An efficient facial expression recognition system with appearance-based fused descriptors

Abstract

Facial expressions have always been defined as the most efficient way to characterize emotions. Therefore, they have been used in various fields of applications such as human-computer interaction. With the continuous progress in terms of image processing and machine learning techniques, several new methods are proposed each year. However, their performance still needs to be improved and this task remains challenging. In this paper, we propose an automatic emotion recognition system through facial analysis. We compare the use of two distinct image descriptors namely, Local Binary Pattern and Histogram of Oriented Gradients with two different dimensionality reduction techniques namely, Principal Component Analysis and Locally Linear Embedding. Moreover, we also combine both descriptors in order to improve the system efficiency. For the classification part of the system, we choose to use the multi-class Support Vector Machine classifier for its generalization capabilities to distinguish between the six basic emotions. Finally, we assess the performance of the proposed system using three different benchmark facial expression datasets namely, JApanese Female Facial Expression (JAFFE), Karolinska Directed Emotional Faces (KDEF) and Radboud Faces Database (RaFD) which yield promising recognition rates with 97.16%, 90.12% and 95.54%, respectively.