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Emotion-Recognition

Introduction

This is a machine learning project which aims to recognize human emotions through facial features. The model has been successfully implemented inside of a desktop application which can now recognize human emotions through a live web camera feed or a video of your choice.

Prerequisites

Dataset - fer2013
Install required libraries mentioned in Requirements.txt

Build Instructions

Preparing Dataset

After downloading the fer2013 .csv file.
While in the same directory as the repository:

python3 ./prepare_dataset.py /path/to/fer2013.csv

Now Repo directory should be like:

  • app (folder)
  • Data (folder)
    • test (sub-folder)
    • train (sub-folder)
  • epare_dataset.py (file)
  • main.py (file)
  • haarcascade_frontalface_default.xml (file)

Machine Learning Model

The Model uses Tensorflow, Keras utility and OpenCV.

To train the model,

python3 main.py --mode train

This will generate a model.h5 file in the directory which will take some time depending on the computational speed, and is required in the next step. If you want to download the pretrained model file use can the download the file from here

To use the model for real time prediction,

python3 main.py --mode display

Desktop Application

The desktop application was designed using Pyqt library along with OpenCV for video and web camera feed. The following dependencies are :- PySide2, Pyqt6, opencv. You must have these dependencies if you want to build the application from source.

There is a model included in the app directory, along with the xml file which are necessary for running the application.

Then

cd app
python3 app.py

Now you should be able to see a new window on your screen.

Refrences

  • "Challenges in Representation Learning: A report on three machine learning contests." I Goodfellow, D Erhan, PL Carrier, A Courville, M Mirza, B Hamner, W Cukierski, Y Tang, DH Lee, Y Zhou, C Ramaiah, F Feng, R Li, X Wang, D Athanasakis, J Shawe-Taylor, M Milakov, J Park, R Ionescu, M Popescu, C Grozea, J Bergstra, J Xie, L Romaszko, B Xu, Z Chuang, and Y. Bengio. arXiv 2013.