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Decoding depression with computer vision-assisted analysis of synchronized facial expressions.

Seohyeon Lee1, Yunsu Kim2, Hayoung Ryu1

  • 1Department of Psychology, Sungkyunkwan University, Seoul, South Korea.

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Facial expression synchronization patterns can accurately detect depression. Analyzing action unit (AU) inter-subject correlation (ISC) reveals key diagnostic cues, outperforming models based on average AU activity.

Keywords:
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Area of Science:

  • Neuroscience
  • Psychology
  • Computer Science

Background:

  • Facial expressions are crucial nonverbal indicators of emotional states.
  • They show potential as markers for affective disorders like depression.

Purpose of the Study:

  • To identify diagnostic cues for depression using facial expression synchronization.
  • To develop depression detection models based on naturalistic emotional responses.

Main Methods:

  • Utilized a naturalistic paradigm and inter-subject correlation (ISC) framework.
  • Analyzed time-series activity patterns of action units (AUs) from facial expressions.
  • Developed depression detection models using AU-ISC vectors as features.

Main Results:

  • Models achieved 72-90% accuracy in detecting depression.
  • AU-ISC models demonstrated significant diagnostic utility.
  • Models using mean AU activity showed poor performance.

Conclusions:

  • Facial expression data can advance objective diagnostic tools for depression.
  • Facial expression dynamics offer a new direction for emotion research.
  • This approach can complement established self-report measures.