Face Recognition Attendance

Abstract

1. Introduction

Learned and implemented the Face detection and recognition using face_recognition api

Using face recognition api, self implemented algo in SceneChangeDetect.py and Multithreading pool developed a system in which one can pass argument as past cctv footage and can do facial_recognition(attendance).

50sec(changes rapid) -> 10 sec processing and largely depends upon the visual it is getting as it skips most of the part of frame in which no change occured and also no face detected.

2. Objective

Face recognition using OpenCV and developed Advance high speed Attendance System on old CCTV footage

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

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/Akash16s/Face-Recognition-Attendance.git
cd Face-Recognition-Attendance

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 Akash16s and published on GitHub under the Apache License 2.0. Please follow the license terms and credit the original author when you use or modify this code.