Understanding the Multidimensional and Dynamic Nature of Facial Expressions Based on Indicators for Appraisal
Meng Zhang1, Klas Ihme1, Uwe Drewitz1
1Institute of Transportation Systems, German Aerospace Center/Deutsches Zentrum für Luft- und Raumfahrt (DLR), Braunschweig, Germany.
Frontiers in Psychology
|March 8, 2021
Summary
Drivers' facial expressions during fear reveal distinct appraisal patterns. High novelty appraisals precede low power appraisals, offering insights into in-vehicle emotion recognition.
Area of Science:
- Psychology
- Human-Computer Interaction
- Affective Computing
Background:
- Facial expressions are key implicit measures for in-vehicle affective computing.
- The temporal dynamics and mechanisms of facial expressions in driving contexts remain under-explored.
- The Component Process Model suggests emotions arise from sequential appraisals.
Purpose of the Study:
- To investigate the multidimensional and dynamic nature of drivers' fear through facial expression analysis.
- To examine the relationship between appraisal components and facial expressions during simulated driving.
- To assess the utility of dynamic facial expression analysis for in-vehicle emotion detection.
Main Methods:
- A driving simulator experiment involving 37 participants.
- Induction of fear and relaxation states during simulated driving scenarios.
- Analysis of facial expression data to identify indicators of appraisal components.
Main Results:
- Facial indicators for high novelty and low power appraisals were significantly activated post-fear event.
- High novelty appraisals were observed to occur earlier in time than low power appraisals.
- Statistical significance was noted for both high novelty (p < 0.01) and low power (p < 0.05) activations.
Conclusions:
- Multidimensional facial expression analysis is a viable method for in-vehicle driver emotion measurement.
- Dynamic analysis of facial expressions, considering appraisal components, enhances in-vehicle emotion assessment.
- Understanding appraisal sequencing provides deeper insights into driver emotional states.
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