A Python-based face recognition attendance system that uses Firebase for database management and Tkinter for the graphical user interface (GUI). This project enables real-time attendance marking and includes features to register new students with their details and photos.

• Face Recognition: Automatically detects and recognizes faces to mark attendance.
• Student Registration: Add new students with their details and photo through a GUI.
• Database Integration: Firebase Realtime Database is used for storing student data and attendance records.
• GUI Interface: User-friendly interface built with Tkinter.
• Excel Export: Attendance records are saved in an Excel file for easy access.

• Python 3.10 or later
• Firebase account with a Realtime Database and Cloud Storage setup
• Installed libraries:

  pip install firebase-admin face_recognition opencv-python-headless numpy cvzone openpyxl

1. Clone the repository:

   git clone https://github.com/your_username/your_repo_name.git
   cd your_repo_name

2. Add your Firebase configuration:

   • Place your serviceAccountKey.json file in the project directory.

3. Prepare the environment:

   • Create an Images folder and add student photos named as student_id.png.
   • Ensure the required directories and files exist as per the project setup.

4. Run the application:

   • Start the GUI:

     python main_with_gui_excel2.py

   • Optional: Generate encodings for new student photos:

     python EncodeGenerator.py

   • Optional: Initialize the database:

     python AddData_to_database.py

1. Register a Student:

   • Open the GUI.
   • Click on "Register New Student."
   • Enter the student details and upload their photo.

2. Mark Attendance:

   • Start the recognition system via the GUI.
   • The system will detect and mark attendance for recognized faces.

3. Check Records:

   • Attendance records are saved in the Firebase database.
   • An Excel file (attendance_record.xlsx) is generated locally for offline access.

• Python: Core programming language.
• OpenCV: For image processing.
• Face Recognition: Library for facial encoding and recognition.
• Tkinter: For building the GUI.
• Firebase: Backend database and storage.
• OpenPyXL: For working with Excel files.

• Riyaz Momin - Developer and Project Owner

• Add multi-camera support.
• Integrate email or SMS notifications.
• Enhance security with multi-factor authentication.
• Add a dashboard for viewing and managing attendance records online.

This project is licensed under the MIT License. See the LICENSE file for details.