Physically blurred faces are more recognizable at a distance
Bibliographic record
Abstract
Previous work has shown that faces with a high degree of blur are more recognizable at smaller sizes. In addition, blurry faces generate identity adaptation aftereffects at small, but not large, sizes. This small-size advantage for blurry faces has been observed using digitally scaled and blurred face images. Here, we examine whether the small-size advantage persists in a paradigm with greater ecological validity, where size variation is achieved through viewing distance, and blurring is achieved via physical blurring filters. We tested 12 participants (5 females, 7 males, ages: 20-44 years, M=26.67, SD=7.16) in a face recognition protocol at two viewing distances (1m and 0.35m) corresponding to small (1.66 degree) and large (4.75 degree) face sizes. At each trial, a celebrity face was displayed on a computer screen until response. Trials were blocked by viewing distance (close vs. far), where a randomly selected half of 100 celebrity images were viewed at each distance. The order of blocks was counterbalanced across participants. Faces were viewed behind a 1-degree Luminit holographic Light Shaping Diffusers (LSD®), such that blur level was fixed throughout the experiment. Participants were asked to identify the celebrity at each trial. If the participant was unable to correctly identify the face, they were immediately shown intact images of the celebrity to determine whether they were unfamiliar with the celebrity, in which case the trial was discarded. Face recognition accuracy was 66.22% at the small size, significantly higher than the 60.19% at the large size (p=0.014, d=0.39). Ten out of 12 participants showed the small-size advantage at the individual level. These results replicate earlier findings of better recognition for blurry faces at smaller sizes with digital manipulation of blur and size, and extend them by demonstrating that this small-size advantage generalizes to physically blurred faces viewed at varying distances.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".