Students Attention Monitoring and Alert System for Online Classes using Face Landmarks
Mukul Lata Roy, D. Malathi, J. D. Dorathi Jayaseeli · 2021 IEEE 4th International Conference on Computing, Power and Communication Technologies (GUCON) · 2021
With the advantage of the web and present-day innovation, it has been made conceivable to direct and conduct daily classes and regular assessments for students at educational institutions remotely. This has become all the more important with the recent shift of working life for students and teachers to the online realm. Nonetheless, this sort of web-based learning does not have the same advantage of intuitive human interaction and correspondence that physical homeroom learning has. In order to improve the experience of web-based learning, instructors may find that it is useful to have some system to alert them when a student appears to stop focusing during online sessions. Facial recognition and facial expression recognition have seen great strides forward in the recent years, with various methods being developed to facilitate the detection of human faces as well as the classification of facial expressions. Various techniques have seen success in the past, such as the use of machine learning techniques that use Histogram of Oriented Gradients (HOG), Local Binary Patterns (LBP), etc., and deep learning methods such as Residual Neural Network (RESNET), Visual Group Geometry 16 (VGG16), LeNet, to name but a few. To solve this problem of waning student concentration due to lack of human supervision during online classes, this paper aims to develop a Students Attention Monitoring and Alert Model (S-AMAM).