Face Recognition Attendance Management System With Power BI Dashboard

Abstract

1. Introduction

Face Recognition based Attendance Management System with a Flask web application and Power BI attendance dashboard.

2. Objective

Face Recognition based Attendance Management System with a Flask web application and Power BI attendance dashboard.

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

3. Key Features / Modules

• Face detection and recognition
• Attendance management
• Generates attendance reports in a csv file
• Secure admin login
• Interactive user interface
• Can detect multiple faces and mark attendance at a time
• Works in bright and low light conditions
• Attendance dashboards using Power BI

4. Technology Stack

• Programming Languages: Python
• Libraries: OpenCV, dlib, face-recognition
• Database: SQLite
• Web Application: Flask, HTML, CSS, JavaScript
• Data Visualization: Power BI

5. System Requirements

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

• A modern web browser
• VS Code or any code editor
• 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/amlanmohanty1/face-recognition-attendance-management-system-with-PowerBI-dashboard.git
cd face-recognition-attendance-management-system-with-PowerBI-dashboard

1. Clone the repository:
2. Install the required dependencies:
3. Replace the training images with your own set of images in the folder Training images.
4. Open the app.py file and change the file paths as per your system.
5. Run the app.py file.

git clone https://github.com/amlanmohanty1/face-recognition-attendance-management-system-with-PowerBI-dashboard.git

pip 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 amlanmohanty1 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.