High performance iris recognition based on 1-D circular feature extraction and PSO-PNN classifier

In this paper, a novel and simple iris feature extraction technique is proposed for iris recognition of high performance. We use one dimensional circular ring to represent iris features. The reduced and significant features afterward are extracted by Sobel operator and 1D wavelet transform. So as to improve the accuracy, this paper combines Probabilistic Neural Network (PNN) and Particle Swarm Optimization (PSO) for an optimized PNN model. A comparative experiment of existing methods for iris recognition is evaluated on CASIA iris image databases. The experimental results reveal the proposed algorithm provides superior performance in iris recognition.