https://github.com/hyba-ab/CyberSecurity-Facial-Recognition-based-Attendance-System/assets/164689889/906bbddf-d478-4f39-b073-bebb55c721a2
Introduction
Our project, the Facial Recognition Based Attendance System, is a solution designed to automate the process of recording attendance using facial recognition technology. By harnessing the power of computer vision and machine learning, our system offers a convenient and efficient alternative to traditional attendance tracking methods.
Technologies Used:
- Frontend Tools: HTML, CSS, Bootstrap
- Backend Tools: Python, OpenCV (cv2), Flask, NumPy, Scikit-learn (sklearn), Pandas, Joblib
Development Process:
- Design and Planning: Conceptualized the system, planned functionalities, and designed frontend and backend components.
- Backend Development: Implemented face detection, recognition, attendance recording, and user management functionalities.
- Machine Learning Model Training: Trained the face recognition model using the K-Nearest Neighbors algorithm.
- Integration and Testing: Integrated frontend and backend components, performed thorough testing, and implemented error handling mechanisms.
- Deployment: Deployed the Flask application to a server for production use.
Getting Started :
1.Prerequisites:
Ensure you have Python, OS, OpenCV, Flask, NumPy, Scikit-learn, Pandas, and Joblib installed on your development environment.
2.Set Up Environment:
Create a virtual environment to manage project dependencies
python -m venv venv
source venv/bin/activate
3.Install Dependencies: Install the required Python libraries using
pip install -r requirements.txt.
4.Data Preparation: Create a directory structure to store user images and attendance records (CSV files).
5.Configuration: Modify any configuration settings in the code as needed.
6.Run the Application: Execute the appropriate script to launch the application.
Authors :
Isra Mariem Thabti , Hiba Abdelli