Project Overview:

The AI-Enabled Smart Attendance System is a face recognition-based attendance solution integrated with IoT and AI technologies. It automates the attendance process by using a camera module (e.g., Raspberry Pi Camera or ESP32-CAM) to capture student faces, verify identities using AI/ML face recognition models, and store attendance data in a centralized web dashboard.

This eliminates manual attendance, prevents proxy attendance, and provides an accurate, contactless, and smart solution for schools, colleges, and workplaces.

Technologies Used:

Hardware:

• ESP32-CAM / Raspberry Pi + Camera Module

• Power Supply or battery (if portable)

• (Optional) RFID module for secondary check

Frontend:

• HTML, CSS, Bootstrap

• JavaScript (for dynamic updates and camera preview)

Backend:

• Option 1: PHP + MySQL

• Option 2: Node.js + MongoDB

• Option 3: Java Spring Boot + PostgreSQL

AI/ML:

• Python (OpenCV, face_recognition library, TensorFlow/Keras)

• Face recognition using:

  • HOG or CNN-based face detection

  • LBPH / FaceNet / Dlib for encoding and matching

System Architecture:

1. Camera module captures the face of the person in front of the device.

2. Image is sent to a Python server (or processed on-device) for face detection and recognition.

3. The recognized face is matched with the database using ML algorithms.

4. If matched:

   • Attendance is marked automatically in the backend database.

   • Date, time, and image are logged.

5. A web dashboard displays:

   • Attendance list (with name, photo, and time)

   • Student-wise report

   • Daily attendance summary