🔗 GitHub Repository

Description

The AI-powered Attendance Tracking System is a real-time facial recognition solution designed to automate and secure attendance marking in workplaces, educational institutions, and restricted access areas. By leveraging deep learning, this system eliminates manual attendance marking, prevents proxy attendance, and ensures high accuracy and efficiency.

The system integrates an MTCNN-based face detection model for real-time facial recognition, ensuring seamless identification and verification. The backend is built with ASP.NET Web API, allowing secure CRUD operations, while the MSSQL database with triggers automates key processes. A modern Angular frontend with Tailwind CSS provides a smooth user experience, and SignalR enables real-time attendance updates across all connected devices.

Key Features 🚀

✅ Automated Face Recognition-Based Attendance:

  • Detects and recognizes faces using MTCNN deep learning model.
  • Prevents fraudulent attendance marking (proxy prevention).

✅ Real-Time Updates & Notifications:

  • Uses SignalR for instant attendance updates across all connected devices.
  • Admins and users receive immediate feedback on attendance status.

✅ Secure & Scalable Backend:

  • Built using ASP.NET Web API (C#) for efficient and secure CRUD operations.
  • MSSQL database with triggers to automate updates and logs.

✅ Comprehensive Attendance Logs & Reports:

  • Stores timestamps, user details, and face embeddings for authentication.
  • Generates attendance reports with filtering options (daily, weekly, monthly).

✅ Modern & Responsive Frontend:

  • Angular for a dynamic user experience.
  • Tailwind CSS for a clean and responsive UI.

✅ Role-Based Access Control (RBAC):

  • Admins can manage users, configure rules, and generate reports.
  • Employees/students can view their own attendance records.

System Workflow 🔄

1️⃣ User Registration & Enrollment

  • Users register their face data in the system.
  • Face embeddings are stored securely in the database.

2️⃣ Attendance Marking

  • The system detects and recognizes faces in real-time.
  • Matches against the stored dataset to mark attendance.

3️⃣ Data Storage & Automation

  • Attendance records are stored in MSSQL database.
  • Triggers ensure real-time updates and prevent duplicate entries.

4️⃣ Live Updates & Reports

  • Users get real-time confirmation via SignalR notifications.
  • Admins can generate attendance reports and monitor logs.

Tech Stack 🛠️

Backend

  • ASP.NET Web API (C#) – Handles authentication, CRUD operations, and business logic.
  • MSSQL Database – Stores user details, face embeddings, and attendance records.
  • SQL Triggers – Automates attendance updates and prevents redundancy.

Face Recognition & AI

  • Python (MTCNN Model) – Detects and recognizes faces in real-time.
  • OpenCV – Handles image processing for facial recognition.
  • TensorFlow/Keras – Used for deep learning-based facial verification.

Frontend

  • Angular – A modern, dynamic front-end framework.
  • Tailwind CSS – Provides a responsive and sleek UI.

Real-Time Communication

  • SignalR (ASP.NET Core) – Enables real-time updates and notifications.

Deployment

  • Hosted on an enterprise-grade cloud server.
  • Supports containerized deployment (Docker, Kubernetes ready).

Screenshots

This project aims to revolutionize attendance tracking by combining AI-driven face recognition, real-time updates, and secure automation, ensuring a seamless and efficient attendance management experience! 🚀