Human emotion recognition using real 3D visual features from Gabor library
Bibliographic record
Abstract
Emotional state recognition is an important component for efficient human-computer interaction. Most existing works address this problem using 2D features, but they are sensitive to head pose, clutter, and variations in lighting conditions. The general 3D based methods only consider geometric information for feature extraction. In this paper, we present a real 3D visual features based method for human emotion recognition. 3D geometric information plus colour/density information of the facial expressions are extracted by 3D Gabor library to construct visual feature vectors. The filter's scale, orientation, and shape of the library are specified according to the appearance patterns of the 3D facial expressions. An improved kernel canonical correlation analysis (IKCCA) algorithm is proposed for final decision. From training samples, the semantic ratings that describe the different facial expressions are computed by IKCCA to generate a seven dimensional semantic expression vector. It is applied for learning the correlation with different testing samples. According to this correlation, we estimate the associated expression vector and perform expression classification. From experiment results, our proposed method demonstrates impressive performance.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".