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.

翻译：面部微表情是短暂、无意识的面部运动，能够揭示隐藏的情绪。大多数依赖光流的微表情识别方法通常只关注起始到峰值阶段，而忽略了包含关键时间动态的峰值到消退阶段。本研究提出了一种组合光流方法，整合了两个阶段以增强特征表示。该方法提供了更全面的运动分析，从而提升了微表情识别的性能。在CASMEII和SAMM数据集上的实验结果表明，该组合光流方法优于基于单一光流的方法，证明了其在捕捉微表情动态方面的有效性。