Neural Network Based Accurate Biometric Recognition and Identification of Human Iris Patterns

Abstract

Problem statement: 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. Approach: Most commercial iris recognition systems use patented algorithms developed by Daugman and these algorithms are able to produce perfect recognition rates. However, published results have usually been produced under favorable conditions and there have been no independent trials of the technology. Results: In this study after providing brief picture on development of various techniques for iris recognition, hamming distance coupled with neural network based iris recognition techniques were discussed. Perfect recognition on a set of 150 eye images has been achieved through this approach. Further, Tests on another set of 801 images resulted in false accept and false reject rates of 0.0005 and 0.187% respectively, providing the reliability and accuracy of the biometric technology. Conclusion/Recommendations: This study provided 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.