Head pose estimation in the wild using approximate view manifolds
Bibliographic record
Abstract
In this paper, we present a head pose estimation method for unconstrained images using feature-based manifold embedding. The main challenge of manifold embedding methods is to learn a similarity kernel that is reflective of variations only due to head pose and ignore other sources of variation. To address this challenge, we have used the feature correspondences of identity-invariant Geometric Blur features to learn a similarity kernel. To speed up the computation of the similarity kernel, we have used spatial pyramidal matching to approximate feature correspondences and random subsampling of training samples to approximate graph neighborhood. In addition to these approximations, we have used the Nyström approximation to embed out-of-sample test images in an efficient manner. Using these approximations, an approximate view manifold was learned for 14000 images in the Annotated Facial Landmarks in the Wild (AFLW) dataset. With the learned manifold, head pose estimation was performed on four in-the-wild face datasets - AFLW (remaining 7000 images), AFW, McGill and YouTube Faces. The Approximate View Manifold training achieves a 7X speedup compared to the non-approximated Learning-manifold-in-the-wild approach [15]. Further, pose estimation using the proposed approach shows significant improvement in accuracy and reduced Mean Angular Error(MAE) compared to other methods [36, 1, 29] on the challenging AFLW (7041 images), McGill (6833 images) and YouTube Faces (22534 images) datasets.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".