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dc.creatorGalar Idoate, Mikeles_ES
dc.creatorDerrac, Joaquínes_ES
dc.creatorPeralta, Danieles_ES
dc.creatorTriguero, Isaaces_ES
dc.creatorPaternain Dallo, Danieles_ES
dc.creatorLópez Molina, Carloses_ES
dc.creatorGarcía, Salvadores_ES
dc.creatorBenítez, José Manueles_ES
dc.creatorPagola Barrio, Migueles_ES
dc.creatorBarrenechea Tartas, Edurnees_ES
dc.creatorBustince Sola, Humbertoes_ES
dc.creatorHerrera, Franciscoes_ES
dc.date.accessioned2015-07-23T16:36:23Z
dc.date.available2017-02-23T00:00:15Z
dc.date.issued2015
dc.identifier.issn0950-7051
dc.identifier.urihttps://hdl.handle.net/2454/17644
dc.description.abstractIn the first part of this paper we reviewed the fingerprint classification literature from two different perspectives: the feature extraction and the classifier learning. Aiming at answering the question of which among the reviewed methods would perform better in a real implementation we end up in a discussion which showed the difficulty in answering this question. No previous comparison exists in the literature and comparisons among papers are done with different experimental frameworks. Moreover, the difficulty in implementing published methods was stated due to the lack of details in their description, parameters and the fact that no source code is shared. For this reason, in this paper we will go through a deep experimental study following the proposed double perspective. In order to do so, we have carefully implemented some of the most relevant feature extraction methods according to the explanations found in the corresponding papers and we have tested their performance with different classifiers, including those specific proposals made by the authors. Our aim is to develop an objective experimental study in a common framework, which has not been done before and which can serve as a baseline for future works on the topic. This way, we will not only test their quality, but their reusability by other researchers and will be able to indicate which proposals could be considered for future developments. Furthermore, we will show that combining different feature extraction models in an ensemble can lead to a superior performance, significantly increasing the results obtained by individual models.en
dc.description.sponsorshipThis work was supported by the Research Projects CAB(CDTI), TIN2011-28488, and TIN2013-40765-P.en
dc.format.mimetypeapplication/pdfen
dc.language.isoengen
dc.publisherElsevieren
dc.relation.ispartofKnowledge-Based Systems 81 (2015) 98–116en
dc.rights© 2015 Elsevier B.V. The manuscript version is made available under the CC BY-NC-ND 4.0 license.en
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectFingerprint classificationen
dc.subjectFeature extractionen
dc.subjectClassificationen
dc.subjectFingerprint recognitionen
dc.subjectSVMen
dc.subjectNeural networksen
dc.subjectEnsemblesen
dc.subjectOrientation mapen
dc.subjectSingular pointsen
dc.subjectExperimental evaluationen
dc.titleA survey of fingerprint classification Part II: experimental analysis and ensemble proposalen
dc.typeArtículo / Artikuluaes
dc.typeinfo:eu-repo/semantics/articleen
dc.contributor.departmentUniversidad Pública de Navarra. Departamento de Automática y Computaciónes_ES
dc.contributor.departmentNafarroako Unibertsitate Publikoa. Automatika eta Konputazioa Sailaeu
dc.rights.accessRightsAcceso abierto / Sarbide irekiaes
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessen
dc.embargo.terms2017-02-23
dc.identifier.doi10.1016/j.knosys.2015.02.015
dc.relation.projectIDinfo:eu-repo/grantAgreement/ES/6PN/TIN2011-28488en
dc.relation.projectIDinfo:eu-repo/grantAgreement/ES/1PE/TIN2013-40765-Pen
dc.relation.publisherversionhttps://dx.doi.org/10.1016/j.knosys.2015.02.015
dc.type.versionVersión aceptada / Onetsi den bertsioaes
dc.type.versioninfo:eu-repo/semantics/acceptedVersionen


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© 2015 Elsevier B.V. The manuscript version is made available under the CC BY-NC-ND 4.0 license.
Except where otherwise noted, this item's license is described as © 2015 Elsevier B.V. The manuscript version is made available under the CC BY-NC-ND 4.0 license.