IoT-Based Smart E - Voting System using Face Authentication

Abstract: Traditional voting processes often grapple with critical vulnerabilities. To address these security and accessibility challenges, this paper presents the design and implementation of an "IoT-Based Smart E-Voting System using Face Authentication." The proposed system leverages Internet of Things (IoT) architecture integrated with advanced biometric facial recognition to create a secure, transparent, and highly efficient electoral platform. Utilizing a Raspberry pi interfaced with a camera module, the system authenticates voters in real-time by cross-referencing live facial capture against a pre-registered, secure database. Upon successful verification, the system grants the user access to a digital ballot. The cast vote is subsequently encrypted and transmitted via an IoT network to a centralized cloud server for real-time tallying, completely eliminating the possibility of duplicate voting or unauthorized access. The integration of biometric facial authentication ensures non-repudiation, while the IoT framework provides a scalable, rapid-response infrastructure for data management. System testing demonstrates high accuracy in face detection under varied lighting conditions and minimal latency in voter registration. Ultimately, this smart e-voting framework offers a robust, cost- effective, and user-friendly alternative to conventional paper-based methods, significantly enhancing the integrity and modernization of the electoral process.

Electronic voting system evolving rapidly. This project proposes a Smart Biometric E-Voting System that integrates face recognition, fingerprint verification, and OTP (One-Time Password) authentication to achieve a multi-layered secure voting mechanism.

During user registration, the voter’s face image is captured and trained using the Haar cascade classifier, and a fingerprint sample is enrolled into the system’s database. The trained model is then stored for future authentication. At the time of voting, the voter logs into the system where facial recognition is performed using the pre-trained model. If the face is successfully authenticated, the system proceeds to fingerprint verification to confirm the voter’s identity. Additionally, an OTP is sent to the registered mobile number for final authentication. Only upon successful verification of all three credentials does the system grant access to the voting panel, allowing the voter to cast their vote.

Keywords: Raspberry Pi, Face Recognition, Fingerprint Verification, OTP Authentication, Haar cascade Classifier, Secure E-Voting System, Biometric Authentication, Machine Learning, Python, Digital Voting, IOT Etc.