i-manager Publications

Smart Voting System with Face Recognition and OTP Verification using Convolutional Neural Network

Gudipalli Rajani*, Guntapalli Leeladhar**, Garlapati Rupesh Chowdary***, Kanakala Lalitha Kumari****, Kornepati Hema Phaneeshwar*****

*-***** Department of Computer Science and Engineering, Vasireddy Venkatadri Institute of Technology, Guntur, Andhra Pradesh, India.

Periodicity:January - June'2025
DOI : https://doi.org/10.26634/jpr.12.1.21945

Abstract

In an era where secure digital solutions are essential for democratic integrity, this paper introduces a Smart Voting System that combines facial recognition and one-time password (OTP) verification to ensure reliable voter authentication. Utilizing Convolutional Neural Networks (CNNs) and the pre-trained MobileNetV2 architecture, the system accurately identifies users through webcam-based facial recognition. A secondary OTP layer, sent through email, ensures that only legitimate voters proceed to vote. Implemented as a web-based platform using Flask, the system offers real-time validation, robust fraud prevention, and a user-friendly interface. Experimental evaluations reveal high classification accuracy and reliability, proving its effectiveness in reducing impersonation and ensuring secure, transparent, and efficient elections. This approach provides a scalable framework adaptable to future electoral enhancements.

Keywords

Smart Voting, Facial Recognition, OTP, CNNs, MobileNetV2, Flask, Voter Authentication, Biometric Verification.

How to Cite this Article?

Rajani, G., Leeladhar, G., Chowdary, G. R., Kumari, K. L., and Phaneeshwarv, K. H. (2025). Smart Voting System with Face Recognition and OTP Verification using Convolutional Neural Network. i-manager’s Journal on Pattern Recognition, 12(1), 16-25. https://doi.org/10.26634/jpr.12.1.21945

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	North Americas,UK, Middle East,Europe		India	Rest of world
	USD	EUR	INR	USD-ROW
Pdf	35	35	200	20
Online	15	15	200	15
Pdf & Online	35	35	400	25

Smart Voting System with Face Recognition and OTP Verification using Convolutional Neural Network

Abstract

Keywords

How to Cite this Article?

References

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