Face Recognition Attendance System

Abstract

1. Introduction

This is a demo of face recognition attendance system on live video from your webcam.

2. Objective

This is a demo of face recognition attendance system on live video from your webcam.

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

3. Key Features / Modules

• Requires less memory (RAM) to operate
• Recognizes known faces from real time live video
• full automated, no need to maintain registeration system
• Easy to add new students
• Easy to remove old students

4. Technology Stack

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/abhineetraj1/face-recognition-attendance-system.git
cd face-recognition-attendance-system

1. Download python3.x
2. Install all the required libraries :-
3. If you want to add student's face data, then click the picture of student's front face.
4. Make sure image file should be in jpg format
5. Rename the image file of student as "section-roll.jpg"
6. Example is there in students folder (A19-2205007.jpg, A19-22050XX.jpg, etc).

pip3 install -r requirements.txt

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 abhineetraj1 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.