A simple yet effective convolutional neural network model to classify facial expressions

Abstract

Facial Expression Recognition (FER) is a form of nonverbal communication; it translates the internal and emotional state of a human being by changing one or several facial muscles. Automated classification of facial expressions has known a great progress over the last decade; we observed the appearance of new methods based on Deep Learning (DL) instead of traditional classification methods. In this paper we propose an improved method based on Convolutional Neural Networks (CNN) that responds to the problem of classification of the six basic emotions(anger, disgust, fear, happy, sad and surprise) plus the neutral case. We validated our model on three public databases and we achieved better results than the state-of-the-art: CK+ 88,23%, JAFFE 86.24%, KDEF 82.38%. Our accuracies out perform results from recently proposed traditional methods as well as DL based methods.