{"ID":2891329,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.17450","arxiv_id":"2507.17450","title":"Persistent Patterns in Eye Movements: A Topological Approach to 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.","short_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 rand...","url_abs":"https://arxiv.org/abs/2507.17450","url_pdf":"https://arxiv.org/pdf/2507.17450v1","authors":"[\"Arsha Niksa\",\"Hooman Zare\",\"Ali Shahrabi\",\"Hanieh Hatami\",\"Mohammadreza Razvan\"]","published":"2025-07-23T12:14:17Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[]","has_code":false}