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

TL;DR

This paper introduces a topological method using persistent homology to analyze eye-tracking data for emotion recognition, achieving high accuracy and revealing that gaze dynamics encode emotional states effectively.

Contribution

It presents a novel topological pipeline leveraging persistent homology for multiclass emotion recognition from eye movements, a new approach in affective computing.

Findings

Achieved up to 75.6% accuracy on four emotion classes.

Persistence diagram features effectively encode discriminative gaze dynamics.

Demonstrated the potential of topological data analysis in emotion recognition.

Abstract

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.