用拓扑方法分析眼动轨迹，识别四种情绪，准确率最高75.6%。
Persistent Patterns in Eye Movements: A Topological Approach to Emotion Recognition
- 通过延时嵌入与持久同调分析眼动轨迹的拓扑结构
- 基于持久图特征的随机森林分类器达75.6%准确率
- 适合情感计算与人类行为分析研究者参考
我们提出一种用于从眼动数据中自动进行多类情绪识别的拓扑流程。将注视轨迹的延时嵌入利用持久同调进行分析，从生成的持久图中提取均值持续性、最大持续性和熵等形状特征。基于这些特征训练的随机森林分类器，在四个情绪类别（即情感环形模型的四象限）上最高达到75.6%的准确率。结果表明，持久图的几何结构能有效编码具有区分性的注视动态，为情感计算与人类行为分析提供了一种有前景的拓扑方法。
原文摘要 · Abstract (English)
We present a topological pipeline for automated multiclass emotion recognition from eye-tracking data. Delay embeddings of gaze trajectories are analyzed using persistent homology. From the resulting persistence diagrams, we extract shape-based features such as mean persistence, maximum persistence, and entropy. A random forest classifier trained on these features achieves up to $75.6\%$ accuracy on four emotion classes, which are the quadrants the Circumplex Model of Affect. The results demonstrate that persistence diagram geometry effectively encodes discriminative gaze dynamics, suggesting a promising topological approach for affective computing and human behavior analysis.
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