MACHINE LEARNING BASED IRIS RECOGNITION MODERN VOTING SYSTEM

Abstract

The integrity and transparency of electoral processes are essential for maintaining public trust in democratic systems. Traditional voting methods often face challenges such as voter impersonation, identity fraud, manual errors, and lengthy verification procedures. Biometric authentication technologies provide a secure and reliable solution for addressing these issues. This paper presents a Machine Learning-Based Iris Recognition Modern Voting System that utilizes iris biometrics for accurate voter identification and authentication. The proposed framework captures iris images from registered voters, performs image preprocessing and feature extraction, and employs machine learning algorithms to recognize and verify voter identities. Iris recognition offers a highly unique and stable biometric characteristic, making it suitable for secure voting applications. The system ensures that only authorized voters can cast their votes while preventing duplicate voting and identity-related fraud. Furthermore, machine learning techniques enhance recognition accuracy and improve system efficiency under varying environmental conditions. Experimental analysis demonstrates that the proposed framework achieves high authentication accuracy, strengthens election security, and streamlines the voting process. The developed system provides a secure, efficient, and intelligent solution for modern electronic voting environments.