Abstract
Emotion recognition from facial images is a crucial task in human-computer interaction, enabling machines to learn human emotions through facial expressions. Previous studies have shown that facial images can be used to train deep learning models; however, most of these studies do not include a through dataset analysis. Visualizing facial landmarks can be challenging when extracting meaningful dataset insights; to address this issue, we propose facial landmark box plots, a visualization technique designed to identify outliers in facial datasets. Additionally, we compare two sets of facial landmark features: (i) the landmarks' absolute positions and (ii) their displacements from a neutral expression to the peak of an emotional expression. Our results indicate that a neural network achieves better performance than a random forest classifier.
Abstract (translated)
面部图像的情感识别是人机交互中的一个关键任务，它使机器能够通过面部表情来学习人类的情绪。以往的研究表明，可以使用面部图像来训练深度学习模型；然而，大多数这些研究并未进行彻底的数据集分析。在提取有意义的数据集洞察时，可视化面部特征点可能会具有挑战性；为了解决这个问题，我们提出了面部特征框图技术，这是一种用于识别面部数据集中异常值的可视化方法。此外，我们将两组面部特征点进行了比较：（i）特征点的绝对位置和（ii）从中性表情到情感顶峰的表情变化中的位移。我们的研究结果表明，神经网络的表现优于随机森林分类器。
URL
https://arxiv.org/abs/2506.17191
