Biometric Based Voter Authentication System

Voting with Fingerprint Technology and the Internet of Things (IoT) will encourage greater confidence in voting due to added security and efficiency during elections. Technology has evolved and traditional voting systems suffer from various issues including impersonation, duplicate voters, and human error when counting or recording the vote. The Fingerprint-Based Voting System using IoT will use fingerprint technology to authenticate votes in order to ensure that only registered voters will be able to vote. Once the voter is verified by the system, that voter can cast his/her vote via push buttons and the system will record the vote at that time. Since data transmissions occur in real-time from IoT Devices to a cloud services provider, the vote can be monitored from anywhere and results will be processed more quickly than under current voting standards. The Fingerprint Based Voting will help avoid multiple voting by changing the status of the voter after they vote. Most importantly, the Fingerprint Voting System will minimize human business and improve voting accuracy, transparency, and reliability for today's digital voting methodologies.

Fingerprint Recognition, IoT, Biometric Authentication

1. Atharva Jamkar et al., “Biometric Voting Machine Using Fingerprint Scanner and Arduino,” ICCT, 2019. [Google Scholar] [Crossref]

2. R. Akila Mukesh et al., “Fingerprint-Based Voting System Using Aadhaar Card,” IJESR, 2019. [Google Scholar] [Crossref]

3. Shubham Gupta et al., “Electronic Voting System Using Face Recognition,” ICCMC, 2021. [Google Scholar] [Crossref]

4. A.M. Jagtap et al., “Biometric Voting System Using Raspberry Pi,” ICOEI, 2019. [Google Scholar] [Crossref]

5. Poornima Kamble et al., “Fingerprint-Based Electronic Voting Machine,” Journal of Analog and Digital Devices, 2019. [Google Scholar] [Crossref]

• What the Desert Fathers Teach Data Scientists: Ancient Ascetic Principles for Ethical Machine-Learning Practice
• Comparative Analysis of Some Machine Learning Algorithms for the Classification of Ransomware
• Comparative Performance Analysis of Some Priority Queue Variants in Dijkstra’s Algorithm
• Transfer Learning in Detecting E-Assessment Malpractice from a Proctored Video Recordings.
• Dual-Modal Detection of Parkinson’s Disease: A Clinical Framework and Deep Learning Approach Using NeuroParkNet