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Stress recognition identifying relevant facial action units through explainable artificial intelligence and machine

Giorgos Giannakakis1, Anastasios Roussos2, Christina Andreou3

  • 1Institute of Computer Science, Foundation for Research and Technology Hellas (FORTH), N. Plastira 100, Heraklion, 70013, Crete, Greece; Department of Electronic Engineering, Hellenic Mediterranean University, Chania, 73133, Greece; Institute of Agri-food and Life Sciences, University Research and Innovation Center, Hellenic Mediterranean University, Heraklion, 71003, Greece.

Computer Methods and Programs in Biomedicine
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Summary

Facial Action Units (AUs) can automatically detect acute stress. Specific AU combinations accurately differentiate stress from neutral states, achieving over 93% accuracy in recognizing stress levels.

Keywords:
Deep learningExplainable artificial intelligenceFacial action units (AU)Machine learningStress

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Area of Science:

  • Psychophysiology
  • Machine Learning
  • Computer Vision

Background:

  • Facial cues and expressions offer insights into stress levels.
  • Facial Action Units (AUs) model facial muscle movements for stress assessment.
  • This study explores automatic acute stress recognition using AUs.

Purpose of the Study:

  • Investigate automatic acute stress recognition based on facial Action Units (AUs).
  • Utilize conventional Machine Learning and Deep Learning techniques for stress detection.
  • Identify relevant AU combinations for stress condition classification.

Main Methods:

  • Developed a new dataset with 58 participants and 11 stress/non-stress tasks.
  • Employed computational feature selection for robust AU subset identification.
  • Integrated AUs with Machine Learning and Deep Learning using Layer-Wise Relevance Propagation for analysis.

Main Results:

  • Acute stress significantly increased AU presence and intensity compared to neutral states.
  • Identified and ranked the most relevant AU combinations for specific stress types.
  • Achieved a mean classification accuracy greater than 93% for stress vs. neutral conditions.

Conclusions:

  • Specific AU combinations are key indicators of acute stress conditions.
  • These AU combinations improve the separability between neutral and stress states.
  • Facial Action Units offer a reliable method for automatic stress recognition.