Classification of upper and lower face action units and facial expressions using hybrid tracking system and probabilistic neural networks
Bibliographic record
Abstract
The most of the human emotions are communicated by changes in one or two of discrete facial features. Theses changes are coded as Action Units (AUs). In this paper, we develop a lower and upper face AUs classification as well as six basic emotions classification system. We use an automatic hybrid tracking system, based on a novel two-step active contour tracking system for lower face and cross-correlation based tracking system for upper face to detect and track of Facial Feature Points (FFPs). Extracted FFPs are used to extract some geometric features to form a feature vector which is used to classify input image sequences into AUs and basic emotions, using Probabilistic Neural Networks (PNN) and a Rule-Based system. Experimental results show robust detection and tracking and reasonable classification where an average AUs recognition rate is 85.98% for lower face and 86.93% for upper face and average basic emotions recognition rate is 96.11%.
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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) | 0.000 | 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) | 0.001 | 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 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".