Biometric pattern recognition emerged as one of the predominant research directions in modern security systems. It plays a crucial\r\nrole in authentication of both real-world and virtual reality entities to allow system to make an informed decision on granting\r\naccess privileges or providing specialized services. The major issues tackled by the researchers are arising from the ever-growing\r\ndemands on precision and performance of security systems and at the same time increasing complexity of data and/or behavioral\r\npatterns to be recognized. In this paper, we propose to deal with both issues by introducing the new approach to biometric pattern\r\nrecognition, based on chaotic neural network (CNN). The proposed method allows learning the complex data patterns easily while\r\nconcentrating on the most important for correct authentication features and employs a unique method to train different classifiers\r\nbased on each feature set. The aggregation result depicts the final decision over the recognized identity. In order to train accurate\r\nset of classifiers, the subspace clustering method has been used to overcome the problem of high dimensionality of the feature\r\nspace. The experimental results show the superior performance of the proposed method.
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