This document discusses iris recognition as a biometric security method. It provides an overview of how iris recognition works, including segmentation of the iris region, normalization, and feature extraction and matching. The accuracy of iris recognition is close to 82%, with an equal error rate of 18.3%. While iris recognition has advantages like the uniqueness and stability of iris patterns, concerns include the high cost of implementation and challenges with non-ideal iris images under different lighting conditions.

1. Guided by: -
Dr.Mohammed Shiri
By: -
Ahmed AL Tememe

2. BIOMETRIC SECURITY
 Modern and reliable method
 Hard to breach
 Wide range
 Why Iris Recognition
Highly protected and stable,
template size is small and
image encoding and matching
is relatively fast.

3. INTRODUCTION TO IRIS RECOGNITION
John Daugman, University of
Cambridge – Pioneer in Iris
Recognition.
Sharbat Gula – aged 12 at
Afghani refugee camp.
18 years later at a remote
location in Afghanistan.

4. OVERVIEW OF OUR SYSTEM
Figure Steps In Iris
Segmentation & Hough
Transform Process
Figure Iris Recognition Process

5. SEGMENTATION
 Detecting the pupil edges
 Detecting the iris edges
 Extracting the iris region

6. Canny Edge Detection
Algorithm
The Canny edge detection algorithm
consists of following steps:
 Smooth the image with a Gaussian filter
 Compute the gradient of image.
 Apply nonmaxima suppression to the
gradient image
 Use double thresholding algorithm to
detect and link edges.

7. The gradient amplitude image
while inding outer and inner
boundary of iris.
shows image after Non Maxima
suppression
Hysteresis Thresholding

8. Upper eyelids
Segmented iris showing both circles.
Lower eyelids segmented iris with the noise mask

9. NORMALISATION
Variations in eye: Optical size (iris), position (pupil), Orientation (iris).
Fixed Dimension, Cartesian co-ordinates to Polar co-ordinates.
Daugman’s Rubber Sheet
Model:
(R, theta) to unwrap iris and easily
generate a template code.

10. FEATURE EXTRACTION AND
MATCHING
 Generate a template code along with a
mask code.
 Compare 2 iris templates using
Hamming distances.
 Shifting of Hamming distances: To
counter rotational inconsistencies.
 <0.32: Iris Match
 >0.32: Not a Match

11. RESULTS AND CASE STUDIES
 FAR, FRR
 EER: 18.3 % which gives an accuracy close to 82%
ROC: Receiver Operator
Characteristics

12. Advantages
 Uniqueness of iris patterns hence improved
accuracy.
 Highly protected, internal organ of the eye
 Stability : Persistence of iris patterns.
 Non-invasive : Relatively easy to be
acquired.
 Speed : Smaller template size so large
databases can be easily stored and
checked.
 Cannot be easily forged or modified.

13. Concerns / Possible
improvements
 High cost of implementation
 Person has to be “physically” present.
 Capture images independent of surroundings
and environment / Techniques for dark eyes.
 Non-ideal iris images
Pupil Dilation Inconsistent Iris size Eye Rotation

14. REFERENCES
1] Wildes, R.P, “Iris Recognition: An Emerging
Biometric Technology”, Proceedings of the IEEE,
VOL. 85, NO. 9, September 1997, pp. 1348-1363.
2] John G. Daugman. How Iris Recognition Works.
Proceedings of 2002 International Conference on
Image Processing, Vol. 1, 2002.
5) J. Daugman “High confidence visual recognition of
persons by a test of statistical independence ,”IEEE
Trans. Pattern Analyse Machine Intell., vol. 15, pp.
1148–1161, Nov. 1993.
6) R. Wildes, “Iris recognition: an emerging biometric

15. THANK YOU!!!