Robust human iris pattern recognition system using neural network approach

This paper proposes a prototype model of robust iris pattern recognition (PR) system for classification of ten different persons using neural network. Feature extraction algorithms are developed and an optimal feature vector comprising of features in relation to image statistics, texture and 2-D transform domain is formed. It is observed that 2D Walsh Hadamard Transform (WHT) entails the best performance as compared to other image transforms. Different neural network configurations, such as, Multi Layer Perceptron (MLP), Radial Basis Function (RBF) and Support Vector Machine (SVM) are implemented after systematically varying the concerned parameters of the respective networks and MLP with single hidden layer is seen to outperform all others with respect to performance on cross validation dataset derived from CASIA iris image database. Further, the sensitivity analysis is carried out over this network in order to reduce time and space complexity of the network. To make this network more robust; controlled Gaussian and Uniform noise are injected in all input features and it is noticed that for both types of noise, the proposed PR system can sustain the variance up to 0.1.