Facial Emotion Recognition

This repository contains the source code for the tutorial on facial emotion recognition. The project is designed taking into account all the steps of a machine learning project. Data collection, data labeling and preprocessing, model training and parameter tuning and finally deployment of the model to a real world scenario. The required packages are pytorch, OpenCV, torchvision, numpy and matplotlib.

Install dependancies

pip install pytorch opencv torchvision numpy matplotlib

Run pretrained model for emotion recognition from your webcam video

To test the final render of the project with the pretrained model run the file live_emotion_recognition.py:

python3 live_emotion_recognition.py

Jupyter Notebook

The notebook file notebook.ipynb contains all the steps for the model training

Project structure

The project is structured as follow:

• We first generate our own dataset using our webcam. With opencv, we will capture different face pause ('Happy', 'Normal', 'Sad', 'Surprised') and store them in different folders named with corresponding labels.
• We preprocess the images by extracting faces using pretrained face detector model (haar_cascade). Other face detector algorithms such as MTCNN or Mediapipe can be used. The dataset is then splited into test and valitation sets in different folders.
• We then build a CNN model with Pytorch to classify face emotions . The model is then trained on the training set and saved.
• Finally we will used the pre-trained model with opencv to recognize facial emotion on live streams videos.