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Multiscale wavelet attention convolutional network for facial expression recognition.

Jing-Wei Liu1,2, Xiao-Yuan Lin1, Peng-Fei Ji3

  • 1Department of Computer Science, Capital University of Economics and Business, Beijing, 100070, China.

Scientific Reports
|July 2, 2025
PubMed
Summary
This summary is machine-generated.

This study enhances facial expression recognition by introducing Multi-scale Convolutional (MsC) layers and wavelet Channel Attention (wCA) mechanisms into Convolutional Neural Networks (CNNs), achieving significant accuracy improvements.

Keywords:
Convolutional neural networkExpression recognitionMulti-scale convolutional layerWavelet channel attention

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Facial expression recognition (FER) is crucial for human-computer interaction.
  • Current Convolutional Neural Networks (CNNs) require accuracy enhancements for robust FER applications.

Purpose of the Study:

  • To improve the accuracy of facial expression recognition systems.
  • To introduce novel deep learning architectures for enhanced FER.

Main Methods:

  • Proposed Multi-scale CNN (MCNN) by replacing the first convolutional layer with a Multi-scale Convolutional (MsC) layer.
  • Introduced wavelet Channel Attention CNN (wCA-CNN) by incorporating a wavelet Channel Attention (wCA) mechanism.
  • Developed wCA-based Multi-scale CNN (wCA-MCNN) combining MsC and wCA.
  • Applied these methods to baseline Residual Network (ResNet18).

Main Results:

  • MCNN improved accuracy by 1.339% over CNN.
  • wCA-CNN improved accuracy by 1.414% over CNN.
  • wCA-MCNN achieved a 2.921% accuracy improvement over CNN.
  • ResNet18 variants showed improvements up to 1.810%.

Conclusions:

  • The proposed MCNN, wCA-CNN, and wCA-MCNN architectures significantly enhance facial expression recognition accuracy.
  • The integration of MsC layers and wCA mechanisms offers a promising direction for advancing FER systems.
  • The methods were validated on both real-world (FESR) and standard (KDEF) datasets.