😊 Facial Expression Detection

Overview

This project detects facial expressions using OpenCV and a deep learning model. It classifies emotions such as happy, sad, angry, surprised, neutral, and more. 🎭

Features

• 🎥 Real-time facial expression detection
• 🧠 Pre-trained deep learning model for emotion recognition
• 📷 Integration with OpenCV for face detection
• 😀 Supports multiple expressions

Installation

# Clone the repository
git clone https://github.com/your-repo/facial-expression-detection.git
cd facial-expression-detection

# Create a virtual environment (optional but recommended)
python -m venv venv
source venv/bin/activate  # On Windows use `venv\Scripts\activate`

# Install dependencies
pip install -r requirements.txt

Usage

python detect_expression.py

Dependencies

• 🐍 Python 3.x
• 🖼️ OpenCV
• 🔬 TensorFlow/Keras
• 🔢 NumPy
• 📊 Matplotlib

Model

The project uses a pre-trained deep learning model, such as MobileNetV2 or a custom CNN trained on a facial expression dataset (e.g., FER-2013). 🤖

Dataset

The dataset used for training includes labeled facial images with different expressions. A common dataset for this task is FER-2013, which contains:

• 📷 35,000+ grayscale images
• 😃 7 emotion classes: Happy, Sad, Angry, Neutral, Fear, Surprise, Disgust

Future Improvements

• 🚀 Improve accuracy with a larger dataset
• 🌍 Deploy the model as a web or mobile application
• ⚡ Optimize performance for real-time detection

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change. 🙌

License

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