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Machine learning accurately estimates emotional arousal using pixel-level facial thermal imaging, outperforming traditional methods. This non-invasive technique reveals nonlinear temperature patterns linked to emotional states.

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Area of Science:

  • Thermography
  • Affective Computing
  • Machine Learning

Background:

  • Facial thermal patterns correlate with emotional states.
  • Previous linear analysis of facial thermal data (regions of interest) may miss complex, nonlinear information.
  • Accurate emotion sensing is valuable for various applications.

Purpose of the Study:

  • To investigate machine learning (ML) for pixel-level analysis of facial thermal images.
  • To estimate dynamic emotional arousal ratings using ML.
  • To compare ML performance against traditional linear regression models.

Main Methods:

  • Collected facial thermal data from 20 participants viewing emotion-eliciting films.
  • Utilized ML models: random forest regression, support vector regression, ResNet-18, and ResNet-34.
  • Interpreted nonlinear relationships using saliency maps and integrated gradients for ResNet-34.

Main Results:

  • ML models significantly outperformed linear regression in estimating arousal.
  • ResNet-18 and ResNet-34 showed superior performance.
  • Nonlinear associations were found between arousal and temperature changes in the nose tip, forehead, and cheeks.

Conclusions:

  • ML-based pixel-level analysis of facial thermal images is effective for estimating emotional arousal.
  • Nonlinear thermal patterns provide valuable insights into emotional states.
  • Potential applications include non-invasive emotion sensing in mental health, education, and human-computer interaction.