Confidence-Based Rank Level Fusion For Multimodal Biometric Systems

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atmire.migration.oldid3850
dc.contributor.advisorGavrilova, Marina
dc.contributor.authorTalebi, Hossein
dc.date.accessioned2015-11-17T23:10:49Z
dc.date.embargolift2016-02-22T07:00:00Z
dc.date.issued2015-11-17
dc.date.submitted2015en
dc.description.abstractIn recent years, the inevitable need for reliable biometric identity management systems in applications such as border crossing, welfare distribution, and accessing critical facilities has drawn researchers' attention to the area of biometric. The intrinsic limitations of unimodal biometric systems such as non-universality, sensitivity to noisy sensor data, inter and intra class variations and spoof attacks have resulted in significant attention toward multimodal biometric systems. An important aspect of a multimodal biometric system is the fusion of information from multiple biometric sources. This thesis focuses on using the notion of Resemblance Probability Distributions to calculate confidence measures for different biometric matchers and use these confidence measures in the fusion module to improve the identification rate of the system. This thesis approaches the problem of low inter class variation and low quality data by proposing Rank List Reinforcement and Confidence-based Ranked List Selection methods.en_US
dc.identifier.citationTalebi, H. (2015). Confidence-Based Rank Level Fusion For Multimodal Biometric Systems (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/25415en_US
dc.identifier.doihttp://dx.doi.org/10.11575/PRISM/25415
dc.identifier.urihttp://hdl.handle.net/11023/2639
dc.language.isoeng
dc.publisher.facultyGraduate Studies
dc.publisher.facultyScience
dc.publisher.institutionUniversity of Calgaryen
dc.publisher.placeCalgaryen
dc.rightsUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.
dc.subjectComputer Science
dc.subject.classificationMultimodal Biometricsen_US
dc.subject.classificationRank Level Fusionen_US
dc.subject.classificationResemblance Probability Distributionen_US
dc.subject.classificationConfidence-Based Ranked List Selectionen_US
dc.subject.classificationRanked List Reinforcementen_US
dc.titleConfidence-Based Rank Level Fusion For Multimodal Biometric Systems
dc.typemaster thesis
thesis.degree.disciplineComputer Science
thesis.degree.grantorUniversity of Calgary
thesis.degree.nameMaster of Science (MSc)
ucalgary.item.requestcopytrue
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