Kaggle Challenge - https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge/data
Facial Emotion Recognition on FER2013 Dataset Using a Convolutional Neural Network.
80-10-10 ratio for training-validation-test sets.
Winner - 71.161% accuracy
This Model - 66.369% accuracy
These instructions will get this model up and running. Follow them to make use of the main.py file to recognize facial emotions using custom images. This model can also be used as facial emotion recognition part of projects with broader applications
Install these prerequisites before proceeding-
pip install tensorflow
pip install keras
pip install numpy
pip install pandas
pip install opencv-python
If you don't want to train the classifier from scratch, you can make the use of main.py directly as the the repository already has fer.json (trained model) and fer.h5 (parameters) which can be used to predict emotion on any test image present in the folder.
Clone this repository. Download and extract the dataset from Kaggle link above.
Run the Emotion.ipynb file, which would generate CNN.json and weights.h5 files for you.
The layers in the Convolution Neural Network used in implementing this classifier can be summarized as follows.
