SIRISHA SUMANTH and LATIF ALIANTO 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 - Focus on high resolution and quality - Moderate illumination - Elimination of artifacts Image localization Adjustments for imaging contrast, illumination and camera gain

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

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

Extraction Process  

Respone of LoG to a step edge

Discrete approximation of LoG function ( σ= 1.4)

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

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

Images of different irises

Results Database Iris 1 Iris 2 Iris 3 Iris 4 Iris 5 Iris 6 Iris 7 8824 9005 8983 9349 9211 8788 9053 9082 7371 7767 7883 7625 6924 7319 7524 7886 8212 7800 7115 7504 7679 8368 8190 7329 7950 7951 8458 7555 8188 8455 7469 7740 8059 7069 7450 7693