Face Recognition Attendance System

Face Detection | Recognition | Attendance

AI & Machine LearningPythonMIT

Abstract

Face Recognition Attendance System is an open-source AI & Machine Learning project. Face Detection | Recognition | Attendance. We seek to provide a valuable attendance service for both teachers and students. Reduce manual process errors by provide automated and a reliable attendance system uses face recognition technology. It is built using Python, OpenCV, NumPy, Pandas. Key capabilities include: Check Camera; Capture Faces; Train Faces. The complete source code is publicly available on GitHub under the MIT License, making it a useful reference for students building an AI & Machine Learning mini project or final-year project.

1. Introduction

We seek to provide a valuable attendance service for both teachers and students. Reduce manual process errors by provide automated and a reliable attendance system uses face recognition technology.

2. Objective

Face Detection | Recognition | Attendance

This project demonstrates how Python, OpenCV, NumPy can be applied to a real-world AI & Machine Learning problem.

3. Key Features / Modules

  • Check Camera
  • Capture Faces
  • Train Faces
  • Recognize Faces & Attendance
  • Automatic Email

4. Technology Stack

PythonOpenCVNumPyPandas
  • Python 3.7
  • OpenCV (Contrib 4.0.1)
  • Haar Cascade
  • LBPH (Local Binary Pattern Histogram)
  • Pycharm 2019.2

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

  • Python 3.8 or later
  • pip / virtualenv for dependencies
  • VS Code, PyCharm or Jupyter Notebook
  • Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/kmhmubin/Face-Recognition-Attendance-System.git
cd Face-Recognition-Attendance-System
pip install opencv-contrib-python
pip install numpy
pip install pandas
pip install Pillow

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

  • Deploy the model as a web app with Streamlit, Flask or FastAPI
  • Compare against an additional model and report the metric difference
  • Add explainability (SHAP / Grad-CAM)

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

  1. What dataset does the project use and how was it pre-processed?
  2. Which algorithm / model architecture is used and why was it chosen over alternatives?
  3. How are training and testing data split, and how is overfitting avoided?
  4. Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
  5. How would you deploy this model for real users?

9. Source Code & License

This project is developed by kmhmubin and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.

View Source on GitHubDownload ZIP

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