通过颜色均衡与微调提升手语数据中面部表情识别准确率
Color histogram equalization and fine-tuning to improve expression recognition of (partially occluded) faces on sign language datasets
- 采用直方图均衡化进行颜色归一化，结合微调优化表情识别
- 整体识别敏感度达83.8%，类别间方差仅0.042
- 上半脸识别效果超人类水平，适用于听障人群表情研究
本研究旨在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能力。实验扩展至仅使用面部上半部分或下半部分进行表情识别，以探究听力正常与聋哑群体在情绪表达上的差异。针对数据集特有的色彩特征，提出基于直方图均衡化的颜色归一化流程并结合微调。结果表明，表情识别的平均敏感度达到83.8%，各类别间方差仅为0.042。与人类表现类似，下半脸表情识别准确率（79.6%）略高于上半脸（77.9%）。值得注意的是，上半脸表情识别精度已超过人类水平。
原文摘要 · Abstract (English)
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.
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