A Comparative Analysis and Implementation of Face Recognition Voting Systems

Abstract

The Face Recognition Voting System uses facial recognition technology to authenticate voters in an effort to modernize and secure the election process. Voter fraud, impersonation, and lengthy verification processes are among the major problems with traditional voting techniques that this approach seeks to resolve. The technology ensures that only qualified voters can cast their ballots by swiftly and precisely verifying voters\' identities through the capture and analysis of their distinctive face features. It streamlines the procedure and increases efficiency by reducing reliance on manual verification and physical ID cards. The solution enhances accessibility while upholding high security standards and may be used in both physical polling places and distant electronic voting platforms. All things considered, this strategy encourages a clear, impenetrable, and easy-to-use voting process appropriate for contemporary democracies.

Introduction

Facial recognition technology is emerging as a secure and efficient solution to improve voter identification, reduce fraud, and enhance accessibility in modern voting systems. Unlike traditional methods (e.g., voter ID cards, signatures), it offers real-time, automated identity verification by comparing facial features with stored biometric data, allowing only eligible voters to cast a single vote.

The technology supports both on-site and remote voting, benefiting elderly, disabled, or rural populations. It reduces human errors, speeds up the voting process, and boosts election integrity and transparency.

Literature Review Highlights:

• Advanced Techniques: Deep learning models (CNNs) and methods like 3D facial mapping and liveness detection improve recognition accuracy, even under varying lighting or facial changes.

• Real-Time Applications: Already successful in surveillance and attendance systems, facial recognition has shown >97% accuracy in controlled settings (e.g., DeepFace, FaceNet).

• E-Voting Integration: Some systems combine facial recognition with OTPs or blockchain for secure remote voting, increasing trust and accessibility.

Methodology:

• System Architecture: Combines face detection, feature extraction, and biometric matching with secure backend and database.

• Algorithms Used: Includes Linear Discriminant Analysis (LDA), PCA, and deep neural networks to improve recognition accuracy and system robustness.

• Security Measures: Includes liveness detection, threshold-based matching, and fallback verification methods like OTPs.

Implementation & Performance:

• Accuracy Rates:

  • Overall: 96.7%

  • Normal lighting: 98%

  • Low lighting: 92%

  • Face partially covered: 89%

• Authentication Speed: Average of 1.8 seconds per user

• Voting Success Rate: 100% for authenticated users

• Duplicate Voting: Prevented using face re-verification logic

Technical Aspects:

• Database Design: Secures encrypted biometric and voting data.

• Deep Learning Models: Uses embeddings from models like FaceNet, ArcFace.

• Hyperparameters:

  • Learning Rate: 0.001

  • Epochs: 20–100

  • Embedding Size: 128 or 512

Conclusion

In contemporary cultures that seek to ensure secure and fair elections, voting process management has become essential. Identity theft and inefficiency are two common criticisms leveled at traditional voting methods. While some technological advancement, such computerized voting, has attempted to address these problems, the development of facial recognition technology presents a potential solution. Despite numerous attempts to boost voter turnout, problems persist, including the potential for fraud and the need for stringent security. This study introduces a face recognition-based voting system and assesses its efficacy in a simulated election environment. The accuracy of facial recognition, the voting system\'s dependability throughout the election, its effect on voter turnout and skip rate, and the usefulness of the interface were the four key considerations. The results substantiate the assertion that the facial recognition technology offers a reliable method of verifying voter identification by attaining a high degree of accuracy.

References

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Copyright

Copyright © 2025 B Manoj, Prakash O. Sarangamath. 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.