A Novel Approach to Human Iris Recognition And Verification Framework Using Machine Learning Algorithm

The component of a computer system that is in responsibility of user security is one of the most important modules. It was demonstrated that simple logins and passwords are easy for hackers to obtain and cannot guarantee high efficiency. Biometric identity recognition is the preferred alternative. The most popular and commonly utilised method for system security, according to the researchers, is biometrics. Iris-based biometric systems are the most secure of biometric systems. A valuable and effective tool for human identification, the iris pattern is a different biological feature for every individual. Even though a number of approaches to this purpose have been described, features, including the high memory consumption, long run times, and computational complexity. New iris verification algorithms have recently been developed as a result of the advancement of machine learning technology. Hence, a novel approach to human recognition and verification framework using Machine Learning algorithm is described in this analysis. The texture-based features are extracted by use of the Gray Level Co-occurrence Matrix (GLCM) and the Hausdorff Dimension (HD). The Support Vector Machine (SVM) algorithm is used to identify and verify the human iris in this strategy. The accuracy, sensitivity, and specificity of the presented approach are evaluated. The robustness of the system is demonstrated by its performance.