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Facial Expression Recognition Based on Squeeze Vision Transformer.

Sangwon Kim1, Jaeyeal Nam1, Byoung Chul Ko1

  • 1Department of Computer Engineering, Keimyung University, Daegu 42601, Korea.

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|May 28, 2022
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Summary
This summary is machine-generated.

Squeeze ViT improves facial expression recognition (FER) by combining global and local image features. This novel approach enhances FER performance while reducing computational complexity, outperforming existing methods on diverse datasets.

Keywords:
facial expression recognitionlandmark tokensqueeze modulevision transformervisual token

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Vision Transformers (ViT) excel in image classification by preserving global features.
  • ViTs struggle with Facial Expression Recognition (FER) due to loss of critical local features.
  • FER demands sensitivity to subtle, localized changes in facial imagery.

Purpose of the Study:

  • To introduce Squeeze ViT, a novel method for enhancing FER performance.
  • To address ViT's limitations in capturing local features crucial for FER.
  • To reduce computational complexity in FER models.

Main Methods:

  • Squeeze ViT combines global and local image features for FER.
  • Feature dimension reduction is employed to decrease computational load.
  • The method was evaluated on both lab-controlled and in-the-wild FER datasets.

Main Results:

  • Squeeze ViT demonstrated superior FER performance compared to state-of-the-art methods.
  • The proposed method achieved excellent results on both controlled and wild datasets.
  • Reduced feature dimensions did not impede, but rather enhanced, FER accuracy.

Conclusions:

  • Squeeze ViT effectively overcomes ViT's limitations in FER.
  • The method offers a computationally efficient and high-performing solution for FER.
  • Squeeze ViT represents a significant advancement in automated facial expression recognition technology.