Human emotion recognition using a deformable 3D facial expression model
Bibliographic record
Abstract
Automatic emotion recognition from facial expression is one of the most intensively researched topics in affective computing and human-computer interaction. However, due to the lack of 3D feature and dynamic analysis the functional aspect of affective computing is insufficient for natural interaction. This paper presents an automatic emotion recognition approach from video sequences based on a fiducial point controlled 3D facial model. As a physics-based transformation, elastic body spline technology is applied on a facial mesh to generate a smooth warp that reflects the control point corresponding to the displacement of fiducial points. It also extracts the deformation feature from the realistic emotional expressions. Discriminative Isomap based classification is used to embed the deformation feature into a low dimensional manifold that spans in an expression space with one neutral and six emotion class centers. The final decision is made by computing the Nearest Class Center of the feature space.
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How this classification was reachedexpand
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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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; both teacher heads agree on what is shown here.
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".