融合前后阶段光流，提升微表情识别精度
A Novel Combined Optical Flow Approach for Comprehensive Micro-Expression Recognition
- 提出联合光流方法，同时分析微表情起始与峰值阶段
- 在CASMEII和SAMM数据集上准确率显著优于单一光流方法
- 适合需要捕捉动态细节的微表情研究与应用
面部微表情是短暂且不由自主的面部动作，能揭示隐藏情绪。现有大多数微表情识别（MER）方法依赖光流，但通常只关注从起始到峰值的阶段，忽略了包含关键时间动态的峰值到消退阶段。本文提出一种联合光流（COF）方法，整合两个阶段的运动信息，实现更全面的运动分析，从而提升特征表示能力。在CASMEII和SAMM数据集上的实验结果表明，COF性能优于仅使用单一光流的方法，验证了其在捕捉微表情动态方面的有效性。
原文摘要 · Abstract (English)
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.
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