1. Biometric Iris Recognition System INTRODUCTION Iris recognition is fast developing to be a foolproof and fast identification technique that can be administered cost effectively. It is a classic biometrics application that is in an advanced stage of research all over the world.

2. Biometric Iris Recognition System Iris localization and alignment procedure

3. Image acquisition

4. Original iris image present in the database Formation of a circular contour around the iris The circular contour is concentric with the circular pupil Removing the portion of the iris occluded by the eyelids

5. The ratio of limbus diameter and pupil diameter forms the first criterion in the comparison of any two irises. First step involves determining the limbus diameter

6. Determining the pupil diameter The ratio of the limbus diameter and the pupil diameter is determined which is an important criterion in the identification/comparison of the irises.

7. Pattern matching To decide if this pattern matches with the one existing in the database. Multi-scale representation of the iris is achieved using the pyramid algorithm of Laplacian of Gaussian filters (4 levels) employing down sampling in the Gaussian and up sampling in the Laplacian of the Gaussian, filtered image. Construction of the Laplacian Pyramid begins first with convolution of the Iris Image with LoG Mask ‘W’, so as to yield low-pass Gaussian filtered images gk The expression is as follows: gk=(W*gk-1)↓2, g1–g4—low-pass Gaussian images each obtained after filtering the previous image and down sampling by 2

8. The LoG filter can be specified as where σ—standard deviation of the Gaussian, ρ—radial distance of a point from the filter's center. A discrete approximation which is derived from the above filter expression. W = [1 4 6 4 1]/ 16

9. Laplacian pyramid lk Is formed as the difference between gk and gk+1 lk=gk-4W*(gk+1)↑2, gk+1 expanded before subtraction so that it matches the sampling rate of gk The expansion is accompanied by up sampling and interpolation: Up sampling is achieved by the insertion of zeros between each row and column of the down sampled Gaussian image. The Mask ‘W’ is used as an interpolation filter and the factor 4 is necessary because 3/4 pixels are newly inserted zeros We then evaluate the degree of match between the acquired image and images from the database. The approach taken is to quantify for the degree of match using normalized correlation between the acquired image and the images from the database.

10. Normalized correlation between the acquired image and the images from the database Normalized correlation can be defined in discrete form as follows Let lk1[i,j] and lk2[i,j] be the two iris images of size r×c (rows×columns) Normalized correlation between lk1and lk2 can be defined as

11. Pattern Matching Result obtained from this iris recognition system for iris images of two different persons

12. Pattern Matching 2 • Result obtained from this iris recognition system for iris images of same person • Corr0=1.0000—Normalized correlation value for the 1st Laplacian of Gaussian of the 2 iris images • Corr0=1.0000—Normalized correlation value for the 2nd Laplacian of Gaussian of the 2 iris images • Corr0=1.0000—Normalized correlation value for the 3rd Laplacian of Gaussian of the 2 iris images • Corr0=1.0000—Normalized correlation value for the 4th Laplacian of Gaussian of the 2 iris images Both the irises are identical Both the irises are identical

13. Result obtained from this iris recognition system for iris image of a person with the iris image of the same person but modified by incorporating 3 dots in the iris region

14. Conclusion • The systems considers the ratio of limbus to pupil diameter as the initial criterion for recognition, which saves the processing time involved in pattern matching and calculating the correlation when the two ratios are not equal. • The pattern matching technique employs multiscale pyramid representation using a LoG filter, which provides optimal enhancement of the features of the iris patterns, and then normalized correlation is employed for evaluating the degree of similarity and has been shown to give accurate results.