Facial Emotion Recognition is a Python-based project that utilizes Deep Learning models to recognize human facial expressions.

The project includes the following features:

• An interface that captures the user's webcam feed and applies the trained model to recognize the facial expression of the user in real-time.
• A trained Deep Learning model for facial expression recognition that has been trained on the FER2013 dataset using Keras with a Tensorflow backend.
• A Python script to train the model on the FER2013 dataset, and a Jupyter notebook with the model's training process.
• A requirements file that includes all the Python packages needed to run the project.

1. Clone the repository:

git clone https://github.com/OmarEhab007/Facial-emotion-recognition.git

2. Change the directory to the project's root directory:

cd Facial-emotion-recognition

3. Create a virtual environment:

python3 -m venv env

4. Activate the virtual environment:

source env/bin/activate

5. Install the required Python packages:

pip install -r requirements.txt

6. Run the application:

python3 facial_expression_recognition.py

• The application will prompt the user to allow access to the webcam.
• The user can then look into the camera, and the model will recognize their facial expression.
• The recognized facial expression will be displayed on the screen.

• The train.py script can be used to train the model on the FER2013 dataset.
• The Jupyter notebook facial_expression_recognition.ipynb shows the model's training process step-by-step and includes visualizations of the data and the model's performance.

• The FER2013 dataset was created by Pierre-Luc Carrier and Aaron Courville, and can be found on Kaggle.
• The trained model architecture is based on the VGG16 architecture.
• The code for capturing and processing the webcam feed is based on the OpenCV library.