dc.contributor.author | Fiérrez Aguilar, Julián | |
dc.contributor.author | Morales Moreno, Aythami | |
dc.contributor.author | Vera Rodríguez, Rubén | |
dc.contributor.author | Camacho, David | |
dc.contributor.other | UAM. Departamento de Ingeniería Informática | es_ES |
dc.contributor.other | UAM. Departamento de Tecnología Electrónica y de las Comunicaciones | es_ES |
dc.date.accessioned | 2018-08-23T14:22:20Z | |
dc.date.available | 2018-08-23T14:22:20Z | |
dc.date.issued | 2017-12-22 | |
dc.identifier.citation | Information Fusion 44 (2018): 57-64 | en_US |
dc.identifier.issn | 1566-2535 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10486/684685 | |
dc.description.abstract | We 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 them | en_US |
dc.description.sponsorship | This 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.extent | 27 pag. | es_ES |
dc.format.mimetype | application/pdf | en |
dc.language.iso | eng | en |
dc.publisher | Elsevier B.V. | en_US |
dc.relation.ispartof | Information Fusion | en_US |
dc.rights | © 2017 Elsevier B.V. | en_US |
dc.subject.other | Classifier | en_US |
dc.subject.other | Fusion | en_US |
dc.subject.other | Biometrics | en_US |
dc.subject.other | Multimodal | en_US |
dc.subject.other | Adaptive | en_US |
dc.subject.other | Context | en_US |
dc.title | Multiple classifiers in biometrics. part 1: Fundamentals and review | en_US |
dc.type | article | en |
dc.subject.eciencia | Informática | es_ES |
dc.relation.publisherversion | https://doi.org/10.1016/j.inffus.2017.12.003 | es_ES |
dc.identifier.doi | 10.1016/j.inffus.2017.12.003 | es_ES |
dc.identifier.publicationfirstpage | 57 | es_ES |
dc.identifier.publicationissue | 44 | es_ES |
dc.identifier.publicationlastpage | 64 | es_ES |
dc.relation.projectID | Gobierno de España. TEC2015-70627-R | es_ES |
dc.type.version | info:eu-repo/semantics/submittedVersion | en |
dc.rights.accessRights | openAccess | en |
dc.authorUAM | Camacho Fernández, David (261274) | |
dc.authorUAM | Fierrez Aguilar, Julián (261834) | |
dc.facultadUAM | Escuela Politécnica Superior | |