• Santhi
• Abhi Rama Sarma
• N. Saikrishna
• P. Naresh
• P. Dhinakar

The COVID-19 pandemic has accelerated the shift to online learning, highlighting the need for innovative strategies to maintain academic integrity in virtual exams. This project introduces an advanced Proctored Online Examination System using deep learning and computer vision technologies to enhance security and efficiency. The system supports faculty and students, managed by super admins. Faculty members log in manually, while students register and authenticate securely. The student dashboard provides access to courses and exams, featuring a compiler for coding practice in C, C++, SQL, Python, and Java. Sophisticated deep learning algorithms, including OpenCV’s LBPH (Linear Binary Pattern Histogram), are used for face detection and recognition, ensuring continuous identity verification during exams. Initial testing shows high accuracy in verifying student identities, reducing cheating. This system offers a secure, interactive environment for online exams, supporting robust identity verification and dynamic learning. It has the potential to revolutionize online education by ensuring exam integrity, fostering greater trust in online learning platforms, and promoting equitable access to education.

Online Learning, Proctored Exams, Deep Learning, Computer Vision, Academic Integrity, Face Recognition, LBPH, Identity Verification, OpenCV, Interactive Learning

"AI – ENHANCED REMOTE EXAM MONITORING WITH COMPUTER VISION TECHNIQUES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 6, page no.165-173, June-2024, Available :http://www.jetir.org/papers/JETIRGJ06026.pdf

"AI – ENHANCED REMOTE EXAM MONITORING WITH COMPUTER VISION TECHNIQUES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 6, page no. pp165-173, June-2024, Available at : http://www.jetir.org/papers/JETIRGJ06026.pdf