VOTERS FRAUD DETECTION SYSTEM

Artificial Intelligence and Machine Learning Engineering

The integrity of the electoral process is a cornerstone of democratic governance, yet it faces persistent threats from fraudulent activities such as impersonation and duplicate voting. Traditional verification methods often fall short of providing robust security and efficiency. This paper presents a comprehensive Voter Fraud Detection System that leverages multi-modal biometric technology to enhance election integrity. The proposed system integrates fingerprint processing and facial recognition to ensure accurate and secure voter authentication. A centralized cloud architecture, built using frameworks like Flask/Django, facilitates real-time data management, verification, and scalability. The system employs Minutiae Points and AI Embeddings for biometric matching, achieving high accuracy rates of 90% and 95% for fingerprint and facial recognition, respectively. By providing a fast, reliable, and transparent verification process, this solution aims to build public trust, mitigate fraud, and modernize the electoral landscape. Future enhancements, including the integration of blockchain technology and additional biometric modalities, are also discussed.

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