Abstract
Iris recognition is regarded as the most reliable and accurate biometric identification system available. The work presented in this paper involves improving iris segmentation to reduce execution time. To determine the performance of the iris system two databases of digitized grayscale eye images are used.
The segmentation process in the iris recognition system is used to localize the circular iris and pupil regions, excluding eyelids and eyelashes. New techniques are proposed and implemented for pupil detection. These techniques are mask, profile and the combined profile mask (CPM) technique. The extracted iris region is normalized into a rectangular block with constant dimensions to account for imaging inconsistencies.
The feature extraction technique is based on 2D Gabor filters. The Hamming distance is used to classify the iris templates, and the FAR, FRR and RR are calculated.
The results of the study proved that the best technique for pupil detection is when using the combined technique. It gives about 100% success rate for pupil detection.
