Adaptive Appearance Model and Condensation Algorithm for Robust Face Tracking
Why this work is in the frame
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Bibliographic record
Abstract
We present an adaptive framework for condensation algorithms in the context of human-face tracking. We attack the face tracking problem by making factored sampling more efficient and appearance update more effective. An adaptive affine cascade factored sampling strategy is introduced to sample the parameter space such that coarse face locations are located first, followed by a fine factored sampling with a small number of particles. In addition, the local linearity of an appearance manifold is used in conjunction with a new criterion to select a tangent plane for updating an appearance in face tracking. Our proposed method seeks the best linear variety from the selected tangent plane to form a reference image. We demonstrate the effectiveness and efficiency of the proposed method on a number of challenging videos. These test video sequences show that our method is robust to illumination, appearance, and pose changes, as well as temporary occlusions. Quantitatively, our method achieves the average root-mean-square error at 4.98 on the well-known dudek video sequence while maintaining a proficient speed at 8.74 fps. Finally, while our algorithm is adaptive during execution, no training is required.
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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 | 0.001 | 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 | 0.001 | 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) | 0.000 | 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 it