Gavrilova, MarinaMONWAR, MD. MARUF2013-01-072013-06-152013-01-072012http://hdl.handle.net/11023/385In recent years, biometric based security systems achieved more attention due to continuous terrorism threats around the world. However, a security system comprised of a single form of biometric information cannot fulfill users’ expectations and may suffer from noisy sensor data, intra and inter class variations and continuous spoof attacks. To overcome some of these problems, multimodal biometric aims at increasing the reliability of biometric systems through utilizing more than one biometric in decision-making process. In order to take full advantage of the multimodal approaches, an effective fusion scheme is necessary for combining information from various sources. Such information can be integrated at several distinct levels, such as sensor level, feature level, match score level, rank level and decision level. In this doctoral research, I present a new methodology based on fusion at the rank level, which is a relatively new approach compared to others, to combine multimodal biometric information from three biometric identifiers (face, ear and iris). I investigate different rank fusion methods, such as highest rank, Borda count and logistic regression. I introduce a novel rank fusion algorithm based on Markov chain which significantly increases the recognition performance of the multimodal biometric system, can handle partial ranking lists, and satisfies the Condorcet criteria essential for fair ranking process. In order to increase the processing speed and to obtain the level of confidence of recognition outcomes of the multimodal biometric system, I further employ fuzzy logic based fusion for biometric authentication. The fuzzy fusion method is based on fuzzy logic and uses match score and rank information of the multimodal biometric system. The experiment results tested within different multimodal biometric database framework show superiority of the proposed approaches to other biometric information fusion methods. The developed system can be effectively used by security and intelligence services for controlling access to prohibited areas and protecting important national or public information.engUniversity 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.Computer ScienceBiometricMultimodal BiometricsFusionRank Level FusionMarkov ChainFuzzy Logicdoctoral thesis10.11575/PRISM/24804