The role of the upper and lower face in the recognition of facial identity in dynamic stimuli.
Bibliographic record
Abstract
Background: Studies show that the information from the upper half of a face plays a greater role in the recognition of facial identity than the lower half. However, in daily life faces are usually encountered as dynamic stimuli, and recent research has shown that dynamic signatures in facial motion contribute to face recognition. Given that the lower half of the face has more mobile structures, this raises the question whether the upper face advantage is also present with dynamic faces. Objective: Our goal was to determine the relative contribution of the upper and lower face in a short-term face familiarity task. Methods: During the encoding phase, 30 subjects learned 12 whole faces, six as static images and six as dynamic video-clips. During the retrieval phase, subjects saw upper or lower halves and reported which of 3 stimuli belonged to one of the learned set. Half of the subjects saw static images and half saw video-clips in the retrieval phase.. Results: There was an interaction between image type at encoding and retrieval, with 10% better recognition when faces were learned from dynamic video-clips than from static images, but only when tested with dynamic stimuli. Regardless of the type of learning, testing with static images showed a small 3% advantage for the upper face, whereas testing with dynamic images had a .3% advantage for the lower face, but these differences were too small to be significant or to generate an interaction involving face halves. Conclusion: Dynamic presentation of faces enhance encoding of identity, but the influence of face-half at retrieval is modest or non-existent. Meeting abstract presented at VSS 2017
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".