Use of Automated Facial Image Analysis for Measurement of Emotion Expression

Facial expressions are a key index of emotion. They have consistent correlation with self-reported emotion (Keltner, 1995; Rosenberg & Ekman, 1994; Ekman & Rosenberg, in press) and emotion-related central and peripheral physiology (Davidson, Ekman, Saron, Senulis, & Friesen, 1990; Fox & Davidson, 1988; Levenson, Ekman, & Friesen, 1990). They putatively share similar underlying dimensions with self-reported emotion (e.g., positive and negative affect) (Bullock & Russell, 1984; Gross & John, 1997; Watson & Tellegen, 1985). Facial expressions serve interpersonal functions of emotion by conveying communicative intent, signaling affective information in social referencing (Campos, Bertenthal, & Kermoian, 1992), and more generally contributing to the regulation of social interaction (Cohn & Elmore, 1988; Fridlund, 1994; Schmidt & Cohn, 2001). As a measure of trait affect, stability in facial expression emerges early in life (Cohn & Campbell, 1992; Malatesta, Culver, Tesman, & Shephard, 1989). By adulthood, stability is moderately strong, comparable to that for selfreported emotion (Cohn, Schmidt, Gross, & Ekman, 2002), and predictive of favorable outcomes in emotion-related domains including marriage and personal well-being over periods as long as 30 years (Harker & Keltner, 2001). Expressive changes in the face are a rich source of cues about intraand interpersonal functions of emotion (cf. Keltner & Haitd, 1999).