Real-Time Facial Expression Detection

This project implements a real-time facial expression detection system using a Convolutional Neural Network (CNN) in Python. It classifies facial expressions into three categories: sad, happy, and surprise. The system captures live video feed from a webcam and displays the predicted expression on the video.

Table of Contents

1. Overview
2. Project Structure
3. Requirements
4. Setup
5. Acknowledgments

Overview

This project is designed to detect facial expressions in real-time using a pre-trained model on a dataset containing images categorized as sad, happy, and surprise. It is organized into separate scripts for data preprocessing, model training, and real-time detection.

Project Structure

.
├── prepare_data.py        # Script for data preprocessing
├── train_model.py         # Script for building and training the model
├── main.py                # Script for real-time facial expression detection
├── requirements.txt       # Required dependencies
└── README.md              # Project documentation

## Requirements
Python 3.8+
TensorFlow 2.x
OpenCV
Numpy
Scitkit-Learn

Install the necessary dependencies using:
pip install -r requirements.txt

## Setup
1. Clone the Repository
git clone https://github.com/yourusername/facial-expression-detection.git
cd facial-expression-detection

## Prepare Dataset
The dataset used for this project is not included. Ensure your dataset is organized as follows:
dataset/
├── sad/
├── happy/
└── surprise/

Note: This repository does not include any pre-trained models or datasets due to size and privacy limitations. Make sure to prepare your dataset and follow the setup instructions carefully.

This `README.md` provides a full setup guide and clear instructions for usage, covering dependencies, data preparation, and each script’s function within the project. Let me know if any additional details are needed!