Here’s a sample markdown documentation for your Emotion Recognition System project:

# Emotion Recognition System

The **Emotion Recognition System** is a deep learning-based project designed to detect human emotions from facial expressions. It utilizes Convolutional Neural Networks (CNNs) to classify seven emotions: Angry, Disgust, Fear, Happy, Sad, Surprise, and Neutral. This project also includes real-time emotion detection using a webcam.

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## Features

- **Emotion Detection**: Identifies seven different emotions.
- **Deep Learning Model**: Built with TensorFlow/Keras for accurate emotion classification.
- **Real-Time Recognition**: Integrated webcam feed for live emotion analysis.
- **Data Preprocessing**: Converts images to grayscale, resizes them, and normalizes pixel values.
- **Interactive Visualization**: Displays training metrics and real-time predictions.

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## Requirements

Install the required dependencies using the following:
```bash
pip install -r requirements.txt

Key Libraries:

• TensorFlow/Keras
• NumPy
• OpenCV
• Matplotlib
• Scikit-learn

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Project Structure

Emotion_Recognition/
├── model.py          # Model training and evaluation
├── test.py           # Real-time emotion recognition
├── requirements.txt  # Dependencies
├── Dataset/          # Organized dataset for training and testing
└── README.md         # Project documentation

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Usage

Training the Model

1. Prepare the dataset:

   Dataset/
   ├── train/
   │   ├── angry/
   │   ├── disgust/
   │   ├── fear/
   │   ├── happy/
   │   ├── sad/
   │   ├── surprise/
   │   └── neutral/
   └── test/
       ├── angry/
       ├── disgust/
       ├── fear/
       ├── happy/
       ├── sad/
       ├── surprise/
       └── neutral/
   

2. Train the model:

   python model.py

Real-Time Recognition

1. Connect a webcam to your system.
2. Run the test script:

   python test.py

3. Press Q to exit the live feed.

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Results

The model achieves a high accuracy in detecting emotions from facial expressions. During real-time testing, it overlays the detected emotion on the individual's face in the video feed.

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Sample Output