The increasing demand for enhanced security in the daily life has directed the improvement of the reliable and intelligent personal identification system based on biometrics. Iris recognition has been regarded as one of the most reliable biometrics technologies in recent years. In this book, an iris recognition scheme is presented as a biometrically based technology for person identification using Multi-Class Support Vector Machines (SVM). The Multi- Objectives Genetic Algorithms (MOGA) is used to select the most significant features in order to increase the matching accuracy. The traditional SVM is modified into an asymmetrical SVM to treat the cases of False Accept and False Reject differently and also to control the unbalanced data of a specific class with respect to the other classes. In order to improve the generalization performance of SVM, the optimal values of SVM parameters are selected. The experimental results indicate that the proposed iris recognition scheme can be applied to a wide range of security-related application areas with an encouraging recognition rate.