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A Fast Preprocessing Method for Micro-Expression Spotting via Perceptual Detection of Frozen Frames.

Vittoria Bruni1,2, Domenico Vitulano1,2

  • 1Department of Basic and Applied Sciences for Engineering, "La Sapienza" Rome University, Via A. Scarpa 14-16, 00161 Rome, Italy.

Journal of Imaging
|August 30, 2021
PubMed
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This study introduces a fast preprocessing method to detect frozen facial expressions, serving as an early indicator for microexpressions (MEs). This technique aids in simplifying and accelerating microexpression spotting in videos.

Area of Science:

  • Computer Vision
  • Human-Computer Interaction
  • Biomedical Engineering

Background:

  • Facial microexpressions (MEs) are subtle, involuntary facial movements crucial for understanding human emotion.
  • Accurate and efficient detection of MEs in video sequences remains a challenge, particularly in long recordings.
  • Current methods may require extensive computational resources, limiting real-time applications.

Purpose of the Study:

  • To develop a rapid preprocessing technique for facial microexpression (ME) spotting.
  • To identify 'frozen' facial expression frames as precursors or successors to MEs.
  • To enhance the efficiency and simplicity of ME detection in video analysis.

Main Methods:

  • Utilized a fast preprocessing approach for facial microexpression spotting.
Keywords:
facial microexpressionsmotion energypreattentive visionspatio-temporal filteringstandard deviation

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  • Employed global visual perception-based features inspired by motion energy models.
  • Focused on detecting frames with static or 'frozen' expressions as indicators.

    • Main Results:

    • Preliminary results demonstrate successful detection of frozen frames in both controlled and uncontrolled video settings.
    • The method effectively identifies frames signaling the presence of nearby microexpressions.
    • Detection accuracy is independent of microexpression type and facial region.

    Conclusions:

    • The proposed method offers a viable strategy for accelerating facial microexpression spotting.
    • Detecting frozen frames serves as an effective preliminary step for ME analysis.
    • This approach can significantly streamline ME detection, especially in extensive video data acquisition.