Enhancing multimodal biometric frameworks using fuzzy fusion at matching score level

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dc.contributor.authorFares, Abdelhakim
dc.contributor.authorMegri, Abderrahim Faycal
dc.contributor.authorMeraoumia, Abdallah
dc.date.accessioned2025-04-20T17:36:12Z
dc.date.available2025-04-20T17:36:12Z
dc.date.issued2021
dc.description.abstractMultimodal biometric systems aim to achieve the best performance in persons recognition, by combining more than one biometric modality so that the combination is done using the fusion principle. In fact, although different levels of fusion are available, score-level fusion is the most commonly used. Indeed, in this process and before combining, the heterogeneous scores provided by the different biometric subsystems must be normalized. In this paper and in order to cancel the scores normalization step, a fuzzy fusion strategy has been proposed. Our experimental results, using a PolyU finger-knuckle-print database, show that the proposed fusion scheme improves the results compared to the Z-score normalization method followed by the min and max classical combining arithmetic rules.
dc.identifier.urihttp://dspace.univ-oeb.dz:4000/handle/123456789/21916
dc.language.isoen
dc.publisherUniversity of Oum El Bouaghi
dc.subjectBiometrics; Multimodal biometrics; Data fusion; Fusion at score level. Fuzzy logic
dc.titleEnhancing multimodal biometric frameworks using fuzzy fusion at matching score level
dc.typeArticle
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The 1 ST international conference on electronics, artificial intelligence and technologies ICEAINT 2021