Facial Expression Recognition Using 3D Facial Feature Distances

Face plays an important role in human communication. Facial expressions and gestures incorporate nonverbal information which contributes to human communication. By recognizing the facial expressions from facial images, a number of applications in the field of human computer interaction can be facilitated. Last two decades, the developments, as well as the prospects in the field of multimedia signal processing have attracted the attention of many computer vision researchers to concentrate in the problems of the facial expression recognition. The pioneering studies of Ekman in late 70s have given evidence to the classification of the basic facial expressions. According to these studies, the basic facial expressions are those representing happiness, sadness, anger, fear, surprise, disgust and neutral. Facial Action Coding System (FACS) was developed by Ekman and Friesen to code facial expressions in which the movements on the face are described by action units. This work inspired many researchers to analyze facial expressions in 2D by means of image and video processing, where by tracking of facial features and measuring the amount of facial movements, they attempt to classify different facial expressions. Recent work on facial expression analysis and recognition has used these seven basic expressions as their basis for the introduced systems. Almost all of the methods developed use 2D distribution of facial features as inputs into a classification system, and the outcome is one of the facial expression classes. They differ mainly in the facial features selected and the classifiers used to distinguish among the different facial expressions. Information extracted from 3D face models are rarely used in the analysis of the facial expression recognition. This chapter considers the techniques using the information extracted from 3D space for the analysis of facial images for the recognition of facial expressions. The first part of the chapter introduces the methods of extracting information from 3D models for facial expression recognition. The 3D distributions of the facial feature points and the estimation of characteristic distances in order to represent the facial expressions are explained by using a rich collection of illustrations including graphs, charts and face images. The second part of the chapter introduces 3D distance-vector based facial expression recognition. The architecture of the system is explained by the block diagrams and flowcharts. Finally 3D distance-vector based facial expression recognition is compared with the conventional methods available in the literature.