Bimodal information analysis for emotion recognition
Bibliographic record
Abstract
We present a bimodal information analysis system for automatic emotion recognition. Our approach is based on the analysis of video sequences which combines facial expressions observed visually with acoustic features to automatically recognize five universal emotion classes: anger, disgust, happiness, sadness and surprise. We address the challenges posed during the temporal analysis of the bimodal data and introduce a novel technique for combining the best features of instantaneous and temporal based visual recognition systems. We obtain robust appearance-based visual features which we classify instantaneously and aggregate it temporally to improve the recognition rates when compared to single-frame based instantaneous classification. The performance of the system is further boosted by using the complementary audio information for the bimodal emotion recognition. We combine the two modalities at both feature and score level to compare the respective joint emotion recognition rates. The emotions are instantaneously classified using a support vector machine and sequentially aggregated based on their classification probabilities. This approach is validated on a posed audio-visual database and a natural interactive database. The experiments performed on these databases provide encouraging results with the best combined recognition rate being 82%.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".