PresenSee – Face Recognition Attendance System

PresenSee is a lightweight face recognition-based attendance management system built using OpenCV.
It captures and recognizes faces through a webcam and marks attendance automatically in a CSV file. The attendance can later be exported to Excel format for easy viewing and reporting.

This project is intended for learning and prototyping purposes, using simple and lightweight models.

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Features

• Face registration through webcam with user ID and name input.
• Face detection and recognition using OpenCV’s LBPH model.
• Attendance auto-logged into daily CSV files.
• One-click export of CSV attendance logs to Excel format.
• Prevents duplicate entries per user per day.
• Simple and cross-platform GUI with Python's TKinter.
• Clean and modular file structure.

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Getting Started

1. Clone the Repository

git clone https://github.com/your-username/PresenSee.git
cd PresenSee

2. Create and Activate a Virtual Environment (Recommended)

For Linux/Mac

python -m venv myvenv 
source myvenv/bin/Activate

For Windows

python -m venv myvenv
myvenv\Scripts\activate

3. Install Required Packages

pip install -r requirements.txt

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How to Use

Step 1: Run the GUI Application

python gui.py

Step 2: Register Face

Click the Register Face button to start face registration.

Step 3: Enter User Details

Enter your User ID and User Name in the popup window and click Register.

Step 4: Face Registration in Progress

The system will capture multiple face images automatically.

Step 5: Train the Model

Click the Train Model button to train the face recognition model with registered data.

Step 6: Mark Attendance

Click the Recognize & Mark Attendance button to start face recognition and attendance marking.

Step 7: Convert CSV to Excel

Click the Convert CSV to Excel button to export attendance logs as Excel files.

Step 8: View Attendance

You can view the Excel files saved in the database/ folder.

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Project Structure

PresenSee/
│
├── attendance/               # Stores daily attendance CSVs (auto-generated)
├── database/                 # Stores Excel (.xlsx) exports
├── dataset/
│   └── users/                # User image folders (e.g. 101_John/)
├── models/
│   ├── haarcascade_frontalface_default.xml
│   └── trainer.yml
├── src/core/
│   ├── face_register.py      # Register a new user
│   ├── face_trainer.py       # Train recognition model
│   ├── face_recognition.py   # Detect and mark attendance
│   └── convertToExcel.py     # CSV ➝ Excel converter
├── gui.py                    # Graphical Interface
├── requirement.txt           # Required Packages
└── README.md

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Requirements

Only the essentials:

opencv-contrib-python
pandas
numpy
openpyxl
pillow

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Notes

• Camera auto-closes after a timeout (default: 60 seconds) or if you press q.
• Ensure good lighting and clear face visibility for best recognition accuracy.

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Author

Made with ❤️ for learning and experimentation by Sumanxhub.