Show simple item record

dc.contributor.authorFiérrez Aguilar, Julián 
dc.contributor.authorMorales Moreno, Aythami 
dc.contributor.authorVera Rodríguez, Rubén 
dc.contributor.authorCamacho, David
dc.contributor.otherUAM. Departamento de Ingeniería Informáticaes_ES
dc.contributor.otherUAM. Departamento de Tecnología Electrónica y de las Comunicacioneses_ES
dc.date.accessioned2018-08-23T14:22:20Z
dc.date.available2018-08-23T14:22:20Z
dc.date.issued2017-12-22
dc.identifier.citationInformation Fusion 44 (2018): 57-64en_US
dc.identifier.issn1566-2535es_ES
dc.identifier.urihttp://hdl.handle.net/10486/684685
dc.description.abstractWe provide an introduction to Multiple Classifier Systems (MCS) including basic nomenclature and describing key elements: classifier dependencies, type of classifier outputs, aggregation procedures, architecture, and types of methods. This introduction complements other existing overviews of MCS, as here we also review the most prevalent theoretical framework for MCS and discuss theoretical developments related to MCS The introduction to MCS is then followed by a review of the application of MCS to the particular field of multimodal biometric person authentication in the last 25 years, as a prototypical area in which MCS has resulted in important achievements. This review includes general descriptions of successful MCS methods and architectures in order to facilitate the export of them to other information fusion problems. Based on the theory and framework introduced here, in the companion paper we then develop in more technical detail recent trends and developments in MCS from multimodal biometrics that incorporate context information in an adaptive way. These new MCS architectures exploit input quality measures and pattern-specific particularities that move apart from general population statistics, resulting in robust multimodal biometric systems. Similarly as in the present paper, methods in the companion paper are introduced in a general way so they can be applied to other information fusion problems as well. Finally, also in the companion paper, we discuss open challenges in biometrics and the role of MCS to advance themen_US
dc.description.sponsorshipThis work was funded by projects CogniMetrics (TEC2015-70627-R) from MINECO/FEDER and RiskTrakc (JUST-2015-JCOO-AG-1). Part of thisthis work was conducted during a research visit of J.F. to Prof. Ludmila Kuncheva at Bangor University (UK) with STSM funding from COST CA16101 (MULTI-FORESEE)en_US
dc.format.extent27 pag.es_ES
dc.format.mimetypeapplication/pdfen
dc.language.isoengen
dc.publisherElsevier B.V.en_US
dc.relation.ispartofInformation Fusionen_US
dc.rights© 2017 Elsevier B.V.en_US
dc.subject.otherClassifieren_US
dc.subject.otherFusionen_US
dc.subject.otherBiometricsen_US
dc.subject.otherMultimodalen_US
dc.subject.otherAdaptiveen_US
dc.subject.otherContexten_US
dc.titleMultiple classifiers in biometrics. part 1: Fundamentals and reviewen_US
dc.typearticleen
dc.subject.ecienciaInformáticaes_ES
dc.relation.publisherversionhttps://doi.org/10.1016/j.inffus.2017.12.003es_ES
dc.identifier.doi10.1016/j.inffus.2017.12.003es_ES
dc.identifier.publicationfirstpage57es_ES
dc.identifier.publicationissue44es_ES
dc.identifier.publicationlastpage64es_ES
dc.relation.projectIDGobierno de España. TEC2015-70627-Res_ES
dc.type.versioninfo:eu-repo/semantics/submittedVersionen
dc.rights.accessRightsopenAccessen
dc.authorUAMCamacho Fernández, David (261274)
dc.authorUAMFierrez Aguilar, Julián (261834)
dc.facultadUAMEscuela Politécnica Superior


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record