Extracting Unique Personal Identification Number from Iris
Nenad Nestorovic, P.W.C. Prasad, Abeer Alsadoon and Amr Elchouemi
DOI : 10.3844/ajassp.2017.701.710
American Journal of Applied Sciences
Volume 14, Issue 7
The aim of this research is to propose an enhanced model that significantly reduces the noise in the iris templates and thus present an efficient and reliable iris recognition system. Accuracy of Iris recognition system significantly depends on reducing noise at every stage of recognition process especially at eye image acquisition and iris segmentation stages. The proposed Iris recognition model combines Canny edge detector and circular Hough transform which yields more rapid and accurate localization of Iris inner and outer boundaries. The eyelid and eyelashes occlusion is also dealt with by applying non maxima radial suppression technique. The segmented Iris area is then normalized and converted from annular shape to fixed-sized rectangular block. In this stage all iris templates converted from circular to rectangular shape will have standard dimensions thus enabling Iris recognition system to perform comparison between them. Fast Fourier Transform function is then applied to normalized Iris image. Input image is viewed as a signal that is converted from time to frequency domain in which the number of frequencies is equal to number of image pixels. The resulting matrix is consisted of real and imaginary part represented by complex numbers. The matrix is encoded according to the pre established encoding pattern that assigns certain sequence of binary numbers to each possible combination of complex numbers thus producing Iris code. In the matching phase the iris codes are compared and if the determined degree of dissimilarity between the two is below the pre-established threshold value than the codes belong to the same user. The proposed Iris recognition method, therefore, successfully treats eyelids and eyelashes noise issues and present the efficient set of algorithms whose performance is superior compared to other existing methods.
© 2017 Nenad Nestorovic, P.W.C. Prasad, Abeer Alsadoon and Amr Elchouemi. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.