Photogenic Facial Expression Discrimination

Abstract

Facial Expression Recognition Systems (FERS) are usually applied to human-machine interfaces, enabling services that require identification of the emotional state of the user. This paper presents a new approach to the facial expression recognition problem, by addressing the question of whether or not it is possible to classify previously labeled photogenic and non-photogenic face images, based on their appearance. A Multi-Layer Perceptron (MLP) is trained with PCA representations of the face images to learn the relationships between facial expressions and the concept of a good photography of the face of a person. In the experiments, the generalization performances using MLP and Support Vector Machines (SVM) were analyzed. The results have shown that Principal Component Analysis (PCA) combined with MLP represent a promising approach to the problem.