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Facial Feedback Hypothesis01:24

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Charles Darwin proposed that facial expressions are an evolutionary adaptation for communication. He argued that these expressions are not influenced by culture but are universal across species. For example, a snarling expression with exposed teeth signals a threat in many animals, including humans. Darwin also suggested that displaying an emotion can intensify the feeling. Smiling, for example, could enhance one's sense of happiness. This idea laid the foundation for understanding the role...
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SFTNet: A microexpression-based method for depression detection.

Xingyun Li1, Xinyu Yi1, Jiayu Ye1

  • 1Faculty of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China.

Computer Methods and Programs in Biomedicine
|November 21, 2023
PubMed
Summary

This study introduces SFTNet, a novel deep learning model for automatic depression detection using microexpressions. SFTNet accurately identifies depression from facial cues, aiding clinical diagnosis and early intervention.

Keywords:
Depression detectionEmotional stimuliFacial expressionsMicroexpression

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Area of Science:

  • Computational psychiatry
  • Affective computing
  • Machine learning for mental health diagnostics

Background:

  • Depression screening is crucial for preventing condition exacerbation.
  • Microexpressions offer potential biomarkers for mental illness detection.
  • Automatic depression detection using microexpressions remains underexplored.

Purpose of the Study:

  • To develop an automatic depression detection method based on microexpressions.
  • To evaluate the efficacy of the proposed SFTNet model.
  • To analyze facial expression characteristics in depressed individuals.

Main Methods:

  • Collected a dataset of 156 participants (76 depression cases, 80 controls).
  • Proposed a two-stream model, SFTNet, integrating single-temporal (STNet) and full-temporal (FTNet) networks.
  • Analyzed Average Number of Occurrences (ANO) and Average Duration (AD) of facial expressions.

Main Results:

  • Depressed subjects exhibited fewer and less rich facial expressions compared to controls.
  • SFTNet achieved high accuracy (0.873), precision (0.888), and recall (0.846) on an emotional stimulus dataset.
  • SFTNet demonstrated strong performance on a doctor-patient conversation dataset (Accuracy: 0.829, Precision: 0.817, Recall: 0.837).

Conclusions:

  • Depressed individuals are more prone to displaying negative emotions.
  • SFTNet outperforms state-of-the-art models in microexpression-based depression detection.
  • The proposed method can assist clinicians in diagnosing depression.