Excuse Me, Do I Know You From Somewhere? Unaware Facial Recognition Using Brain-Computer Interfaces
Bibliographic record
Abstract
While a great deal of research has been done on \ the human brain’s reaction to seeing faces and \ reaction to recognition of these faces, the unaware \ recognition of faces is an area where further research \ can be conducted and contributed to. We performed a \ preliminary experiment where participants viewed \ images of faces of individuals while we recorded their \ EEG signals using a consumer-grade BCI headset. \ Pre-selection of the images used in each of the three \ phases in the experiment allowed us to tag each image \ based on what state of recognition we expect the image \ to take – No Recognition, a Possible Unaware \ Recognition, and a Possible Aware Recognition. We \ find, after filtering, artifact removal, and analysis of \ the participants’ EEG signals recorded from a \ consumer-grade BCI headset, obvious differences \ between the three classes of recognition (as defined \ above) and, more specifically, unaware recognitions, \ can be easily identified.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.035 | 0.017 |
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".