Emotion_Detection

Uncover the emotions behind expressions. Analyze faces in real-time using this deep learning project.

What it does:

• Classifies emotions into seven categories: angry, disgusted, fearful, happy, neutral, sad, and surprised.
• Employs a convolutional neural network (CNN) trained on the FER-2013 dataset.
• Detects emotions from your webcam feed or images.

Key features:

• Lightweight and efficient: Runs on standard computers with basic GPUs.
• Pre-trained model included: Get started easily without training.
• Customizable: Train on your own datasets for unique use cases.
• Open-source and easy to use: Contribute to the code and make it your own.

Get started:

Clone the repository:

git clone [https://github.com/atulapra/Emotion-detection.git](https://github.com/atulapra/Emotion-detection.git)
cd Emotion-detection

• Download the FER-2013 dataset inside the src folder.

• If you want to train this model, use:

cd src
python emotions.py --mode train

cd src
python emotions.py --mode display

• The folder structure is of the form:
  src:
  • data (folder)
  • emotions.py (file)
  • haarcascade_frontalface_default.xml (file)
  • model.h5 (file)