Citation

S., Sudha and A., Srinivasan and T., Gayathri Devi and Roslee, Mardeni (2024) Biometric Smart Voting System Using Deep Learning with Internet of Things. In: 2024 Multimedia University Engineering Conference (MECON), 23-25 July 2024, Cyberjaya, Malaysia.

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Abstract

This paper proposes a smart biometric voting system. Every election has a number of various kinds of challenges that the electoral commission must deal with. The election commission's most common problems include improper confirmation of votes cast, duplicate votes cast, or votes cast illegally. In this study, a new and secure voting system that combines IOT technology with time management to improve security is designed. One of the safest biometric methods for identifying a person is facial recognition. Preventing duplicate voting is the primary objective. This paper focuses on sophisticated voting systems using face recognition and Finger print technologies. In this system, when a voter shows up to cast their ballot, the system recognizes their face and compares it to the voter’s database image. Once the faces match, we obtain the voter's data from our computer and verify the voter's finger print. If both details match, the voter is permitted to vote for a ballot. There is a possibility of dual voter registration, and phoney votes can be cast. The current voting mechanism needs to be more secure. So, this article addresses voter security with the overall goal of improving the robustness and reliability of the voting system through the removal of dummy voters. Following a successful voting session, the voting details are stored in the cloud using IOT, and the voter's vote data is counted automatically. The election result can be displayed at the end of the day. It will reduce the manipulation of the voting machine.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Face recognition, IOT cloud, Finger print
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Engineering (FOE)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	06 Feb 2025 06:37
Last Modified:	06 Feb 2025 06:37
URII:	http://shdl.mmu.edu.my/id/eprint/13365

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