Facial expressions serve as a crucial medium for human emotional communication. Based on duration and intensity, expressions can be categorized into macro-expressions and micro-expressions. Characterized by their involuntary and fleeting nature (lasting less than 0.5 seconds), micro-expressions (MEs) hold significant value in fields such as business negotiations, criminal investigations, and clinical diagnosis due to their uncontrollable properties.
Traditional micro-expression recognition methods primarily rely on onset-apex frames or fixed-length sequences, often limited by insufficient utilization of temporal information. A study by Professor Haifeng Li's team at Harbin Institute of Technology, published in Frontiers of Computer Science, introduces an innovative solution modeling the dynamic evolution of micro-expressions to significantly improve recognition performance. This approach preserves richer temporal variation features through complete sequence analysis, marking a technological breakthrough in the field. The code is available at https://github.com/hitheyuhong/TA_MER_FOR_CCAC.git.
The research team pioneered a self-attention based micro-expression temporal feature analysis network:
The team announced plans to focus on developing unsupervised learning methods for ME feature extractors. By leveraging massive unlabeled data, this direction aims to enhance model noise immunity and address current overfitting challenges caused by limited annotated samples, potentially benefiting high-robustness scenarios like clinical diagnosis and security monitoring.
Frontiers of Computer Science
Experimental study
Not applicable
Modeling the evolution dynamics to enhance micro-expression recognition
15-Mar-2026