Automated facial coding: validation of basic emotions and FACS AUs in FaceReader

Abstract

In this study, we validated automated facial coding (AFC) software—FaceReader (Noldus, 2014)—on 2 publicly available and objective datasets of human expressions of basic emotions. We present the matching scores (accuracy) for recognition of facial expressions and the Facial Action Coding System (FACS) index of agreement. In 2005, matching scores of 89% were reported for FaceReader. However, previous research used a version of FaceReader that implemented older algorithms (version 1.0) and did not contain FACS classifiers. In this study, we tested the newest version (6.0). FaceReader recognized 88% of the target emotional labels in the Warsaw Set of Emotional Facial Expression Pictures (WSEFEP) and Amsterdam Dynamic Facial Expression Set (ADFES). The software reached a FACS index of agreement of 0.67 on average in both datasets. The results of this validation test are meaningful only in relation to human performance rates for both basic emotion recognition and FACS coding. The human emotions recognition for the 2 datasets was 85%, therefore FaceReader is as good at recognizing emotions as humans. To receive FACS certification, a human coder must reach an agreement of 0.70 with the master coding of the final test. Even though FaceReader did not attain this score, action units (AUs) 1, 2, 4, 5, 6, 9, 12, 15, and 25 might be used with high accuracy. We believe that FaceReader has proven to be a reliable indicator of basic emotions in the past decade and has a potential to become similarly robust with FACS.