EFFECTIVE ONLINE IRIS IMAGE REDUCTION AND RECOGNITION METHOD BASED ON EIGEN VALUES
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Abstract
The efficient Eigen values-based approach for online iris image compression and human
identification, including the situation of identical twins, is introduced in this study. The iris image is
retrieved after eliminating the pupil, eyebrow, skin, and other noise disturbances from an accurate
picture. The retrieved iris picture is partitioned into several blocks, each 16 by 16 pixels. Eigenvalues
are now generated for each block to identify each block better and save it in the smart card memory.
Therefore, all that is required to determine whether two iris pictures are the same is to compare the
stored Eigenvalues with the online-calculated Eigenvalues. The identical Eigenvalues between two
iris scans indicate they belong to the same individual. According to our study, various people's iris
images—including those of identical twins—have distinct Eigenvalues. We tested our Eigen Values
Based Iris Image Identification Technique using datasets of iris images from CASIA and Multimedia
University, and we discovered that it provides 99.99% accuracy for matching identical twin and
individual photos. According to the implementation, our strategy seems to give the most extraordinary
matching results for identical twins and people. It is a practical, cost-effective, and effective method
for online personal identification.
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