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

Date
2012
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Abstract
Humans 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.
Description
Bibliography: p. 135-142
Some pages are in colour.
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Citation
Ahmadian, 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/5023
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