Abstract
This article shows and tests a good eye recognition method that works well in image situations with fewer restrictions. The Circular Hough Transform (CHT) and Truncated Total Variation model were used to separate the iris from other parts and noises in an eye picture to locate and separate the iris accurately. This helps get the most exact pictures of the eye. Doughman’s rubber sheet model makes the split eye area regular and normalized. The Principal Component Analysis (PCA) method is used to identify features of iris patterns that come from Eigen irises. A test image is projected onto the subspace occupied by the eigen iris to do recognition. Then, recorded eigen irises are compared with the test image by finding the normalized hamming distance between them. Up to 95.5% of the accurate, the planned method has given correct findings.
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