Person identification across actions from biological motion
Bibliographic record
Abstract
A significant amount of past research has studied person identification from point light displays of walking humans, investigating parameters such as viewing angle and the differential contributions of structural and kinematic information. However, little is known about the ability of human observers to generalize identity across different activities. In this study we use a same/different paradigm to compare observers' ability to identify point light displays within and across activities. We drew from a database of 100 motion-captured humans, each of which encompassed both walking and running activities. Subjects were shown successive paired stimuli and had to indicate whether the stimuli represented the same or different person. In either case, the two displays were at slightly different viewpoints. Two independent factors were examined: stimulus pairing (walker/walker, runner/runner, walker/runner) and information content (structural only, kinematic only, full information). For all information contents for stimulus pairing of matching activities (walker/walker, runner/runner) subjects performed significantly better than chance (t(5)=2.71, p0.05). The main effect of Pairing was significant (F(2, 30)=35.7, p[[lt]]0.001), with the walker/runner pairing being the most difficult. Information was not a significant factor. However, there was a significant interaction between Pairing and Information (F(4, 30)=4.03, p[[lt]]0.01) that manifested in performance on the runner/runner task in particular being better for full information than for structural or kinematic only. Results are discussed in light of a principal components-based linear model that estimates a runner time series from a given walker time series by equating principal component coordinates.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".