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Facial Micro-Expression Recognition Enhanced by Score Fusion and a Hybrid Model from Convolutional LSTM and Vision

Yufeng Zheng1, Erik Blasch2

  • 1Department of Data Science, University of Mississippi Medical Center, Jackson, MS 39216, USA.

Sensors (Basel, Switzerland)
|July 8, 2023
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Summary

This study introduces a hybrid neural network for real-time micro-expression recognition, enhancing human-machine interaction by accurately detecting subtle facial emotions. The model combines convolutional neural networks, recurrent neural networks, and vision transformers for superior performance.

Keywords:
convolutional neural network (CNN)deep learningfacial micro-expressionhuman-machine interactionlong short-term memory (LSTM)score fusionvision transformer

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Human-Computer Interaction

Background:

  • Facial emotion expression is universal across cultures, yet machines struggle to interpret subtle, involuntary micro-expressions.
  • Accurate micro-expression recognition is crucial for advanced human-machine interaction, enabling machines to understand true human emotions for better decision-making.
  • Applications include detecting dangerous situations, alerting caregivers, and providing context-aware responses in real-time.

Purpose of the Study:

  • To propose and evaluate a novel hybrid neural network model for real-time micro-expression recognition.
  • To compare the performance of the hybrid model against several individual neural network architectures.
  • To demonstrate the effectiveness of combining different neural network components for enhanced micro-expression detection.

Main Methods:

  • A hybrid neural network model was developed, integrating a Convolutional Neural Network (CNN) for spatial feature extraction, a Recurrent Neural Network (RNN, specifically LSTM) for temporal summarization, and a Vision Transformer for sparse spatial relation capture.
  • The model processes short facial videos as input to recognize various micro-expressions (happiness, fear, anger, surprise, disgust, sadness).
  • Experiments involved training and testing on publicly available micro-expression datasets, employing score fusion techniques and analyzing improvement metrics.

Main Results:

  • The proposed hybrid neural network model significantly outperformed individual neural network models in micro-expression recognition accuracy.
  • Score fusion techniques were shown to dramatically increase the recognition performance of the hybrid model.
  • The model's results were validated against literature-reported methods on identical datasets, confirming its superior efficacy.

Conclusions:

  • The developed hybrid neural network offers a robust solution for real-time micro-expression recognition, advancing the capabilities of human-machine interaction.
  • Combining CNN, LSTM, and Vision Transformer architectures with score fusion provides a powerful approach for capturing both spatial and temporal dynamics of facial expressions.
  • This research paves the way for more emotionally intelligent machines capable of nuanced understanding of human affective states.