1. IRIS RECOGNITION SYSTEM
By: -
Deepak Attarde
Mayank Gupta
Vishwanath Srinivasan
Guided by: -
Dr. Aditya Abhyankar

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

4. OVERVIEW OF OUR SYSTEM

5. SEGMENTATION Canny Edge Detection Algorithm Detecting the pupil edges
Detecting the iris edges
Extracting the iris region
Canny Edge Detection Algorithm

6. NORMALISATION Daugman’s Rubber Sheet Model:
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.

7. 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

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

9. 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.

10. 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
Inconsistent Iris size
Pupil Dilation
Eye Rotation

11. THANK YOU!!!