Facial Emotion Recognition On Ravdess
Metrics
Accuracy
Results
Performance results of various models on this benchmark
Model Name | Accuracy | Paper Title | Repository |
|---|---|---|---|
| bi-LSTM+Attention | 62.13% | A proposal for Multimodal Emotion Recognition using aural transformers and Action Units on RAVDESS dataset | |
| MTCAE-DFER | 83.69% | MTCAE-DFER: Multi-Task Cascaded Autoencoder for Dynamic Facial Expression Recognition | - |
| Intermediate-Transformer-Fusion, visual branch only | 74.92% | Self-attention fusion for audiovisual emotion recognition with incomplete data | - |
| Guided-ST and bi-LSTM with attention | 57.08% | Multimodal Emotion Recognition on RAVDESS Dataset Using Transfer Learning | - |
0 of 4 row(s) selected.