MUMBAI, India, Aug. 31 -- Intellectual Property India has published a patent application (202541016348 A) filed by Manivannan K; B. Srinivas; D. Maalini; V. S. B. Engineering College, Karur; V. Sudarshana; M. Sri Someshwari; E. Shopiga; and M. Saranya on February 25, 2025, for Enhanced Electoral Voting System With Ai-Based Face And Iris Recognition For Secure And Efficient Elections.
Inventors include Manivannan K; B. Srinivas Number; D. Maalini; V. S. B. Engineering College, Karur; V. Sudarshana; M. Sri Someshwari; E. Shopiga; and M. Saranya.
The application for the patent was published on August 28, 2026, under issue no. 35/2026.
Abstract: 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 AI-driven face and iris recognition with OTP (One-Time Password) verification to establish a highly secure, fraud-resistant, and efficient voting framework. By utilizing Multi-CNN (Convolutional Neural Network) algorithms, the system ensures precise voter identification, eliminating impersonation and unauthorized voting. Facial recognition captures unique facial structures, while iris recognition analyzes 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 AI-driven 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.
Disclaimer: Curated by HT Syndication.
