FaceSecure-ATM-Pinless-Banking-System

FaceSecure ATM is a pinless banking system that uses face recognition for secure authentication. It integrates a webcam, Arduino-based RFID triggers, and a Streamlit web interface, allowing users to check balance, deposit, withdraw, and view transaction history while ensuring safety and convenience.

Features

• Face recognition authentication using OpenCV, face_recognition, and Mediapipe
• RFID-based ATM hardware integration via Arduino
• Web interface for balance inquiry, deposit, withdrawal, and transaction history
• Secure user data storage with SQLite database
• Real-time webcam verification with face mesh overlay
• Modular and extensible design for future upgrades

Components

Hardware

• Arduino Uno
• LCD Display and Keypad (optional)
• RFID Module and Reader

Software

• Python 3.12
• OpenCV, Mediapipe, face_recognition
• Streamlit for web interface
• SQLite for database management
• PySerial for Arduino communication

Setup and Installation

Hardware Setup

1. Connect Arduino, RFID module, and optional LCD/keypad.
2. Upload the Arduino code to your Arduino Uno.
3. Ensure correct COM port for serial communication.

Software Setup

1. Clone the repository:

   git clone 

2. Navigate to the project directory:

   cd FaceSecure-ATM

3. Install required Python packages:

   pip install -r requirements.txt

4. Initialize the database:

   python atm_db.py

5. Ensure face images are stored in the image_Folder.

Running the Project

1. Face Recognition & Hardware Integration

   python face_recognition.py

   • The system waits for RFID triggers from Arduino.
   • Recognized users can access the ATM functions.

2. Streamlit Web Interface

   streamlit run app.py

   • Log in with your username and password.
   • Access balance, deposit, withdrawal, and transaction history.

Project Structure

  FaceSecure-ATM/
│
├── app.py                 # Streamlit web interface
├── face_recognition.py    # Face recognition and Arduino integration
├── database.py            # SQLite database management
├── requirements.txt       # Python dependencies
├── image_Folder/          # Folder containing face images
└── ArduinoCode.ino        #

• the face recognition script (face_recognition.py) to authenticate users.
• Open the Streamlit web interface (app.py) to manage account functions like balance inquiry, deposits, withdrawals, and transaction history.
• Use the ATM interface securely without PINs, relying on facial verification.

Notes

• Ensure your webcam is connected and functional.
• Use the correct COM port in face_recognition.py for Arduino communication.
• For additional users, add face images in the image_Folder and update the database if needed.