| dc.description.abstract | Various methods have been developed to examine human facial expressions, including a
recent facial coding software known as FaceReader. Although a number of studies that examined its performance in identifying emotions reported promising results, the majority of studies relied on still images as emotion-inducing stimuli and exclusively examined FaceReader peak-values. The current study aimed to further examine FaceReader’s emotion recognition by assessing the utility of measurements that were taken over periods of time. Furthermore, this study directly compared FaceReader to facial electromyography (fEMG), a well-established method of measuring facial expressions, as well as to self-reports of emotion experience. In a repeated-measures and within-subject design, the emotions of sadness, disgust and fear were induced using video stimuli. The facial reactions of 26 participants were video recorded, while changes in facial muscle activity associated with the expression of the emotions were recorded using fEMG. Instead of peak values, the current study analysed the average measurements that were recorded during each clip. The video-clips were repeatedly presented, which was expected to result in a decrease in emotional reaction to allow evaluation of the instruments’ performances when emotion intensity decreases. The performance of both FaceReader and fEMG was inconsistent for all three emotions. However, FaceReader appeared to have a bias to identify neutral facial states as expressing sadness. Limitations of the current study that prevent from definite conclusions about the performance of the instruments are pointed out and are followed by suggestions for future research. | |
