Face Recognition based Attendance Management System with a Flask web application and Power BI attendance dashboard.
- Features
- Youtube Demo
- Installation and Usage
- Technologies Used
- Methodology
- User Interface Demo
- License
- 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
Here's the link of the Youtube video demonstrating this project.
- Clone the repository:
git clone https://github.com/amlanmohanty1/face-recognition-attendance-management-system-with-PowerBI-dashboard.git
- Install the required dependencies:
pip install -r requirements.txt
- Replace the training images with your own set of images in the folder
Training images
. - Open the
app.py
file and change the file paths as per your system. - Run the
app.py
file.
- Programming Languages: Python
- Libraries: OpenCV, dlib, face-recognition
- Database: SQLite
- Web Application: Flask, HTML, CSS, JavaScript
- Data Visualization: Power BI
- Environment Setup: Created a conda environment and installed necessary dependencies including OpenCV, dlib, face-recognition, and Flask.
- Face Detection: Converted images to black and white, then used HOG to detect faces by comparing image gradients.
- Face Embedding: Used 128-dimensional vectors and the triplet loss function for distinguishing between faces.
- Face Recognition: Utilized Euclidean distance with a threshold of 0.5 to compare the generated face encodings with the actual encodings of the training images to recognize the faces.
- Database Connection: Stored attendance data in a SQLite database and exported it to CSV for Power BI integration.
- Web Application: Developed a Flask-based web app for real-time attendance capturing and management.
- Power BI Dashboard: Connected the attendance data to Power BI to create dashboards. Embedded Power BI reports into the web app for real-time insights.
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Fig.1: Home page of FRAMS
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Fig.2: Attendance Punching using FRAMS
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Fig.3: Face is detected in low light conditions
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Fig.4: Face is detected from different angles
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Fig.5: Administrator Login Page
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Fig.6: Page showing the current day’s attendance
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Fig.7: Attendance Dashboard in Power BI
This project is licensed under the MIT License. Check out the LICENSE file for more details.