MEDN: Motion-Emotion Feature Decoupling Network for Micro-Expression Recognition

• URL: http://arxiv.org/abs/2604.17899v1
• Date: Mon, 20 Apr 2026 07:19:20 GMT
• Title: MEDN: Motion-Emotion Feature Decoupling Network for Micro-Expression Recognition
• Abstract summary: This paper presents a Motion Emotion Feature Decoupling Network (MEDN) for microexpression recognition.<n>We propose a dual-branch framework to separately extract motion and emotion features.<n>Experiments on three benchmark datasets validate that MEDN effectively decouples motion and emotion features and achieves superior recognition performance.
• Score: 28.87936777678119
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Unlike macro-expression, micro-expression does not follow a strictly consistent mapping rule between emotions and Action Units (AUs). As a result, some micro-expressions share identical AUs yet represent completely opposite emotional categories, making them highly visually similar. Existing microexpression recognition (MER) methods mostly rely on explicit facial motion cues (e.g., optical flow, frame differences, AU features) while ignoring implicit emotion information. To tackle this issue, this paper presents a Motion Emotion Feature Decoupling Network (MEDN) for MER. We design a dual-branch framework to separately extract motion and emotion features. In the motion branch, an AU-detection task restricts features to the explicit motion domain, and orthogonal loss is adopted to reduce motion emotion feature coupling. For implicit emotion modeling, we propose a Sparse Emotion Vision Transformer (SEVit) that sparsifies spatial tokens to highlight local temporal variations with multi-scale sparsity rates. A Collaborative Fusion Module (CoFM) is further developed to fuse disentangled motion and emotion features adaptively. Extensive experiments on three benchmark datasets validate that MEDN effectively decouples motion and emotion features and achieves superior recognition performance, offering a new perspective for enhancing recognition accuracy and generalization.

