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Lightweight Representation of Motion-Magnified Facial Dynamics for Micro Expression Sensing.
1School of Future Technology, Korea University of Technology and Education, Cheonan 31253, Republic of Korea.
Sensors (Basel, Switzerland)
|June 26, 2026
Summary
This study introduces an apex-free micro expression recognition (MER) framework for biomedical sensing. The novel method efficiently analyzes subtle facial dynamics, achieving high accuracy without frame-level apex detection.
Area of Science:
- Biomedical Engineering
- Computer Vision
- Affective Computing
Background:
- Reliable spontaneous affect monitoring is crucial in biomedical sensing.
- Micro expression recognition (MER) is challenging due to subtle, rapid facial movements.
- Existing MER methods often rely on apex frame detection, limiting real-world deployment.
Purpose of the Study:
- To develop an apex-free framework for robust micro expression recognition.
- To analyze subtle facial dynamics without frame-level annotations.
- To create a computationally efficient MER model for biomedical applications.
Main Methods:
- Proposed an apex-free framework using Eulerian motion magnification across multiple levels.
- Introduced a shared temporal mixer (STM) to analyze motion evolution.
- Employed a dual-branch CNN with attention mechanisms for feature extraction.
Main Results:
- Achieved approximately 80% accuracy on CASME II and 70% on SMIC datasets under cross-dataset evaluation.
- Demonstrated high computational efficiency with only 0.94 M parameters and 262 MFLOPs.
- Outperformed or matched state-of-the-art MER methods in rigorous testing.
Conclusions:
- The apex-free MER framework offers competitive accuracy and high efficiency.
- The model shows significant potential for real-time affect monitoring in biomedical settings.
- This approach overcomes limitations of traditional apex-detection-based MER systems.
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