Real-time Expression Detection using Deep Learning

This project demonstrates real-time expression detection using deep learning. It uses a convolutional neural network (CNN) model trained on facial expression images to detect emotions in real-time video streams captured from a webcam.

Features

• Detects facial expressions in real-time video streams
• Supports multiple expressions such as Angry, Disgust, Fear, Happiness, Sad, Surprise, and Neutral
• Displays predicted emotion labels on the video stream
• Draws rectangles around detected faces for better visualization

Requirements

• Python 3.x
• OpenCV (pip install opencv-python)
• TensorFlow (pip install tensorflow)

Usage

1. Clone this repository: git clone https://github.com/Tushar-Jagatap/Facial-Expression-Detection-using-CNN.git

2. Navigate to the project directory: cd Facial-Expression-Detection-using-CNN

3. Run the Python script: python real_time_run.py

4. Press q to exit the program.

Credits

• The model used in this project is trained using the FER-2013 dataset from Kaggle.

License

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