Abstract
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.
Highlights
Iris recognition has become the dynamic theme for security applications, with an emphasis on personal identification based on biometrics
We described an iris recognition algorithm using Modified Fuzzy Hypersphere Neural Network (MFHSNN) which has ability to learn patterns faster by creating /expanding HSs
It has been verified on CASIA database the result is as shown in Table 1 and Table II
Summary
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. The human iris, the annular part between pupil and sclera, has distinctly unique features such as freckles, furrows, stripes, coronas and so on. An iris is normally segmented by detecting its inner areas (pupil) and outer (limbus) boundaries [3] [4]. In 1993, Daugman proposed an integrodifferential operator to find both the iris inner and outer borders Wildes represented the iris texture with a laplacian pyramid constructed with four different resolution levels and used the normalized correlation to determine whether the input image and the model image are from the same class [5]. Ruggero Donida Labati, et al had represented the detection of the iris center and boundaries by using neural networks. A trained neural network processes the parameters associated to the extracted boundaries and it estimates the offsets in the vertical and horizontal axis with respect to the estimated center [12]
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