Polaris

Face recognition attendance system .

AI & Machine LearningPythonMIT

Abstract

Polaris is an open-source AI & Machine Learning project. Face recognition attendance system . It is built using Python, Deep Learning, Machine Learning, Computer Vision. Key capabilities include: Register new people :; Search for people using their pictures :. 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

Face recognition attendance system .

2. Objective

Face recognition attendance system .

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

3. Key Features / Modules

  • Register new people :
  • Search for people using their pictures :

4. Technology Stack

PythonDeep LearningMachine LearningComputer Vision

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/ChibaniMohamed/Polaris.git
cd Polaris

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 ChibaniMohamed 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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