Browsing by Author "Ahmadian, Kushan"
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- ItemOpen AccessChaotic Neural Network for Biometric Pattern Recognition(2012-08-30) Ahmadian, Kushan; Gavrilova, MarinaBiometric pattern recognition emerged as one of the predominant research directions in modern security systems. It plays a crucial role in authentication of both real-world and virtual reality entities to allow system to make an informed decision on granting access privileges or providing specialized services. The major issues tackled by the researchers are arising from the ever-growing demands on precision and performance of security systems and at the same time increasing complexity of data and/or behavioral patterns to be recognized. In this paper, we propose to deal with both issues by introducing the new approach to biometric pattern recognition, based on chaotic neural network (CNN). The proposed method allows learning the complex data patterns easily while concentrating on the most important for correct authentication features and employs a unique method to train different classifiers based on each feature set. The aggregation result depicts the final decision over the recognized identity. In order to train accurate set of classifiers, the subspace clustering method has been used to overcome the problem of high dimensionality of the feature space. The experimental results show the superior performance of the proposed method.
- ItemOpen AccessChaotic neural networks and multi-dimensional data analysis in biometric applications(2012) Ahmadian, Kushan; Gavrilova, Marina L.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.