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Face Emotion Detection

This project implements a face expression detection system using machine learning techniques. It can identify various facial expressions in images or real-time video streams.

Table of Contents

Features

  • Detect faces in images and video streams
  • Classify facial expressions into categories (e.g., happy, sad, angry, surprised)
  • Real-time processing capability
  • Easy-to-use command-line interface
  • Pre-trained model included

Installation

  1. Clone this repository:

    git clone https://github.com/harsh6045/facial-emotion-detection.git
cd face-emotion-detection

  2. Create a virtual environment (optional but recommended):

    python -m venv venv
source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

  3. Install the required dependencies:

    pip install -r requirements.txt

Usage

  1. To detect expressions in an image:

    python realtimedetection.py --image path/to/your/image.jpg

  2. To run real-time detection using your webcam:

    python realtimedetection.py --webcam

  3. For additional options:

    python realtimedetection.py --help

Dataset

We used the FER2013 dataset for training and evaluation. This dataset contains 48x48 pixel grayscale images of faces, categorized into 7 emotions:

  1. Angry
  2. Disgust
  3. Fear
  4. Happy
  5. Sad
  6. Surprise
  7. Neutral

Training

To train the model on your own dataset or fine-tune the existing model:

  1. Prepare your dataset
  2. Run the training script

Evaluation

Model performance metrics on the test set:

  • Accuracy: 52.2%
  • F1-Score: 0.63

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

  1. Fork the repository
  2. Create your feature branch (git checkout -b feature/AmazingFeature)
  3. Commit your changes (git commit -m 'Add some AmazingFeature')
  4. Push to the branch (git push origin feature/AmazingFeature)
  5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.