Facial Action Units Detection to Identify Interest Emotion: An Application of Deep Learning

Abstract

For human beings, facial expression is one of the most powerful and natural ways to communicate their emotions and intentions. A human being is able to recognize facial expressions effortlessly, but for a machine, this task is very difficult. Today, facial expression recognition is proving to be one of the most relevant applications in many fields such as human–computer interaction, medicine, security, education, etc. Facial Action Coding System (FACS) is a method that describes face movements. This later became a main description tool used in the studies concerned with facial expression. In this paper, we propose an action units recognition system which reflects interest emotion using deep learning, particularly the Convolutional Neural Network (CNN) architecture “MobileNetV2”. Our choice of this system is motivated by its success in image classification. Our system allows detecting action units which define interest emotion from an input image. In other words, our classifier differentiates interest facial movements from other affective states. This identification is very useful in several areas, particularly in e-learning: For example, knowing whether a distance learner is interested or not in the course certainly influences the quality of learning and reduces the dropout rate. Our proposed approach presented a very satisfactory recognition rate despite the absence of a large database.