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Multi-scale fusion visual attention network for facial micro-expression recognition.

Hang Pan1, Hongling Yang1, Lun Xie2

  • 1Department of Computer Science, Changzhi University, Changzhi, China.

Frontiers in Neuroscience
|August 14, 2023
PubMed
Summary

This study introduces a novel multi-scale fusion visual attention network (MFVAN) to improve micro-expression recognition by focusing on key facial areas and reducing redundant features. The MFVAN achieves state-of-the-art results on multiple datasets.

Keywords:
attention mechanismfeature fusionmask operatemicro-expression recognitionmulti-scale feature

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • Micro-expressions, subtle facial movements, are challenging to detect due to low intensity.
  • Existing methods often struggle with feature redundancy from imprecise region localization.
  • Individual identity attributes can interfere with accurate micro-expression analysis.

Purpose of the Study:

  • To develop a novel network for robust micro-expression recognition.
  • To address the challenges of low-intensity expressions and feature redundancy.
  • To mitigate the impact of individual identity attributes on recognition accuracy.

Main Methods:

  • A multi-scale fusion visual attention network (MFVAN) was proposed.
  • The model extracts multi-scale features and uses attention to mask redundant regions.
  • Self-supervision and transfer learning enhance feature map robustness.

Main Results:

  • MFVAN achieved state-of-the-art performance on SMIC, CASME II, SAMM, and 3DB-Combined datasets.
  • Experimental results confirm the benefit of multi-scale local attention for micro-expression recognition.
  • The study revealed the impact of individual attributes on region localization.

Conclusions:

  • The MFVAN effectively combines visual attention and multi-scale feature fusion for micro-expression recognition.
  • This approach successfully tackles the interference of identity attributes and low-intensity movements.
  • The findings highlight the significance of multi-scale attention in enhancing micro-expression analysis.