Year: 2013
Type: dissertation
Abstract: FaceReader is software designed to measure six emotional facial expressions \nquickly and easily. We tested the validity of FaceReader by asking 30 participants to \nimitate unambiguous pictures of ???happy??? and ???angry??? emotional expressions. Participants \nwere video recorded while facial electromyography (EMG) measured activity of their \nzygomaticus major (i.e., cheek muscle) and corrugator supercilii (i.e., brow muscle). As \nhypothesized, FaceReader???s assessments of ???happy??? expressions were most strongly \ncorrelated with zygomaticus activity (median r = .723, p < .001) and assessments of \n???angry??? expressions were most strongly correlated with corrugator activity (median r = \n.55, p < .05). Corrugator activity was also significantly correlated with FaceReader???s \nassessments of ???disgusted??? (median r = .51, p < .05) and ???sad??? (median r = .42, p < .01) \nexpressions, consistent with research that the corrugator is involved in negative affect in general. These data provide preliminary evidence that FaceReader is a valid measure of \nemotional expressions.
Author: J. Trevor D’Arcey
Language: English
Topic: Emotion and Mood Recognition
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2193632595