Unfamiliar Face Matching With Frontal and Profile Views
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Research has systematically examined how laboratory participants and real-world practitioners decide whether two face photographs show the same person or not using frontal images. In contrast, research has not examined face matching using profile images. In Experiment 1, we ask whether matching unfamiliar faces is easier with frontal compared with profile views. Participants completed the original, frontal version of the Glasgow Face Matching Test, and also an adapted version where all face pairs were presented in profile. There was no difference in performance across the two tasks, suggesting that both views were similarly useful for face matching. Experiments 2 and 3 examined whether matching unfamiliar faces is improved when both frontal and profile views are provided. We compared face matching accuracy when both a frontal and a profile image of each face were presented, with accuracy using each view alone. Surprisingly, we found no benefit when both views were presented together in either experiment. Overall, these results suggest that either frontal or profile views provide substantially overlapping information regarding identity or participants are unable to utilise both sources of information when making decisions. Each of these conclusions has important implications for face matching research and real-world identification development.
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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) | 0.002 | 0.002 |
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 it