ADVANCED IRIS RECOGNITION SYSTEM USING DAUGMAN’S ALGORITHM AND ARTIFICIAL NEURAL NETWORKS

Abstract

We have an assortment of techniques of biometric identification. One of the most advanced and efficient approaches is iris recognition. Pattern recognition is its primary mechanism; it finds unique and easily identifiable patterns in the iris to positively identify the person. This method of identification is more accurate and produces better results. Security breaches and authentication fraud are on the rise, making the implementation of a strict biometric system imperative. The proposed research makes use of Daugman's method for iris localization; this is an integro-differential operator that may separate or segment regular shapes. The Daugman algorithm also has the ability to successfully decrease noise. Iris localization is best accomplished using the Daugman method because of these two features. Feature extraction, which follows iris localization, finds the consistent and unique aspects of an iris picture. Mean, Standard Deviation, Entropy, Root Mean Square, Smoothness, Kurtosis, Energy, Homogeneity, Contrast, and Variance are all computed in the research. In reaction to various iris pictures, the features display unique behavior. Possible overlap of values exists. The features are then fed into a neural network using the Levenberg-Marquardt back propagation training algorithm. After training using feature values extracted from permitted photos, the neural network is then tested for correctness. The traditional MMU database was included into the design of the system. Compared to the prior technique that used the same database, the suggested method achieved a better degree of accuracy, namely 99.7 percent.