iris recognisation
DESCRIPTION
TRANSCRIPT
Guided by: - Dr. Aditya Abhyankar
By: -Deepak AttardeMayank GuptaVishwanath Srinivasan
BIOMETRIC SECURITY
Modern and reliable method Hard to breach Wide range
Why Iris RecognitionHighly protected and stable, template size is small and image encoding and matching is relatively fast.
INTRODUCTION TO IRIS RECOGNITION
John Daugman, University of Cambridge – Pioneer in Iris Recognition.
Sharbat Gula – aged 12 at Afghani refugee camp.
18 years later at a remote location in Afghanistan.
OVERVIEW OF OUR SYSTEM
SEGMENTATION
Detecting the pupil edges Detecting the iris edges Extracting the iris region
Canny Edge Detection Algorithm
NORMALISATION
Daugman’s Rubber Sheet Model:
(R, theta) to unwrap iris and easily generate a template code.
Fixed Dimension, Cartesian co-ordinates to Polar co-ordinates.
Variations in eye: Optical size (iris), position (pupil), Orientation (iris).
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
THANK YOU!!!