Assessing the convergent validity between the automated emotion recognition software Noldus FaceReader 7 and Facial Action Coding System Scoring

Skiendziel, Tanja; Rösch, Andreas; Schultheiss, Oliver C.

doi:10.1371/journal.pone.0223905

Cited by 149 publications

(152 citation statements)

References 27 publications

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“…Although this is a relatively novel method, it has been validated and used in multiple research settings. [38][39][40] After taking a seat on the exercise bicycle (both adenosine and exercise), the video-recordings started with 'baseline', approximately 1 minute before cardiac stress testing actually began, and served as the baseline measure of facial expression of anxiety. The three other time blocks during cardiac stress testing were 'start cardiac stress testing' when patients started exertion, 'maximal cardiac stress testing', when heart rate was at peak level (or at 2 minutes after adenosine injection), and 'recovery', when patients stopped exertion and slowly recovered.…”

Section: State Anxiety Measurementioning

confidence: 99%

Anxiety and hemodynamic reactivity during cardiac stress testing: The role of gender and age in myocardial ischemia

Bekendam

Mommersteeg

Kop

et al. 2021

Journal of Nuclear Cardiology

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Background. The prevalence of myocardial ischemia is associated with anxiety. State and trait anxiety are more common in younger women compared to men, and high anxiety levels could affect hemodynamic reactivity during cardiac stress testing. The aim is to examine whether anxiety plays a role in gender differences in patients £ 65 and > 65 years in hemodynamic reactivity and ischemia during cardiac stress testing. Methods and results. Included were 291 patients (66.8 ± 8.7 years, 45% women) with suspect ischemia undergoing myocardial perfusion single-photon emission computed tomography (MPI-SPECT). Primary outcomes were semi-quantitative summed difference score (SDS) and summed stress score (SSS), as continuous indicators of myocardial ischemia. Analyses were stratified by age. Trait anxiety was measured using a validated questionnaire (GAD-7) and state anxiety using facial expression analyses software. Overall, trait and state anxiety were not associated with the prevalence of ischemia (N = 107, 36%). A significant interaction was found between gender and trait anxiety in women £ 65 years for SDS (F(1,4) = 5.73, P = .019) and SSS (F(1,10) = 6.50, P = .012). This was not found for state anxiety. Conclusion. SDS and SSS were significantly higher in women younger than 65 years with high trait anxiety. This interaction was not found in men and women over 65 years. (J Nucl Cardiol 2020

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Section: State Anxiety Measurementioning

confidence: 99%

Anxiety and hemodynamic reactivity during cardiac stress testing: The role of gender and age in myocardial ischemia

Bekendam

Mommersteeg

Kop

et al. 2021

Journal of Nuclear Cardiology

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“…While Facet was found to exceed human judges in classifying emotions on these standardized sets of static emotional portrayals, its accuracy dropped to 63% for dynamic stimuli depicting real-life facial expression imitations. A performance index of 80% was recently reported using FaceReader in the context of dynamic expressions that were enacted to also mimic a basic emotion display [39]. When testing the software CERT (a precursor of Facet) on subtle dynamic (i.e., non-prototypical) facial stimuli, Yitzhak et al [40] found that emotion classification accuracy for subtle expressions (21%) was significantly reduced in comparison to highly intense and stereotypical expressions (89%).…”

Section: Introductionmentioning

confidence: 99%

A performance comparison of eight commercially available automatic classifiers for facial affect recognition

et al. 2020

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In the wake of rapid advances in automatic affect analysis, commercial automatic classifiers for facial affect recognition have attracted considerable attention in recent years. While several options now exist to analyze dynamic video data, less is known about the relative performance of these classifiers, in particular when facial expressions are spontaneous rather than posed. In the present work, we tested eight out-of-the-box automatic classifiers, and compared their emotion recognition performance to that of human observers. A total of 937 videos were sampled from two large databases that conveyed the basic six emotions (happiness, sadness, anger, fear, surprise, and disgust) either in posed (BU-4DFE) or spontaneous (UT-Dallas) form. Results revealed a recognition advantage for human observers over automatic classification. Among the eight classifiers, there was considerable variance in recognition accuracy ranging from 48% to 62%. Subsequent analyses per type of expression revealed that performance by the two best performing classifiers approximated those of human observers, suggesting high agreement for posed expressions. However, classification accuracy was consistently lower (although above chance level) for spontaneous affective behavior. The findings indicate potential shortcomings of existing out-of-thebox classifiers for measuring emotions, and highlight the need for more spontaneous facial databases that can act as a benchmark in the training and testing of automatic emotion recognition systems. We further discuss some limitations of analyzing facial expressions that have been recorded in controlled environments.

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“…Clemson researchers observed that many participants smile excessively when being video recorded. Furthermore, individual physiognomy and resting facial expression influences the emotion coding process [71]. Calibration aids in reducing the influence of individual bias.…”

Section: Expression Bias Interpretation and Calibrationmentioning

confidence: 99%

“…Three validation studies on FaceReader showed that the software was accurate at predicting intended emotion 79%-89% of the time. Validation studies in the literature may give researchers confidence in FaceReader over other software [71,74].…”

Section: Software Influencementioning

confidence: 99%

The State of Automated Facial Expression Analysis (AFEA) in Evaluating Consumer Packaged Beverages

2020

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In the late 1970s, analysis of facial expressions to unveil emotional states began to grow and flourish along with new technologies and software advances. Researchers have always been able to document what consumers do, but understanding how consumers feel at a specific moment in time is an important part of the product development puzzle. Because of this, biometric testing methods have been used in numerous studies, as researchers have worked to develop a more comprehensive understanding of consumers. Despite the many articles on automated facial expression analysis (AFEA), literature is limited in regard to food and beverage studies. There are no standards to guide researchers in setting up materials, processing data, or conducting a study, and there are few, if any, compilations of the studies that have been performed to determine whether any methodologies work better than others or what trends have been found. Through a systematic Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) review, 38 articles were found that were relevant to the research goals. The authors identified AFEA study methods that have worked and those that have not been as successful and noted any trends of particular importance. Key takeaways include a listing of commercial AFEA software, experimental methods used within the PRISMA analysis, and a comprehensive explanation of the critical methods and practices of the studies analyzed. Key information was analyzed and compared to determine effects on the study outcomes. Through analyzing the various studies, suggestions and guidance for conducting and analyzing data from AFEA experiments are discussed.

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Assessing the convergent validity between the automated emotion recognition software Noldus FaceReader 7 and Facial Action Coding System Scoring

Cited by 149 publications

References 27 publications

Anxiety and hemodynamic reactivity during cardiac stress testing: The role of gender and age in myocardial ischemia

Anxiety and hemodynamic reactivity during cardiac stress testing: The role of gender and age in myocardial ischemia

A performance comparison of eight commercially available automatic classifiers for facial affect recognition

The State of Automated Facial Expression Analysis (AFEA) in Evaluating Consumer Packaged Beverages

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