Facial Emotion Recognition through Neural Networks

Abstract

Humans often express themselves through facial expressions. Deep learning techniques are used as an efficient system application process in research on the advancement of artificial intelligence technology in human-computer interactions. As an illustration, let’s say someone tries to communicate by using facial expressions. Some people who see it occasionally cannot foresee the expression or emotion it may evoke. Psychology includes study and evaluation of inferences in interpreting a person’s or group of people’s emotions when interacting in order to recognize emotions or facial expressions. Indeed, a convolutional neural networks (CNN) model may be learned to assess images and recognize facial expressions. This study suggests developing a system that can classify and forecast facial emotions using feature extraction and real-time Convolution Neural Network (CNN) technology from the OpenCV library. We have chosen FER 2013 Dataset as the main dataset for our study. Face detection, extraction of facial features, and facial emotion categorization are the three key procedures that make up the research that was implemented.