Investigating Spontaneous Facial Action Recognition through AAM Representations of the Face

The Facial Action Coding System (FACS) [Ekman et al., 2002] is the leading method for measuring facial movement in behavioral science. FACS has been successfully applied, but not limited to, identifying the differences between simulated and genuine pain, differences betweenwhen people are telling the truth versus lying, and differences between suicidal and non-suicidal patients [Ekman and Rosenberg, 2005]. Successfully recognizing facial actions is recognized as one of the “major” hurdles to overcome, for successful automated expression recognition. How one should represent the face for effective action unit recognition is the main topic of interest in this chapter. This interest is motivated by the plethora of work in existence in other areas of face analysis, such as face recognition [Zhao et al., 2003], that demonstrate the benefit of representation when performing recognition tasks. It is well understood in the field of statistical pattern recognition [Duda et al., 2001] given a fixed classifier and training set that how one represents a pattern can greatly effect recognition performance. The face can be represented in a myriad of ways. Much work in facial action recognition has centered solely on the appearance (i.e., pixel values) of the face given quite a basic alignment (e.g., eyes and nose). In our work we investigate the employment of the Active Appearance Model (AAM) framework [Cootes et al., 2001, Matthews and Baker, 2004] in order to derive effective representations for facial action recognition. Some of the representations we will be employing can be seen in Figure 1. Experiments in this chapter are run across two action unit databases. The CohnKanade FACS-Coded Facial Expression Database [Kanade et al., 2000] is employed to investigate the effect of face representation on posed facial action unit recognition. Posed facial actions are those that have been elicited by asking subjects to deliberately make specific facial actions or expressions. Facial actions are typically recorded under controlled circumstances that include full-face frontal view, good lighting, constrained head movement and selectivity in terms of the type and magnitude of facial actions. Almost all work in automatic facial expression analysis has used posed image data and the Cohn-Kanade database may be the database most widely used [Tian et al., 2005]. The RU-FACS Spontaneous Expression Database is employed to investigate how these same representations affect spontaneous facial action unit recognition. Spontaneous facial actions are representative of “real-world” facial