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 a...
INTRODUCTION TO IRIS RECOGNITION
Sharbat Gula – aged 12 at
Afghani refugee camp.
18 years later at a remote
location in Af...
OVERVIEW OF OUR SYSTEM
SEGMENTATION




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

Canny Edge
Detection
Al...
NORMALISATION

Variations in eye: Optical size (iris), position (pupil), Orientation (iris).
Fixed Dimension, Cartesian co...
FEATURE EXTRACTION AND
MATCHING






Generate a template code along with a
mask code.
Compare 2 iris templates using...
RESULTS AND CASE STUDIES



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

ROC: Receiver Operator
Characteri...
Advantages








Uniqueness of iris patterns hence improved
accuracy.
Highly protected, internal organ of the eye
...
Concerns / Possible
improvements





High cost of implementation
Person has to be “physically” present.
Capture image...
THANK YOU!!!
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IRIS RECOGNISATION

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  • IRIS RECOGNISATION

    1. 1. By: Deepak Attarde Mayank Gupta Vishwanath Srinivasan Guided by: Dr. Aditya Abhyankar
    2. 2. 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.
    3. 3. 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.
    4. 4. OVERVIEW OF OUR SYSTEM
    5. 5. SEGMENTATION    Detecting the pupil edges Detecting the iris edges Extracting the iris region Canny Edge Detection Algorithm
    6. 6. 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.
    7. 7. 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
    8. 8. RESULTS AND CASE STUDIES   FAR, FRR EER: 18.3 % which gives an accuracy close to 82% ROC: Receiver Operator Characteristics
    9. 9. 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.
    10. 10. 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 Pupil Dilation Eye Rotation Inconsistent Iris size
    11. 11. THANK YOU!!!

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