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Charles Darwin proposed that facial expressions are an evolutionary adaptation for communication. He argued that these expressions are not influenced by culture but are universal across species. For example, a snarling expression with exposed teeth signals a threat in many animals, including humans. Darwin also suggested that displaying an emotion can intensify the feeling. Smiling, for example, could enhance one's sense of happiness. This idea laid the foundation for understanding the role...
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ME-PLAN: A deep prototypical learning with local attention network for dynamic micro-expression recognition.

Sirui Zhao1, Huaying Tang2, Shifeng Liu2

  • 1School of Computer Science and Technology, University of Science and Technology of China (USTC), Hefei, An Hui, China; School of Computer Science and Technology, Southwest University of Science and Technology (SWUST), Mianyang, Si Chuan, China; State Key Laboratory of Cognitive Intelligence, Hefei, An Hui, China.

Neural Networks : the Official Journal of the International Neural Network Society
|July 8, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces ME-PLAN, a deep learning framework for recognizing micro-expressions (MEs). It effectively identifies subtle facial movements, improving lie detection and psychological analysis by learning precise ME features.

Keywords:
3D residual networkEmotion recognitionFacial micro-expressionME spottingPrototypical learning

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Area of Science:

  • Psychology
  • Computer Science
  • Artificial Intelligence

Background:

  • Micro-expressions (MEs) are subtle, spontaneous facial movements indicating genuine emotions.
  • Micro-expression Recognition (MER) has vital applications in lie detection, criminal investigation, and psychological healing.
  • Current MER research faces challenges due to subtle ME features and limited data.

Purpose of the Study:

  • To propose a novel deep prototypical learning framework, ME-PLAN, for Micro-expression Recognition (MER).
  • To develop an apex frame spotting method to overcome the reliance on manually annotated data.
  • To enhance the learning of discriminative ME features through a local attention mechanism.

Main Methods:

  • A deep prototypical learning framework (ME-PLAN) comprising a 3D residual prototypical network and a local-wise attention module.
  • An apex frame spotting method using Unimodal Pattern Constrained (UPC) to automatically identify key ME frames.
  • End-to-end training of ME-PLAN using extracted ME key-frame sequences.

Main Results:

  • The proposed ME-PLAN framework demonstrates superior performance in Micro-expression Recognition.
  • The apex frame spotting method effectively identifies crucial frames without manual annotation.
  • Experimental analysis confirms the effectiveness of the local attention mechanism in capturing subtle facial movements.

Conclusions:

  • ME-PLAN offers an effective solution for Micro-expression Recognition, addressing data limitations and feature learning challenges.
  • The integrated apex frame spotting method enhances the practicality and efficiency of MER systems.
  • The research contributes a robust framework for analyzing subtle emotional cues in facial expressions.