Facial expression analysis of students in classroom using machine learning technique

Facial Expression recognition is an important aspect in building pervasive environments. Such Facial expression recognition plays a key role in many domains and applications, such as: Video Retrieval, Video Surveillance, Health Care, Human-Computer Interaction and Entertainment Industry. The aim of the paper is to analyze the facial expression of the students in a classroom which in turn helps in motivating and increasing the students’ attention. The methodology involves expression detection that uses Open Source Computer Vision Library (Open CV) and Dlib for Feature extraction which detects the location of facial landmark points and finally classifies the facial expressions that is implemented using SVM (Support Vector Machines). This recognition of facial expression provides insights about the following findings such as i) the students who are not attentive inside the classroom ii) students distraction rate iii) time interval between various expressions. This in turn helps the faculty in improvising their teaching learning process. The idea could also be further enhanced in a live video streaming for the entire educational institution that identifies the students’ emotion in every part of the campus with the availability of high computing capacity.