Transcript IRIS RECOGNITION SYSTEM - Santa Clara University

By: Deepak Attarde
Mayank Gupta
Vishwanath Srinivasan
Guided by: Dr. Aditya Abhyankar
BIOMETRIC SECURITY
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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
Detecting the pupil edges
 Detecting the iris edges
 Extracting the iris region
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Canny Edge
Detection
Algorithm
NORMALISATION
Variations in eye: Optical size (iris), position (pupil), Orientation (iris).
Fixed Dimension, Cartesian co-ordinates to Polar coordinates.
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
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RESULTS AND CASE STUDIES
FAR, FRR
 EER: 18.3 % which gives an accuracy close to 82%
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ROC: Receiver Operator
Characteristics
Advantages
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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
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Pupil Dilation
Inconsistent Iris size
Eye Rotation
THANK YOU!!!