AI Attendance Project App.

An AI-powered attendance management system that uses facial recognition to automatically mark and track attendance, eliminating the need for manual roll calls or biometric hardware.

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📌 Overview

• Traditional attendance systems (manual roll call, ID cards, fingerprint scanners) are slow, error-prone, and easy to manipulate (proxy attendance).
• This project uses computer vision and facial recognition to detect and identify individuals in real time via a webcam, then automatically logs their attendance with a timestamp.
• Built as part of the Apna College Prime AI/ML Course capstone project.

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✨ Features

• Real-time face detection and recognition using a webcam feed
• Automatic attendance marking with date and time stamps
• Prevents duplicate attendance entries for the same session
• Simple Flask-based web interface to interact with the system
• Stores attendance records for easy tracking and export
• Lightweight and easy to set up locally

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🛠️ Tech Stack

Component	Technology
Backend	Python, Flask
Face Detection/Recognition	OpenCV, face-recognition library
Data Storage	CSV / local file storage
Frontend	HTML, CSS (via Flask templates)

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📂 Project Structure

ai-attendance-project-app/
│
├── src/                  # Core application logic and helper modules
├── app.py                # Main Flask application entry point
├── requirements.txt      # Python dependencies
└── README.md             # Project documentation

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⚙️ Installation & Setup

1. Clone the repository

   git clone https://github.com/sumitjhadev/ai-attendance-project-app.git
   cd ai-attendance-project-app

2. Create a virtual environment (recommended)

   python -m venv venv
   venv\Scripts\activate       # Windows
   source venv/bin/activate    # macOS/Linux

3. Install dependencies

   pip install -r requirements.txt

4. Run the application

   python app.py

5. Open in browser

   http://127.0.0.1:5000
   

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▶️ How It Works

1. User faces are registered/enrolled into the system beforehand.
2. When the webcam feed detects a face, it compares it against the stored/known faces.
3. If a match is found, the system marks that person as "present" along with the current timestamp.
4. Attendance data is saved and can be reviewed or exported.

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🚀 Future Improvements

• Add a proper database (SQLite/MongoDB) instead of flat-file storage
• Add admin authentication and role-based access
• Export attendance reports as PDF/Excel
• Deploy the app with a public-facing landing page (see companion repo: ai-attendance-project-landing)
• Improve recognition accuracy in low-light conditions

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🙌 Acknowledgements

Built as part of the Apna College Prime AI/ML Course.

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📄 License

This project is for educational purposes as part of a course capstone project.