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Decoupling facial motion features and identity features for micro-expression recognition.

Tingxuan Xie1, Guoquan Sun1, Hao Sun1

  • 1School of Information Science and Engineering, Shandong University, Qingdao, Shandong, China.

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Summary
This summary is machine-generated.

This study introduces a novel deep learning approach to accurately recognize micro-expressions by separating facial motion and identity features. The proposed method enhances human-computer interaction and emotional state analysis.

Keywords:
Deep learningFacial motion featuresFeature decouplingIdentity featuresMicro-expression recognition

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Area of Science:

  • Artificial Intelligence
  • Computer Vision
  • Human-Computer Interaction

Background:

  • Micro-expressions are involuntary, brief facial movements revealing true emotions, crucial for understanding human affect.
  • Automatic micro-expression recognition aids human-computer interaction, security, and psychological treatment.
  • Current deep learning methods struggle with subtle micro-expressions due to interference from identity features.

Purpose of the Study:

  • To develop a micro-expression recognition algorithm that effectively decouples facial motion and identity features.
  • To improve the accuracy and robustness of micro-expression recognition systems.
  • To enhance the discriminative power of extracted features for better emotion analysis.

Main Methods:

  • Proposed a novel algorithm with a Micro-Expression Motion Information Features Extraction Network (MENet) and an Identity Information Features Extraction Network (IDNet).
  • Incorporated a Diverse Attention Operation (DAO) module and a divergence loss function within MENet for enhanced motion feature extraction.
  • Utilized global attention in IDNet for identity feature extraction and a Mutual Information Neural Estimator (MINE) to decouple features.

Main Results:

  • Achieved competitive results on benchmark datasets including SDU, MMEW, SAMM, and CASME II.
  • Demonstrated the superiority of the proposed algorithm in micro-expression recognition tasks.
  • Successfully decoupled facial motion and identity features, leading to more discriminative micro-expression representations.

Conclusions:

  • The proposed method effectively distinguishes between facial motion and identity features for improved micro-expression recognition.
  • The algorithm shows significant potential for advancing emotion recognition and human-computer interaction applications.
  • The decoupling strategy enhances feature discriminability, paving the way for more accurate affective computing.