Multimodal Person Recognition using Social Behavioral Biometric

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dc.contributor.advisorGavrilova, Marina L.
dc.contributor.advisorYanushkevich, Svetlana N.
dc.contributor.authorSultana, Madeena
dc.contributor.committeememberLeung, Henry
dc.contributor.committeememberLi, Zongpeng
dc.contributor.committeememberRuhe, Guenther
dc.contributor.committeememberYang, Xiaosong
dc.date2018-06
dc.date.accessioned2018-04-10T21:30:37Z
dc.date.available2018-04-10T21:30:37Z
dc.date.issued2018-04-06
dc.description.abstractThe goal of a biometric recognition system is to make a human-like decision on individuals’ identity by recognizing their physiological and/or behavioral traits. Nevertheless, decision-making process by either a human or a biometric recognition system can be highly complicated due to low quality of data or an uncertain environment. Human brain has an advantage over computer system due to its ability to perform a massive parallel processing of auxiliary information such as visual cues, cognitive and social interactions, contextual and spatio-temporal data. Similarly to a human brain, social behavioral cues can aid the reliable decision-making of an automated biometric system. Being an integral part of human behavior, social interactions are likely to possess unique behavioral patterns. However, the significance of social behavior for automated user recognition has been noted in the scientific community only recently. In this doctoral thesis, a novel person recognition approach is presented that relies on the knowledge of individuals’ social behavior in order to enhance the performance of a traditional biometric system. The social behavioral information of individuals’ has been mined from an Online Social Network (OSN) and fused with traditional face and ear biometrics. This research identified a set of Social Behavioral Biometric (SBB) features from the online social information and proposed a framework to utilize these features for an automated person recognition for the first time. Extensive experiments confirm that human social behavior expressed through OSN can provide a unique insight onto person recognition. Performance of the proposed multimodal approach has been evaluated to determine the effectiveness of fusing social behavioral information. Experimental results on virtual and semi-real databases demonstrate significant performance gain in the proposed method over traditional biometric system. This doctoral research contributes to an emerging research direction in biometric domain as well as opens new frontier of studying social behavior in virtual domain.en_US
dc.identifier.citationSultana, M. (2018). Multimodal Person Recognition using Social Behavioral Biometric (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/31776en_US
dc.identifier.doihttp://dx.doi.org/10.11575/PRISM/31776
dc.identifier.urihttp://hdl.handle.net/1880/106488
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.subjectBiometrics
dc.subjectInformation Security
dc.subjectCybersecurity
dc.subjectSocial Behavioral Biometrics
dc.subjectInformation Fusion
dc.subjectOnline Social Network
dc.subjectPerson Recognition
dc.subjectSocial Behavior
dc.subject.classificationComputer Scienceen_US
dc.titleMultimodal Person Recognition using Social Behavioral Biometric
dc.typedoctoral thesis
thesis.degree.disciplineComputer Science
thesis.degree.grantorUniversity of Calgary
thesis.degree.nameDoctor of Philosophy (PhD)
ucalgary.item.requestcopytrue
ucalgary.thesis.checklistI confirm that I have submitted all of the required forms to Faculty of Graduate Studies.en_US
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