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M3ENet: A Multi-Modal Fusion Network for Efficient Micro-Expression Recognition.

Ke Zhao1,2, Xuanyu Liu3, Guangqian Yang4

  • 1Electronic Science and Technology Museum, University of Electronic Science and Technology of China, Chengdu 611731, China.

Sensors (Basel, Switzerland)
|October 29, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces M³ENet, a novel network for micro-expression recognition (MER) that combines appearance and motion cues. M³ENet achieves state-of-the-art results efficiently, improving emotion detection in challenging scenarios.

Keywords:
convolution neural networkdeep learningmicro-expression recognitionmulti-modal

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • Micro-expression recognition (MER) is crucial for understanding suppressed emotions but faces challenges like short duration and limited data.
  • Existing MER methods often rely on single cues (appearance or motion), hindering comprehensive emotional expression capture.
  • Accurate MER is vital for applications like user experience analysis and security surveillance.

Purpose of the Study:

  • To develop a lightweight, efficient multi-modal network for micro-expression recognition (MER).
  • To effectively integrate both motion and appearance cues for enhanced MER performance.
  • To improve MER generalization in low-data scenarios and address class imbalance.

Main Methods:

  • Proposed M³ENet, a lightweight multi-modal fusion network integrating optical flow (motion) and RGB (appearance) features.
  • Employed early-stage feature fusion within two modality-specific subnetworks.
  • Utilized targeted data augmentation and focal loss for improved generalization and class imbalance mitigation.

Main Results:

  • M³ENet achieved state-of-the-art performance across five benchmark datasets (CASME I, CASME II, CAS(ME)², SAMM, MMEW).
  • The network demonstrated high efficiency, outperforming existing methods.
  • Ablation studies and Grad-CAM visualizations validated the model's effectiveness and interpretability.

Conclusions:

  • The proposed M³ENet effectively integrates multi-modal features for robust and efficient micro-expression recognition.
  • The approach shows significant promise for real-world MER applications, especially in data-scarce environments.
  • The study highlights the importance of multi-modal fusion and targeted training strategies for advancing MER technology.