Dealing with biometric multi-dimensionality through chaotic neural network methodology

Acquiring a group of different biometrics characteristic and specifications results in a number of issues that should be addressed in a modern biometric system. One of the common problems is the high dimensionality of the data, which may impact negatively the biometric system performance. The complexity of data is rarely considered in multimodal biometric systems due to the gap between recently developed dimensionality reduction techniques in data mining and data analysis of biometric features. To remedy the situation, this paper proposes a unique methodology for shrinking down the finite search space of all possible subspaces. The approach also utilises the function approximation capabilities of chaotic neural networks to act as an associative memory to learn the biometric patterns. In summary, the contribution of this paper is in novel methodology based on the axis-parallel dimension reduction technique and chaotic neural network to improve the performance and circumvention of biometric system.