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

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.
• 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.