The Importance of Internal and External Features in Face Recognition
Bibliographic record
Abstract
Past research in the field of face perception has found that external facial features (hair, ears, face contour) better facilitate recognition of unfamiliar faces, whereas internal facial features (eyes, nose, mouth) support the recognition of familiar faces. In the current study, we investigate the differential use of internal and external features for the recognition of faces that vary in familiarity and race. White-Canadian participants (target sample size = 260) complete an online card sorting task in which they are given a set of cards containing photographs depicting individual faces. They are instructed to sort the cards into piles for each identity present in the set. Each participant sorts a set of cards with either own-race familiar, own-race unfamiliar, other-race familiar, or other-race unfamiliar faces. These faces are modified to contain only internal features, only external features, or both. Face recognition accuracy is measured by calculating the number of piles (or perceived identities) and the number of misidentification errors. Using a signal detection framework, we will calculate a sensitivity score for each participant to assess their recognition accuracy. Preliminary data from 44 adult participants in the whole face conditions reveal a) an own-race advantage in unfamiliar face recognition; participants perceived more identities when sorting other-race unfamiliar faces (mean = 6.93) versus own-race unfamiliar faces (mean = 4.89), and b) a familiar face advantage (mean = 3.98). We are currently recruiting 220 participants for the internal features and external features conditions and expect to find greater reliance on internal features for familiar faces and external features for unfamiliar faces. Moreover, we expect there to be a greater reliance on internal features for own-race as compared to other-race faces. These results will offer insight into how reliance on internal and external facial features differs based on familiarity and race.
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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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".