Iris recognition based on weighting selection and fusion fuzzy model of iris features to improve recognition rate

The study suggests designing a weighting model for iris features and selection of the best ones to show the effect of weighting and selection process on system performance. The search introduces a new weighting and fusion algorithm depends on the inter and intra class differences and the fuzzy logic. The output of the algorithm is the feature’s weight of the selected features. The designed system consists of four stages which are iris segmentation, feature extraction, feature weighting_selection_fusion model implementation and recognition. System suggests using region descriptors for defining the center and radius of iris region, then the iris is cropped and transformed into the polar coordinates via rotation and selection of radius-size pixels of fixed window from center to circumference. Feature extraction stage is done by wavelet vertical details and the statistical metrics of 1st and 2nd derivative of normalized iris image. At weighting and fusion step the best features are selected and fused for classification stage which is done by distance classifier. The algorithm is applied on CASIA database which consists of iris images related to 250 persons. It achieved 100% segmentation precision and 98.7% recognition rate. The results show that segmentation algorithm is robust against illumination and rotation variations and occlusion by eye lash and lid, and the weighting_selection_fusion algorithm will increase the system performance.

Ali Mahmoud Mayya and Mariam Mohammad Saii, D.
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Int J Inf Res Rev
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