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In the context digital transformation, the necessity for secure and efficient virtual identity verification has become paramount. Traditional methods often fail to balance security, speed, and usability, leaving gaps in user authentication systems. This paper addresses the critical challenge of creating a virtual ID system that identifies a single profile with improved security, speed, and effectiveness. An innovative face recognition algorithm using dynamic content loading and deep learning techniques is proposed. The utilisation of OpenCV for face recognition and feature extraction, combined with advanced similarity calculation methods, the system achieves superior accuracy in profile authentication tasks. Extensive testing, including identical twin scenarios, demonstrates the robustness of the algorithm and its superiority over existing solutions such as Apple’s Face ID. In 150 of the tests conducted with identical twins, the algorithm consistently achieved 100% recognition accuracy. This breakthrough in facial recognition technology promises to create a triple authentication system, which will solve the problem of false positives in terms of identifying and authenticating people. This paper integrates principles from Computer Intelligence and Chatbots, emphasizing the application of deep learning techniques in enhancing virtual identity verification systems. This research contributes to the broader discourse on improving authentication mechanisms in the digital age.