This work is to demonstrate the below problem: https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge

A real time face detector and emotion classifier is built using Convolution Neural Network and OpenCV. The CNN model is tuned for fine performance even on a low end device.

Follow the guided tutorial for neural network training.

Files Structure:

• FER_CNN.ipynb - Tutorial to train the CNN
• FER.py - Uses the pre-trained model to give inferences
• model.json - Neural network architecture
• weights.h5 - Trained model weights

Using Python virtual environment will be advisable.

• For model prediction

  pip install -r requirements.txt

  OR

  pip install opencv-python

  pip install tensorflow (Note here we are installing tensorflow-cpu)

  pip install keras

• For model training, pandas numpy tensorflow keras matplotlib scikit-learn seaborn

• Running the inference engine

Use the webcam

python FER.py webcam <fps>

Use a video file

python FER.py <video_file_name> <fps>

• Report issues on issue tracker
• Fork this repo
• Make awesome changes
• Raise a pull request

Copyright (C) 2018 Mayur Madnani

Licensed under MIT License

See the LICENSE.