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Related Experiment Video

Updated: Feb 28, 2026

Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation
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Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation

Published on: August 26, 2016

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Cross-Dataset Facial Micro-Expression Recognition with Regularization Learning and Action Unit-Guided Data

Ju Zhou1,2, Xinyu Liu3, Lin Wang1,4

  • 1Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.

Entropy (Basel, Switzerland)
|February 27, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces novel methods to improve facial micro-expression recognition across different datasets. The techniques address feature distribution inconsistencies and data imbalance, enhancing recognition accuracy in real-world applications.

Keywords:
action unitcross-dataset recognitiondata augmentationmicro-expression recognitionregularization learning

Related Experiment Videos

Last Updated: Feb 28, 2026

Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation
07:12

Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation

Published on: August 26, 2016

9.9K

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Facial micro-expression recognition is crucial for real-world applications.
  • Cross-dataset evaluation is standard but challenging due to inconsistent feature distributions and data imbalance.
  • Existing methods struggle with domain shift and imbalanced data in micro-expression recognition.

Purpose of the Study:

  • To develop robust methods for cross-dataset facial micro-expression recognition.
  • To address feature distribution discrepancies and data imbalance in training datasets.
  • To enhance the accuracy and generalizability of micro-expression recognition models.

Main Methods:

  • A plug-and-play batch regularization module using information-theoretic regularization to learn domain-invariant representations.
  • An Action Unit (AU)-guided generative adversarial network (GAN) for synthesizing balanced micro-expression samples.
  • K-means clustering to guide GAN in generating samples based on AU intensity cluster centers.

Main Results:

  • The proposed methods significantly improve performance in cross-dataset micro-expression recognition.
  • Experiments on CNN, ResNet, and PoolFormer architectures demonstrate superior results compared to state-of-the-art methods.
  • The approach effectively handles feature distribution inconsistencies and data imbalance.

Conclusions:

  • The developed regularization module and AU-guided GAN effectively address key challenges in cross-dataset micro-expression recognition.
  • The proposed techniques lead to more accurate and reliable facial micro-expression recognition systems.
  • This work advances the practical applicability of micro-expression recognition technology.