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Emotional labeling is a cognitive process that involves identifying and naming one's emotions, such as anger, fear, happiness, or sadness. It allows individuals to recognize and express their internal emotional states, a critical aspect of emotional regulation and communication. Labeling emotions requires more than mere recognition; it also involves drawing upon memory and contextual cues to understand the current situation and apply a corresponding emotional label. For instance, feeling...
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Machine Learning-Based Ear Thermal Imaging for Emotion Sensing.

Budu Tang1,2, Wataru Sato1,2

  • 1Graduate School of Informatics, Kyoto University, Yoshida-Honmachi, Sakyo, Kyoto 606-8507, Japan.

Sensors (Basel, Switzerland)
|February 27, 2026
PubMed
Summary
This summary is machine-generated.

Thermal imaging can detect emotions by measuring ear temperature. Advanced machine learning models, especially deep learning, show nonlinear relationships between ear temperature changes and emotional arousal, outperforming linear methods for emotion sensing.

Keywords:
arousal and valenceear thermal imagingemotional arousalmachine learningpixel-level analysis

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Area of Science:

  • Psychophysiology
  • Machine Learning
  • Biomedical Engineering

Background:

  • Thermal imaging offers non-contact, light-independent measurement of skin temperature, reflecting autonomic nervous system activity.
  • Previous studies suggest a linear link between ear temperature and emotional arousal, but nonlinear relationships remain unexplored.
  • Accurate emotion sensing is crucial for various applications, including mental health monitoring and human-computer interaction.

Purpose of the Study:

  • To investigate nonlinear relationships between ear thermal changes and subjective emotional arousal.
  • To compare the performance of machine learning models against linear regression for emotion sensing using thermal imaging.
  • To identify specific ear regions associated with emotional arousal through model interpretation.

Main Methods:

  • Reanalyzed thermal ear images and self-reported arousal ratings from participants watching emotion-eliciting films.
  • Employed linear regression, random forest, and ResNet-50 convolutional neural network models.
  • Evaluated models using mean squared error and correlation coefficients; interpreted the best model with Gradient-weighted Class Activation Mapping and Shapley additive explanation.

Main Results:

  • Both machine learning models significantly outperformed linear regression in predicting arousal ratings.
  • The ResNet-50 deep learning model demonstrated superior performance compared to the random forest model.
  • Model interpretation revealed nonlinear associations between temperature variations in specific ear regions and subjective arousal levels.

Conclusions:

  • Ear thermal imaging, when combined with advanced machine learning techniques like deep learning, shows significant promise for accurate emotion sensing.
  • Nonlinear modeling approaches are essential for capturing the complex relationship between physiological signals and subjective emotional states.
  • This study highlights the potential of thermal imaging as a non-invasive tool for understanding and quantifying human emotions.