AI Enhanced E-Voting System Securing Elections with Face Recognition and OTP Authentication in India

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This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2024 | Volume : 14 | Issue : 03 | Page : 21 30
    By

    Vaibhav Digole,

  • Trupti Mate,

  • Pranit Dhondji,

  • Sanskar Malpnai4,

  1. Student,, Department of Electronics & Telecommunication, Smt. Kashibai Navale College of Engineering (SKNCOE),Vadgaon, Pune,, Maharashtra, India
  2. Student,, Department of Electronics & Telecommunication, Smt. Kashibai Navale College of Engineering (SKNCOE),Vadgaon, Pune,, Maharashtra, India
  3. Student,, Department of Electronics & Telecommunication, Smt. Kashibai Navale College of Engineering (SKNCOE),Vadgaon, Pune,, Maharashtra, India
  4. Student,, Department of Electronics & Telecommunication, Smt. Kashibai Navale College of Engineering (SKNCOE),Vadgaon, Pune,, Maharashtra, India

Abstract

The E-Voting domain seeks to leverage technology to address these issues, enabling citizens to vote securely and conveniently while maintaining the transparency of the electoral process. Machine learning algorithms like Haar cascade and CNN will enhance the system’s security, accuracy, and efficiency by leveraging data- driven approaches. Conventional voting techniques frequently encounter obstacles including identity theft, convoluted processes, and hold-ups in the processing of results. This article suggests a complex voting system that combines face recognition with One-Time Password (OTP) authentication to improve efficiency and security to overcome these problems. By utilizing machine learning techniques such as Convolutional Neural Networks (CNN) and Haar cascades, along with Python’s robust libraries, the system seeks to enhance the electoral process’s dependability and efficiency. By improving the process’ accessibility, security, and transparency for all citizens, the solution aims to guarantee voting integrity and operational effectiveness. This sophisticated method presents a possible alternative to standard electronic voting machines, which have shown to be more labor-intensive and unreliable in democracies like India. This approach constitutes a major step in modernizing and enhancing India’s political process, given the growing need for secure electronic services and the difficulty of low voter turnout owing to regional mobility.  

Keywords: Machine Learning, OTP verification, Haar-cascade Algorithm, CNN, face recognition

[This article belongs to Current Trends in Signal Processing ]

How to cite this article:
Vaibhav Digole, Trupti Mate, Pranit Dhondji, Sanskar Malpnai4. AI Enhanced E-Voting System Securing Elections with Face Recognition and OTP Authentication in India. Current Trends in Signal Processing. 2024; 14(03):21-30.
How to cite this URL:
Vaibhav Digole, Trupti Mate, Pranit Dhondji, Sanskar Malpnai4. AI Enhanced E-Voting System Securing Elections with Face Recognition and OTP Authentication in India. Current Trends in Signal Processing. 2024; 14(03):21-30. Available from: https://journals.stmjournals.com/ctsp/article=2024/view=183892


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Regular Issue Subscription Review Article
Volume 14
Issue 03
Received 13/08/2024
Accepted 05/10/2024
Published 18/11/2024
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