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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".