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A framework for improving accuracy of multimodal biometrics security based on bayesian network

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dc.contributor.supervisor Hlomani, Hlomani
dc.contributor.supervisor Mpoeleng, Dimane
dc.contributor.author Morwaagole, Emmanuel E.
dc.date.accessioned 2019-03-22T13:15:16Z
dc.date.available 2019-03-22T13:15:16Z
dc.date.issued 2018-10
dc.identifier.citation Morwaagole,Emmanuel. E (2018) A framework for improving accuracy of multimodal biometrics security based on bayesian network,Masters Theses,Botswana International University of Science and Technology:Palapye en_US
dc.identifier.uri https://repository.biust.ac.bw/handle/123456789/76
dc.description Theses(MSc Computer Science)-------Botswana International university of Science and Technology,2018 en_US
dc.description.abstract This thesis addresses the problem of biometrics security and accuracy through the fusion of fingerprint and face images. The Biometrics community in recent years came up with different approaches to improve the accuracy and security of systems. Multimodal authentication has attracted a lot of attention because of its advantage over single biometrics matchers. Even though efforts were made to improve these systems, they are, however, still vulnerable to security threats such as spoofing where a forged biometric copy and/or artificially recreated biometric data (which maybe legitimate) may be used to spoof the system. Multimodal biometric systems overcome various limitations of uni-modal biometric systems, such as nonuniversality, lower false acceptance, and higher genuine acceptance rates. We thus propose a framework, based on Bayesian Network, which is tailored to deal with the fusion of fingerprint and face. In our proposed framework, face and fingerprint data are fused at Feature level, using probabilistic inference to map posterior probability distributions of unobserved variable to make a basic decision of authentication. We present our proposed framework for fusing bimodal biometrics systems by combining face and fingerprint images to better the performance of biometrics security system. In this research we wanted to exploit prior knowledge as much as possible hence our use of the Bayesian Network to provide reasoning to our framework. Although Bayesian Networks have been used before, our focus in this study is to fully exploit the graphical structure of Bayesian Networks and explicitly model their statistical dependencies between relevant variables per sample measurement. The evaluation of the proposed framework used multimodal chimerical databases formed from publicly available databases. The evaluation of our framework shows an improved performance of 0.03 Error Equal Rate over the performance of the face or finger biometric modalities when each is implemented. The Error Equal Rate for Face and Finger biometric modalities were 0.31 and 0.28, respectively. en_US
dc.language.iso en en_US
dc.publisher Botswana International University of Science and Technology en_US
dc.subject Bayesian Networks en_US
dc.subject Inference en_US
dc.subject Multimodal Biometrics en_US
dc.subject Principal Component Analysis en_US
dc.subject Linear Discriminant Analysis en_US
dc.subject Pre-processing en_US
dc.subject Probabilistic Inference en_US
dc.subject Feature extraction en_US
dc.title A framework for improving accuracy of multimodal biometrics security based on bayesian network en_US
dc.description.level msc en_US
dc.description.accessibility unrestricted en_US
dc.description.department cis en_US


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