Multiple classifiers in biometrics. Part 2: Trends and challenges
Entity
UAM. Departamento de Ingeniería Informática; UAM. Departamento de Tecnología Electrónica y de las ComunicacionesPublisher
Elsevier B.V.Date
2017-11-22Citation
10.1016/j.inffus.2017.12.005
Information Fusion 44 (2018): 103-112
ISSN
1566-2535DOI
10.1016/j.inffus.2017.12.005Funded by
This work was funded by projects CogniMetrics (TEC2015-70627-R) from MINECO/FEDER and RiskTrakc (JUST-2015-JCOO-AG-1). Part of this 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)Project
Gobierno de España. TEC2015-70627-REditor's Version
https://doi.org/10.1016/j.inffus.2017.12.005Subjects
Classifier; Fusion; Biometrics; Multimodal; Adaptive; Context; InformáticaRights
© 2017 Elsevier B.V.Abstract
The present paper is Part 2 in this series of two papers. In Part 1 we provided an introduction to Multiple Classifier Systems (MCS) with a focus into the fundamentals: basic nomenclature, key elements, architecture, main methods, and prevalent theory and framework. Part 1 then overviewed 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. Here in Part 2 we present in more technical detail recent trends and developments in MCS coming 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 Part 1, methods here are described in a general way so they can be applied to other information fusion problems as well. Finally, we also discuss here open challenges in biometrics in which MCS can play a key role
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Google Scholar:Fiérrez Aguilar, Julián
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Morales Moreno, Aythami
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Vera Rodríguez, Rubén
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Camacho, David
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