Micro-expression Consistency

Dynamic analysis of micro-expressions for authenticity verification

Our research focuses on micro-expression consistency analysis to verify the authenticity of emotional expressions. Micro-expressions are brief, involuntary facial expressions that reveal genuine emotions, making them crucial for distinguishing between authentic and deceptive emotional displays.

Research Focus: Developing advanced frameworks for micro-expression recognition and analysis that integrate frequency analysis, structural embedding, and multimodal learning to enhance emotion authenticity verification.

Key Contributions

  • EMO-LLaMA Framework: Enhancing facial emotion understanding through instruction tuning in large language models
  • Task-Specific Micro-expression Recognition: Integrating frequency analysis and structural embedding for improved recognition performance
  • FEALLM System: Advancing facial emotion analysis in multimodal large language models with emotional synergy and reasoning
  • Multi-granularity Representation: Learning facial emotional representations with unlabeled data and textual supervision
  • Spatiotemporal Pattern Analysis: Discriminative analysis of spontaneous facial micro-expressions using improved projection methods
EMO-LLaMA: Enhancing Facial Emotion Understanding with Instruction Tuning
B. Xing, Z. Yu, X. Liu*, K. Yuan, Q. Ye, W. Xie, H. Yue, J. Yang, and H. Kälviäinen
International Journal of Computer Vision, 2025
Advancing Micro-Expression Recognition: A Task-Specific Framework Integrating Frequency Analysis and Structural Embedding
L. Zheng, Y. Zong, C. Lu, T. Zhang, J. Shi, X. Liu, W. Zheng
IEEE Transactions on Affective Computing, 2025
FEALLM: Advancing Facial Emotion Analysis in Multimodal Large Language Models with Emotional Synergy and Reasoning
Z. Hu, K. Yuan, X. Liu*, Z. Yu, Y. Zong, J. Shi, H. Yue, and J. Yang
ACM International Conference on Multimedia (ACMMM), 2025
source code
Multi-granularity Facial Emotional Representation with Unlabeled Data and Textual Supervision
K. Yuan, Z. Yu, X. Liu*, B. Xing, Y. Zhang, W. Xie, L. Shen, B. Schuller
IEEE Transactions on Image Processing, 2025
Discriminative spatiotemporal local binary pattern with improved revisited projection for spontaneous facial micro-expression recognition
X. Huang, S. Wang, X. Liu, G. Zhao, X. Feng, and M. Pietikäinen
IEEE Transactions on Affective Computing, vol. 10, no. 1, pp. 32–47, 2019
« Back to Research Framework