Real-Time Facial Expression Detection Using Convolutional Neural Network

Abstract

This project involves identification of facial expressions that reveal human emotions can help com- puters to better assess the human state of mind, so as to provide a more customized interaction.

We explore the recognition of human facial expressions through a deep learning approach using a Convolutional Neural Network (CNN) algorithm. The system uses a labelled data set containing around 32,298 images with multiple facial expressions for training and testing. The pretraining phase involves a face detection subsystem with noise removal, including feature extraction. The generated classification model used for prediction can identify seven emotions of the Facial Action Coding System (FACS). The Facial Action Coding System (FACS) refers to a set of facial muscle

movements that correspond to a displayed emotion. Using FACS, we are able to determine the dis- played emotion of a participant. This analysis of facial expressions is one of very few techniques

available for assessing emotions in real-time.