By Nigel Mun (1005031), Xu Muzi (1005641), Emmanuel Lopez (1005407)

Usage of code:

1. visualisation

• data-visualisation.ipynb displays all the different distributions of the csv dataset
• face-recognition-analysis.ipynb shows a plot of the images on a graph, showing their similarities/differences to each other

2. Preprocessing

• CSV_to_Dataset.ipynb converts all the csv data into a Tensor object, one for training, one for testing
• DataAug.ipynb performs data augmentation to the Tensor object, and saving it as a new Tensor object

3. Models

• All the files in this folder are the models we have tested during our initial trial phase, to pick out the best 2 models that we are going to employ in our project.

4. Final models

• The models in here are the models we will use to train and test with live data from the web cam.

5. VideoCapture

• You can run the video capture file using:
• cd /VideoCapture
• python VideoCapture.py
• This will cause a popup whereby your computer camera will be activated, and you can test what emotion is predicted from your face.
• Make sure the following libraries are installed:
• pip install opencv-python
• pip install tensorflow
• pip install keras