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...
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Format: | Article |
Language: | English |
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Asociación Española para la Inteligencia Artificial
2023-04-01
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Series: | Inteligencia Artificial |
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Online Access: | https://journal.iberamia.org/index.php/intartif/article/view/770 |
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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.
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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 |
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