• Stefan Lautenbacher, Teena Hassan, Dominik Seuss, Frederik W Loy, Jens-Uwe Garbas, Ute Schmid, and Miriam Kunz.
• Physiological Psychology, University of Bamberg, Bamberg 96047, Germany.
• Pain Res Manag. 2022 Jan 1; 2022: 6635496.

IntroductionThe experience of pain is regularly accompanied by facial expressions. The gold standard for analyzing these facial expressions is the Facial Action Coding System (FACS), which provides so-called action units (AUs) as parametrical indicators of facial muscular activity. Particular combinations of AUs have appeared to be pain-indicative. The manual coding of AUs is, however, too time- and labor-intensive in clinical practice. New developments in automatic facial expression analysis have promised to enable automatic detection of AUs, which might be used for pain detection.ObjectiveOur aim is to compare manual with automatic AU coding of facial expressions of pain.MethodsFaceReader7 was used for automatic AU detection. We compared the performance of FaceReader7 using videos of 40 participants (20 younger with a mean age of 25.7 years and 20 older with a mean age of 52.1 years) undergoing experimentally induced heat pain to manually coded AUs as gold standard labeling. Percentages of correctly and falsely classified AUs were calculated, and we computed as indicators of congruency, "sensitivity/recall," "precision," and "overall agreement (F1)."ResultsThe automatic coding of AUs only showed poor to moderate outcomes regarding sensitivity/recall, precision, and F1. The congruency was better for younger compared to older faces and was better for pain-indicative AUs compared to other AUs.ConclusionAt the moment, automatic analyses of genuine facial expressions of pain may qualify at best as semiautomatic systems, which require further validation by human observers before they can be used to validly assess facial expressions of pain.Copyright © 2022 Stefan Lautenbacher et al.

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