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A New Feature Extraction and Recognition Method for Microexpression Based on Local Non-negative Matrix Factorization.

Junli Gao1, Huajun Chen1, Xiaohua Zhang2

  • 1School of Automation, Guangdong University of Technology, Guangzhou, China.

Frontiers in Neurorobotics
|December 14, 2020
PubMed
Summary
This summary is machine-generated.

This study enhances microexpression recognition by improving data augmentation. The novel macro-to-micro algorithm boosts accuracy for subtle facial expressions.

Keywords:
CK+/CASME2/SAMM datasetsfeature extractionmacro-expressionmacro-to-micro transformationmicro-expressionnon-negative matrix factorization

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Human-Computer Interaction

Background:

  • Microexpressions are challenging for standard algorithms due to their brief duration and subtle movements.
  • Non-negative matrix factorization (NMF) is effective for facial feature extraction.
  • Existing microexpression datasets lack sufficient samples for robust classifier training.

Purpose of the Study:

  • To improve microexpression recognition accuracy.
  • To address limitations in microexpression dataset size and feature vector properties.
  • To develop an effective data augmentation method for microexpressions.

Main Methods:

  • Exploration of local non-negative matrix factorization (LNMF) for microexpression feature extraction.
  • Development of an improved macro-to-micro algorithm for augmenting microexpression samples using macroexpression data.
  • Manipulation of macroexpression data based on LNMF to meet non-negative properties.

Main Results:

  • The proposed improved macro-to-micro algorithm effectively augments microexpression samples.
  • The method achieves higher recognition accuracy for microexpressions compared to existing algorithms.
  • Experimental validation on CK+/CASME2/SAMM datasets confirms the effectiveness.

Conclusions:

  • The enhanced macro-to-micro algorithm based on LNMF significantly improves microexpression recognition.
  • This approach provides a viable solution for limited microexpression dataset sizes.
  • The method offers a promising direction for advancing facial expression analysis.