https://github.com/radanpro/ai-proctoreye
Student Identity Verification and Behavior Monitoring System An AI-powered system for verifying student identity during exams, tracking their allotted time, and monitoring their behavior to detect potential cheating. The system uses cameras and AI techniques to ensure a secure and fair exam environment.
https://github.com/radanpro/ai-proctoreye
Last synced: 23 days ago
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Student Identity Verification and Behavior Monitoring System An AI-powered system for verifying student identity during exams, tracking their allotted time, and monitoring their behavior to detect potential cheating. The system uses cameras and AI techniques to ensure a secure and fair exam environment.
- Host: GitHub
- URL: https://github.com/radanpro/ai-proctoreye
- Owner: radanpro
- Created: 2024-10-16T15:50:41.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2025-01-28T16:23:39.000Z (3 months ago)
- Last Synced: 2025-02-15T16:57:51.240Z (3 months ago)
- Language: Python
- Size: 639 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# AI ProctorEye
**⚠️ This project is currently under active development. Expect frequent updates and changes.**
AI ProctorEye is an intelligent system designed to verify student identities, monitor their behavior during exams, and ensure a secure and fair examination environment. The project leverages AI techniques such as face recognition, barcode scanning, and behavior analysis to detect suspicious activity and prevent cheating.
---
## Features
- **Student Identity Verification**: Matches a student's current image with the one stored in the database using barcode information.
- **Exam Seat Verification**: Confirms that the student is seated in the correct location.
- **Behavior Monitoring**: Analyzes student behavior during the exam to detect cheating attempts.
- **Progressive Steps**: The project is structured in steps, where each step builds on the previous one to gradually enhance the system's capabilities.
- **Student Registration**: New feature to register students via a user-friendly frontend, uploading their image and details to the backend for processing.
- **Real-time Error Reporting**: The API now returns specific error messages, such as "No face detected in the image," which are displayed in the frontend for better user feedback.
## Project Structure
The project is divided into two main parts:
1. **Real Project** (Python-based API):
- Contains the core functionality of student identity verification and behavior monitoring.
- Developed using Python and organized into steps for incremental development.
2. **Frontend Project** (React-based UI):
- A user interface for registering students, displaying their information, and handling image uploads.
- Integrated with the API to facilitate seamless communication between frontend and backend.
### Directory Structure
```bash
AI-proctoreye/
│
├── api/ # Core system and API
| ├── databse/
| ├── routes/
| ├── services/
| ├── services_v2/
| ├── main.py
| └── requirements.txt
| └── README.md
│
├── frontend/ # React-based UI
| ├── src/
| | ├── monitoring/
| | | └── monitoring.jsx
| | ├── students/
| | | ├── AddStudent.js
| | | └── StudentList.js
| | | └── ...
│
└── README.md # Project documentation
```
## Installation
1. **Clone the Repository**:
```bash
git clone https://github.com/radabpro/AI-proctoreye.git
```
2. **Navigate to the Project Directory**:
```bash
cd AI-proctoreye
```
3. **Create a Virtual Environment**:
```bash
cd api
python -m venv venv
```
4. **Activate the Virtual Environment**:
- On **Windows**:
```bash
venv\Scripts\activate
```
- On **macOS/Linux**:
```bash
source venv/bin/activate
```
5. **Install Dependencies**:
After activating the virtual environment, install the required dependencies from the `requirements.txt` file (on api folder):
```bash
pip install -r api/requirements.txt
```
6. **Set Up MySQL Database**:
- Ensure you have **XAMPP** or another MySQL server running to:
- Create a new database for your project and configure the connection settings in your code in `database/database_manager.py`.
7. **Frontend Setup (React)**:
- Navigate to the `frontend` folder and install necessary dependencies:
```bash
cd frontend
npm install
```
## Usage
1. **Run the API Server**:
In the `api` folder, run:
```bash
uvicorn main:app --reload
```
This will start the backend API server on `http://localhost:8000`.
2. **Run the React Frontend**:
In the `frontend` folder, run:
```bash
npm start
```
This will start the React development server on `http://localhost:3000`.
- Open your browser and go to `http://localhost:3000` to access the frontend interface where you can add new students, view the student list, and interact with the backend API.
## Development Steps
1. **Step 1**: Create an algorithm to compare the current student's image with the stored one using the barcode.
2. **Step 2**: Identify the student by comparing their image with all students stored in the database, starting with those taking the exam at the same time.
3. **Step 3**: Verify the student is seated in the correct location by matching their image with the expected student for that seat.
4. **Step 4**: Monitor student behavior and detect cheating during the exam.
5. **Step 5**: Add frontend functionality for registering students with their images and details.
## Error Handling and API Updates
- The API now returns more specific error messages for the frontend to handle, such as:
- "No face detected in the image"
- "Error saving student data"
- The React frontend now properly displays these errors using an alert box, ensuring users receive relevant feedback when errors occur.
### Example:
If the API encounters an error such as "No face detected in the image", the frontend will display:
```plaintext
Error: No face detected in the image
```
## Future Improvements
- Implement live video analysis for real-time behavior monitoring.
- Add scalability to handle larger datasets and more students efficiently.
- Integrate with external exam management systems for seamless operation.
## Contributing
We welcome contributions to enhance the project! Please follow these steps:
1. Fork the repository.
2. Create a new branch for your feature or bug fix:
```bash
git checkout -b feature-branch
```
3. Make your changes.
4. Commit your changes with a descriptive message:
```bash
git commit -m "Add new feature"
```
5. Push to the branch you created:
```bash
git push origin feature-branch
```
6. Create a new Pull Request:
- Go to your forked repository on GitHub.
- Click on the "Pull Requests" tab.
- Click on the "New Pull Request" button.
- Select your branch from the dropdown and create the pull request, adding any relevant comments.
## .gitignore
To maintain a clean and organized repository, the following items are included in the `.gitignore` file:
- Python bytecode and cache files
- Virtual environment directories
- Environment variable files
- Logs and debugging files
- SQLite databases (if used)
- Mock databases used for testing
- Node.js dependencies (if frontend uses Node.js)
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.