SmartEye: An Accurate Infrared Eye Tracking System for Smartphones
Bibliographic record
Abstract
The capability to estimate where a user is looking on a screen is known as gaze estimation or eye tracking. It has been used in medical applications including assessment of mood and learning disorders, and brain injury diagnosis. If accurate eye tracking could be integrated into commodity smartphones these diagnostics could be broadly deployed at very low cost. The highest accuracy and most robust eye tracking methods employ infrared cameras and illumination which are not yet available on all standard smartphones. In this paper, we present an accurate infrared eye tracking system on a smartphone, named SmartEye, on an industrial prototype phone equipped with an infrared camera and illumination. The system is accurate in the presence of head pose variation and device movements in the user's hands, and requires only a one-time calibration routine to measure specific parameters of the user's eye. Our system achieves a gaze estimation bias of 0.57°at a 20cm distance from the user, 5 times better than state-of-the art mobile device eye-tracking systems that do not use infrared illumination. Our system also allows for free head movements at distances between 20-40cm with a moderate increase in average gaze bias (to ~1°), and can operate at 12fps. This enhanced accuracy and increased mobility can expand significantly the range of eye-tracking applications that can be supported by smartphones.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".