Year: 2025
Type: conference-paper
Abstract: Micro-expressions are involuntary facial movements that cannot be consciously controlled, conveying subtle cues with substantial real-world applications. The analysis of micro-expressions generally involves two main tasks: spotting micro-expression intervals in long videos and recognizing the emotions associated with these intervals. Previous deep-learning methods have primarily relied on classification networks utilizing sliding windows. However, fixed window sizes and window-level hard classification introduce numerous constraints. Additionally, these methods have not fully exploited the potential of complementary pathways for spotting and recognition. In this paper, we present a novel temporal state transition architecture grounded in the state space model, which replaces conventional window-level classification with video-level regression. Furthermore, by leveraging the inherent connections between spotting and recognition tasks, we propose a synergistic strategy that enhances overall analysis performance. Extensive experiments demonstrate that our method achieves state-of-the-art performance. The codes are available at https://github.com/zizheng-guo/ME-TST.
Authors: Bochao Zou, Zizheng Guo, Wenfeng Qin, Xin Li, Kangsheng Wang, Huimin Ma
Institution: University of Science and Technology Beijing
Language: English
Topic: Emotion and Mood Recognition
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W4408352329