CNN Emotion Detection

Introduction

This project is focused on detecting human emotions using Convolutional Neural Networks (CNNs). It leverages the power of deep learning to analyze facial expressions and classify them into various emotion categories. The core of this project is built using Keras and TensorFlow, showcasing the practical application of CNNs in real-world scenarios.

Features

• Emotion classification using a CNN model.
• Real-time emotion detection from webcam feed.
• Detailed analysis and visualization of the detection process.

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.

Prerequisites

Before you begin, ensure you have the following installed:

• Python 3.x
• Pip (Python package installer)

Installation

1. Clone the repository to your local machine:

   git clone https://github.com/Zatch07/CNN-Emotion-Detection.git
   

2. Navigate to the project directory:

   cd CNN-Emotion-Detection
   

3. Install the required Python packages:

   pip install -r requirements.txt
   
   

Running the Project

To run the emotion detection model, execute the following command:

python main.py

This will start the application and begin detecting emotions using your webcam.

Built With

• Keras - The deep learning framework used
• TensorFlow - Backend for Keras
• OpenCV - Used for capturing and processing webcam feed

Model Information

The model architecture and training process are detailed in the Jupyter notebook emotion-classification-cnn-using-keras.ipynb.