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Micro-Expression Recognition Based on Dual-Stream Motion-Anchored Cross-Fusion Network.

Junxian Li1, Tian Li2, Shucheng Huang3

  • 1School of Information Engineering, Yangzhou Polytechnic University, Yangzhou 225009, China.

Sensors (Basel, Switzerland)
|June 26, 2026
PubMed
Summary

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This summary is machine-generated.

This study introduces a new Motion-Anchored Cross-Modal Fusion Network (MACFN) for micro-expression recognition (MER). MACFN effectively enhances the identification of subtle facial movements by focusing on motion dynamics and appearance.

Area of Science:

  • Affective Computing
  • Computer Vision
  • Machine Learning

Background:

  • Micro-expression recognition (MER) is challenging due to subtle, fleeting facial movements.
  • Existing spatial-temporal networks struggle with static facial features, leading to noise bias.
  • Need for advanced models to accurately capture dynamic facial expressions.

Purpose of the Study:

  • To propose a novel dual-stream ViT architecture, the Motion-Anchored Cross-Modal Fusion Network (MACFN).
  • To explicitly decouple and synergize spatial appearance and optical flow dynamics for MER.
  • To improve the accuracy and robustness of micro-expression recognition systems.

Main Methods:

  • Developed a motion-anchored spatial attention module to guide texture stream attention to relevant regions.
Keywords:
attentionemotion recognitionmicro-expression recognitionoptical flow

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  • Introduced a cross-modal bilinear fusion module to capture second-order interactions between modalities.
  • Utilized a dual-stream Vision Transformer (ViT) architecture for processing spatial and motion information.

    • Main Results:

    • MACFN effectively bypasses static backgrounds, focusing on genuine micro-expression regions.
    • The cross-modal fusion captures complex feature interactions, enhancing discriminative power.
    • Achieved competitive performance on CASME II, SAMM, and SMIC databases using a leave-one-subject-out protocol.

    Conclusions:

    • MACFN demonstrates significant effectiveness in micro-expression recognition.
    • The proposed architecture successfully addresses limitations of conventional spatial-temporal networks.
    • MACFN offers a promising approach for advancing affective computing research.