Abstract
Most experimental studies of facial expression processing have used static stimuli (photographs), yet facial expressions in daily life are generally dynamic. In its original photographic format, the Karolinska Directed Emotional Faces (KDEF) has been frequently utilized. In the current study, we validate a dynamic version of this database, the KDEF-dyn. To this end, we applied animation between neutral and emotional expressions (happy, sad, angry, fearful, disgusted, and surprised; 1,033-ms unfolding) to 40 KDEF models, with morphing software. Ninety-six human observers categorized the expressions of the resulting 240 video-clip stimuli, and automated face analysis assessed the evidence for 6 expressions and 20 facial action units (AUs) at 31 intensities. Low-level image properties (luminance, signal-to-noise ratio, etc.) and other purely perceptual factors (e.g., size, unfolding speed) were controlled. Human recognition performance (accuracy, efficiency, and confusions) patterns were consistent with prior research using static and other dynamic expressions. Automated assessment of expressions and AUs was sensitive to intensity manipulations. Significant correlations emerged between human observers’ categorization and automated classification. The KDEF-dyn database aims to provide a balance between experimental control and ecological validity for research on emotional facial expression processing. The stimuli and the validation data are available to the scientific community.
Highlights
Research on facial expression processing has generally utilized static faces as stimuli, obtained from standardized databases such as the Pictures of Facial Affect (PoFA; Ekman and Friesen, 1976), the Karolinska Directed Emotional Faces (KDEF; Lundqvist et al, 1998), the NimStim Stimulus Set (Tottenham et al, 2002), the Radboud Faces Database (RaFD; Langner et al, 2010), FACES (Ebner et al, 2010) and others
We wanted to relate human observers’ performance and automated facial expression analysis, which had to be conducted for each stimulus
The statistical analyses were performed on the stimuli as the error term. This means that the recognition performance scores of the 96 participants were averaged for each of the 240 video-clip stimuli, which served as the units of analysis, with an N = 40 for each expression category
Summary
Research on facial expression processing (see reviews in Nelson and Russell, 2013; Calvo and Nummenmaa, 2016) has generally utilized static faces as stimuli, obtained from standardized databases such as the Pictures of Facial Affect (PoFA; Ekman and Friesen, 1976), the Karolinska Directed Emotional Faces (KDEF; Lundqvist et al, 1998), the NimStim Stimulus Set (Tottenham et al, 2002), the Radboud Faces Database (RaFD; Langner et al, 2010), FACES (Ebner et al, 2010) and others (for a review and evaluation, see Cowie et al, 2005; Anitha et al, 2010; Sandbach et al, 2012). The control of possible perceptual confounds with non-expressive factors that may affect expression recognition They involve low-level image properties of the stimuli, such as illumination and light source, size of the face relative to the background, head-face orientation, or changes in facial appearance like hair, make up, eyeglasses, jewelry, etc. The control of such factors may be critical for paradigms using neurophysiological (such as eventrelated potentials, ERPs; see Naples et al, 2015) or eyetracking (e.g., probability of first fixation in a particular face region, or pupillometry; e.g., Calvo and Nummenmaa, 2011) measures, which are sensitive to physical image properties To this end, all the face stimuli in our KDEF-dyn set are standardized in size, resolution, location, and frontal view, in addition to multiple low-level image properties (luminance, contrast, etc.)
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