This paper proves that in iris recognition, the concepts of sheep, goats, lambs and wolves - as proposed by Doddington and Yager in the socalled Biometric Menagerie, are at most fuzzy and at least not quite well defined. They depend not only on the users or on their biometric templates, but also on the parameters that calibrate the iris recognition system. This paper shows that, in the case of iris recognition, the extensions of these concepts have

This chapter assumes the hypothesis that human learning is perception based, and consequently, the learning process and perceptions should not be represented and investigated independently or modeled in different simulation spaces. In order to keep the analogy between the artificial and human learning, the former is assumed here as being based on the artificial perception. Hence, instead of choosing to apply or develop a Computational Theory of (human)

The main topic discussed in this paper is how to use intelligence for biometric decision defuzzification. A neural training model is proposed and tested here as a possible solution for dealing with natural fuzzification that appears between the intra- and inter-class distributions of scores computed during iris recognition tests. It is shown here that the use of proposed neural network support leads to an improvement in the artificial perception

This paper shows that maintaining logical consistency of an iris recognition system is a matter of finding a suitable partitioning of the input space in enrollable and unenrollable pairs by negotiating the user comfort and the safety of the biometric system. In other words, consistent enrollment is mandatory in order to preserve system consistency. A fuzzy 3valent disambiguated model of iris recognition is proposed and analyzed in terms of completeness,

This paper discusses some topics related to the latest trends in the field of evolutionary approaches to iris recognition. It presents the results of an exploratory experimental simulation whose goal was to analyze the possibility of establishing an Interchange Protocol for Digital Identities evolved in different geographic locations interconnected through and into an Intelligent Iris Verifier Distributed System (IIVDS) based on multi-enrollment.

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

This papers introduces a new family of iris encoders which use 2-dimensional Haar Wavelet Transform for noise attenuation, and Hilbert Transform to encode the iris texture. In order to prove the usefulness of the newly proposed iris encoding approach, the recognition results obtained by using these new encoders are compared to those obtained using the classical Log-Gabor iris encoder. Twelve tests involving single/multienrollment and conducted on

The present paper aims to bring some interesting iris recognition related problems in attention of the artificial intelligence research community and also to mark the release of the first publicly available set of processing tools for University of Bath Iris Image Database (UBIID), tools that can be used to generate test data sets, without losing precious time. New recognition results for UBIID are also presented here.

The relation between self awareness and intelligence is an open problem these days. Despite the fact that self awarness is usually related to Emotional Intelligence, this is not the case here. The problem described in this paper is how to model an agent which knows (Cognitive) Binary Logic and which is also able to pass (without any mistake) a certain family of Turing Tests designed to verify its knowledge and its discourse about the modal states

This paper presents a formal theory which describes propositional binary logic as a semantically closed formal language, and allows for syntactically and semantically well-formed formulae, formal proofs (demonstrability in Hilbertian acception), deduction (Gentzen's view of demonstrability), CNF-ization, and deconstruction to be expressed and tested in the same (computational) formal language, using the same data structure. It is also shown here