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dc.contributor.authorAlsaade, F.
dc.contributor.authorAriyaeeinia, A.
dc.contributor.authorMalegaonkar, A.
dc.contributor.authorPawlewski, M.
dc.contributor.authorPillay, S.G.
dc.date.accessioned2012-12-18T13:59:37Z
dc.date.available2012-12-18T13:59:37Z
dc.date.issued2008
dc.identifier.citationAlsaade , F , Ariyaeeinia , A , Malegaonkar , A , Pawlewski , M & Pillay , S G 2008 , ' Enhancement of multimodal biometric segregation using unconstrained cohort normalisation ' , Pattern Recognition , vol. 41 , no. 3 , pp. 814-820 . https://doi.org/10.1016/j.patcog.2007.06.028
dc.identifier.issn0031-3203
dc.identifier.otherPURE: 113527
dc.identifier.otherPURE UUID: f18122b2-5907-4be1-b12b-f4badd241f0f
dc.identifier.otherdspace: 2299/1024
dc.identifier.otherScopus: 35448973731
dc.identifier.urihttp://hdl.handle.net/2299/9445
dc.descriptionOriginal article can be found at: http://www.sciencedirect.com/science/journal/00313203 Copyright Elsevier Ltd.
dc.description.abstractThis paper presents an investigation into the effects, on the accuracy of multimodal biometrics, of introducing unconstrained cohort normalisation (UCN) into the score-level fusion process. Whilst score normalisation has been widely used in voice biometrics, its effectiveness in other biometrics has not been previously investigated. This study aims to explore the potential usefulness of the said score normalisation technique in face biometrics and to investigate its effectiveness for enhancing the accuracy of multimodal biometrics. The experimental investigations involve the two recognition modes of verification and open-set identification, in clean mixed-quality and degraded data conditions. Based on the experimental results, it is demonstrated that the capabilities provided by UCN can significantly improve the accuracy of fused biometrics. The paper presents the motivation for, and the potential advantages of, the proposed approach and details the experimental study. 2007 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.en
dc.language.isoeng
dc.relation.ispartofPattern Recognition
dc.titleEnhancement of multimodal biometric segregation using unconstrained cohort normalisationen
dc.contributor.institutionSchool of Engineering and Technology
dc.contributor.institutionCentre for Engineering Research
dc.contributor.institutionScience & Technology Research Institute
dc.contributor.institutionCommunications and Intelligent Systems
dc.description.statusPeer reviewed
rioxxterms.versionofrecordhttps://doi.org/10.1016/j.patcog.2007.06.028
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue


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