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This repository contains the code and resources for a facial recognition system. The system is designed to identify and authenticate individuals based on facial features using deep learning techniques.

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Face Recognition Attendance System

A robust system designed to authenticate individuals and record attendance using facial recognition technology powered by deep learning. This project simplifies attendance tracking for classrooms, workplaces, or events.


📋 Features

  • Role-based access for administrators, lecturers.

  • Manage courses, units, venues, and attendance records through an intuitive interface.

  • Capture and store multiple images for accurate identification.

  • Good for college project

Project Structure

## Project Structure

```plaintext
Face-Recognition-Attendance-System/
├── database/
│   ├── attendance-db.sql         # SQL file to set up the database
│   └── database_connection.php   # Database connection script
├── models/
│   └── face-api-models.js        # JavaScript models for Face API
├── resources/
│   ├── assets/
│   │   ├── css/                  # CSS files
│   │   └── javascript/           # JavaScript files
│   ├── images/                   # Images directory
│   ├── labels/                   # Stored images of registered students
│   ├── lib/
│   │   └── global-functions.php  # Global PHP functions
│   ├── pages/
│   │   ├── admin/                # Admin-specific pages
│   │   ├── lecturer/             # Lecturer-specific pages
│   │   └── login.php             # Login page
├── index.php                     # Main entry point for all pages
├── .htaccess                     # Redirect rules
└── README.md                     # Project documentation


🚀 Setup Procedure

Follow these steps to set up and run the project:

1. Clone or Download the Repository

  • Clone the repository using Git:
    git clone https://github.com/francis-njenga/Face-Recognition-Attendance-System.git
    -Download zip file

2. Place the Project in the Server Directory

If you’re using XAMPP, place the project folder inside the htdocs directory:

xampp/htdocs/Face-Recognition-Attendance-System

Use a simple folder name, as it will be part of the URL (e.g., attendance-system).

3. Start XAMPP

  • Open the XAMPP Control Panel.
  • Start the Apache and MySQL services.

4. Set Up the Database

  • Visit phpMyAdmin.

  • Create a new database.

    • Recommended name: attendance_db (You can choose any name, but ensure it matches the configuration in your project files).
  • Import the SQL file:

  • Locate the attendance-db.sql file in the database/ folder of the project.

  • Import it into the newly created database.

5. Launch the Application

Visit the application in your browser:

http://localhost/{your-project-folder-name}

🧑‍💻 User Guide

1. Login as Administrator

Once logged in, you can:

  • Add students.
  • Manage courses, units, and venues.

⚠️ Important:

  • Ensure to add at least two students and capture five clear images for each.
  • Poor image quality will affect recognition accuracy. You can retake any image by clicking on it.

2. Login as Lecturer

  • Create a lecturer account via the admin panel or use a pre-existing one.

Select lecture user type, to be able to login as lecture

if you have issues using this email and password, create your lecture on admin panel

As a lecturer:

  • Select a course, unit, and venue on the home page.
  • Launch the Face Recognition feature to begin attendance.

Additional Features for the Lecturer Panel

  • You can also export the attendance to an Excel sheet.
  • Other simple features are available for managing the lecture panel.

📜 License This project is licensed under the MIT License.

📧 Support For any issues or inquiries, feel free to reach out via email: Francis Njenga.

Visit My Website

https://www.frankcodes.tech

You can send donations to my PayPal account: [email protected]

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This repository contains the code and resources for a facial recognition system. The system is designed to identify and authenticate individuals based on facial features using deep learning techniques.

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