Secure Biometric Voting System using Deep Learning and IOT

In order to greatly improve voting security and efficiency, this study investigates the creation and deployment of a unique Electronic Voting Machine (EVM) system coupled with biometric identification. Voting procedures have historically relied on paper ballots, a system beset by a number of issues, such as excessive voting, ballot paper loss or misplacement, environmental damage from paper consumption, and a drawn-out process for compiling results. To solve these problems, a sophisticated EVM system is suggested, which uses distinct biometric identifiers-facial recognition and fingerprints-for voter authentication and safe vote recording. Vote repetition and fake voting, which have been major issues with earlier voting systems, are successfully prevented by our EVM technology. This strong voter authentication strategy reduces the possibility of voting fraud, making the voting process more secure and dependable. The switch to this cutting-edge EVM technology is difficult, though. The study highlights important ramifications, such as how automation may affect employment, possible biases and mistakes in biometric technologies, and crucial privacy issues with using sensitive biometric data. Notwithstanding these difficulties, the suggested method offers a strong basis for upcoming improvements. Adding more biometric identifiers, such as iris recognition, improving the precision of existing biometric technologies, and fortifying data privacy protocols are some opportunities for future advancement.