Boosting Micro-Expression Analysis via Prior-Guided Video-Level Regression

TL;DR

This paper introduces a prior-guided video-level regression approach for micro-expression analysis, improving the detection of expression phases and recognition accuracy by considering temporal evolution and shared optimization.

Contribution

It proposes a scalable interval selection strategy and a synergistic optimization framework for more accurate and efficient micro-expression analysis.

Findings

Achieved state-of-the-art results on CAS(ME)$^3$ and SAMMLV datasets.

Enhanced micro-expression spotting and recognition accuracy.

Efficient utilization of limited data through shared parameters.

Abstract

Micro-expressions (MEs) are involuntary, low-intensity, and short-duration facial expressions that often reveal an individual's genuine thoughts and emotions. Most existing ME analysis methods rely on window-level classification with fixed window sizes and hard decisions, which limits their ability to capture the complex temporal dynamics of MEs. Although recent approaches have adopted video-level regression frameworks to address some of these challenges, interval decoding still depends on manually predefined, window-based methods, leaving the issue only partially mitigated. In this paper, we propose a prior-guided video-level regression method for ME analysis. We introduce a scalable interval selection strategy that comprehensively considers the temporal evolution, duration, and class distribution characteristics of MEs, enabling precise spotting of the onset, apex, and offset phases. In addition, we introduce a synergistic optimization framework, in which the spotting and recognition tasks share parameters except for the classification heads. This fully exploits complementary information, makes more efficient use of limited data, and enhances the model's capability. Extensive experiments on multiple benchmark datasets demonstrate the state-of-the-art performance of our method, with an STRS of 0.0562 on CAS(ME)$^3$ and 0.2000 on SAMMLV. The code is available at https://github.com/zizheng-guo/BoostingVRME.