Automated recognition of individual performers from de-identified video sequences
Bibliographic record
Abstract
Identification of individual humans from RGB image data is well-established. However, in many domains, such as in healthcare or applications involving children, ethical issues have been raised around using traditional RGB image data because individuals can be identified from these data. The widespread availability of reliable depth data, and the associated human skeleton data derived from these data, presents an opportunity to differentiate between individuals while potentially avoiding individually identifiable features. Using skeleton data only, we developed a unique 20-dimensional bone segment length feature vector for 1,761 trials (1,759,980 image frames) of data, captured from 14 participants who engaged in a one-hour group intervention playing Xbox One Kinect Bowling twice-weekly for 24 weeks. We then evaluated our novel feature using representative batch processing (k-nearest neighbour) and real-time (multi-layer perceptron) models, validated against manually-labelled ground-truth data. Our results suggest that our skeleton feature can differentiate between instances (i.e., individuals) with an accuracy over all participants of 100% for batch processing and 96.57% in real-time, and deals well with class imbalances. Our results suggest that we can reliably differentiate between individual persons using only skeleton data derived from depth image data in medical research.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".