IRIS RECOGNISATION
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IRIS RECOGNISATION

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IRIS RECOGNISATION IRIS RECOGNISATION Presentation Transcript

  • 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 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 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
  • 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 Pupil Dilation Eye Rotation Inconsistent Iris size
  • THANK YOU!!!