用新可视化方法提升面部表情识别准确率
Facial Landmark Visualization and Emotion Recognition Through Neural Networks
- 提出地标箱线图，可视化检测数据集异常点
- 位移特征比绝对位置更利于情绪识别
- 神经网络性能优于随机森林，适合情感计算研究
从面部图像中识别情绪是人机交互中的关键任务，使机器能够通过面部表情理解人类情绪。以往研究虽已证明可利用面部图像训练深度学习模型，但多数缺乏对数据集的深入分析。在提取有意义的数据洞察时，面部地标可视化存在挑战。为此，本文提出面部地标箱线图这一可视化技术，用于识别面部数据集中的异常值。同时，我们对比了两组面部地标特征：(i) 地标绝对位置；(ii) 从中性表情到情绪峰值时的地标位移。实验结果表明，神经网络的性能优于随机森林分类器。
原文摘要 · Abstract (English)
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.
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