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Related Experiment Video

Updated: Aug 5, 2025

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
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Dual-ATME: Dual-Branch Attention Network for Micro-Expression Recognition.

Haoliang Zhou1,2, Shucheng Huang1, Jingting Li2,3

  • 1School of Computer, Jiangsu University of Science and Technology, Zhenjiang 212100, China.

Entropy (Basel, Switzerland)
|March 29, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces the Dual-branch Attention Network (Dual-ATME) for improved micro-expression recognition (MER). Dual-ATME effectively combines hand-crafted and automated feature selection for more discriminative representations, enhancing MER performance.

Keywords:
attention mechanismmicro-expression recognitionregions of interest

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Biometrics

Background:

  • Micro-expression recognition (MER) is challenging due to subtle, rapid facial movements.
  • Deep learning with attention mechanisms shows promise but struggles with limited data and feature representation.
  • Existing methods often fail to capture the full spectrum of micro-expression (ME) dynamics.

Purpose of the Study:

  • To propose a novel Dual-branch Attention Network (Dual-ATME) for enhanced micro-expression recognition.
  • To address the limitations of single-scale features in representing micro-expressions.
  • To improve the discriminative power of features for more accurate MER.

Main Methods:

  • Introduced the Dual-branch Attention Network (Dual-ATME) comprising Hand-crafted Attention Region Selection (HARS) and Automated Attention Region Selection (AARS).
  • HARS utilizes prior knowledge for manual feature extraction from regions of interest.
  • AARS employs attention mechanisms for automatic extraction of hidden data features.
  • Feature fusion combines dual-scale features for effective ME representation learning.

Main Results:

  • Dual-ATME demonstrated superior or competitive performance compared to state-of-the-art MER methods.
  • Experiments were conducted on spontaneous micro-expression datasets: CASME II, SAMM, and SMIC, including the composite MEGC2019-CD dataset.
  • The proposed method effectively learns micro-expression representations through fused dual-scale features.

Conclusions:

  • The Dual-branch Attention Network (Dual-ATME) offers a significant advancement in micro-expression recognition.
  • Combining hand-crafted and automated attention mechanisms leads to more robust and discriminative feature representations.
  • Dual-ATME provides a promising solution for the challenging task of micro-expression analysis.