BML22 ID5 Intelligent student Attendance System using CNN and deep transfer learning: A review скачать в хорошем качестве

BML22 ID5 Intelligent student Attendance System using CNN and deep transfer learning: A review

by: Slimane Ennajar, Walid Bouarifi ,Anouar Dalli Student attendance system in a classroom during a learning session and when taking an examination is challenging. It is a time-consuming task due to an unusually large number of students present during a learning session or an examination. However, many studies have been proposed to improve the system, thus all those systems still have issues. In this paper, we present a review of the previous works on attendance systems based on machine learning algorithms and we propose an intelligent student Attendance System using CNN and deep transfer learning. The latter allows us to build a model for facial recognition of learners during a learning session in the classroom. Due to the covid19 pandemic, the students put on facial masks to protect themselves and to minimize the virus's spreading. Which creates an issue that wasn’t previously. In this work, we are taking this into consideration. The final step is to record the presence of students as an Excel file.