Facial expression recognition using Pytorch on FER2013 dataset, achieving accuracy 72.53% (state of the art: 75.2%)

• Clone this repository (only support Python 3+)
• Download FER2013 dataset in Kaggle
• Download VGG19 pretrained (Google Drive)
• Install requirements:

pip install -r requirements.txt

The data consists of 48x48 pixel grayscale images of faces, 7 class (0=Angry, 1=Disgust, 2=Fear, 3=Happy, 4=Sad, 5=Surprise, 6=Neutral). The training set consists of 28,709 examples. The validation set consists of 3,589 examples. The test set consists of 3,589 examples.

Run file train.py:

python train.py --dataset_root path/to/file/fer2013.csv --model_name "VGG19" --checkpoint path/to/the/checkpoint --bs ... --lr ...

Run file eval.py:

python eval.py --dataset_root path/to/file/fer2013.csv --trained_model path/to/the/trained/model

Example:

python eval.py --dataset_root ./fer2013/fer2013.csv --trained_model model_state.pth.tar

Model VGG19 achieved 72.53% accuracy on test set (state of the art 75.2%: paper) Class-wise accuracy:

• Haar Cascades
• MTCNN (from link here)

Predict image, run image_demo.py:

python image_demo.py --trained_model path/to/the/trained/model --mode "haarcascade"(or "mtcnn") --input path/to/input/image --output path/to/output/image

Example:

python image_demo.py --trained_model model_state.pth.tar --mode "haarcascade" --input ./input.jpg --output ./out.jpg

python image_demo.py --trained_model model_state.pth.tar --mode "mtcnn" --input ./input.jpg --output ./out.jpg

With video, run video_demo.py:

python video_demo.py --trained_model path/to/the/trained/model --input path/to/input/video --output path/to/output/video --save_fps 24

Some example in folder images

Install streamlit and run file app.py to facial expression recognition and face detection using Haarcascade:

streamlit run app.py

I hope to complete the to-do list in the near future:

• Improve model face detection and classifier
• Demo with video