Facial-Emotion-Recognition-

This project involves creating and training a Convolutional Neural Network (CNN) to detect emotions from facial expressions using the FER 2013 dataset. The trained model can then be used to perform real-time emotion detection from a webcam feed.

Requirements

The following packages are required to run this project:

• Python 3.7+
• TensorFlow 2.x
• Keras
• OpenCV
• NumPy
• Pillow

You can install the required packages using pip:

pip install numpy opencv-python keras tensorflow pillow

Download FER2013 Dataset

Download the FER 2013 dataset from Kaggle using the following link and place it in the data folder under your project directory: Download FER2013 Dataset

Training the Emotion Detection Model

The TrainEmotionDetector.py script is used to train the emotion detection model. It reads the FER 2013 dataset, builds a Convolutional Neural Network (CNN) model, trains the model on the training data, and saves the trained model architecture and weights.

Training Steps:

1. Initialize Data Generators: Create data generators for training and validation data with rescaling.
2. Build the CNN Model: Define a sequential model with convolutional layers, max-pooling layers, dropout layers, and dense layers.
3. Train the Model: Fit the model using the training data and validate using the validation data.
4. Save the Trained Model: Save the model architecture as a JSON file and the model weights as an HDF5 file.

Running the Training Script:

To train the model, execute the following command:

python TrainEmotionDetector.py

The training process may take several hours depending on your hardware. For instance, on an Intel i7 processor with 16 GB of RAM, it might take around 4 hours.

After training, the trained model structure and weights will be saved in your project directory:

• emotion_model.json
• emotion_model.h5

Move these files into the Model directory in your project.

Testing the Emotion Detection Model

The TestEmotionDetector.py script is used to test the trained model. It loads the model architecture and weights, captures real-time video from the webcam, detects faces, and predicts emotions.

Testing Steps:

1. Load the Trained Model: Load the model architecture and weights from the saved JSON and HDF5 files.
2. Start the Webcam Feed: Capture real-time video from the webcam.
3. Detect Faces: Use OpenCV's Haar Cascade Classifier to detect faces in the video feed.
4. Predict Emotions: For each detected face, predict the emotion using the trained model.
5. Display the Results: Draw rectangles around faces and display the predicted emotions on the video feed.

Running the Testing Script:

To run the emotion detection in real-time, execute the following command:

python TestEmotionDetector.py

Press the 'q' key to exit the real-time emotion detection.

Notes

• Ensure the FER 2013 dataset is placed correctly in the data/train and data/test directories with appropriate subfolders for each emotion.
• The model and weights should be located in the Model directory after training.
• Make sure your webcam is properly connected and accessible for real-time testing.

Acknowledgments

• The FER 2013 dataset used in this project is available on Kaggle.
• This project uses Keras for building and training the neural network and OpenCV for real-time emotion detection from webcam feed.