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Iris Recognition Using Modified Fuzzy Hypersphere Neural Network with different Distance Measures

In this paper we describe Iris recognition using Modified Fuzzy Hypersphere Neural Network (MFHSNN) with its learning algorithm, which is an extension of Fuzzy Hypersphere Neural Network (FHSNN) proposed by Kulkarni et al. We have evaluated performance of MFHSNN classifier using different distance measures. It is observed that Bhattacharyya distance is superior in terms of training and recall time as compared to Euclidean and Manhattan distance measures. The feasibility of the MFHSNN has been successfully appraised on CASIA database with 756 images and found superior in terms of generalization and training time with equivalent recall time. I. INTRODUCTION Iris recognition has become the dynamic theme for security applications, with an emphasis on personal identification based on biometrics. Other biometric features include face, fingerprint, palm-prints, iris, retina, gait, hand geometry etc. All these biometric features are used in security applications (1). The human iris, the annular part between pupil and sclera, has distinctly unique features such as freckles, furrows, stripes, coronas and so on. It is visible from outside. Personal authentication based on iris can obtain high accuracy due to rich texture of iris patterns. Many researchers work has also affirmed that the iris is essentially stable over a person's life. Since the iris based personal identification systems can be more noninvasive for the users (2).