Authors : Dr. N. Madhusudhana Reddy; Shaik Rafi

Volume/Issue : Volume 10 - 2025, Issue 6 - June

Google Scholar : https://tinyurl.com/4dd3d6t3

DOI : https://doi.org/10.38124/ijisrt/25jun1032

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Voting has traditionally been conducted using a project ballot, an electronic voting machine (evm) based on direct response electronic (dre), or identical ballot boxes. To address the limitations of the existing voting system, this study proposes a digital voting method that utilizes a deep learning algorithm with iris recognition technology. The iris recognition- based voting system is a program that employs an individual's eye iris pattern to verify their identity. An automated biometric identification system known as iris recognition analyses video footage of an individual's iris to identify unique patterns that are distinct, consistent, and easily visible from a distance. The proposed technology ensures that voters can only submit one ballot, and it has the capability to detect and prevent multiple entries by the same individual. Additionally, since the Aadhaar is linked to the voter ID, this approach eliminates the need for the user to carry a voter ID that contains the required information. This enhances digitization by digitally verifying the biometric and iris pattern on each user's aadhar card, allowing the voter's iris to be captured and used as an identity verification method at the polling station through a simple iris scan. The four processes involved in the iris recognition process are image capture, iris segmentation, feature extraction, and pattern matching. Due to its exceptional accuracy, iris recognition is considered one of the most dependable biometric modalities. As a result of incorporating the latest advancements, this system enhances digital voting and eliminates the primary drawbacks associated with traditional voting methods.

Keywords : Deep Learning, Iris Recognition, Image Segmentation, Databases.

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