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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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Lightweight Convolutional Neural Network with Efficient Channel Attention Mechanism for Real-Time Facial Emotion

Juan A Ramirez-Quintana1, Jesus J Muñoz-Pacheco1, Graciela Ramirez-Alonso2

  • 1Graduate Studies and Research Division, Tecnológico Nacional de México/I.T. Chihuahua, Chihuahua 31200, Mexico.

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Summary

A new deep learning model, Lightweight Expression Recognition Network (LiExNet), enables real-time facial emotion recognition with high accuracy and low computational cost. This efficient network is ideal for embedded systems and occupational stress monitoring.

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deep learningemotion recognitionfacial expression recognitionreal-time processing

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Facial expression recognition is crucial for understanding human emotions.
  • Existing methods often require significant computational resources, limiting real-time applications.
  • There is a need for efficient models for emotion recognition in resource-constrained environments.

Purpose of the Study:

  • To introduce the Lightweight Expression Recognition Network (LiExNet), a novel deep neural network for real-time emotion recognition.
  • To optimize the network for low computational complexity and minimal memory footprint.
  • To evaluate LiExNet's performance on diverse facial expression datasets, including a custom dataset for occupational stress.

Main Methods:

  • Developed LiExNet with 42,000 parameters, integrating convolutional, depthwise convolutional, and attention mechanisms.
  • Trained and validated the network on CK+, KDEF, FER2013, and the custom EMOTION-ITCH dataset.
  • Assessed computational requirements (0.03 MB memory, 1.38 GFLOPs) and real-time inference capabilities.

Main Results:

  • Achieved high accuracy: 99.5% (CK+), 88.2% (KDEF), 79.2% (FER2013), and 96% (EMOTION-ITCH).
  • Demonstrated superior performance among real-time methods, ranking first on CK+ and KDEF, and second on FER2013.
  • Confirmed real-time inference feasibility on embedded systems with minimal resources.

Conclusions:

  • LiExNet offers a practical and robust solution for real-time emotion recognition.
  • The model is suitable for applications in hardware-constrained environments, such as embedded systems.
  • LiExNet shows promise for real-time emotion monitoring and assessing emotional dissonance in occupational settings.