The face can communicate varied personal information including subjective emotion, communicative intent, and cognitive appraisal. Accurate interpretation by observer or computer interface depends on attention to dynamic properties of the expression, context, and knowledge of what is normative for a given individual. In two separate studies, we investigated individual differences in the base rate of positive facial expression and in specific facial action units over intervals from 4 to 12 months. Facial expression was measured using convergent measures, including facial EMG, automatic feature-point tracking, and manual FACS coding. Individual differences in facial expression were stable over time, comparable in magnitude to stability of self-reported emotion, and sufficiently strong that individuals were recognized on the basis of their facial behavior alone at rates comparable to that for a commercial face recognition system (Facelt from Identix). Facial action units convey unique information about person identity that can inform interpretation of psychological states, person recognition, and design of individuated avatars.

The Facial Action Coding System (FACS) [23] is an objective method for quantifying facial movement in terms of component actions. This system is widely used in behavioral investigations of emotion, cognitive processes, and social interaction. The coding is presently performed by highly trained human experts. This paper explores and compares techniques for automatically recognizing facial actions in sequences of images. These techniques include analysis of facial motion through estimation of optical flow; holistic spatial analysis, such as principal component analysis, independent component analysis, local feature analysis, and linear discriminant analysis; and methods based on the outputs of local filters, such as Gabor wavelet representations and local principal components. Performance of these systems is compared to naive and expert human subjects. Best performances were obtained using the Gabor wavelet representation and the independent component representation, both of which achieved 96 percent accuracy for classifying 12 facial actions of the upper and lower face. The results provide converging evidence for the importance of using local filters, high spatial frequencies, and statistical independence for classifying facial actions.