Face Recognition Attendance System

A Python-based GUI desktop application that uses real-time face recognition to automatically mark employee attendance. Built with OpenCV, face_recognition, and Tkinter, it records attendance data to an Excel sheet with attendance percentage calculations.

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Features

• Face Recognition Login — Identifies registered employees via webcam and marks attendance instantly.
• New User Registration — Capture a face photo via webcam and store employee details.
• Duplicate Prevention — Prevents marking attendance more than once per day for the same employee.
• Attendance Percentage — Automatically calculates attendance percentage based on working days (excluding Sundays and Indian public holidays).
• Excel-Based Storage — All employee and attendance data is persisted in .xlsx files — no database required.

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Project Structure

PythonProject/
│
├── app.py                  # Main application entry point
├── employees.xlsx          # Stores registered employee records (ID, Name)
├── attendance.xlsx         # Stores attendance logs (Date, Time, %, etc.)
├── faces/                  # Directory storing registered face images
│   └── <empID>_<name>.jpg
└── README.md

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Requirements

Python Version

• Python 3.8+

Dependencies

Install all dependencies using pip:

pip install opencv-python face_recognition numpy pandas openpyxl holidays

Note: face_recognition requires dlib. On Windows, it is recommended to install a prebuilt wheel or use CMake + Visual Studio Build Tools.

pip install cmake
pip install dlib
pip install face_recognition

Hardware

• A working webcam (built-in or external).

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Getting Started

1. Clone the repository

   git clone <your-repo-url>
   cd PythonProject

2. Install dependencies

   pip install opencv-python face_recognition numpy pandas openpyxl holidays

3. Run the application

   python app.py

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How to Use

Registering a New Employee

1. Click "New User Registration" on the main window.
2. Enter the Employee ID and Name.
3. Click "Register and Capture Face".
4. A webcam window will open — look at the camera.
5. Press SPACE to capture your face or ESC to cancel.
6. The employee is saved to employees.xlsx and their face image to the faces/ folder.

Marking Attendance (Login)

1. Click "Login (Face Recognition)" on the main window.
2. The webcam activates and scans for registered faces.
3. If a match is found:
   • A green rectangle appears around the face.
   • Attendance is marked in attendance.xlsx with the current date, time, and attendance percentage.
   • A success dialog is shown.
4. If the face is unrecognized, a red rectangle with "Unknown" is displayed.
5. Press ESC to close the webcam window manually.

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Attendance Data

The attendance.xlsx file tracks the following fields:

Column	Description
S.No.	Serial number
Employee ID	Unique identifier for the employee
Name	Employee name
Date	Date attendance was marked (DD-MM-YYYY)
Time	Time attendance was marked (HH:MM:SS)
Attendance	Status — always Present when marked
Attendance Percentage	% of working days present since Jan 1st

Working days exclude Sundays and Indian public holidays (via the holidays library).

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Configuration

Constant	Default Value	Description
EMPLOYEES_FILE	employees.xlsx	Excel file for employee records
ATTENDANCE_FILE	attendance.xlsx	Excel file for attendance logs
FACES_DIR	faces/	Directory for stored face images

These can be changed at the top of app.py.

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Known Limitations(will improve with time!! stay tuned)

• Only one face is processed per recognition session (first match wins).
• Attendance percentage is calculated from January 1st of the current year.
• Face recognition accuracy may decrease in low-light conditions.
• face_recognition library can be slow on machines without a GPU.

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Tech Stack

Technology	Purpose
Python	Core language
OpenCV (cv2)	Webcam access & image processing
face_recognition	Face encoding & matching
Tkinter	GUI framework
Pandas	Excel read/write operations
OpenPyXL	Excel file engine for pandas
holidays	Indian public holiday detection
NumPy	Numerical operations

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License

This project is created for academic purposes as part of a 6th Semester Python Lab.