Attendance Monitor using Flask and Opencv

AI Attendance Monitor is a cost-efficient AI-powered attendance monitoring system developed using Python Flask. The project utilizes OpenCV-Python, Geocoder, and Geopy libraries to provide a convenient solution for attendance tracking based on face recognition and location verification.

Features

• 📷 Face recognition: The system uses face recognition algorithms to compare captured faces with the reference photos in the attendanceResources folder. If a match is found, an attendance record is created with a corresponding timestamp.
• 📍 Geo-tagging: Before running the Flask app, you need to generate a geo tag of your current location using curr_geo_tag_creator.py. This geo tag is used by the app to verify if the user is within the specified vicinity of the tagged location (default: 100 meters).
• 💰 Cost-efficient: The AI Attendance Monitor eliminates the need for manual attendance tracking and reduces administrative efforts by automating the process using AI technologies.

Prerequisites

• Python 3.x
• OpenCV-Python library
• Geocoder library
• Geopy library

Installation

1. Clone the repository:

   git clone https://github.com/your_username/AI-Attendance-Monitor.git
   cd AI-Attendance-Monitor

2. Install the required dependencies:

   pip install -r requirements.txt

Usage

1. Generate a geo tag of your current location by running:

   python curr_geo_tag_creator.py

2. Start the Flask app:

   python app.py

3. Access the AI Attendance Monitor web interface by navigating to http://localhost:5000 in your web browser.

Customization

• Changing Vicinity Distance: You can modify the vicinity distance in vicinity_checker.py to adjust the range within which a user's location is considered valid.
• Updating Reference Photos: To update the reference photos for face recognition, add the corresponding images to the attendanceResources folder.

License

This project is licensed under the MIT License.

Feel free to use and customize this project.