Smart Teaching Using Human Facial Emotion Recognition (Fer) Model

Abstract

Emotion recognition has attracted  most of   numerous technical/ non-technical   fields due  to its variety of applications such as entertainment, surveillance, psychology, marketing and few tech domains are some of examples. Emotion recognition is done based on some changes in human face, which we call them as regions of interest such as eyes, eyebrows, forehead, cheeks, mouth etc. On the other side, Online mode of education has also been blooming from nowhere especially after the Covid-19 pandemic, all institution including universities and training centers have been adapted to online mode of education, but on a contrary we have to agree to the fact that  this mode of education is not as effective as traditional mode of education such as face-to-face [Classroom] teaching. So in this paper we try to provide a software solution to improve the online mode of education. We use  Facial Emotion Recognition (FER) model to deploy into one of testing web application which streams live video from the students camera and can able to detect the emotions of students/attendees. We use FER2013 datasets to train our model and have  used the google colab platform for testing of the model. we have obtained the good accuracy compared to our previous works. Presently we have developed our application for single device and  have been using only LAN protocol, in future this mode of technology  can be implemented at multiple device level using  WAN protocol . With the help of already available app named “IP webcam” and a web application developed by us using FLASK we were successfully able to recognize the emotion of students in the application.