AL-Rafidain Journal of Computer Sciences and Mathematics

Abstract

This research is aimed to design an iris recognition system. There are two main steps to verify the goal. First: applying image processing techniques on the picture of an eye for data acquisition. Second: applying neural networks techniques for identification.
The image processing techniques display the steps for getting a very clear iris image necessary for extracting data from the acquisition of eye image. This picture contains all the eye (iris, pupil and lashes). So, the localization of the iris is very important. The new picture should be enhanced to bring out the pattern. The enhanced picture is segmented into 100 parts, then a standard Deviation (STD) can easily be computed for every part. These values will be used in the neural network for the identification.
For neural network techniques, Backprobagation neural network was used for comparisons. The weights and output values will be stored in a text file to be used later in identification. The Backprobagation network succeeded in identification and attained to (False Acceptance Rate = 10% - False Rejection Rate = 10%).