This repository contains a project with the aim of building a simple CNN to classify faces images into 7 expressions categories:
| Angry | Disgust | Fear | Happy | Sad | Surprise | Neutral |
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It uses as train/test data the following Kaggle.com dataset: Facial Emotion Expressions.
This project is a work in progress and is not yet completed.
However, it provides a basis Python framework to prepare Kaggle's downloaded data and to customize, build, train and
test Convolutional Neural Networks to perform human facial expression classification on images.
Feel free to fork it and make your own changes/improvements (and eventually create a pull request).
- Python 3.10 or higher (this is what I used but older versions probably also work, typically >=3.8)
List of needed dependencies:
- TensorFlow (for all the DL pipelines)
- OpenCV (to modify/visualize images)
- Matplotlib (not mandatory, to plot curves...)
Clone the repo:
git clone https://github.com/GFaure9/mood-detection.git
And after having created clean virtual environment in project's folder:
pip install -r requirements.txt
Note
Original dataset from https://www.kaggle.com/samaneheslamifar. You can download it directly at: https://www.kaggle.com/datasets/samaneheslamifar/facial-emotion-expressions.
Some insights about implemented objects (in src):
CNN: main class to define, build and train a CNN using TensorFlow lib through Keras APIDataFacesImages: main class to build training and test datasets from faces images databasePipeline: class defining pipelines to run for building, training and testing face expression classification modelscreate_downsampled_datasets(_input_path, output_path sample_size_): function that creates a new folder from a chosen folder, with the same sub-folders but keeping only a given number of files in each sub-folder
In exploration a Jupyter Notebook was started to start testing and using these objects and find the best configuration for the CNN.