Score normalization applied to adaptive biometric systems

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dc.contributor.authorPisani, Paulo Henrique
dc.contributor.authorPoh, Norman
dc.contributor.authorde Carvalho, Andre C. P. L. F.
dc.contributor.authorLorena, Ana Carolina [UNIFESP]
dc.date.accessioned2019-08-19T11:48:29Z
dc.date.available2019-08-19T11:48:29Z
dc.date.issued2017
dc.description.abstractBiometric authentication systems have certain limitations. Recent studies have shown that biometric features may change over time, which can entail a decrease in recognition performance of the biometric system. An adaptive biometric system addresses this problem by adapting the biometric reference/template over time, thereby tracking the changes automatically. However, the use of these systems usually requires the adoption of a high threshold value to avoid the inclusion of impostor patterns into the genuine biometric reference. In this study, we hypothesize that score normalization procedures, which have been used to improve the recognition performance of biometric systems through a better refinement of their decision, can also improve the overall performance of adaptive systems. With such a normalization, a better threshold choice could also be made, which would then increase the number of genuine samples used for adaptation. To the best of our knowledge, this is the first investigation towards the use of score normalization to enhance adaptive biometric systems dealing with the change of user features over time. Through a systematic experimental design tested on two behavioral biometric traits, the obtained results indeed support our conjecture. Moreover, the experimental results show that the performance gain brought by adaptation can have a higher overall impact than score normalization alone. (C) 2017 Elsevier Ltd. All rights reserved.en
dc.description.affiliationUniv São Paulo, Inst Ciencias Matemat & Comp, Av Trabalhador Sao Carlense 400, Sao Carlos, SP, Brazil
dc.description.affiliationUniv Surrey, Dept Comp, Fac Engn & Phys Sci, Guildford, Surrey, England
dc.description.affiliationUniv Fed São Paulo, Inst Ciencia & Tecnol, Rua Talim 330, Sao Jose Dos Campos, Brazil
dc.description.affiliationUnifespUniv Fed São Paulo, Inst Ciencia & Tecnol, Rua Talim 330, Sao Jose Dos Campos, Brazil
dc.description.sourceWeb of Science
dc.description.sponsorshipCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description.sponsorshipIDCAPES: 2012/25032-0
dc.description.sponsorshipIDCNPq: 2012/22608-8
dc.description.sponsorshipIDFAPESP: 2013/07375-0
dc.format.extent565-580
dc.identifierhttp://dx.doi.org/10.1016/j.cose.2017.07.014
dc.identifier.citationComputers & Security. Oxford, v. 70, p. 565-580, 2017.
dc.identifier.doi10.1016/j.cose.2017.07.014
dc.identifier.issn0167-4048
dc.identifier.urihttp://repositorio.unifesp.br/handle/11600/51264
dc.identifier.wosWOS:000413127000033
dc.language.isoeng
dc.publisherElsevier Advanced Technology
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.subjectAdaptive biometric systemsen
dc.subjectTemplate updateen
dc.subjectScore normalizationen
dc.subjectKeystroke dynamicsen
dc.subjectAccelerometer biometricsen
dc.titleScore normalization applied to adaptive biometric systemsen
dc.typeinfo:eu-repo/semantics/article
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