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This study introduces a novel facial expression recognition (FER) method that transforms profile images to frontal views and uses multimodal learning. This approach significantly improves accuracy for non-frontal facial images.

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Area of Science:

  • Computer Science
  • Artificial Intelligence

Background:

  • Facial expression recognition (FER) systems struggle with accuracy for non-frontal or rotated faces.
  • Current FER methods are limited by pose variations, hindering real-world applications.

Purpose of the Study:

  • To enhance FER accuracy by addressing challenges posed by large pose variations.
  • To develop a robust FER method capable of recognizing expressions from profile and large-angle rotated facial images.

Main Methods:

  • Proposed a novel FER method combining profile-to-frontal image transformation and multimodal learning.
  • Utilized Qwen-Image-Edit for transforming profile images to frontal views, preserving expression features.
  • Employed the CLIP model for vision-language joint learning to improve semantic representation of expression features.

Main Results:

  • Achieved high accuracy on benchmark datasets: RAF (89.39%), EXPW (67.17%), and AffectNet-7 (62.66%).
  • Demonstrated superior performance compared to existing FER approaches, particularly for challenging poses.

Conclusions:

  • The proposed profile-to-frontal transformation and multimodal learning approach effectively mitigates pose variation issues in FER.
  • This method offers a significant advancement for practical and accurate facial expression recognition systems.