Secure E-voting: Leveraging Blockchain technology and Face recognition for enhanced authentication

Abstract

Technology advancements have prompted researchers to investigate electronic voting, or "e-voting," to update and simplify the electoral process. In this research, we present a novel solution to these problems by fusing deep learning methods specifically, facial detection with blockchain technology to create an electronic voting system. To further enhance the security and integrity of the voting process, we apply deep learning techniques for facial detection. The voter's identification is then confirmed by deep learning models analyzing this data, which stops fraudulent voting and guarantees that only qualified people take part in the election process. Furthermore, our approach places a high priority on protecting voter privacy by utilizing methods like homomorphic encryption and zero-knowledge proofs, which enable vote counting without jeopardizing the voter's identity. Because blockchain technology is transparent, audits and verification are made easier, allowing interested parties to confirm the fairness of the electoral process. Our suggested electronic voting system provides a strong solution that tackles the primary issues of security, integrity, and privacy by merging deep learning combined with blockchain technologies for facial recognition.