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Multimodal Attention Dynamic Fusion Network for Facial Micro-Expression Recognition.

Hongling Yang1, Lun Xie2, Hang Pan1

  • 1Department of Computer Science, Changzhi University, Changzhi 046011, China.

Entropy (Basel, Switzerland)
|September 28, 2023
PubMed
Summary

Facial micro-expression recognition is improved by a new model that enhances local details and fuses facial image features with action unit data. This multimodal dynamic attention fusion network (MADFN) boosts emotional feature discrimination.

Keywords:
dynamic fusionlearnable class tokenmicro-expression recognition

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • Facial micro-expressions convey emotional changes through combinations of action units.
  • Action units offer auxiliary data to enhance facial micro-expression recognition.
  • Existing methods often overlook the impact of action units on facial image feature extraction.

Purpose of the Study:

  • To propose a novel local detail feature enhancement model for micro-expression recognition.
  • To address the limitations of current fusion techniques by considering action unit impact on feature extraction.
  • To introduce the multimodal dynamic attention fusion network (MADFN).

Main Methods:

  • Utilized a masked autoencoder with learnable class tokens to refine micro-expression images by removing low-expressivity areas.
  • Employed an action unit dynamic fusion module to integrate action unit representations with image features.
  • Developed the multimodal dynamic attention fusion network (MADFN) for enhanced feature representation.

Main Results:

  • The MADFN model achieved state-of-the-art performance on benchmark datasets: SMIC (81.71%), CASME II (82.11%), and SAMM (77.21%).
  • Demonstrated improved discrimination of facial image emotional features through effective fusion of image and action unit data.
  • Verified the model's effectiveness on SMIC, CASME II, SAMM, and a combined 3DB-Combined dataset.

Conclusions:

  • The proposed MADFN model significantly enhances facial micro-expression recognition by improving the discrimination of emotional features.
  • Integrating action unit information dynamically with image features is crucial for robust micro-expression analysis.
  • The findings highlight the potential of MADFN for advancing the field of micro-expression recognition.