Classroom student emotions classification from facial expressions and speech signals using deep learning

Abstract

Classroom environment is a competent platform for the students to learn and improve their understanding of the subject. An instructor’s primary responsibility lies in managing the students in a way they feel interested and focused during the class. With the aid of automated systems based on artificial intelligence, an instructor can get feedback on the students’ attention span in the class by monitoring their emotions using learning algorithms that can prove to be effective to improve the teaching style of the instructor that can in turn have positive effects on the class. In this paper, we propose an LSTM recurrent neural network trained on an emotional corpus database to extract the speech features and convolutional neural networks trained on the FER2013 facial emotion recognition database were used to predict the speech and facial emotions of the students respectively, in real-time. The live video and audio sequence of the students captured is fed to the learned model to classify the emotions individually. Once the emotions such as anger, sadness, happiness, surprise, fear, disgust and neutral were identified, a decision-making mechanism was used to analyze the predicted emotions and choose the overall group emotion by virtue of the highest peak value achieved. This research approach has the potential to be deployed in video conferences, online classes etc. This implementation proposal should effectively improve the classification accuracy and the relatability of the detected student emotions and facilitate in the design of sophisticated automated learning systems that can be a valuable tool in evaluating both the students and the instructors. The adapted research methodologies and their results are discussed and found to perform suggestively better than the other research works used in the comparison.