Emotion Recognition

To train the model from scratch -

1. Download the Cohn-Kanade dataset.
2. Create a folder named "data" in the project directory.
3. And paste the dataset with the name "Dataset_images" and emotions with the name "Emotion" in the above created folder.
4. Finally, open terminal in the project directory and then type in-

python train.py

After training or if you directly want to try the trained model -

python main.py

Dependencies -

• Numpy
• Pandas
• scikit-learn
• opencv

The emotions being classified are Neutral, Anger, Contempt, Disgust, Fear, Happy, Sad, Surprise
Currently the accuracy on the test data is 53.42% and live feed is not performing great.

The size of the dataset is not large enough to classify 8 different emotions, so probably train on less number of classes?

Currently under development