Facial Expression Recognition System - Sure Trust 😊

Welcome to the Facial Expression Recognition System repository! This project harnesses the power of YOLOv9 and Flask to detect emotions in images and live camera feeds. It identifies five emotions: Angry, Happy, Natural, Sad, and Surprised, achieving a mean Average Precision (mAP50) of 0.731. The system features a user-friendly web interface that supports file uploads, real-time processing, and emoji feedback.

Table of Contents

• Project Overview
• Features
• Technologies Used
• Installation
• Usage
• Demo
• Contributing
• License
• Contact
• Releases

Project Overview

Facial expressions are a vital part of human communication. This project aims to develop a system that can recognize and interpret these expressions. Using deep learning techniques, we built a model that can classify emotions from facial images. This technology has applications in Human-Computer Interaction (HCI), emotion analysis, and more.

Features

• Emotion Detection: Accurately detects five emotions from images and live video feeds.
• Web Interface: Easy-to-use interface for uploading images and viewing results.
• Real-Time Processing: Analyze live camera input for immediate feedback.
• Emoji Feedback: Provides emoji suggestions based on detected emotions.
• Open Source: Contribute to the project and improve the system.

Technologies Used

This project utilizes the following technologies:

• Python: The primary programming language for the application.
• OpenCV: For image processing and computer vision tasks.
• Flask: A lightweight web framework for creating the web interface.
• HTML/CSS/JS: For building the front end of the application.
• YOLOv9: A state-of-the-art object detection model used for emotion recognition.
• TensorFlow: For deep learning tasks and model training.
• Roboflow Dataset: A dataset used for training the emotion detection model.

Installation

To get started with this project, follow these steps:

1. Clone the Repository:

   git clone https://github.com/Bananacat123-hue/Facial_Expression_Recognition-Sure_Trust-.git

2. Navigate to the Project Directory:

   cd Facial_Expression_Recognition-Sure_Trust-

3. Install Required Packages:

   Make sure you have Python installed. Then, install the required packages using pip:

   pip install -r requirements.txt

4. Run the Application:

   Start the Flask server:

   python app.py

5. Access the Web Interface:

   Open your web browser and go to http://127.0.0.1:5000 to access the application.

Usage

Once the application is running, you can use it in the following ways:

1. Upload an Image: Click on the upload button to select an image file from your device. The system will analyze the image and display the detected emotion.

2. Use the Live Camera: Allow the application to access your camera. It will process the video feed in real-time and show the detected emotions as you move.

3. View Emoji Feedback: Based on the detected emotion, the application will display an appropriate emoji for quick feedback.

Demo

Here’s a brief demonstration of how the application works:

You can find the latest releases and updates here.

Contributing

We welcome contributions to improve this project. Here’s how you can help:

1. Fork the Repository: Click on the fork button to create a copy of the repository in your account.

2. Create a New Branch: Use a descriptive name for your branch.

   git checkout -b feature/YourFeatureName

3. Make Your Changes: Implement your feature or fix a bug.

4. Commit Your Changes: Write a clear commit message.

   git commit -m "Add your message here"

5. Push to Your Branch:

   git push origin feature/YourFeatureName

6. Create a Pull Request: Go to the original repository and submit a pull request.

License

This project is licensed under the MIT License. Feel free to use, modify, and distribute this software.

Contact

For questions or feedback, you can reach out to the project maintainer:

• Email: your-email@example.com
• GitHub: Bananacat123-hue

Releases

For the latest updates and downloadable files, visit the Releases section.

Thank you for your interest in the Facial Expression Recognition System! We hope you find it useful for your projects and research. Your contributions and feedback are always welcome.