VoteEase: An Online Voting Application Using Blockchain and Facial Recognition

Abstract

Election system trust must change as society is reshapedbydigitaltransformation.Ouronlinevotingplat- form offers safe, transparent, and decentralised elections by fusing DeepFace-powered facial recognition with the Avalanche blockchain. We guarantee smooth authentication while preserv- ing voter privacy by incorporating real-time facial verification. Everystage,fromvoterregistrationtovotecounting,isprotected by biometric and cryptographic measures. This working proto- type shows that safe, remote voting is not only a goal for the future but is something we can accomplish now.

Introduction

The project “Online Voting System Using Blockchain Technology and Facial Recognition” aims to modernize traditional voting by addressing issues like long queues, human error, and security risks. It integrates Avalanche blockchain for immutable, transparent vote recording and DeepFace facial recognition with liveliness checks for accurate voter identity verification, preventing fraud and impersonation.

Key features include OTP-based registration, candidate management, and secure smart contract-based voting. The system ensures speed, reliability, scalability, and strong security, with trade-offs like internet and Avalanche network dependency deemed acceptable.

The literature review highlights existing blockchain voting systems' limitations (Ethereum’s cost/scalability, spoofing risks in identity verification, usability issues) and explains how this system improves on them by combining fast Avalanche blockchain, dynamic facial recognition (using OpenCV and MediaPipe), and privacy-preserving cryptography (“accountable anonymity”).

The system architecture comprises:

• Frontend: React.js interface with facial recognition, OTP, MetaMask integration for secure voting.

• Backend: Node.js/Express.js managing user authentication, database (MongoDB), and facial recognition API.

• Blockchain: Avalanche smart contracts ensure tamper-proof vote recording and automated result calculation.

Performance results show improved speed (2-3 seconds per vote), enhanced security (no fraud detected), and greater transparency versus traditional voting. Additional features include multi-language support and real-time notifications.

Conclusion

In a increasingly digital world that cuts across industries such as finance, education, and healthcare, it is natural thatthe electoral process also has to adapt alongside. The purpose ofthisprojectwasnotsimplytodesignanotherapp;rather, it was to reimagine the form a secure and open electoral system in the 21st century might take. With the combinationof the transparency and security of blockchain with the high degreeofaccuracyinfacialrecognition,wehavebuiltasystem which guarantees that every vote is not only recorded but also respected and protected. Each voter is authenticated, every transaction is trackable, and the entire process is contained within a simple, user-friendly interface for all people regard- lessoftheirtechnologicalexpertise.Thedeploymentonthe Avalanche blockchain was intentional, as it enabled us to bypass the slow transaction times and prohibitive gas prices that define alternative networks like Ethereum. Furthermore, byincorporatingDeepFacefacialrecognitiontech,webrought a level of trust to the system whereby every vote is being cast by the right person. Combined with our React frontend, the result is a platform that is not just secure and fast but actually user-friendly as well. This demonstration guarantees us that secure internet voting is not only possible but also viable,with the potential for widespread application. It is not coding; it is a powerful declaration about the future development of democratic processes. That being said, we are aware that the journeycontinues.There’salwaysscopeforinnovation—beit with features that incorporate differently-abled users, or even AI-driven fraud detection. This isn’t about technology. This is about rebuilding trust—voter by voter, person by person, and community by community

References

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Copyright

Copyright © 2025 Taneeshka Naganath Reddy, Madhusudhan , Harsha N C, Sanket , Bindiya A R. 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.