Biometric 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. 1. Introduction Growing efforts are devoted to develop and implement new security systems based on biometric features. System subjects could be either human or virtual reality entities, and they are accepted or rejected by the system based on biological or behavioral biometric features. Biometric pattern recognition includes recognition of fingerprint, face, gait, signature, voice, ear, iris, or other physiological or behavioral features. As one of the key biometric features, facial biometrics plays a key role in user authentication. It consists of a set of high-dimensional vectors representing topological, color, or texture information. The feature set is very complex and may contain hundreds of features. This makes it a difficult biometric pattern recognition problem to deal with [1]. Many of the earlier face recognition algorithms are based on feature-based methods. These methods identify a set of geometrical features on the face such as eyes, eyebrows, mouth, and nose [2]. Properties and relations between the feature points, such as areas, distances, and angles are used as descriptors for face recognition. Statistical methods are usually used to lower the number of dimensions; however, there are no universal answers to the problem of how many points give the best performance. In addition, there is no clear answer on what
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