Face Recognition For Mess Management System

A full-stack authentication system that uses OpenCV (LBPH) for face recognition and PostgreSQL for user data management.

Tech Stack

Frontend:

React.js, Vite, React-Webcam

Backend (Manager):

Node.js, Express, PG (PostgreSQL driver)

AI Engine (Brain):

Python, FastAPI, OpenCV

Database:

PostgreSQL

Project Setup

Guide to Use

1. Clone the Project

git clone https://github.com/CS253-Mess-Management/Final-codes.git
cd Final-codes/

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Start Face Recognition Model (Backend - Python)

Make sure you are in the root directory of the project.

Install Dependencies

pip install fastapi uvicorn opencv-contrib-python numpy python-multipart pydantic

Run the Server

uvicorn main:app --port 8000 --reload

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Start Frontend

Make sure you are in the root directory of the project.

cd "Mess automation login page"

Install Dependencies

npm install

Run Frontend

npm run dev

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Start Backend (Node.js)

Make sure you are in the root directory of the project.

cd Mess_Management_Backend/

Install Dependencies

npm install

Create a .env file and the following :

PORT=5000
DB_URL=...
JWT_SECRET=...
EMAIL_USER=...
EMAIL_PASS=...

Add dummy data in data base

node src/seed.js 

Manadatory to get approve by manager after new regesteration of new students as this will add a default Manager

Use the following credentials to log in as the Manager:

email : manager@mess.com
password : abcd1234

NOTE :

• Newly registered students cannot log in immediately.
• A Manager must approve each student before they are allowed access to the system.

Run Backend

npm run dev

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Notes

• Ensure Python and Node.js are installed.

• Run all services in separate terminals.

• Default ports:

  • FastAPI: 8000
  • Frontend: 5173
  • Backend: 5000

STEP 1 Create datasabe:

Create a database named facereco and run the following SQL command to create the user table:

SQL Code:

CREATE TABLE students ( id SERIAL PRIMARY KEY, face_id INTEGER UNIQUE NOT NULL, name VARCHAR(100), email VARCHAR(100), last_login TIMESTAMP DEFAULT CURRENT_TIMESTAMP );

Step 2 Python Setup:

Download Requirments , create a virtual environment , start servers

Bash Commands

Run the commands inside project folder

1. python -venv venv
2. python\scipts\activate
3. pip install fastapi uvicorn opencv-contrib-python numpy Pilow
4. start python server: uvicorn main:app --reload --port 8000

Step 3 Backend Setup:

Open a new terminal in parallel

Bash Commands:

Run the commands inside project folder:

1. npm install express cors axios pg
2. start backend server:node server.js

Step 4 React Frontend:

Navigate to the frontend folder(cd frontend) inside the project folder, then run in a new parallel terminal

Bash commands:

1. npm install react-webcam react-router-dom
2. Start fontend server: npm run dev

Workflow

Capture:

React captures a base64 image and sends it to Node.js (Port 5000).

Recognition:

Node.js forwards the image to Python (Port 8000).

Branching Logic

Known Face:

If the AI recognizes you (e.g., ID 1), Node looks for you in PostgreSQL. If found, it logs you in. If not found in the DB (but known by AI), it auto-appends a record to sync them.

Unknown Face:

If the AI doesn't recognize you, Node triggers train_my_face.py locally to start a new training session and adds a new record to the Database.

Important Notes:

Camera Lock: The React webcam must be turned off when train_my_face.py starts, otherwise the Python script will crash (only one app can use the camera at a time).

Ports:

• React: 5173

• Node.js: 5000

• Python AI: 8000