Improved Biometric Recognition and Identification of Human Iris Patterns Using Neural Networks

Abstract

A biometric system provides automatic identification of an individual based on a unique feature or characteristic possessed by the individual. Iris recognition is regarded as the most reliable and accurate biometric identification system available. An approach for accurate Biometric Recognition and Identification of Human Iris Patterns using Neural Network has been illustrated by gopikrishnan et. al. It has been concluded by Yingzi Du et. al that the partial iris portion of the iris pattern describes the uniqueness and the pupil has no direct effect on the accuracy of the biometric recognition. In this paper the Iris recognition has been carried out employing a template of size 10 × 480 pixels instead of 20 × 480 pixels as employed in the earlier paper. The results of the two sizes of the templates have been compared and it has been observed that the accuracy of the results obtained with the limited template size is comparable with that of the one with the full size. The reason for this is also discussed in this paper. The improved methodology suggested has resulted in the reduction of the space requirement as well as time complexity with no loss in accuracy. This paper also provides results of iris recognition performed applying Hamming distance, Feed forward back propagation, Cascade forward back propagation, Elman forward back propagation and perceptron. It has been established that the method suggested applying perceptron provides the best accuracy in respect of iris recognition with no major additional computational complexity.