What's in a face: Automatic facial coding of untrained study participants compared to standardized inventories.

Automatic facial coding (AFC) is a novel research tool to automatically analyze emotional facial expressions. AFC can classify emotional expressions with high accuracy in standardized picture inventories of intensively posed and prototypical expressions. However, classification of facial expressions of untrained study participants is more error prone. This discrepancy requires a direct comparison between these two sources of facial expressions. To this end, 70 untrained participants were asked to express joy, anger, surprise, sadness, disgust, and fear in a typical laboratory setting. Recorded videos were scored with a well-established AFC software (FaceReader, Noldus Information Technology). These were compared with AFC measures of standardized pictures from 70 trained actors (i.e., standardized inventories). We report the probability estimates of specific emotion categories and, in addition, Action Unit (AU) profiles for each emotion. Based on this, we used a novel machine learning approach to determine the relevant AUs for each emotion, separately for both datasets. First, misclassification was more frequent for some emotions of untrained participants. Second, AU intensities were generally lower in pictures of untrained participants compared to standardized pictures for all emotions. Third, although profiles of relevant AU overlapped substantially across the two data sets, there were also substantial differences in their AU profiles. This research provides evidence that the application of AFC is not limited to standardized facial expression inventories but can also be used to code facial expressions of untrained participants in a typical laboratory setting.

Automatic facial coding versus electromyography of mimicked, passive, and inhibited facial response to emotional faces.

Decoding someone's facial expressions provides insights into his or her emotional experience. Recently, Automatic Facial Coding (AFC) software has been developed to provide measurements of emotional facial expressions. Previous studies provided first evidence for the sensitivity of such systems to detect facial responses in study participants. In the present experiment, we set out to generalise these results to affective responses as they can occur in variable social interactions. Thus, we presented facial expressions (happy, neutral, angry) and instructed participants (N = 64) to either actively mimic, to look at them passively (n = 21), or to inhibit their own facial reaction (n = 22). A video stream for AFC and an electromyogram (EMG) of the zygomaticus and corrugator muscles were registered continuously. In the mimicking condition, both AFC and EMG differentiated well between facial expressions in response to the different emotional pictures. In the passive viewing and in the inhibition condition AFC did not detect changes in facial expressions whereas EMG was still highly sensitive. Although only EMG is sensitive when participants intend to conceal their facial reactions, these data extend previous findings that Automatic Facial Coding is a promising tool for the detection of intense facial reaction.

Read My Face: Automatic Facial Coding Versus Psychophysiological Indicators of Emotional Valence and Arousal.

Facial expressions provide insight into a person's emotional experience. To automatically decode these expressions has been made possible by tremendous progress in the field of computer vision. Researchers are now able to decode emotional facial expressions with impressive accuracy in standardized images of prototypical basic emotions. We tested the sensitivity of a well-established automatic facial coding software program to detect spontaneous emotional reactions in individuals responding to emotional pictures. We compared automatically generated scores for valence and arousal of the Facereader (FR; Noldus Information Technology) with the current psychophysiological gold standard of measuring emotional valence (Facial Electromyography, EMG) and arousal (Skin Conductance, SC). We recorded physiological and behavioral measurements of 43 healthy participants while they looked at pleasant, unpleasant, or neutral scenes. When viewing pleasant pictures, FR Valence and EMG were both comparably sensitive. However, for unpleasant pictures, FR Valence showed an expected negative shift, but the signal differentiated not well between responses to neutral and unpleasant stimuli, that were distinguishable with EMG. Furthermore, FR Arousal values had a stronger correlation with self-reported valence than with arousal while SC was sensitive and specifically associated with self-reported arousal. This is the first study to systematically compare FR measurement of spontaneous emotional reactions to standardized emotional images with established psychophysiological measurement tools. This novel technology has yet to make strides to surpass the sensitivity of established psychophysiological measures. However, it provides a promising new measurement technique for non-contact assessment of emotional responses.