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. 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.

The work presented in this thesis involved developing an iris recognition system in order to verify both the uniqueness of the human iris and also its performance as a best biometric technology. The iris recognition system consists of an automatic segmentation system that is based on the Hough transform, and is able to localize the circular iris and pupil region, Occluding eyelids and eyelashes, and reflections. The extracted iris region was then normalized into a rectangular block with constant dimensions to account for imaging inconsistencies. Finally, the phase data was extracted and quantized to four levels to encode the unique pattern of the iris into a bit-wise Biometric template.

The Hamming distance was employed for classification of iris templates, and two templates were found to match if a test of statistical independence was failed. The system can perform with perfect recognition on a set eye images; The false accept and false reject rates of iris is found to be very less. Therefore, iris recognition is shown to be a reliable and accurate biometric technology.