A Decentralized Facial Verification-Based Voting System Using Blockchain and Smart Contracts System

Abstract

This paper presents a decentralized facial recognition-based voting system that ensures secure, transparent, and tamper-proof elections using blockchain technology. The system integrates facial recognition for authentication and blockchain for immutable vote storage. Votes are encrypted using SHA-256 and stored securely in a Ganache blockchain environment. AWS cloud services are used for managing facial datasets. The system ensures transparency, prevents fraud, and enables real-time result monitoring.

Introduction

This project proposes a secure blockchain-based electronic voting (e-voting) system integrated with facial recognition to improve the security, transparency, and reliability of elections. Traditional voting systems suffer from issues such as voter impersonation, duplicate voting, data tampering, centralized storage vulnerabilities, and slow vote counting. The proposed system addresses these challenges by combining blockchain technology, biometric authentication, and cryptographic security.

Methodology

The system follows five main stages:

1. User Registration and Authentication: Voters register by providing personal details and facial images, which are securely stored in a MySQL database and AWS cloud. Only approved users can participate.
2. Facial Recognition Verification: During login, a live facial image is matched with the stored image to verify the voter's identity, preventing impersonation.
3. Vote Casting and Encryption: Eligible users can vote only on the election date. Each vote is encrypted using the SHA-256 hashing algorithm, ensuring data integrity and preventing duplicate voting.
4. Blockchain-Based Vote Storage: Encrypted votes are stored as immutable transactions on an Ethereum blockchain (simulated using Ganache). Smart contracts written in Solidity manage vote recording and counting, ensuring that votes cannot be modified or deleted.
5. Real-Time Result Generation: Votes are automatically counted from the blockchain, allowing transparent, accurate, and immediate result generation while preserving voter anonymity.

Results

The system was evaluated using 200 simulated user samples (150 for validation and 50 for performance testing). The results demonstrated:

• Authentication Accuracy: 97.8%
• Vote Integrity: 100%
• System Reliability: 98.5%
• Average Response Time: Less than 2 seconds

The facial recognition module accurately authenticated users with very low false acceptance and rejection rates, while the blockchain ensured secure, tamper-proof vote storage. The system also prevented unauthorized voting and duplicate votes.

References

[1] W. A. Mahmood, J. Waleed, A. R. Abbas, H. Alaskar, M. Altulyan, and A. J. Hussain, “Intelligent Gesture-Enhanced Blockchain Voting: A New Era of Secure and Accessible E-Voting,” IEEE Access, vol. 12, pp. 1–15, 2024. [2] J. Yao, B. Yang, T. Wang, and W. Zhang, “A Distributed Self-Tallying Electronic Voting System Using Smart Contracts,” Chinese Journal of Electronics, vol. 33, no. 2, pp. 1–10, 2024. [3] D. Granata, M. Rak, P. Palmieri, and A. Pastena, “A Methodology for Vulnerability Assessment and Threat Modelling of an E-Voting Platform Based on Ethereum Blockchain,” IEEE Access, vol. 12, pp. 1–12, 2024. [4] R. Barelli, M. Bellini, and F. Bianchi, “Toward Secure Electronic Voting: A Survey on Systems and Attacks,” IEEE Access, vol. 13, pp. 1–20, 2025. [5] A. K. Vangujar, S. Sharma, and P. Singh, “A Novel Approach to E-Voting With Group Identity-Based Identification,” IEEE Access, vol. 12, pp. 1–10, 2024. [6] H. Baniata and G. Caluna, “BP-Vot: Blockchain-Based E-Voting With Differential Privacy and Self-Sovereign Identities,” IEEE Access, vol. 13, pp. 1–12, 2025. [7] M. Alown, A. Alharbi, and S. Alshammari, “Enhancing Democratic Processes: A Survey of Electronic Voting Systems,” IEEE Access, vol. 13, pp. 1–18, 2025. [8] T. Treier and K. Düüna, “Identifying and Solving a Vulnerability in the Estonian i-Voting Process,” IEEE Access, vol. 12, pp. 1–10, 2024. [9] D. Dabpimjub and S. Kiattisin, “Success Factors for Conceptual Digital Voting Model,” Journal of Mobile Multimedia, vol. 20, no. 1, pp. 1–12, 2024. [10] P. R. Saha, A. Kumar, and R. Singh, “Algorithmic Approach of Majority Voting With Agents’ Inclusiveness,” IEEE Access, vol. 13, pp. 1–10, 2025. [11] K. Christidis and M. Devetsikiotis, “Blockchains and Smart Contracts for the Internet of Things,” IEEE Access, vol. 4, pp. 2292–2303, 2016. [12] A. Reyna, C. Martín, J. Chen, E. Soler, and M. Díaz, “On Blockchain and Its Integration With IoT: Challenges and Opportunities,” Future Generation Computer Systems, vol. 88, pp. 173–190, 2018. [13] M. T. Hammi, B. Hammi, P. Bellot, and A. Serhrouchni, “Bubbles of Trust: A Decentralized Blockchain-Based Authentication System,” Computers & Security, vol. 78, pp. 126–142, 2018. [14] Face++ Research Team, “Face Recognition Technology for Identity Verification,” Megvii Technology, 2023. [Online]. Available: https://www.faceplusplus.com [15] Ethereum Foundation, “Ethereum Whitepaper: A Next-Generation Smart Contract and Decentralized Application Platform,” 2014. [Online]. Available: https://ethereum.org

Copyright

Copyright © 2026 Ms. P. Jenifer, Ms. D. Amali. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.