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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.17450 |
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| _version_ | 1866916858957922304 |
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| author | Niksa, Arsha Zare, Hooman Shahrabi, Ali Hatami, Hanieh Razvan, Mohammadreza |
| author_facet | Niksa, Arsha Zare, Hooman Shahrabi, Ali Hatami, Hanieh Razvan, Mohammadreza |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_17450 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Persistent Patterns in Eye Movements: A Topological Approach to Emotion Recognition Niksa, Arsha Zare, Hooman Shahrabi, Ali Hatami, Hanieh Razvan, Mohammadreza Machine Learning 55N31 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. |
| title | Persistent Patterns in Eye Movements: A Topological Approach to Emotion Recognition |
| topic | Machine Learning 55N31 |
| url | https://arxiv.org/abs/2507.17450 |