A Dual-Layer Verification Framework for Efficient Digital Voting System Based on Facial Detection

The rapid digital transformation of governance systems has intensified the demand for secure, transparent, and efficient voting mechanisms.Conventional electronic voting systems often suffer from authentication weaknesses, susceptibility to impersonation, and limited public trust.This paper proposes a novel dual-layer verification framework for an efficient digital voting system based on facial detection, designed to enhance voter authentication reliability and system integrity.The framework integrates facial biometric verification as the primary layer and encrypted credential validation as the secondary layer to mitigate identity fraud and unauthorized access.A critical architectural analysis is presented, followed by a structured methodology that ensures scalability, robustness, and operational feasibility.Experimental evaluation demonstrates significant improvement in authentication accuracy, system responsiveness, and resistance to malicious attacks compared to prior approaches.The analytical results validate the effectiveness of the proposed framework in addressing contemporary digital voting challenges.The study establishes a strong foundation for future optimization through intelligent and meta-heuristic techniques.