Modern and reliable method
Hard to breach
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
Detecting the pupil edges
Detecting the iris edges
Extracting the iris region
Variations in eye: Optical size (iris), position (pupil), Orientation (iris).
Fixed Dimension, Cartesian co-ordinates to Polar coordinates.
Daugman’s Rubber Sheet
(R, theta) to unwrap iris and easily
generate a template code.
FEATURE EXTRACTION AND
Generate a template code along with a
Compare 2 iris templates using
Shifting of Hamming distances: To
counter rotational inconsistencies.
<0.32: Iris Match
>0.32: Not a Match
RESULTS AND CASE STUDIES
EER: 18.3 % which gives an accuracy close to 82%
ROC: Receiver Operator
Uniqueness of iris patterns hence improved
Highly protected, internal organ of the eye
Stability : Persistence of iris patterns.
Non-invasive : Relatively easy to be
Speed : Smaller template size so large
databases can be easily stored and
Cannot be easily forged or modified.
Concerns / Possible
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