Abstract
Election officials encounter a range of challenges during electoral processes, notably issues like inaccurate vote confirmation and instances of unauthorized voting. To tackle these challenges, we introduce a cutting-edge online voting system that enhances the efficiency and security of elections. This autonomous system employs a camera to capture the images of voters, which are subsequently stored in a secure database. Utilizing Blockchain technology and Convolutional Neural Networks (CNN), the captured images are analyzed to facilitate accurate voter identification. The CNN is trained on a diverse dataset of labeled images to ensure high prediction accuracy. Furthermore, our system integrates a dual-factor authentication approach, combining facial recognition with Email One-Time Password (OTP) verification to confirm the identities of voters. This innovative methodology aims to bolster the integrity and trustworthiness of online voting mechanisms, paving the way for a more transparent and inclusive digital democratic process.
Published Version
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