FOVAL: Calibration-Free and Subject-Invariant Fixation Depth Estimation Across Diverse Eye-Tracking Datasets
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866911163545026560 |
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| author | Hosp, Benedikt W. |
| author_facet | Hosp, Benedikt W. |
| contents | Accurate fixation depth estimation is essential for applications in extended reality (XR), robotics, and human-computer interaction. However, current methods heavily depend on user-specific calibration, which limits their scalability and usability. We introduce FOVAL, a robust calibration-free approach that combines spatiotemporal sequence modelling via Long Short-Term Memory (LSTM) networks with subject-invariant feature engineering and normalisation. Compared to Transformers, Temporal Convolutional Networks (TCNs), and CNNs, FOVAL achieves superior performance, particularly in scenarios with limited and noisy gaze data. Evaluations across three benchmark datasets using Leave-One-Out Cross-Validation (LOOCV) and cross-dataset validation show a mean absolute error (MAE) of 9.1 cm and strong generalisation without calibration. We further analyse inter-subject variability and domain shifts, providing insight into model robustness and adaptation. FOVAL's scalability and accuracy make it highly suitable for real-world deployment. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2408_03591 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | FOVAL: Calibration-Free and Subject-Invariant Fixation Depth Estimation Across Diverse Eye-Tracking Datasets Hosp, Benedikt W. Computer Vision and Pattern Recognition Artificial Intelligence Human-Computer Interaction Machine Learning Signal Processing Accurate fixation depth estimation is essential for applications in extended reality (XR), robotics, and human-computer interaction. However, current methods heavily depend on user-specific calibration, which limits their scalability and usability. We introduce FOVAL, a robust calibration-free approach that combines spatiotemporal sequence modelling via Long Short-Term Memory (LSTM) networks with subject-invariant feature engineering and normalisation. Compared to Transformers, Temporal Convolutional Networks (TCNs), and CNNs, FOVAL achieves superior performance, particularly in scenarios with limited and noisy gaze data. Evaluations across three benchmark datasets using Leave-One-Out Cross-Validation (LOOCV) and cross-dataset validation show a mean absolute error (MAE) of 9.1 cm and strong generalisation without calibration. We further analyse inter-subject variability and domain shifts, providing insight into model robustness and adaptation. FOVAL's scalability and accuracy make it highly suitable for real-world deployment. |
| title | FOVAL: Calibration-Free and Subject-Invariant Fixation Depth Estimation Across Diverse Eye-Tracking Datasets |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Human-Computer Interaction Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2408.03591 |