Paper Title
AUTOMATICCHEATINGDETECTIONINEXAMHALLEXAMHALLUSING YOLO WITH FACE RECOGNITION

Abstract
The proliferation of technology in education has raised concerns about academic integrity, particularly during examinations. This study proposes an innovative approach for automatic cheating detection in exam halls using yolo with face recognition techniques. By leveraging computer vision and machine learning algorithms, the system aims to monitor students' activities during exams and detect suspicious behaviors indicative of cheating. The proposed system utilizes yolo for real-time video analysis to identify faces and track students' movements. Face recognition algorithms are then applied to authenticate students' identities and detect unauthorized individuals. Furthermore, the system employs image processing techniques to analyze exam papers for signs of cheating, such as copying or referencing external materials. Through the integration of these technologies, the proposed system offers a robust solution for enhancing exam integrity and maintaining academic standards in educational institutions. Keywords - Alogorithm,YOLOv7,CCTV,PatternRecognition,LiveMonitoring,ProctoringSoftware