I am working on comparing john daugman and Wilde's method of iris recognition systems. Can anyone kindly forward the MATLAB code to me sot_d242000[at]yahoo.com
Thank you in advance

iris code matlab code

clear all;
close all;
clc;
%Reading the image
Img=imread('002L_1.png');
%%Pre Processing and Normalisation
figure;imshow(Img);title('INPUT EYE IMAGE');
%%Step 1: Converting to Gray sclae from rgb
Gray_imag=rgb2gray(Img);
figure;imshow(Gray_imag);title('IMAGE after Gray conversion');
%Deleting extra portion
t2=Gray_imag(:,65:708);
t3=t2(18:563,;
figure;imshow(t3);title('IMAGE after Deleting extra portion');
%%Step 2: Resizing the image(546x644) to 512 x 512
t4=imresize(t3,[512,512],'bilinear');
figure;imshow(t4);title('IMAGE after resize');
%%Step 3: Histogram Equlisation
Hist_eq_img = histeq(t4,512);
figure;imshow(Hist_eq_img);title('IMAGE after Histogram Equlisation');
% Step 4: Gaussian Filtering
G = fspecial('gaussian',[512 512],20);
%Filter it
Hist_eq_img=double(Hist_eq_img);
Ig = imfilter(Hist_eq_img,G,'same');
%Display
%%Step 5: Canny Edge detection
BW2 = edge(Ig,'canny',0.53,1);
figure;imshow(BW2);title('IMAGE after canny edge detection');

The iris of each eye is unique. No two irises are alike in their mathematical detail--even between identical twins and triplets or between one's own left and right eyes. Unlike the retina, however, it is clearly visible from a distance, allowing easy image acquisition without intrusion. The iris remains stable throughout one's lifetime, barring rare disease or trauma. The random patterns of the iris are the equivalent of a complex "human barcode," created by a tangled meshwork of connective tissue and other visible features. The iris recognition process begins with video-based image acquisition that locates the eye and iris.

The boundaries of the pupil and iris are defined, eyelid occlusion and specular reflection are discounted, and quality of image is determined for processing. The iris pattern is processed and encoded into a record (or "template"), which is stored and used for recognition when a live iris is presented for comparison. Half of the information in the record digitally describes the features of the iris, the other half of the record controls the comparison, eliminating specular reflection, eyelid droop, eyelashes, etc.

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 favourable conditions, and there have been no independent trials of the technology.

The iris recognition system consists of an automatic segmentation system that is based on the Hough transform, and is able to localise the circular iris and pupil region, occluding eyelids and eyelashes, and reflections. The extracted iris region was then normalised into a rectangular block with constant dimensions to account for imaging inconsistencies. Finally, the phase data from 1D Log-Gabor filters was extracted and quantised 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 performed with perfect recognition on a set of 75 eye images; however, tests on another set of 624 images resulted in false accept and false reject rates of 0.005% and 0.238% respectively. Therefore, iris recognition is shown to be a reliable and accurate biometric technology.