Learning Iris Biometric Digital Identities for Secure Authentication: A Neural-Evolutionary Perspective Pioneering Intelligent Iris Identification
This chapter discusses the latest trends in the field of evolutionary approaches to iris recognition, approaches which are compatible with the task of multi-enrollment in a biometric authentication system based on iris recognition, and which are also able to ensure strong discrimination between the enrolled users. A new authentication system based on supervised learning of iris biometric identities is proposed here. It is the first neural-evolutionary approach to iris authentication that proves an outstanding power of discrimination between the intra- and inter-class comparisons performed for the test database (Bath Iris Image Database). It is shown here that when using digital identities evolved by a logical and intelligent artificial agent (Intelligent Iris Verifier/Identifier) the separation between inter- and intra-class scores is so good that it ensures absolute safety for a very large percent of accepts (97%, for example), i.e. recognition is no longer a statistical event, or in other words, the statistical aspect of iris recognition becomes residual while the logical binary aspect prevails. In this way, iris recognition theory and practice advance from inconsistent verification to consistent verification/identification.
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