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A Convolutional Neural Network for Compound Micro-Expression Recognition.

Yue Zhao1, Jiancheng Xu1

  • 1School of Electronics and Information, Northwestern Polytechnical University, Xi'an 710129, China.

Sensors (Basel, Switzerland)
|January 1, 2020
PubMed
Summary
This summary is machine-generated.

Researchers developed a new method to recognize compound micro-expressions, which combine basic emotions. This technique enhances subtle facial movements and uses a novel network to analyze them, improving emotion recognition accuracy.

Keywords:
3D-FFTCNNEVMFACSTV-L1 optical flowcompound micro-expressions

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Area of Science:

  • Psychology and Computer Science
  • Human-Computer Interaction
  • Affective Computing

Background:

  • Human emotions are often expressed through subtle, short-duration micro-expressions.
  • Current research primarily focuses on recognizing six basic emotions, neglecting more complex compound expressions.
  • Compound micro-expressions combine basic emotions, reflecting richer human emotional states.

Purpose of the Study:

  • To develop a method for recognizing compound micro-expressions.
  • To synthesize a Compound Micro-expression Database (CMED) for training and testing.
  • To enhance the detection of subtle facial motion features in micro-expressions.

Main Methods:

  • Synthesized a Compound Micro-expression Database (CMED) from existing datasets.
  • Employed Eulerian Video Magnification (EVM) to enhance facial motion features.
  • Utilized 3D Fast Fourier Transform (3D-FFT) and optical flow to extract features.
  • Designed a shallow network for high-level feature extraction from optical flow maps.

Main Results:

  • Successfully synthesized the CMED by combining four spontaneous micro-expression databases.
  • The proposed method effectively enhanced subtle facial motion features.
  • The shallow network demonstrated proficiency in recognizing both basic and compound micro-expressions.

Conclusions:

  • The developed deep network framework can effectively recognize compound micro-expressions.
  • This research expands the scope of micro-expression recognition beyond basic emotions.
  • The study provides a foundation for more nuanced understanding of human emotional expression.