Abstract
Iris recognition is a well-known biometric technique. John Daugman has proposed a method for iris recognition,
which is divided into four steps: segmentation, normalization, feature extraction and matching. In this
paper, we evaluate, modify and extend John Daugman�s method. We study the images of CASIA and UBIRIS
databases to establish some modifications and extensions on Daugman�s algorithm. The major modification is
on the computationally demanding segmentation stage, for which we propose a template matching approach.
The extensions on the algorithm address the important issue of pre-processing, that depends on the image
database, being especially important when we have a non infra-red red camera (e.g. a WebCam). For this
typical scenario, we propose several methods for reflexion removal and pupil enhancement and isolation. The
tests, carried out by our C# application on grayscale CASIA and UBIRIS images, show that our template
matching based segmentation method is accurate and faster than the one proposed by Daugman. Our fast
pre-processing algorithms efficiently remove reflections on images taken by non infra-red cameras.
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