Prototype-Based Modeling for <newline/>Facial Expression Analysis
Bibliographic record
Abstract
Automatic facial expression analysis systems are aiming towards the application of computer vision techniques in human computer interaction, emotion analysis, and even medical care via a space mapping between the continuous emotion and a set of discrete expression categories. The main difficulty with these systems is the inherent problem of facial alignment due to person-specific appearance. Beside the facial representation problem, the same displayed facial expression may vary differently across humans; this can be true even for the same person in different contexts. To cope with these variable factors, we introduce the concept of prototype-based model as anchor modeling through a SIFT-flow registration. A set of prototype facial expression models is generated as a reference space of emotions on which face images are projected to generate a set of registered faces. To characterize the facial expression appearance, oriented gradients are processed on each registered image. We obtained the best results 87% with the person–independent evaluation strategy on JAFFE dataset (7-class expression recognition problem), and 83% on the complex setting of the GEMEP-FERA database (5-class problem).
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".