Fiducial point tracking for facial expression using multiple particle filters with kernel correlation analysis
Bibliographic record
Abstract
Detecting and tracking fiducial points successfully can generate necessary dynamic and deformable information for facial image interpretation tasks with numerous potential applications. In this paper we propose an automatic fiducial points tracking method using multiple Differential Evolution Markov Chain (DE-MC) particle filters with kernel correlation techniques. Fiducial points are initialized through the scale invariant feature based detectors. By taking the advantage of the ability to approximate complicated proposal distributions, multiple DE-MC particle filters are applied for fiducial points tracking by building a path connecting sampling with measurements, based on the fact that the posteriori depends on both the previous state and the current observation. A Kernel correlation analysis approach is proposed to find the detection likelihood with maximization of the similarity criterion between the target points and the candidate points. Sampling efficiency is improved and computational time is substantially reduced by making use of the intermediate results obtained in particle allocation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".