Latent semantic analysis of facial action codes for automatic facial expression recognition

Abstract

For supervised training of automatic facial expression recognition systems, adequate ground truth labels that describe relevant facial expression categories are necessary. One possibility is to label facial expressions into emotion categories. Another approach is to label facial expressions independently from any interpretation attempts. This can be achieved via the facial action coding system (FACS). In this paper we present a novel approach that allows to automatically cluster FACScodes into meaningful categories. Our approach exploits the fact that FACScodes can be seen as documents containing terms -the action units (AUs) present in the codes-and so text modeling methods that capture co-occurrence information in low-dimensional spaces can be used. The FACScode derived descriptions are computed by Latent Semantic Analysis (LSA) and Probabilistic Latent Semantic Analysis (PLSA). We show that, as a high-level description of facial actions, the newly derived codes constitute a competitive alternative to both basic emotion and FACScodes. We have used them to train different types of artificial neural networks