Oriented-Filters Based Head Pose Estimation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The aim of this study is to elaborate and validate a methodology to automatically assess head orientation with respect to a camera in a video sequence. The proposed method uses relatively stable facial features (upper points of the eyebrows, upper nasolabial-furrow corners and nasal root) that have symmetric properties to recover the face slant and tilt angles. These fiducial points are characterized by a bank of steerable filters. Using the frequency domain, we present an elegant formulation to linearly decompose a Gaussian steerable filter into a set of x, y separable basis Gaussian kernels. A practical scheme to estimate the position of the occasionally occluded nasolabial-furrow facial feature is also proposed. Results show that head motion can be estimated with sufficient precision to obtain the gaze direction without camera calibration or any other particular settings are required for this purpose.
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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 | 0.000 | 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 | 0.000 |
| 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