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Absolute Motion Analysis- General Plane Motion01:24

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Visualize a drone, with its propellers spinning rapidly, hovering mid-air. The fascinating movements and operations of this drone can be comprehended by applying the principle of general plane motion.
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Related Experiment Video

Updated: Feb 28, 2026

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
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Learnable Feature Disentanglement with Temporal-Complemented Motion Enhancement for Micro-Expression Recognition.

Yu Qian1, Shucheng Huang1, Kai Qu2

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

Entropy (Basel, Switzerland)
|February 27, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces LFD-TCMEN, a novel network for micro-expression recognition (MER). It effectively disentangles facial identity and motion, improving emotion detection accuracy in challenging scenarios.

Keywords:
feature disentanglementmicro-expression recognitionmotionoptical flow

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Related Experiment Videos

Last Updated: Feb 28, 2026

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Biometrics

Background:

  • Micro-expressions (MEs) are subtle, involuntary facial movements crucial for understanding genuine emotions.
  • Micro-expression recognition (MER) faces challenges due to the entanglement of emotional cues with identity-specific features.
  • Existing methods like RPCA struggle with static feature separation, potentially losing vital emotional information.

Purpose of the Study:

  • To develop a novel, learnable framework for disentangling facial identity and motion for improved MER.
  • To enhance the discriminative power of features by adaptively isolating pure motion patterns.
  • To create a more dynamic and effective approach inspired by cognitive models of facial processing.

Main Methods:

  • Proposed LFD-TCMEN network with an end-to-end learnable feature disentanglement framework.
  • Introduced a Disentangle Representation Learning (DRL) module for adaptive isolation of motion patterns.
  • Developed a Temporal-Complemented Motion Enhancement (TCME) module integrating motion representations with optical flow dynamics.

Main Results:

  • Achieved state-of-the-art cross-subject performance on CAS(ME)³ and DFME benchmarks.
  • Demonstrated effective isolation of pure motion patterns, overcoming static preprocessing limitations.
  • Validated the efficacy of synergistic optimization through a multi-task objective unifying various losses.

Conclusions:

  • The proposed LFD-TCMEN network offers a significant advancement in micro-expression recognition.
  • Learnable feature disentanglement and synergistic optimization are key to enhancing MER performance.
  • The method shows strong potential for applications in deception detection and psychological diagnosis.