• Shreyans Jain
• Nikhil Sinha

Due to their ability to streamline attendance tracking and reduce errors, automated attendance monitoring systems have gained popularity in recent years. In this paper, we propose a robotized participation observing framework utilizing simulated intelligence methods, explicitly face acknowledgment. The framework is intended to take various countenances all at once and record them to check participation. The proposed framework utilizes a profound learning-based face acknowledg- ment calculation to naturally distinguish people and imprint their participation. The framework is carried out utilizing Python programming language and OpenCV library. The system’s results demonstrate that it efficiently and accurately records attendance. The proposed framework can possibly change participation following in different settings, including schools, colleges, and working environments. This paper gives a definite portrayal of the framework’s philosophy, execution, and results, alongside a writing survey and future examination bearings

Automated attendance monitoring, AI-based face recognition, Deep learning, Computer vision, OpenCV, Python programming language, Accuracy, Efficiency, Streamlin- ing, Error reduction

"Automated Attendance Monitoring Systems", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 5, page no.120-127, May-2024, Available :http://www.jetir.org/papers/JETIRGG06020.pdf

"Automated Attendance Monitoring Systems", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 5, page no. pp120-127, May-2024, Available at : http://www.jetir.org/papers/JETIRGG06020.pdf