😊 Real-Time Facial Emotion Detection

This project performs real-time emotion detection using a Convolutional Neural Network (CNN) model and OpenCV. It detects human facial expressions like Happy, Sad, Angry, and more from webcam video input 🎥 and displays a live stats overlay including FPS and detected emotions.

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🧠 Features

• 🎯 Accurate emotion classification using deep learning
• 📦 Real-time face detection with OpenCV
• ⚡ Displays FPS and number of faces detected
• 📊 Live emotion stats with color-coded labels
• 🎨 Clean and dynamic on-screen overlay

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🔍 Detected Emotions

• 😠 Angry
• 🤢 Disgust
• 😨 Fear
• 😀 Happy
• 😢 Sad
• 😲 Surprise
• 😐 Neutral

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📁 Dataset

To train your own model or explore the data, download the full dataset from Kaggle:

🔗 Face Expression Recognition Dataset on Kaggle

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🛠️ Setup Instructions

1. Clone the repository

   git clone https://github.com/your-username/your-repo-name.git
   cd your-repo-name

2. Install dependencies

   pip install -r requirements.txt

3. Download or place the pre-trained model

   • Make sure you have emotion.h5 (trained on FER dataset)
   • Place it in the project root directory

4. Run the emotion detection

   python detect_emotion.py

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🧪 Technologies Used

• 🧠 TensorFlow / Keras – for CNN model
• 🎥 OpenCV – for webcam capture and face detection
• 📊 NumPy – for numerical processing
• 💾 Haar Cascades – for real-time face detection
• 🐍 Python – programming language used

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📦 Folder Structure

├── detect_emotion.py       # Main detection script
├── emotion.h5              # Trained CNN model
├── requirements.txt        # Python dependencies
├── README.md               # Project documentation
└── demo.gif                # Sample output (replace with your own)

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🙌 Credits

• Dataset from Kaggle FER Dataset
• CNN Model built with Keras
• Face detection using OpenCV Haar Cascades
• Developed with ❤️ by Aditya Raj

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🔗 Connect with Me

• 🔗 GitHub: @ADiTyaRaj8969
• ✉️ Email: adivid198986@gmail.com
• 💼 LinkedIn: Aditya Raj

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