Modelling, Classification and Synthesis of Facial Expressions

The field of computer vision endeavours to develop automatic approaches to the interpretation of images from the real world. Over the past number of decades researchers within this field have created systems specifically for the automatic analysis of facial expression. The most successful of these approaches draw on the tools from behavioural science. In this chapter we examine facial expression analysis from both a behavioural science and a computer vision perspective. First we will provide details of the principal approach used in behavioural science to analyze facial expressions. This will include an overview of the evolution of facial expression analysis, where we introduce the field of facial expression analysis with Darwin’s initial findings (Darwin, 1872). We then go on to show how his findings were confirmed nearly 100 years later by Ekman et al. (Ekman et al., 1969). Following on from this we provide details of recent works investigating the appearance and dynamics of facial expressions. Given these foundations from behavioural science, we appraise facial expression analysis from a computer vision perspective. Here researchers attempt to create automated computational models for facial expression classification and synthesis. This chapter is divided into three sections, each of which deals with a different, but related problem within the field of facial expression analysis: I. Classification of facial expressions: Facial expressions play a major role in human communication. A system capable of interpreting and reacting to facial expressions would represent a major advancement in the field of human computer interaction. Although initial investigations into facial expressions focussed on classifying the six primary expressions (joy, sadness, fear, surprise, anger and disgust), in more recent years the focus has changed, due to a need for a consistent representation of facial expressions. Researchers began to concentrate on classifying expressions in terms of the individual movements that make up facial expressions. In this section, we appraise the current state of the art in facial expression classification, and provide details of our research to date in this area. II. Modelling the dynamics of facial expressions: Recent research has shown that it is not just the particular facial expression, but also the associated dynamics that are important when attempting to decipher its meaning. The