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A novel facial expression recognition framework using deep learning based dynamic cross-domain dual attention

Ahmed Omar Alzahrani1, Ahmed Mohammed Alghamdi2, M Usman Ashraf3

  • 1Department of Information Systems and Technology, College of Computer Science and Engineering, University of Jeddah, Jeddah, Makkah, Saudi Arabia.

Peerj. Computer Science
|June 26, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel dynamic cross-domain dual attention network to improve facial expression recognition by learning domain-invariant global and local features, overcoming challenges posed by domain shifts.

Keywords:
Artificial intelligenceCross-domainsDeep learningFacial expression recognition

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Facial expression recognition (FER) faces challenges due to domain shifts, impacting global and local feature transfer.
  • Existing methods struggle with transferring local features and suffer from reduced target domain representation during training.

Purpose of the Study:

  • To propose a dynamic cross-domain dual attention network for robust facial expression recognition.
  • To address the limitations of current methods in handling domain shifts and insufficient discriminative supervision.

Main Methods:

  • Developed a network with separate modules for global and local adversarial learning to achieve domain-invariant feature learning.
  • Introduced a semantic-aware module for generating pseudo-labels using both global and local features.
  • Employed extensive experiments across multiple benchmark datasets (RAF-DB, FER-PLUS, AffectNet, ExpW, SFEW 2.0, JAFFE).

Main Results:

  • The proposed dynamic cross-domain dual attention network achieved state-of-the-art performance on multiple datasets.
  • Recognition accuracies reached 93.18% (RAF-DB), 92.35% (FER-PLUS), 82.13% (AffectNet), 78.37% (ExpW), 72.47% (SFEW 2.0), and 70.68% (JAFFE).

Conclusions:

  • The novel network effectively learns domain-invariant features, significantly improving facial expression recognition across diverse domains.
  • The approach demonstrates superior performance compared to existing methods, offering a promising solution for real-world FER applications.