Self-Revoking Identity Driven Secure Systems represent an advanced approach to user authentication by integrating continuous biometric verification with dynamic session control. This system leverages facial recognition technology to ensure that only the legitimate user maintains access throughout an active session. Built using a MERN stack (MongoDB, Express.js, React.js, Node.js) combined with Python-based machine learning, the solution enables secure registration, login with face verification, and real-time monitoring. During authentication, facial data is captured and trained using image pre-processing and a Local Binary Patterns Histogram (LBPH) recognition model. Once logged in, a continuous verification loop periodically checks the user’s identity. If a mismatch is detected, the system automatically revokes access and logs out the user, preventing unauthorized usage. The system also includes features such as account management, password recovery, and security checks to enhance usability and robustness. By combining biometric verification with automated session termination, this approach significantly reduces risks associated with identity theft, session hijacking, and unauthorized access, making it highly suitable for secure applications in finance, healthcare, and e-governance.

Keywords: Self-Revoking Authentication, Continuous User Verification, Facial Recognition, LBPH Algorithm