ADVANCEMENT IN CONTINUOUS MONITERING OF MULTIMODAL BIOMETRIC USING MODULAR POINT SENSITIVITY TRACKING
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Abstract
In the Evolving landscape of Digital Examinations and remote interviews, ensuring continuous and reliable identity verification has become increasingly critical. After the covid pandemic, Online examination and interviews have made a remarkable impact on Global Education and Recruitment process for which in this modern examination and interview environment, ensuring authenticity and continuous monitoring of candidate is very much essential , Hence continuous user authentication is critical to maintaining academic integrity and security. Ensuring the Authenticated user at the login time is one time Security, monitoring the candidate is very much essential as they can use AI for searching the answers or any other person sitting next to them for helping to solve the given question. using multimodal Biometric by giving a sensitivity in the Border value along with recognizing new voice, moving of foreign items behind the candidate will be the key during continues Monitoring. Where this paper proposes a multimodal biometric framework using facial point tracking and sensitivity based co-ordinate analysis. A System is designed to capture and compare real time co-ordinate points which will be compared with pre-recorded values in Data Base. Where, the deviation beyond sensitivity threshold triggers a monitoring mechanism which logs inconsistencies and flags potential anomalies. Unlike conventional one-time authentication systems, the proposed solution continuously captures and compares real-time biometric data against pre-recorded reference Senstivity increased Border points, Coordinates of Foreign Objects behind the Candidate stored in a database. Facial coordinates are tracked within a predefined sensitivity threshold for physical movement and recognizes if any extra Noise is heard during the session.
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