A Combined Support Vector Machine and Statistical Method for Iris Recognition

This paper introduces an innovative hybrid method for iris recognition by combining a Modified Fuzzy C-Means (MFCM) algorithm with Support Vector Machine (SVM) techniques, aiming to improve both accuracy and efficiency. The research focuses on advancing iris recognition systems to strengthen security, enhance precision, and safeguard privacy in authentication and identification processes. The proposed method incorporates preprocessing techniques alongside classification strategies to enable precise identification of human iris patterns. Specifically, it utilizes MFCM and high-order statistical features for effective iris localization and feature extraction, followed by classification through Principal Component Analysis (PCA) integrated with SVM. Experimental results show that this approach significantly boosts the performance of iris recognition systems. Overall, the findings suggest that the proposed framework offers a reliable and secure solution, suitable for a wide range of identification and authentication applications.