Highly Secure Online Voting System Using Multi-Level Authentication and Blockchain

Abstract

This project proposes a highly secure and intelligent online voting system designed to eliminate fraud, ensure voter authenticity, and improve transparency. The system begins with the user accessing a web or mobile application and selecting the type of election, such as national, university, corporate, or egovernance voting. After selecting the election type, the system automatically activates a camera to perform iris (eye) scanning using deep-learning techniques. If the iris scan fails due to environmental or userrelated issues, the system provides alternative biometric authentication methods such as face recognition or fingerprint scanning, ensuring accessibility and reliability. Once biometric authentication is successful, the system verifies the voter’s identity using official credentials such as Voter ID or institutional ID, and checks whether the user has already voted to prevent duplicate voting. If the user is eligible, they proceed to select their voting category and candidate. After the vote is submitted, the data is securely encrypted and stored on a blockchain ledger, ensuring immutability, transparency, and tamper-proof record keeping, and the system finally enables secure result computation and declaration. The prototype is implemented in Python using OpenCV for image processing, TensorFlow/Keras for the deep-learning iris model, and Flask for backend integration, with the blockchain layer providing immutable storage of votes. This multi-layered approach combining biometric authentication, identity verification, and blockchain storage provides a robust, secure, and user-friendly online voting platform suitable for modern digital governance systems.