Related papers

• Emotion Across Speech and Faces: Shared Affective Mechanisms in Multimodal Foundation Models [7.8273212511900985]
  We explore emotion-sensitive neurons (ESNs), sparse decoder neurons selectively associated with emotion categories.<n>Visual ESNs are causally meaningful: deactivating them selectively impairs recognition of the associated facial emotion.<n>Cross-modal interventions reveal bidirectional causal transfer: ESNs identified from one modality produce emotion-specific effects when applied to the other.
  arXiv  Detail & Related papers  (2026-08-17T20:26:22Z)
• From Syntax to Emotion: A Mechanistic Analysis of Emotion Inference in LLMs [1.4692712025142225]
  We investigate the internal mechanisms of emotion recognition in large language models (LLMs)<n>By analyzing sparse feature activations across layers, we identify a consistent three-phase information flow.<n>We show that emotion representations comprise both shared features across emotions and emotion-specific features.
  arXiv  Detail & Related papers  (2026-04-28T17:03:16Z)
• Emotion-Director: Bridging Affective Shortcut in Emotion-Oriented Image Generation [23.10502994564729]
  Emotion-Director is a cross-modal collaboration framework consisting of two modules.<n>We propose a cross-Modal Collaborative diffusion model, abbreviated as MC-Diffusion.<n>We also propose MC-Agent, a cross-Modal Collaborative Agent system that rewrites textual prompts to express the intended emotions.
  arXiv  Detail & Related papers  (2025-12-22T15:32:18Z)
• EmoCAST: Emotional Talking Portrait via Emotive Text Description [56.42674612728354]
  EmoCAST is a diffusion-based framework for precise text-driven emotional synthesis.<n>In appearance modeling, emotional prompts are integrated through a text-guided decoupled emotive module.<n>EmoCAST achieves state-of-the-art performance in generating realistic, emotionally expressive, and audio-synchronized talking-head videos.
  arXiv  Detail & Related papers  (2025-08-28T10:02:06Z)
• Emotion Transfer with Enhanced Prototype for Unseen Emotion Recognition in Conversation [64.70874527264543]
  We introduce the Unseen Emotion Recognition in Conversation (UERC) task for the first time.<n>We propose ProEmoTrans, a prototype-based emotion transfer framework.<n>ProEmoTrans shows promise but still faces key challenges.
  arXiv  Detail & Related papers  (2025-08-27T03:16:16Z)
• From Coarse to Nuanced: Cross-Modal Alignment of Fine-Grained Linguistic Cues and Visual Salient Regions for Dynamic Emotion Recognition [7.362433184546492]
  Dynamic Facial Expression Recognition aims to identify human emotions from temporally evolving facial movements.<n>Our method integrates dynamic motion modeling, semantic text refinement, and token-level cross-modal alignment to facilitate the precise localization of emotionally salient features.
  arXiv  Detail & Related papers  (2025-07-16T04:15:06Z)
• Disentangle Identity, Cooperate Emotion: Correlation-Aware Emotional Talking Portrait Generation [63.94836524433559]
  DICE-Talk is a framework for disentangling identity with emotion and cooperating emotions with similar characteristics.<n>We develop a disentangled emotion embedder that jointly models audio-visual emotional cues through cross-modal attention.<n>Second, we introduce a correlation-enhanced emotion conditioning module with learnable Emotion Banks.<n>Third, we design an emotion discrimination objective that enforces affective consistency during the diffusion process.
  arXiv  Detail & Related papers  (2025-04-25T05:28:21Z)
• Attention-based Interactive Disentangling Network for Instance-level Emotional Voice Conversion [81.1492897350032]
  Emotional Voice Conversion aims to manipulate a speech according to a given emotion while preserving non-emotion components. We propose an Attention-based Interactive diseNtangling Network (AINN) that leverages instance-wise emotional knowledge for voice conversion.
  arXiv  Detail & Related papers  (2023-12-29T08:06:45Z)
• GM-TCNet: Gated Multi-scale Temporal Convolutional Network using Emotion Causality for Speech Emotion Recognition [14.700043991797537]
  We propose a Gated Multi-scale Temporal Convolutional Network (GM-TCNet) to construct a novel emotional causality representation learning component. GM-TCNet deploys a novel emotional causality representation learning component to capture the dynamics of emotion across the time domain. Our model maintains the highest performance in most cases compared to state-of-the-art techniques.
  arXiv  Detail & Related papers  (2022-10-28T02:00:40Z)
• MSA-GCN:Multiscale Adaptive Graph Convolution Network for Gait Emotion Recognition [6.108523790270448]
  We present a novel Multi Scale Adaptive Graph Convolution Network (MSA-GCN) to recognize emotions. In our model, a adaptive selective spatial-temporal convolution is designed to select the convolution kernel dynamically to obtain the soft-temporal features of different emotions. Compared with previous state-of-the-art methods, the proposed method achieves the best performance on two public datasets.
  arXiv  Detail & Related papers  (2022-09-19T13:07:16Z)
• Shapes of Emotions: Multimodal Emotion Recognition in Conversations via Emotion Shifts [2.443125107575822]
  Emotion Recognition in Conversations (ERC) is an important and active research problem. Recent work has shown the benefits of using multiple modalities for the ERC task. We propose a multimodal ERC model and augment it with an emotion-shift component.
  arXiv  Detail & Related papers  (2021-12-03T14:39:04Z)
• Emotion Recognition from Multiple Modalities: Fundamentals and Methodologies [106.62835060095532]
  We discuss several key aspects of multi-modal emotion recognition (MER) We begin with a brief introduction on widely used emotion representation models and affective modalities. We then summarize existing emotion annotation strategies and corresponding computational tasks. Finally, we outline several real-world applications and discuss some future directions.
  arXiv  Detail & Related papers  (2021-08-18T21:55:20Z)
• Modality-Transferable Emotion Embeddings for Low-Resource Multimodal Emotion Recognition [55.44502358463217]
  We propose a modality-transferable model with emotion embeddings to tackle the aforementioned issues. Our model achieves state-of-the-art performance on most of the emotion categories. Our model also outperforms existing baselines in the zero-shot and few-shot scenarios for unseen emotions.
  arXiv  Detail & Related papers  (2020-09-21T06:10:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.