AI-Driven Voter Authentication and Fraud Detection System
DOI:
https://doi.org/10.15680/IJCTECE.2026.0903004Keywords:
OTP Verification, Iris Scanner, Fingerprint Sensor, Neon Database, 2 Step Verification, Artificial Intelligence.Abstract
Ensuring election integrity is crucial to uphold democratic values and prevent fraudulent activities such as fake voting and multiple voting attempts. Traditional voter verification methods, such as checking physical identity cards or signatures, can be easily manipulated or forged, leading to potential electoral fraud. Biometric systems, especially fingerprint recognition, are widely acknowledged as reliable because fingerprints are unique to each individual and difficult to replicate or fake. Fingerprint authentication eliminates the need for voters to carry physical IDs or documents, which can be lost, forged, or misused, making the voting process more secure and efficient. In the proposed system, fingerprint scanning serves as the first line of defense against fake voters. At the polling station, every voter is required to place their finger on a fingerprint scanner. The system compares the scanned fingerprint with a pre-registered fingerprint database that contains data of all eligible voters. If the scanned fingerprint matches a registered voter’s fingerprint, the system confirms the voter’s identity and eligibility to vote. Therefore, to add a second layer of security, an OTP (One-Time Password) verification step is integrated into the system. Therefore, to add a second layer of security, an OTP (One-Time Password) verification step is integrated into the system. Once the fingerprint scan is successful, an OTP is automatically generated and sent to the voter’s registered mobile number. The voter must enter this OTP at the polling terminal to confirm that they are in physical possession of their phone linked with the registered voter profile. This OTP verification acts as a dynamic security token, making it extremely difficult for someone to vote using stolen or fake biometric data.
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