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

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.

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.

OVERVIEW OF OUR SYSTEM

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

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.

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

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

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.

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

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