Abstract
Previous work in automatic affect analysis (AAA) has emphasized static expressions to the neglect of the dynamics of facial movement and considered head movement only a nuisance variable to control. We investigated whether the dynamics of head and facial movements apart from specific facial expressions communicate affect in infants, an under-studied population in AAA. Age-appropriate tasks were used to elicit positive and negative affect in 31 ethnically diverse infants. 3D head and facial movements were tracked from 2D video. Head angles in the horizontal (pitch), vertical (yaw), and lateral (roll) directions were used to measure head movement; and the 3D coordinates of 49 facial points to measure facial movements. Strong effects were found for both head and facial movements. Angular velocity and angular acceleration of head pitch, yaw, and roll were higher during negative relative to positive affect. Amplitude, velocity, and acceleration of facial movement were higher as well during negative relative to positive affect. A linear discriminant analysis using head and facial movement achieved a mean classification rate of positive and negative affect equal to 65% (Kappa = 0.30). Head and facial movement individually and in combination were also strongly related to observer ratings of affect intensity. Our results suggests that the dynamics of head and facial movements communicate affect at ages as young as 13 months. These interdisciplinary findings from behavioral science and computer vision deepen our understanding of communication of affect and provide a basis for studying individual differences in emotion in socio-emotional development.
Highlights
Within the past 5 years, there have been dramatic advances in automatic affect analysis (AAA)
To understand the beginnings of non-verbal communication, we explored whether AAA could reveal the extent to which the dynamics of head and facial movement of infants communicate affective meaning independent of the morphology of facial expression
We investigated whether AAA was sufficiently advanced to reveal the dynamics of emotion communication in infants
Summary
Within the past 5 years, there have been dramatic advances in automatic affect analysis (AAA). From person-specific feature detection and recognition of facial expressions in posed behavior in controlled settings (Zeng et al, 2012), AAA has progressed to person-independent feature detection and recognition of facial expression in spontaneous behavior in diverse settings (Valstar et al, 2013, 2015; Sariyanidi et al, 2015). These include therapy interviews, psychology research, medical settings, and webcam recordings in homes (Cohn and De la Torre, 2015). Annotating intensity variation from video is arduous; and computational approaches almost exclusively follow the lead of behavioral science in emphasizing static facial expressions and AUs
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