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.