Abstract
Facial micro-expressions are brief, involuntary facial movements that reveal hidden emotions. Most Micro-Expression Recognition (MER) methods that rely on optical flow typically focus on the onset-to-apex phase, neglecting the apex-to-offset phase, which holds key temporal dynamics. This study introduces a Combined Optical Flow (COF), integrating both phases to enhance feature representation. COF provides a more comprehensive motion analysis, improving MER performance. Experimental results on CASMEII and SAMM datasets show that COF outperforms single optical flow-based methods, demonstrating its effectiveness in capturing micro-expression dynamics.
Abstract (translated)
面部微表情是短暂的、非自愿的面部动作，能揭示隐藏的情绪。大多数依赖于光学流的微表情识别（MER）方法通常只关注从开始到峰值的阶段，而忽视了从峰值到结束的关键时间动态变化。这项研究引入了一种结合型光学流（COF），整合这两个阶段来增强特征表示。COF提供了更全面的运动分析，从而提高了微表情识别性能。在CASMEII和SAMM数据集上的实验结果显示，COF优于单一基于光学流的方法，证明了其捕捉微表情动态的有效性。
URL
https://arxiv.org/abs/2510.15471
