How many unique faces do we see in a typical day?
Bibliographic record
Abstract
Face recognition is a form of perceptual expertise that develops into adulthood. Daily visual exposure to faces, described as the 'face-diet,' fundamentally shapes facial recognition abilities. For example, in racially homogenous societies, the lack of exposure to racially diverse faces contributes to the 'other-race-effect.' Furthermore, an enriched face-diet with many unique identities is necessary to support and refine face processing competence. A previous study that examined the adult face-diet characterized exposure on several variables including duration, viewing distance, and pose but did not address exposure statistics for unique facial identities (Oruc et al. 2018). Here, we developed a novel deep learning-based tool to quantify the number of unique facial identities in first-person perspective footage for 30 adults in Metro Vancouver, British Columbia, Canada. We show that an adult observer encounters an average of 40 unique faces during a typical day, with familiar faces accounting for approximately half. Our findings showed that 77% of the total face exposure was familiar, and 70% was accounted for by the top five most frequently encountered identities. These results may reflect the longer encounters typical of social interactions with familiar individuals. For all but two participants, the most frequently occurring face was a familiar identity that accounted for an average of 33% of total face exposure. For the majority of participants (63%), the most frequently occurring familiar face was of the opposite sex. Contrary to the predicted outcomes of studying observers in a region known for its ethnically diverse population, the racial distribution of identities diverged from Metro Vancouver's census data (2016) of Statistics Canada and was biased towards own-race faces for both familiar and unfamiliar identities. This last finding emphasizes the prevalence of societal and cultural barriers that may prevent co-mingling between racially and ethnically diverse communities despite favourable demographics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 teacher head, 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".