Fingerprint classification with the extreme learning machine algorithm for multilayer perceptron

dc.creatorZabala-Blanco, David
dc.creatorQuinteros, Axel
dc.creatorMora, Marco
dc.creatorHernández-García, Ruber
dc.creatorFlores-Calero, Marco
dc.date2023-03-08T13:36:55Z
dc.date2023-03-08T13:36:55Z
dc.date2022
dc.date.accessioned2024-05-02T20:30:39Z
dc.date.available2024-05-02T20:30:39Z
dc.identifierhttp://repositorio.ucm.cl/handle/ucm/4497
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/9274741
dc.descriptionFingerprint classification comes to be a relevant guarantee for efficient as well as accurate fingerprint identification, in particular in the case of dealing with one-to-many fingerprint identification. Nevertheless, owing to massive intraclass variability, insignificant inter-class variability, and perturbations, the current fingerprint classification methods still need to enhance the accuracy without increasing the computational cost. In this paper, we introduce a novel method that combines the best extractor of features reported in the literature (Hong08) with multilayer extreme learning machines to maintain the superior classification capability (more than 90%) by simplifying the training time (feasibility for realization in a commercial firmware).
dc.languageen
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 Chile
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/3.0/cl/
dc.sourceInternational Conference on Automation/XXV Congress of the Chilean Association of Automatic Control (ICA-ACCA), Curicó, Chile, 1-6
dc.subjectTraining
dc.subjectExtreme learning machines
dc.subjectPerturbation methods
dc.subjectFingerprint recognition
dc.subjectMultilayer perceptrons
dc.subjectFeature extraction ,
dc.subjectNonhomogeneous media
dc.titleFingerprint classification with the extreme learning machine algorithm for multilayer perceptron
dc.typeArticle


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