# Persistent Patterns in Eye Movements: A Topological Approach to Emotion Recognition

arXiv: https://arxiv.org/abs/2507.17450 · HTML: https://mlsanity.com/paper/2507.17450/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- First posted on arXiv 23 Jul 2025.

## Abstract (arXiv)

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.