Noise Resistant Identification of Human Iris Patterns Using Fuzzy ARTMAP Neural Network
A biometric system provides automatic identification of an individual based on unique features or characteristics possessed by that person. In this paper, the authors propose an efficient iris recognition system that employs circular Hough transform technique to localize the iris region in the eye image and cumulative sum based gray change analysis method to extract features from the normalized iris template and also fuzzy ARTMAP neural network to classify the iris codes. The results of simulations on a set of 756 eye images illustrate that an accurate and noise resistant personal identification system has been successfully designed. The proposed system achieved 0 false acceptance rate using 1800-bit binary iris codes and recognized all authorized users with 100% accuracy.