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IRIS DETECTION

IRIS DETECTION. SIRISHA SUMANTH and LATIF ALIANTO. The Iris as a Biometric . Why Iris? Data-rich physical structure - The tabecular meshwork The cornea – protection Stability of the iris pattern Non-invasive method Genetic independence. Preprocessing. Image acquisition

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IRIS DETECTION

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  1. IRIS DETECTION SIRISHA SUMANTH and LATIF ALIANTO

  2. The Iris as a Biometric Why Iris? • Data-rich physical structure - The tabecular meshwork • The cornea – protection • Stability of the iris pattern • Non-invasive method • Genetic independence

  3. Preprocessing • Image acquisition - Focus on high resolution and quality - Moderate illumination - Elimination of artifacts • Image localization • Adjustments for imaging contrast, illumination and camera gain

  4. Iris Isolation • Removal of parts other than the iris - Circular mask • Image cropping - Use of the geometry of the eye - Reduction in image size

  5. Feature Extraction • Pattern of the tabecular network • Comparison of edge operators • LoG operator - Calculates second spatial derivative of an image - Not affected by noise due to smoothing operation - Isotropic operator

  6. Extraction Process 

  7. Respone of LoG to a step edge

  8. Discrete approximation of LoG function ( σ= 1.4)

  9. Database creation and data compression • Efficient use of storage space • Edge information is stored in a binary image – use of ones and zeros only • Majority of the data are zeros • Further compression using the run length of zeros • Compression of 662.112 KB to 20.954 KB

  10. Data Validation • Test of statistical independence • Parameter – Hamming distance - gives a measure of the disagreement • Hamming distance = zero =>identity validated • Hamming distance ≠ zero =>invalid ID

  11. Images of different irises

  12. Results

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