Enhancing Emotion Recognition in Video Characters Through Multi-Modal Deep Learning Approaches
Bibliographic record
Abstract
Human emotion recognition is a classic problem for Convolutional Neural Networks (CNNs) in classification tasks, and a plethora of research has emerged in recent years. This paper presents a video character emotion analysis model utilizing the The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) dataset, exploring the integration of audio-visual data for emotion recognition. CNNs were employed to analyze image and audio data separately, then the models were fused using linear summation and greedy selection methods. The multi-modal approach significantly enhances performance over single-modal models, with the linear summation method showing the best results at a 0.4 weight ratio. The model's predictions are visualized to demonstrate the composite emotional analysis of video characters. The findings indicate that multi-modal models can improve accuracy, precision, recall, and F1-score by 30-40 percent compared to single-modal models. Future work will focus on optimizing model performance and leveraging confusion matrices to further refine the greedy selection algorithm.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".