It is quite easy to spoof an automated iris recognition system using
fake iris such as paper print and artificial lens. False Rejection Rate (FRR) and
False Acceptance Rate (FAR) of a specific approach can be as a result of noise
introduced in the segmentation process. Special attention has not been paid to a
modified system in which a more accurate segmentation process is applied to an
already existing efficient algorithm thereby increasing the overall reliability and
accuracy of iris recognition. In this work an improvement of the already existing
wavelet packet decomposition for iris recognition with a Correct Classification
Rate (CCR) of 98.375% is proposed. It involves changing the segmentation
technique used for this implementation from the integro-differential operator
approach (John Daugman’s model) to the Hough transform (Wilde’s model).
This research extensively compared the two segmentation techniques to show
which is better in the implementation of the wavelet packet decomposition.
Implementation of the integro-differential approach to segmentation showed an
accuracy of 91.39% while the Hough Transform approach showed an accuracy
of 93.06%. This result indicates that the integration of the Hough Transform into
any open source iris recognition module can offer as much as a 1.67% improved
accuracy due to improvement in its preprocessing stage. The improved iris
segmentation technique using Hough Transform has an overall CCR of 100%