Bibliographic record
Abstract
In recent years there has been a growing interest in improving all aspects of the interaction between human and computers. Emotion recognition is a new research direction in human-computer interaction (HCI) which is based on affective computing that is expected to significantly improve the quality of HCI system and communications. Most existing works address this problem using 2D features, but they are sensitive to head pose, clutter, and variations in lighting conditions. In light of such problems, two 3D visual feature based approaches are presented in this dissertation. First, we present a recognition method based on the Gabor library for real 3D visual features extraction and an improved kernel canonical correlation analysis (IKCCA) algorithm for emotion classification. Second, to reduce the computation cost and provide a more general approach, we propose using a fiducial points' controlled 3D face model to recognize human emotion from video sequences. An Elastic body spine (EBS) technique is applied for deformation feature extraction and a discriminative Isomap (D-Isomap) based classification is used for the final decision. The most significant contributions of this work are detecting and tracking fiducial points automatically from video sequences to construct a generic 3D face model, and the introduction of EBS deformation features for emotion recognition. The experimental results show the robustness and effectiveness of the proposed methods.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".