Bibliographic record
Abstract
This paper presents a proof-of-concept architecture that investigates if a standard smartphone front camera can estimate face-to-screen distance in real time by using the average human iris diameter as a biological scale reference within a pinhole camera model. The proposed Android implementation uses automatic eye region localization, camera intrinsic calibration, grayscale preprocessing, and random sample consensus circle fitting to measure iris diameter in pixels. To support natural device use, a gaze rectification step compensates for perspective distortion when the user looks at the screen instead of directly into the camera. The method was evaluated in a pilot study with 48 recordings from one participant across eight distances from 200 to 550 millimeters, captured under both screen-directed and camera-directed gaze conditions. The system remained stable over the 250 to 500 millimeter range and produced comparable performance for screen and camera gaze. While accuracy degraded markedly at 550 millimeters. The main limitation was a systematic underestimation of roughly 20 to 35 millimeters, affected by the ground truth measurement setup and the static biological assumption ofnbsp; iris diameter. The findings of this paper show that an on-device, real-time pipeline for iris based monocular distance estimation is computationally feasible for mobile applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".