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Summary
This summary is machine-generated.

This study introduces a new method to improve micro-expression recognition, even when faces are partially hidden. The dual-branch network effectively restores lost facial features for better accuracy in occluded scenarios.

Keywords:
disrupted feature dependenciesexpression semantic surface texturefeature sparsitymicro‐expression recognition under occlusion

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • Micro-expression recognition is crucial for understanding subtle human emotions.
  • Facial occlusions in real-world settings pose significant challenges to current recognition models.
  • Occlusions cause feature sparsity and disconnection, disrupting spatial and temporal information.

Purpose of the Study:

  • To develop a robust micro-expression recognition system that can handle facial occlusions.
  • To introduce a novel approach for separating and reconstructing expression-aware features.

Main Methods:

  • Proposed a dual-branch collaborative network architecture.
  • One branch utilizes optical flow and frame differencing for spatial feature extraction.
  • The second branch employs occlusion position embedding to filter irrelevant features and reconstruct occluded facial regions.

Main Results:

  • The proposed network effectively restores disrupted spatial and temporal features caused by occlusions.
  • Demonstrated superior performance in occluded micro-expression recognition compared to state-of-the-art methods.
  • Successfully separated expression-aware and expression-irrelevant features.

Conclusions:

  • The novel 'expression semantic surface texture' approach enhances micro-expression recognition under occlusion.
  • The dual-branch network provides a promising solution for real-world occluded facial expression analysis.
  • This method significantly improves the accuracy and robustness of micro-expression recognition systems.