Multimodal Cancelable Biometric System

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atmire.migration.oldid4669
dc.contributor.advisorGavrilova, Marina
dc.contributor.authorPaul, Padma Polash
dc.contributor.committeememberElhajj, Reda
dc.contributor.committeememberWang, Yingxu
dc.contributor.committeememberYanushkevich, Svetlana
dc.contributor.committeememberHoward, Newton
dc.date.accessioned2016-07-26T16:35:15Z
dc.date.available2016-07-26T16:35:15Z
dc.date.issued2016
dc.date.submitted2016en
dc.description.abstractCyberattacks against individuals and organizations are increasing at alarming rates all over the world. Traditional password, pin, smart id, and tokens based systems are insufficient to provide reliable authentication. Biometric authentication is now widely used in both physical and virtual worlds. Unfortunately, even the well-established biometric systems are suffering from vulnerabilities, with the most crucial components being biometric templates that store user biometric data. If the biometric of a user is compromised, the identity and privacy of an individual are compromised as well, since it is impossible to revoke or reissue the biometric template. Therefore, secure and revocable biometric template generation algorithms are required to ensure biometric system integrity and user privacy. In this thesis, biometric template generation algorithm for multimodal biometric system is presented. To achieve the template protection for the multimodal biometric verification system, new methods for feature level fusion and feature extraction are proposed, called Cancelable Feature Fusion (CFF) and Cancelable Binary Pattern (CBP), respectively. CFF combines multiple biometric traits using random indexes so that for every fusion it generates a new fused template. Developed 2-Fold Cross-Folding (2-CF) and Generalized Cross-Folding (G-CF) are new algorithms for cancelable feature fusion, which utilize random indexes to combine multiple biometric traits. Another developed method is CBP, a biometric feature extraction algorithm that can generate a new set of features from a single sample as needed. For after matching fusion, Social Network Analysis (SNA) based score fusion is proposed to achieve better verification accuracy. In this thesis, traditional feature fusion and feature extraction algorithms are replaced with CFF and CBP respectively to support the template protection. For the validation of the methodologies, genuine, stolen and fake key scenarios are analyzed using several sets of virtual multimodal biometric databases of face, ear, and signature. Experimental results show that proposed Multimodal Cancelable Biometric System (MCBioS) architectures can achieve 0% Equal Error Rate (EER) and a higher template integrity.en_US
dc.identifier.citationPaul, P. P. (2016). Multimodal Cancelable Biometric System (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/27029en_US
dc.identifier.doihttp://dx.doi.org/10.11575/PRISM/27029
dc.identifier.urihttp://hdl.handle.net/11023/3152
dc.language.isoeng
dc.publisher.facultyGraduate Studies
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.classificationCancelable Feature Fusionen_US
dc.subject.classificationCancelable Binary Patternen_US
dc.subject.classificationCancelable Biometricsen_US
dc.subject.classificationBiometric Template Protectionen_US
dc.subject.classificationMultimodal Biometric Systemen_US
dc.titleMultimodal Cancelable Biometric System
dc.typedoctoral thesis
thesis.degree.disciplineComputer Science
thesis.degree.grantorUniversity of Calgary
thesis.degree.nameDoctor of Philosophy (PhD)
ucalgary.item.requestcopytrue
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