A Deep Learning Based Human Emotions Recommendation System

Abstract

Classroom environments are affected by legions of factors that are difficult to detect by college supervising authorities. Evaluating the student-teacher interaction by looking at the student behaviour from outside the class can simply provide us a shallow understanding of what actually is happening within the classroom. In order to gain a greater depth of understanding, facial expressions of the students can be evaluated. Facial expressions are one of the most important cues for sensing human emotions and behavioral aspects among st humans. Neural networks, and deep learning in general, are far more effective at categorizing such emotions due to their robust designs and accuracy in predictions. We also contrast our deep learning approach with conventional shallow learning based approaches and show that a convolutional neural network is far more effective at learning representations of facial expression data.