Maintaining the integrity and fairness of elections depends on the validity of voter identification, especially in situ- ations where voting is done remotely. In situations where strong cryptographic methods are not accessible, biometrics becomes a feasible substitute. This study investigates the application of facial recognition technology for distant voter identification. It looks at the architectural choices, technical complexities, and lingering issues, such as privacy concerns and dispute settlement. The research suggests integrating strong anti-spoofing strategies into facial recognition systems to combat fraudulent attempts, such as presentation attacks that use fictitious images or movies. The Multi-Task Cascaded Convolutional Neural Network (MTCNN) model, which is well-known for its effectiveness in facial detection and alignment tasks, is a key component of this methodology. Additionally, the paper addresses the integration of feature extraction techniques based on deep learning.