Abstract: As the global demand for secure and efficient voting processes increases, including biometric technology such as face detection and identification into voting systems appears to be a possible response. This literature review explores the advancements in utilizing deep learning-oriented face recognition within smart voting systems. Begin-ning with a concise summary of traditional voting methods and their inherent vulnerabilities, this paper examines the current role of face detection technologies in enhancing voter authentication and preventing electoral fraud. For real-time voter verification, the paper looks at and assesses the performance of a number of deep learning models, includ-ing convolution-based neural networks (CNNs). Additionally, key challenges related to accuracy, data security, and ethical concerns are discussed. By carefully analysing both new and existing systems, this study explains how deep learning has the potential to revolutionize voting processes. In order to ensure scalability, equity, and security, it also points out areas that require further research and development.
Keywords: Face Recognition, Deep learning, Convolution neural network.
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DOI:
10.17148/IJARCCE.2025.14105
