Online Examination And Proctoring System

Abstract

The increasing demand for remote education and online assessments has necessitated the development of secure and reliable exam monitoring systems. This project presents an Online Examination And Proctoring System that ensures academic integrity by monitoring candidates in real-time through webcam and screen activity. Utilizing advanced technologies such as facial recognition, object detection, and behavior analysis, the system identifies and flags suspicious activities including presence of multiple faces, unauthorized device usage, and leaving the test environment. It offers a scalable and automated solution that reduces the need for human invigilators, thus enhancing accessibility and efficiency in digital examinations. The system is developed using Python, OpenCV, and machine learning models, and integrates seamlessly with popular learning management platforms.