How extraneous facial markings affect recognition
Bibliographic record
Abstract
The ability to recognize faces accurately is of significant concern across an array of fields, being critical to our legal system by playing a pivotal role in eyewitness identification and even beginning to be incorporated into artificial intelligence (e.g., facial recognition software). It is important, then, to understand when facial recognition is accurate and when it is not. Most research on facial recognition is limited to the impact of internal facial features (e.g., eyes, nose, and mouth) on accuracy. In the current study, I tested the accuracy of human recognition for faces containing extraneous markings (e.g., moles, scars, tattoos). In Experiment 1, I had participants study a series of faces, some of which were altered to include a mole or a scar, and then completed an old-new recognition task. I found that unaltered faces were more discriminable than faces in either altered condition; there were no differences between the altered conditions. In Experiment 2, I used a similar study phase but tested memory using two-alternative-forced-choice between the studied target and an alternative version of the same face. I once again found better discriminability for unaltered faces compared to faces with scars but only marginal differences compared to those with moles. In Experiment 3, I compared unaltered faces to faces with moles and faces altered to be more traditionally distinct (e.g., altered eye size); an old-new recognition test was used. I found better discriminability for the distinct faces in comparison to the other conditions; no difference was found between the other conditions. Finally, in Experiments 4a and 4b, using a different set of face stimuli, I compared old-new recognition for faces with and without tattoos, with the latter also using inversion at test. I found similar discriminability between faces with tattoos and those without was found in 4a, while better discriminability was found for the faces with tattoos in 4b. Together, these results indicate that the effect of extraneous markings depends more on how different markings are from each other than simply on the presence of one. Additionally, results also indicate that the presence of the markings shifts from processing the markings as extensions of faces and toward processing them as objects. Implications for the memorability of faces with extraneous markings and the multi-dimensional space framework are discussed.
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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 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".