyidentificationandclassificationoffingerprintsviatheextremelearningmachinealgorithm