Optimizable Ensemble Regression for Arousal and Valence Predictions from Visual Features
Bibliographic record
Abstract
The cognitive state of a person can be categorized using the Circumplex model of emotional states, a continuous model of two dimensions: arousal and valence. We exploit the Remote Collaborative and Affective Interactions (RECOLA) database, which includes audio, video, and physiological recordings of interactions between human participants to predict arousal and valance values using machine learning techniques. To allow learners to focus on the most relevant data, features are extracted from raw data. Such features can be predesigned or learned. Learned features are automatically learned and utilized by deep learning solutions. Predesigned features are calculated before machine learning and inputted into the learner. Our previous work on video recordings focused on learned features. In this paper, we expand our work onto predesigned visual features, extracted from video recordings. We process these features by applying time delay and sequencing, arousal/valence labelling, and shuffling and splitting. We then train and test regressors to predict arousal and valence values. Our results outperform those from the literature. We achieve a root mean squared error (RMSE), Pearson’s correlation coefficient (PCC), and concordance correlation coefficient (CCC) of 0.1033, 0.8498, and 0.8001 on arousal predictions; and 0.07016, 0.8473, and 0.8053 on valence predictions, using an optimizable ensemble, respectively.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".