A Smart Mess Management System that automates student attendance using face recognition and simplifies mess operations like meal tracking, payment logging, and user management. Built with ReactJS, Node.js, and Python (OpenCV & face recognition libraries).
- Automatic face detection and recognition for attendance
- Real-time webcam capture or image upload
- No manual sign-ins or ID cards required
- Meal booking & tracking system
- Daily/weekly meal status for each user
- Transaction & payment history tracking
- Admin panel to manage users, meals, and payments
- Students: Face login, view attendance, manage meals
- Admins: Dashboard for attendance logs, mess data, and reports
| Frontend | Backend | AI/ML | Database |
|---|---|---|---|
| ReactJS | Node.js (Express) | Python (OpenCV, face_recognition) | MongoDB |
- Utilizes the
face_recognitionPython library for accurate and fast face matching. - Student face data is collected and stored securely in the backend during registration.
- When attendance is being marked:
- A live image (or frame) is captured from webcam or uploaded.
- The image is sent to the Python-based face recognition API.
- The system matches the face against the stored dataset.
- If a match is found, attendance is logged with timestamp into the database.
Include the following screenshots in your documentation folder:
- ✅ Face Recognition in action (with bounding boxes)
- 📋 Attendance Dashboard view
- 🍛 Meal Tracking UI
- 💳 Payment & Transaction History page
- 👤 Admin User Management Panel
-
🎥 Live Camera Streaming
Replace static image uploads with real-time webcam attendance capture. -
🔔 Notification Alerts
Email or SMS notifications for attendance confirmation, meal status, and payments. -
📱 QR Code Fallback Login
In case face recognition fails, allow secure login via user-specific QR codes. -
🏨 Integration with Hostel/Billing Systems
Seamless sync with hostel room allocation, fee payment, and student profile management. -
📊 Advanced Analytics
Graphs and reports for admin: meal usage patterns, absence records, budget planning.
The Smart Mess Management System is divided into three main layers:
- Frontend (Client)
- Backend (API Server)
- Face Recognition Service (Python)
- Database (MongoDB)
lua
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+--------------------+
| ReactJS Frontend |
+---------+----------+
|
| REST API / HTTP
v
+---------+----------+
| Node.js Backend |
| (Express.js API) |
+---------+----------+
|
+--------------+--------------+
| |
v v
+---------------+ +----------------------+
| MongoDB | | Face Recognition API |
| (Attendance, |<----------->| Python + OpenCV |
| Meals, Users)| Face match | face_recognition |
+---------------+ result +----------------------+
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- Student Dashboard: view attendance, meal plan, and payments
- Admin Panel: manage users, monitor attendance, meals, and transactions
- Interacts with backend via REST APIs
- Handles routing, authentication, session/token management
- Connects to MongoDB for persistent storage
- Sends attendance images to Python face recognition API
- Handles meal booking, payment logs, admin control
- Uses
face_recognitionand OpenCV to:- Encode and compare facial features
- Train model on known faces
- Match real-time input image and return student ID or fail
- Stores:
- User data (name, roll no, face encodings path)
- Attendance logs (timestamp, user ID)
- Meal transactions (booking, consumption, payments)
- Student opens the attendance portal.
- Webcam captures image → image sent to backend.
- Backend sends image to Python Face API.
- Python service compares image with known encodings.
- On successful match:
- Python returns student ID
- Backend logs attendance in MongoDB
- Frontend shows confirmation to user
- Passwords and sensitive data are hashed and encrypted
- Face data is stored securely, encoded (not raw images)
- Token-based authentication (JWT) for session management