颜色直方图均衡化与微调以改进手语数据集上（部分遮挡）人脸的表情识别
Color histogram equalization and fine-tuning to improve expression recognition of (partially occluded) faces on sign language datasets
AI总结：
本文提出结合直方图均衡化、颜色归一化与微调的表情识别方法，在手语数据集上取得83.8%平均敏感度，并比较面部上下半部分识别效果。
摘要：
本研究的目标是量化计算机视觉方法在手语数据集上正确分类面部表情的程度。我们扩展了实验，仅使用面部的上半部分或下半部分来识别表情，这是进一步研究听力正常受试者与失聪受试者之间情绪表现差异所必需的。为考虑数据集特有的颜色分布，我们的方法引入了一个基于直方图均衡化和微调的颜色归一化阶段。结果表明，该方法能够以83.8%的平均敏感度正确识别面部表情，且各类别间的方差极小（.042）。与人类相似，基于面部下半部分的表情识别率（79.6%）高于基于上半部分的识别率（77.9%）。值得注意的是，基于面部上半部分的分类准确率高于人类水平。
英文摘要：
The goal of this investigation is to quantify to what extent computer vision methods can correctly classify facial expressions on a sign language dataset. We extend our experiments by recognizing expressions using only the upper or lower part of the face, which is needed to further investigate the difference in emotion manifestation between hearing and deaf subjects. To take into account the peculiar color profile of a dataset, our method introduces a color normalization stage based on histogram equalization and fine-tuning. The results show the ability to correctly recognize facial expressions with 83.8% mean sensitivity and very little variance (.042) among classes. Like for humans, recognition of expressions from the lower half of the face (79.6%) is higher than that from the upper half (77.9%). Noticeably, the classification accuracy from the upper half of the face is higher than human level.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
