Assessing the convergent validity between the automated emotion recognition software Noldus FaceReader 7 and Facial
Tanja Skiendziel1, Andreas G Rösch1, Oliver C Schultheiss1
1Department of Psychology, Friedrich-Alexander University, Erlangen, Germany.
Plos One
|October 18, 2019
Summary
This study validates automated emotion and action unit (AU) coding using FaceReader 7, finding 80% accuracy for basic emotions and moderate validity for specific AUs compared to manual FACS scoring.
Area of Science:
- Psychology
- Computer Science
- Biometrics
Background:
- Automated facial expression analysis is crucial for understanding human emotion.
- Accurate coding of basic emotions and Action Units (AUs) is essential for research.
- Previous versions of automated systems have shown varying degrees of validity.
Purpose of the Study:
- To validate the accuracy of FaceReader 7 software in coding basic emotions.
- To assess the validity of FaceReader 7 in coding specific Action Units (AUs).
- To compare the performance of FaceReader 7 with its predecessor, FaceReader 6.
Main Methods:
- Utilized the Standardized and Motivated Facial Expressions of Emotion dataset.
- Applied FaceReader 7 for automated emotion and AU coding.
- Correlated automated AU coding with manual Facial Action Coding System (FACS) scoring for 20 AUs.
Main Results:
- FaceReader 7 correctly classified an average of 80% of emotional facial expressions.
- Moderate overall validity was found for AU coding, with highest validity for AUs 1, 5, 9, 17, and 27.
- Validity coefficients were comparable to those reported for FaceReader 6.
Conclusions:
- FaceReader 7 demonstrates reliable performance in automated emotion and AU coding.
- The software shows practical utility for facial expression analysis in research.
- Limitations and implications for automated facial expression analysis are discussed.
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