Artificial Intelligence-Based Real-Time Facial Emotion Monitoring System

Facial expressions are the reflections of internal feelings of humans towards a situation. It is viewed as an effective tool of non-verbal communication. Analyzing these facial expressions give an insight into the human behaviour. In the education field, for effective tutoring system, understanding the emotional state of the students in the class is very important. But it is very difficult for teachers to observe every student all the time, so this gives the motivation to develop an automatic emotion monitoring system and sends the overall emotional behavior report to the teacher at the end of the class. The main objective of this proposed work is to develop a Real-Time facial emotion recognition (FER) system that keeps track of individual facial emotions. With this proposed work 7 universal human facial emotions can be recognized. FER-2013 dataset is used for training the CNN model. The existing models lack of giving accurate results with a conclusive report to understand the emotion analysis towards a situation in real-time. In this proposed work, we have implemented a deep learning model to monitor and recognize facial emotions in real-time and generates a report of all the emotions detected during the time interval and gives the count of each detected emotion. For face detection in real-time Viola-Jones algorithm is used. Our model gives a very good accuracy of 90.40% and can recognize emotions with very good accuracy in real-time.