Soft biometric trait classification from real-world face videos conditioned on head pose estimation
Bibliographic record
Abstract
Recently, soft biometric trait classification has been receiving more attention in the computer vision community due to its wide range of possible application areas. Most approaches in the literature have focused on trait classification in controlled environments, due to the challenges presented by real-world environments, i.e. arbitrary facial expressions, arbitrary partial occlusions, arbitrary and nonuniform illumination conditions and arbitrary background clutter. In recent years, trait classification has started to be applied to real-world environments, with some success. However, the focus has been on estimation from single images or video frames, without leveraging the temporal information available in the entire video sequence. In addition, a fixed set of features are usually used for trait classification without any consideration of possible changes in the facial features due to head pose changes. In this paper, we propose a temporal, probabilistic framework first to robustly estimate continuous head pose angles from real-world videos, and then use this pose estimate to decide on the appropriate set of frames and features to use in a temporal fusion scheme for soft biometric trait classification. Experiments performed on large, real-world video sequences show that our head pose estimator outperforms the current state-of-the-art head pose approaches (by up to 51%), whereas our head pose conditioned biometric trait classifier (for the case of gender classification) outperforms the current state-of-the-art approaches (by up to 31%).
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".