Micro-expression Recognition Based on Dual-branch Feature Extraction and Fusion

Mingjie Zhang; Bo Li; Wanting Liu; Hongyan Cui; Yue Li; Qingwen Li; Hong Li; Ge Gao

cs.CVFeb 27, 2026

Micro-expression Recognition Based on Dual-branch Feature Extraction and Fusion

Authors: Mingjie Zhang, Bo Li, Wanting Liu, Hongyan Cui, Yue Li, Qingwen Li, Hong Li, Ge Gao

Organizations: School of Petroleum China University of Petroleum (Beijing) Karamay Campus Karamay, China · Artificial Intelligence Research Institute China Tower Corporation Limited Beijing, China · Low Carbon Business Department South Electric Power Science and Technology Development Co., Ltd. Guangzhou, China · School of Information and Communication Engineering Beijing University of Posts and Telecommunications Bejing, China

Abstract

Micro-expressions, characterized by transience and subtlety, pose challenges to existing optical flow-based recognition methods. To address this, this paper proposes a dual-branch micro-expression feature extraction network integrated with parallel attention. Key contributions include: 1) a residual network designed to alleviate gradient anishing and network degradation; 2) an Inception network constructed to enhance model representation and suppress interference from irrelevant regions; 3) an adaptive feature fusion module developed to integrate dual-branch features. Experiments on the CASME II dataset demonstrate that the proposed method achieves 74.67% accuracy, outperforming LBP-TOP (by 11.26%), MSMMT (by 3.36%), and other comparative methods.

Micro-Expression Recognition

See all 12 papers

Dual-View Optical Flow for 4D Micro-Expression Recognition - A Multi-Stream Fusion Attention Approach
Mar 27, 2026Luu Tu Nguyen, Thi Bich Phuong Man, Vu Tram Anh Khuong +2Micro-Expression Recognition Facial Expression Recognition
VNU University of Engineering and Technology, Faculty of Information Technology, Hanoi, Vietnam
STAG: Spatio-temporal Evolving Structural Representation of Action Units for Micro-expression Recognition
Jun 26, 2026Nandani Sharma, Varun Sharma, Dinesh SinghFacial Expression Recognition Micro-Expression Recognition
Vision Intelligence and Machine Learning (VIML) Group, School of Computing and Electrical Engineering, Indian Institute of Technology Mandi, Mandi, 175005, Himachal Pradesh, India · Indian Institute of Information Technology Bhagalpur, Sabour Rd, Bhagalpur, 813210, Bihar, India
MEDN: Motion-Emotion Feature Decoupling Network for Micro-Expression Recognition
Apr 20, 2026Chenxing Hu, Kun Xie, Qiguang Miao +3Disentangled Representation Learning Vision Transformer
School of Computer Science and Technology, Xidian University, Xi’an, Shaanxi 710071, China · Xi’an Key Laboratory of Big Data and Intelligent Vision, Xi’an, Shaanxi 710071, China · Key Laboratory of Collaborative Intelligence Systems, Ministry of Education, Xidian University, Xi’an 710071, China +1

Mar 27, 2026J/K move · Enter open · S save

Dual-View Optical Flow for 4D Micro-Expression Recognition - A Multi-Stream Fusion Attention Approach

Luu Tu Nguyen, Thi Bich Phuong Man, Vu Tram Anh Khuong, Thanh Ha Le+1

VNU University of Engineering and Technology, Faculty of Information Technology, Hanoi, Vietnam

Micro-Expression Recognition Facial Expression Recognition

Open paper AskPDF arXiv

Micro-expression recognition is vital for affective computing but remains challenging due to the extremely brief, low-intensity facial motions involved and the high-dimensional nature of 4D mesh data. To address these challenges, we introduce a dual-view optical flow approach that simplifies mesh processing by capturing each micro-expression sequence from two synchronized viewpoints and computing optical flow to represent motion. Our pipeline begins with view separation and sequence-wise face cropping to ensure spatial consistency, followed by automatic apex-frame detection based on peak motion intensity in both views. We decompose each sequence into onset-apex and apex-offset phases, extracting horizontal, vertical, and magnitude flow channels for each phase. These are fed into our Triple-Stream MicroAttNet, which employs a fusion attention module to adaptively weight modality-specific features and a squeeze-and-excitation block to enhance magnitude representations. Training uses focal loss to mitigate class imbalance and the Adam optimizer with early stopping. Evaluated on the multi-label 4DME dataset, comprising 24 subjects and five emotion categories, in the 4DMR IJCAI Workshop Challenge 2025, our method achieves a macro-UF1 score of 0.536, outperforming the official baseline by over 50% and securing first place. Ablation studies confirm that both the fusion attention and SE components each contribute up to 3.6 points of UF1 gain. These results demonstrate that dual-view, phase-aware optical flow combined with multi-stream fusion yields a robust and interpretable solution for 4D micro-expression recognition.

Abstract

Micro-Expression Recognition

Explore similar work

Dual-View Optical Flow for 4D Micro-Expression Recognition - A Multi-Stream Fusion Attention Approach

STAG: Spatio-temporal Evolving Structural Representation of Action Units for Micro-expression Recognition

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