Electronic Voting Machine with Face Recognition

Abstract

Electronic Voting Machines (EVMs) have improved the efficiency of elections; however, voter impersonation and duplicate voting remain critical challenges. This paper proposes) using real-time face recognition for voter authentication. The system integrates a webcam-based face capture module, facial encoding storage in a secure database, and a graphical user interface for vote casting and administration. The framework ensures that only registered voters can vote and prevents multiple voting attempts using biometric verification and database validation. The proposed system is suitable for academic institutions, organizational elections, and prototype-level secure voting research. Experimental evaluation demonstrates reliable face recognition accuracy under controlled lighting conditions with prevention of duplicate voting.

Introduction

Elections are central to democratic governance, and the integrity of voting systems directly affects public trust. Traditional paper ballots and early electronic voting machines (EVMs) face challenges such as ballot tampering, impersonation, and counting errors. Biometric authentication—particularly facial recognition—offers a promising solution, providing contactless, user-friendly, and cost-effective voter verification. Advances in computer vision and deep learning have improved face recognition accuracy, enabling real-time authentication for electoral processes.

Literature Review:

• Early electronic voting systems improved efficiency but lacked strong security measures, leaving them vulnerable to impersonation and tampering.

• Fingerprint-based biometric systems are effective but face hygiene, durability, and usability issues.

• Facial recognition algorithms, from Eigenfaces and Fisherfaces to modern deep learning embeddings, provide robust, non-invasive identification.

• Many prototype systems use a two-stage approach: face detection followed by matching against a registered database, often using Euclidean or cosine similarity with threshold-based decision rules.

• Secure database management, including lightweight options like SQLite, ensures reliable storage of biometric data.

Proposed Methodology:

• Integrates Python-based face recognition with Arduino Uno hardware and SQLite database management.

• Workflow: system initialization → voter registration → face authentication → voting via push buttons/LED feedback → database update → vote confirmation.

• Advantages: contactless authentication, low-cost, real-time verification, scalable for institutional elections, and prevention of duplicate voting.

Results & Discussion:

• The system achieves high recognition accuracy, provides immediate hardware feedback, prevents duplicate voting, and maintains efficient processing times.

Conclusion

This paper presented the design and implementation of a integrated with an Arduino Uno hardware interface. The proposed system combines biometric authentication, real-time facial recognition, database management, and hardware-controlled voting mechanisms to enhance the security and reliability of electronic voting processes in small-scale institutional environments. The system successfully demonstrated: 1) Accurate real-time face recognition using 128-dimensional facial embeddings 2) Secure voter registration with biometric storage in a local database 3) Prevention of duplicate voting through status flag validation 4) Reliable hardware interaction using push buttons, LCD display, and LED indicators 5) Seamless communication between Python software and Arduino via serial interface Experimental evaluation showed high recognition accuracy 95–97% under controlled lighting conditions, with an average total voting time of 6–8 seconds per voter. The integration of biometric verification with hardware-level vote capture significantly reduces the risk of impersonation and unauthorized voting.

References

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Copyright

Copyright © 2026 Ms. Sakshi Kumbhare, Ms. Sejal Patle, Ms. Princy Moon, Ms. Akansha Kankhure, Mr. Tejas Kurve. 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.