Recognition of Masked Faces in the Era of the Pandemic: No Improvement Despite Extensive Natural Exposure
Bibliographic record
Abstract
Face masks, which became prevalent across the globe during the COVID-19 pandemic, have had a negative impact on face recognition despite the availability of critical information from uncovered face parts, especially the eyes. An outstanding question is whether face-mask effects would be attenuated following extended natural exposure. This question also pertains, more generally, to face-recognition training protocols. We used the Cambridge Face Memory Test in a cross-sectional study ( N = 1,732 adults) at six different time points over a 20-month period, alongside a 12-month longitudinal study ( N = 208). The results of the experiments revealed persistent deficits in recognition of masked faces and no sign of improvement across time points. Additional experiments verified that the amount of individual experience with masked faces was not correlated with the mask effect. These findings provide compelling evidence that the face-processing system does not easily adapt to visual changes in face stimuli, even following prolonged real-life exposure.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".