Chaotic neural networks and multi-dimensional data analysis in biometric applications

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dc.contributor.advisorGavrilova, Marina L.
dc.contributor.authorAhmadian, Kushan
dc.date.accessioned2017-12-18T22:36:59Z
dc.date.available2017-12-18T22:36:59Z
dc.date.issued2012
dc.descriptionBibliography: p. 135-142en
dc.descriptionSome pages are in colour.en
dc.description.abstractHumans have used body characteristics such as face, v01ce and gait. Although biometrics emerged from its extensive use in law enforcement (to identify criminals, to provide security clearance for employees in border protection, in fatherhood determination, forensics and positive identification of convicts), it is being increasingly used today to establish person recognition in a large number of civilian applications. In a practical biometric system (i.e., a system that employs biometrics for personal recognition), there are a number of important issues that should be considered, including performance (achievable recognition accuracy and speed) and circumvention (system resistance to noise and to being fooled by fraudulent methods). In order for biometric system to meet the above demands on performance and circumvention, more than one type of biometric is required. Hence, the need arises for the use of multi-modal biometrics, which is a combination of different biometric recognition technologies, varying from physical biometrics (such as face, iris and fingerprint recognition) to behavioral characteristics (i.e. signature, voice, and gate). Acquiring a group of different biometrics with different characteristics and specifications, results in a number of issues that should be addressed in a multi-modal biometric system. In such a system, one of the common problems is the high dimensionality of the data which impacts negatively system performance. Hence, dimensionality reduction methodologies are needed to be used. However, they have not been considered in recent multi-modal biometric systems due to gap between recently developed dimensionality reduction techniques in data mining and data analysis of biometric features. To remedy this situation, I propose a new methodology for shrinking down the finite search space of all possible subspaces by focusing on subspace analysis which is a novel approach in data clustering for biometric dataset. This is also a new contribution in biometric fusion methodology, which allows dealing with noisy data and makes the biometric system more error-proof. In summary, the purpose of this research is to develop a novel methodology based on the subspace clustering dimension reduction technique and chaotic neural network to improve the performance and circumvention of multi-modal biometric system. The focus is on the verification process where the proposed methodology is compared against some distinguished works on multi-modal biometrics. The system implementation and comparison criteria are included in this proposal to validate the developed multi-modal verification system.
dc.format.extentx, 142 leaves : ill. ; 30 cm.en
dc.identifier.citationAhmadian, K. (2012). Chaotic neural networks and multi-dimensional data analysis in biometric applications (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/5023en_US
dc.identifier.doihttp://dx.doi.org/10.11575/PRISM/5023
dc.identifier.urihttp://hdl.handle.net/1880/106024
dc.language.isoeng
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.titleChaotic neural networks and multi-dimensional data analysis in biometric applications
dc.typedoctoral thesis
thesis.degree.disciplineComputer Science
thesis.degree.grantorUniversity of Calgary
thesis.degree.nameDoctor of Philosophy (PhD)
ucalgary.item.requestcopytrue
ucalgary.thesis.accessionTheses Collection 58.002:Box 2100 627942902
ucalgary.thesis.notesUARCen
ucalgary.thesis.uarcreleaseyen
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