IRIS RECOGNITION SYSTEM

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IRIS RECOGNITION SYSTEM
By - Deepak Attarde Mayank Gupta Vishwanath
Srinivasan

• Guided by -
• Dr. Aditya Abhyankar

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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.

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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.
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OVERVIEW OF OUR SYSTEM
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SEGMENTATION

• Detecting the pupil edges
• Detecting the iris edges
• Extracting the iris region

Canny Edge Detection Algorithm
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NORMALISATION
Variations in eye Optical size (iris), position
(pupil), Orientation (iris).
Fixed Dimension, Cartesian co-ordinates to Polar
co-ordinates.
Daugmans Rubber Sheet Model (R, theta) to
unwrap iris and easily generate a template code.
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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.
• lt0.32 Iris Match
• gt0.32 Not a Match

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RESULTS AND CASE STUDIES

• FAR, FRR
• EER 18.3 which gives an accuracy close to 82

ROC Receiver Operator Characteristics
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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.

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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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THANK YOU!!!