🎥 Face Recognition Attendance System

An innovative Face Recognition-based Attendance System that uses deep learning to capture and recognize student faces, update attendance records in real-time using Firebase, and provide a user-friendly GUI for manual database management. This project is inspired by tutorials from Murtaza Hassan and Computer Vision Zone.

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✨ Features

• 🎬 Real-Time Face Recognition: Detects and recognizes student faces in real-time using a webcam and deep learning techniques.
• 📊 Firebase Integration: Seamlessly stores and manages student data and attendance records using Firebase Realtime Database.
• 📈 Automatic Attendance Marking: Automatically updates attendance status in Firebase for recognized students.
• 🖥️ Manual Database Management GUI: Built with Tkinter, this GUI allows for easy addition, removal, and modification of student records.
• 📤 Export to Excel: Easily export student data to an Excel file for reporting and analysis.

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📋 Prerequisites

• Python 3.x
• Required Python Libraries: opencv-python, numpy, face_recognition, cvzone, firebase-admin, tkinter, pandas
• Dlib and CMake: Install Visual Studio with Desktop Development with C++ to compile the necessary C++ libraries required by face_recognition.
• A Firebase project with Realtime Database and Storage configured.
• A valid serviceAccountKey.json file for Firebase authentication.

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🚀 Setup Instructions

1. Clone the Repository:

   git clone https://github.com/Sourabh9267/Face-Recognition-Attendance-System-with-Realtime-Firebase-Integration.git
   cd Face-Recognition-Attendance-System-with-Realtime-Firebase-Integration

2. Install the Required Packages:

   pip install opencv-python numpy face_recognition cvzone firebase-admin pandas

3. Firebase Configuration:

   • Place your serviceAccountKey.json file in the root directory. This is crucial for Firebase authentication.
   • If you encounter any issues setting up Firebase or generating the serviceAccountKey.json, refer to Murtaza Hassan's tutorial for step-by-step instructions.

4. Prepare Student Images:

   • Use .png format for all student images.
   • Name each image file according to the student's unique ID. This convention is essential for accurate identification.

5. Fill Data Manually:

   • Run the studentmanagement.py script to open the database management GUI.
   • Add each student's details manually once through this interface to ensure the data is correctly synchronized with Firebase.

6. Generate Encodings:

   Run the following command to generate the facial encodings required for recognition:

   python EncodeGenerator.py

   This will create EncodeFile.p, containing all known student encodings.

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📂 Running the Project

1. Run the Face Recognition System:

   python Main.py

   This script will start the webcam, detect faces, and update attendance records in real-time.

2. Run the GUI for Manual Management:

   python studentmanagement.py

   Use this GUI to manually add, update, or delete student records and export data to Excel.

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⚙️ Customization Instructions

⏱️ Attendance Re-Update Condition

By default, the attendance system allows re-marking of attendance after 30 seconds. This condition is defined on Line 114 of Main.py:

if secondsElapsed > 30:

The value 30 represents the time in seconds after which attendance can be re-marked. You can customize this setting according to your needs:

To Allow Re-marking after 1 Hour:

Replace 30 with 3600 (since there are 3600 seconds in 1 hour):

if secondsElapsed > 3600:

To Allow Re-marking after 1 Day:

Replace 30 with 86400 (since there are 86400 seconds in 1 day):

if secondsElapsed > 86400:

Explanation of the Re-Update Condition:

The variable "secondsElapsed" calculates the time difference in seconds between the current time and the last recorded attendance time for each student. The condition:

if secondsElapsed > X:

checks if this time difference (secondsElapsed) exceeds the specified value X. When secondsElapsed is greater than X, the system allows the attendance to be marked again for that student. Adjusting the value of X lets you control the frequency of attendance updates based on your needs:

🔍 Code Overview

📝 Main Components

• Main.py: The primary script for face recognition, attendance marking, and database interaction.
• studentmanagement.py: A GUI application for managing the student database manually.
• EncodeGenerator.py: A script to generate and store facial encodings.

🔧 Key Functions in Main.py

• Face Detection and Recognition: Utilizes the face_recognition library to identify students.
• Attendance Marking: Updates Firebase in real-time based on recognized faces.
• Firebase Operations: Manages interactions with the Firebase Realtime Database.

🖥️ GUI Features in studentmanagement.py

• CRUD Operations: Add, remove, update student records with ease.
• Export Functionality: Export data to Excel for further analysis.

🔗 Useful Resources

• Understanding face_recognition Library: Read Adam Geitgey's article to dive deeper into the working of the face_recognition library.
• Project Tutorial by Murtaza Hassan: Watch the tutorial video that inspired this project.
• Setting Up Firebase Database: Follow this tutorial for a comprehensive guide on setting up Firebase.

🙏 Acknowledgements

• Thanks to Adam Geitgey for his fantastic face_recognition library. Read his insightful article on how this library works.
• Special Thanks to Murtaza Hassan and Computer Vision Zone for their inspirational tutorial, which provided a foundation for this project. You can check out their tutorial here.

🎥 Preview

For a detailed demonstration of this project in action, check out the Google Drive link or view the Main project GIF preview below:

Documentation and Project Report for reference

Face_Recognition_Attendance_System_Documentation.docx