Generation of Facial Expression Map using Supervised and Unsupervised Learning

Recently, studies of human face recognition have been conducted vigorously (Fasel & Luettin, 2003; Yang et al., 2002; Pantic & Rothkrantz, 2000a; Zhao et al., 2000; Hasegawa et al., 1997; Akamatsu, 1997). Such studies are aimed at the implementation of an intelligent man-machine interface. Especially, studies of facial expression recognition for humanmachine emotional communication are attracting attention (Fasel & Luettin, 2003; Pantic & Rothkrantz, 2000a; Tian et al., 2001; Pantic & Rothkrantz, 2000b; Lyons et al., 1999; Lyons et al., 1998; Zhang et al., 1998). The shape (static diversity) and motion (dynamic diversity) of facial components such as the eyebrows, eyes, nose, and mouth manifest expressions. Considering facial expressions from the perspective of static diversity because facial configurations differ among people, it is presumed that a facial expression pattern appearing on a face when facial expression is manifested includes person-specific features. In addition, from the viewpoint of dynamic diversity, because the dynamic change of facial expression originates in a person-specific facial expression pattern, it is presumed that the displacement vector of facial components has person-specific features. The properties of the human face described above reveal the following tasks. The first task is to generalize a facial expression recognition model. Numerous conventional approaches have attempted generalization of a facial expression recognition model. They use the distance of motion of feature points set on a face and the motion vectors of facial muscle movements in its arbitrary regions as feature values. Typically, such methods assign that information to so-called Action Units (AUs) of a Facial Action Coding System (FACS) (Ekman & Friesen, 1978). In fact, AUs are described qualitatively. Therefore, no objective criteria pertain to the setting positions of feature points and regions. They all depend on a particular researcher’s experience. However, features representing facial expressions are presumed to differ among subjects. Accordingly, a huge effort is necessary to link quantitative features with qualitative AUs for each subject and to derive universal features therefrom. It is also suspected that a generalized facial expression recognition model that is applicable to all subjects would disregard person-specific features of facial expressions that are borne originally by each subject. For all the reasons described above, it is an important task to establish a method to extract person-specific features using a common approach to every subject, and to build a facial expression recognition model that incorporates these features. O pe n A cc es s D at ab as e w w w .in te ch w eb .o rg