Secure E-voting using Facial Recognition with AI

  • Unique Paper ID: 191207
  • Volume: 12
  • Issue: 8
  • PageNo: 6251-6256
  • Abstract:
  • Electronic voting systems are being widely adopted to improve the efficiency, accessibility, and transparency of the electoral process. Despite these advantages, many existing e- voting platforms are still susceptible to security threats such as identity fraud, unauthorized system access, and multiple voting attempts by the same individual. To overcome these limitations, this research presents a secure e-voting system that integrates Artificial Intelligence–based facial recognition for reliable voter authentication and secure vote casting. The proposed approach employs deep learning techniques for facial feature extraction, real-time face matching, and liveness detection to ensure that only authorized voters are permitted to access the voting interface. The system architecture combines AI-driven recognition models with encrypted voter databases and a secure vote-recording mechanism, thereby minimizing the risk of impersonation and vote manipulation. Implemented as a user-friendly web appli- cation, the system follows an automated verification workflow that effectively prevents double voting and unauthorized logins. Experimental evaluation shows that the proposed system achieves high recognition accuracy, faster authentication, and improved security when compared to conventional password-based or ID- based voting methods. These results suggest that the integration of AI-powered facial recognition can significantly enhance the reliability, transparency, and overall trustworthiness of digital voting systems

Copyright & License

Copyright © 2026 Authors retain the copyright of this article. This article 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.

BibTeX

@article{191207,
        author = {Rohit Hilal Wagh and Pankaj R Patil and Harsh Chandrakant Kotwal and Suraj Sudhir Thoke and Hemali Lalit Firke},
        title = {Secure E-voting using Facial Recognition with AI},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {6251-6256},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=191207},
        abstract = {Electronic voting systems are being widely adopted to improve the efficiency, accessibility, and transparency of the electoral process. Despite these advantages, many existing e- voting platforms are still susceptible to security threats such as identity fraud, unauthorized system access, and multiple voting attempts by the same individual. To overcome these limitations, this research presents a secure e-voting system that integrates Artificial Intelligence–based facial recognition for reliable voter authentication and secure vote casting. The proposed approach employs deep learning techniques for facial feature extraction, real-time face matching, and liveness detection to ensure that only authorized voters are permitted to access the voting interface. The system architecture combines AI-driven recognition models with encrypted voter databases and a secure vote-recording mechanism, thereby minimizing the risk of impersonation and vote manipulation. Implemented as a user-friendly web appli- cation, the system follows an automated verification workflow that effectively prevents double voting and unauthorized logins. Experimental evaluation shows that the proposed system achieves high recognition accuracy, faster authentication, and improved security when compared to conventional password-based or ID- based voting methods. These results suggest that the integration of AI-powered facial recognition can significantly enhance the reliability, transparency, and overall trustworthiness of digital voting systems},
        keywords = {Electronic Voting (E-Voting), Secure E-Voting System, Facial Recognition, Artificial Intelligence (AI), Deep Learning, Biometric Authentication, Liveness Detection.},
        month = {January},
        }

Cite This Article

Wagh, R. H., & Patil, P. R., & Kotwal, H. C., & Thoke, S. S., & Firke, H. L. (2026). Secure E-voting using Facial Recognition with AI. International Journal of Innovative Research in Technology (IJIRT), 12(8), 6251–6256.

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