A Quality Adaptive Multimodal Affect Recognition System for User-Centric Multimedia Indexing
Bibliographic record
Abstract
The recent increase in interest for online multimedia streaming platforms has availed massive amounts of multimedia information that need to be indexed to be searchable and retrievable. User-centric implicit affective indexing employing emotion detection based on psycho-physiological signals, such as electrocardiography (ECG), galvanic skin response (GSR), electroencephalography (EEG) and face tracking, has recently gained attention. However, real world psycho-physiological signals obtained from wearable devices and facial trackers are contaminated by various noise sources that can result in spurious emotion detection. Therefore, in this paper we propose the development of psycho-physiological signal quality estimators for unimodal affect recognition systems. The presented systems perform adequately in classifying users affect however, they resulted in high failure rates due to rejection of bad quality samples. Thus, to reduce the affect recognition failure rate, a quality adaptive multimodal fusion scheme is proposed. The proposed scheme yields no failure, while at the same time classify the users' arousal/valence and liking with significantly above chance weighted F1-scores in a cross-user experiment. Another finding of this study is that head movements encode liking perception of users in response to music snippets. This work also includes the release of the employed dataset including psycho-physiological signals, their quality annotations, and users' affective self-assessments.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".