This project seeks to advance the capabilities of an exam hall monitoring system by incorporating emotion detection, alongside developing a user-friendly interface (UI) and adding features such as unauthorised material detection, hand gesture recognition, an alert system, and an exam management system. Building upon previous studies and leveraging advanced computer vision techniques, this enhanced system is designed to revolutionise exam supervision. The newly proposed UI allows for smooth interaction, serving both exam administrators and supervisors effectively. The integration of unauthorised material detection algorithms helps in identifying prohibited items within the exam hall, enhancing security and maintaining integrity. Hand gesture recognition provides additional scrutiny by detecting gestures that may indicate malpractice or irregular behaviour. The introduction of emotion detection technology aims to further refine the monitoring capabilities by assessing the emotional states of students, which can be indicative of dishonest behaviour. System has been implemented to immediately inform authorities of any suspicious activities, facilitating real-time intervention. The exam management system allows administrators to organise exam views, assign staff to specific halls, and manage various examination process aspects comprehensively. A central dashboard provides a detailed overview of all detected anomalies and irregularities, supporting informed decision-making and intervention strategies. This expanded system represents a significant technological advancement in exam supervision, merging cutting-edge functionalities to ensure a secure, fair, and emotionally considerate examination environment. By combining these features, the system not only detects anomalies but also proactively manages and prevents unethical practices, ultimately safeguarding the integrity of academic evaluations.
Led the development of a sophisticated exam hall allocation and monitoring system using the Django framework, with HTML, CSS, and JavaScript enhancing the user interface for greater accessibility and efficiency. Incorporated YOLOv3 for unauthorized material, emotion and head pose anomaly detection, safeguarding exam integrity. Streamlined administrative tasks and boosted real-time surveillance capabilities with MySQL backend integration.
For support, email ehamzathulfavas@gmail.com or download the project Report
