Enhanced Electoral Voting System Using AI-Based Face and Iris Recognition for Secure and Efficient Elections

Traditional voting systems face multiple challenges, including identity fraud, duplicate voting, ballot tampering, and excessive manpower requirements. Manual voter verification methods are inefficient, error-prone, and vulnerable to manipulation, raising concerns about the integrity of electoral processes. These issues necessitate the development of a secure, transparent, and automated voting system that leverages cutting-edge Artificial Intelligence (AI) and Machine Learning (ML) technologies. This paper proposes an Enhanced Electoral Voting System that integrates AIdriven face and iris recognition with OTP (One-Time Password) verification to establish a highly secure, fraudresistant, and efficient voting framework. By utilizing MultiCNN (Convolutional Neural Network) algorithms, the system ensures precise voter identification, eliminating impersonation and unauthorized voting. Facial recognition captures unique facial structures, while iris recognition analyses intricate biometric patterns, making the authentication process highly reliable and robust. The proposed system reduces duplicate voters by implementing multi-factor authentication, ensuring each voter can cast only one vote. Additionally, automation significantly reduces manpower requirements, optimizing election efficiency. The implementation of advanced AI/ML models such as Facenet and MTCNN enhances recognition accuracy, minimizing errors in voter verification. The dataset used for training these AI models comprises large-scale biometric datasets, ensuring high precision in real-world applications. By integrating biometric authentication and AIdriven security mechanisms, the proposed system guarantees a secure, transparent, and efficient electoral process. It aligns with global electoral security standards, making it ideal for national elections, online voting, and remote voter authentication while offering a scalable, tamper-proof solution for modern democracies.