A simple facial emotion recognition classifier implemented by SVM
conda create -n SVM python=3.8 conda activate SVM pip install -r requirements.txtDownload the corresponding data set from Kaggle
The structure of our model is as follows
SVM | | - log (our experiment result) | - data (you should get this directory from kaggle) | - model (some models we use to extract feature) | - hog.py | - resnet.py | - ... | - main.py | - ... We provide detailed parameter descriptions in main.py
python -u main.py --kernel rbf --method {your_method} --gpu_id {your_gpu_id} --C 5 --gamma 0.02 > res.log| kernel | feature method | other description | score |
|---|---|---|---|
| sigmoid | / | / | 24.97 |
| rbf | / | / | 30.90 |
| linear | / | / | not converged |
| linear | hog | patch(8,2) | 45.31 |
| rbf | hog | patch(8,2) | 45.90 |
| rbf | hog | patch(4,4) | 51.46 |
| rbf | hog+pca | patch(4,4) | 51.52 |
| rbf | align | dim: 11664->136 | 44.10 |
| rbf | hog | C=5, gamma=0.02 | 57.65 |
| rbf | hog+aug | C=5, gamma=0.02 | 59.07 |
| rbf | resnet18+aug | C=5, gamma=0.02 | 68.04 |
for more details, please see ./log directory.