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Progressive Multi-Scale Vision Transformer for Facial Action Unit Detection.

Chongwen Wang1, Zicheng Wang1

  • 1School of Computer Science, Beijing Institute of Technology, Beijing, China.

Frontiers in Neurorobotics
|January 31, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a new Progressive Multi-Scale Vision Transformer (PMVT) for facial action unit (AU) detection. PMVT improves accuracy by learning complex AU relationships without manual landmarks, enhancing affective computing.

Keywords:
affective computingcross-attentionfacial action unit recognitionmulti-scale transformerself-attention

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Affective Computing

Background:

  • Facial Action Unit (AU) detection is crucial for understanding emotions.
  • Existing methods often rely on manual facial landmarks and struggle with complex AU relationships.

Purpose of the Study:

  • To develop a novel method for capturing intricate relationships among facial AUs in a data-driven manner.
  • To overcome limitations of existing AU detection techniques in representing exclusive and concurrent AU combinations.

Main Methods:

  • Proposed a Progressive Multi-Scale Vision Transformer (PMVT) model.
  • Utilized a multi-scale self-attention mechanism for flexible patch attention.
  • Enabled adaptive receptive fields for encoding facial regions.

Main Results:

  • PMVT demonstrated improved AU detection accuracy on BP4D and DISFA datasets.
  • Achieved consistent improvements over state-of-the-art AU detection methods.
  • Visualizations confirmed automatic perception of discriminative facial regions.

Conclusions:

  • PMVT offers a landmark-free approach to AU detection.
  • The model effectively captures complex AU interdependencies for robust expression analysis.
  • PMVT advances the field of affective computing through improved facial expression recognition.