Face Recognition Attendance System

A desktop attendance system built with Python, Tkinter, OpenCV Haar Cascade, and LBPH face recognition.

Features

• Register students with numeric IDs and names.
• Capture face images from the webcam.
• Train an LBPH face recognition model.
• Track faces from the webcam and mark daily attendance.
• Save attendance logs as CSV files.

Download (no setup required)

Don't want to install Python? Download the ready-to-run app:

➡️ https://github.com/Hinsane5/ComputerVisionAttendance/releases/latest

1. Download the file for your computer:
   • Windows → AttendanceSystem-Windows.zip
   • Mac → AttendanceSystem-macOS.zip
2. Unzip it.
3. Double-click AttendanceSystem to launch.

First launch: bypass the security warning

The app is not code-signed, so your system shows a one-time warning the first time you open it. This is expected — here is how to get past it:

• Mac: right-click (or Control-click) the app → Open → click Open again.
• Windows: on the "Windows protected your PC" screen, click More info → Run anyway.

Mac says "AttendanceSystem is damaged and can't be opened"? That is macOS blocking the download, not a real problem. Open the Terminal app and run this once (drag the app onto the Terminal window to fill in the path):

xattr -dr com.apple.quarantine /path/to/AttendanceSystem.app

Then double-click the app again.

The app asks for camera access on first use — click Allow. Your data (students, model, attendance logs) is saved in an AttendanceSystem folder in your home directory.

Run from source (for developers)

The sections below are only needed if you want to run or modify the code directly instead of using the downloadable app above.

Requirements

• Python 3.11+
• Webcam access
• macOS, Windows, or Linux with a working OpenCV camera backend

Installation

python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

On Windows, activate the environment with:

.venv\Scripts\activate

Run

python main.py

Usage

1. Enter a numeric student ID and name.
2. Click Take Images and wait until face samples are captured.
3. Click Train Model.
4. Click Track & Mark Attendance to recognize a student and save attendance.

Generated Files

The app creates these runtime files locally:

• TrainingImage/ stores captured face images.
• TrainingImageLabel/Trainner.yml stores the trained OpenCV model.
• StudentDetails/StudentDetails.csv stores registered student data.
• Attendance/Attendance_YYYY-MM-DD.csv stores attendance logs.

These files are ignored by Git because they can contain personal data and generated model output.

Notes

• Keep haarcascade_frontalface_default.xml in the project root.
• If tracking fails with an invalid model error, click Train Model again to rebuild the model.