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WGGLFA: Wavelet-Guided Global-Local Feature Aggregation Network for Facial Expression Recognition.

Kaile Dong1, Xi Li1,2, Cong Zhang2

  • 1School of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan 430205, China.

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|August 27, 2025
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Summary
This summary is machine-generated.

This study introduces a novel wavelet-guided network for facial expression recognition (FER), improving accuracy by capturing multi-scale details. The proposed method enhances human-computer interaction and affective computing capabilities.

Keywords:
deep learningfacial expression recognitionglobal–local feature fusionmulti-scale feature

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Signal Processing

Background:

  • Facial expression recognition (FER) is crucial for human-computer interaction and affective computing.
  • Existing methods struggle to capture multi-scale structural details, limiting accuracy.
  • Biological visual systems process information at multiple scales.

Purpose of the Study:

  • To propose a novel wavelet-guided global-local feature aggregation network (WGGLFA) for enhanced FER.
  • To address the limitations of current FER methods in capturing multi-scale facial expression features.

Main Methods:

  • Developed the WGGLFA network with three key modules: Scale-Aware Expansion (SAE), Structured Local Feature Aggregation (SLFA), and Expression-Guided Region Refinement (ExGR).
  • Utilized dilated convolution and wavelet transform in SAE for multi-scale feature extraction.
  • Incorporated facial keypoints in SLFA for structured local feature extraction.
  • Employed wavelet transform for high-/low-frequency feature separation and fine-grained representation.

Main Results:

  • Achieved high accuracies: 90.32% on RAF-DB, 91.24% on FERPlus, and 71.90% on FED-RO.
  • Demonstrated WGGLFA's effectiveness compared to state-of-the-art methods.
  • Showcased improved robustness and generalization capabilities.

Conclusions:

  • The WGGLFA network effectively captures multi-scale structural details for superior FER.
  • Wavelet-guided feature aggregation enhances fine-grained expression representation.
  • WGGLFA offers a robust and generalizable solution for advanced facial expression recognition.