A Secure Online Voting System Using Blockchain and Face Recognition with Machine Learning

Electronic voting systems have become increasingly important in modern democratic processes due to their ability to improve voting efficiency, accessibility, and transparency. However, conventional electronic voting systems face significant challenges related to voter authentication, vote manipulation, identity theft, duplicate voting, and centralized database vulnerabilities. To address these issues, this research proposes a secure online voting system that integrates Blockchain technology with Face Recognition using Machine Learning. Blockchain provides a decentralized, immutable, and transparent ledger for securely recording votes, while face recognition ensures reliable voter authentication by verifying the identity of each voter before allowing access to the voting portal. The proposed system utilizes image preprocessing, facial feature extraction, and machine learning-based face recognition to authenticate registered voters. Once authentication is successful, the voter is permitted to cast a vote, which is encrypted and stored as a block within the blockchain network. The decentralized architecture prevents unauthorized modification of voting records and enhances trust in the election process. Experimental evaluation indicates that the proposed system achieves high authentication accuracy, secure vote storage, and resistance against common security attacks such as duplicate voting, identity fraud, and vote tampering. The proposed framework offers a reliable, transparent, and scalable solution for future electronic voting applications in educational institutions, corporate organizations, and governmental elections.

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