The Recognizability of Cropped Unilateral Upper Face Photographs
Bibliographic record
Abstract
PURPOSE: Facial photographs are invaluable medical teaching tools, but patient privacy must be respected. The degree to which unilateral cropped upper face photos are recognizable is not known. METHODS: Oculofacial surgeons were canvassed on a societal email to participate in an online study in February 2025. The surgeons were shown 4 famous individuals. Each of the 4 celebrities was presented initially as a cropped unilateral periorbital photograph with an iris mask, then without an iris mask, and then finally as a full face photograph. For each photograph, the surgeons were asked to identify the celebrity and rate how confident they were in the identification. RESULTS: Eighty-seven surgeons completed the study with a response rate of 62%. Overall, the cropped, unilateral upper face photographs of famous celebrities were correctly identified on average 4.9% of the time, with a mean prediction confidence level of 69 ± 15%. Unmasking the iris did not improve recognition. Mistaken identification of the cropped, iris-masked, unilateral upper face photographs occurred approximately 20% of the time. On average, there was a 16.8-fold increase in recognition of the full face of famous celebrities compared with the cropped unilateral upper face photo. CONCLUSION: In the vast majority of cases, the identity of cropped unilateral periorbital photos is not discernible. Notwithstanding, patient informed consent remains mandatory to obtain and use photos for publication.
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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 | 0.002 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".