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Chaotic Neural Networks and Multi-Modal Biometrics
Abstract
Neural network is a collection of interconnected neurons with the ability to derive conclusion from imprecise data that can be used to both identify and learn patterns. This chapter presents the concept of neural network as an intelligent learning tool for biometric security systems. Neural networks have been extensively used in a variety of computational and optimization problems. In the first half of this chapter, focus is given to a specific topic—chaos in neural network. A detailed description of an on-demand chaotic noise injection method recently developed to deal with a common drawback of non-autonomous methods—their blind noise injecting strategy—is presented. The second part of the chapter discusses the issue of high-dimensionality in the context of a complex biometric security system. The amount of data and its complexity can be overwhelming, and one way of dealing with this issue is to use the dimensionality reduction techniques, which are typically based on clustering or transformations from one space to another. The reduced dimensionality vector can be then used in the energy model for an associative memory, which will learn the data patterns. The benefit is that this is a learner system that converges the given set of vectors to the stored pattern in a network, which can be later used for biometric recognition and also for identifying the most significant biometric patterns. At the end of this chapter, some examples are presented showing the feasibility of using such approach in biometric domain—both for single and multi-modal biometric.
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