Banking Management Desktop Application

A desktop banking system with AI facial recognition for secure transactions.
This project was developed as an academic assignment to practice Fullstack Development with Python and Tkinter.

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📌 Project Information

• Team size: 4
• Role: Fullstack Developer
• Duration: Feb 2025 – May 2025
• Languages/Frameworks: Python, Tkinter

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📌 Overview

The system is designed to manage banking operations including customer accounts, transactions, and services.
It integrates AI facial recognition to enhance security for high-value transactions.

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📌 Features

• Authentication (login/register)
• Main Bank Interface for account management
• Transaction History tracking
• Money Transfer functionality
• Settings and Other Services
• AI Facial Recognition for transactions ≥ 10M VND

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📌 System Design

• Database schema in Microsoft SQL Server
• Integrated cv2 Camera and Haarcascade for face detection
• Built Training Pipeline with real face dataset, monitoring overfitting
• Implemented secure transaction flow with facial recognition

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📌 AI Training Pipeline

The project integrates an AI facial recognition system to secure high-value transactions. The training pipeline was designed to process real facial datasets and includes the following steps:

1. Data Collection:

• Gathered a dataset of real face images.
• Organized into training and validation sets.

2. Preprocessing:

• Applied image resizing and normalization.
• Used Haarcascade for face detection.
• Augmented data to increase variability.

3. Model Training:

• Built a recognition model using OpenCV (cv2).
• Trained with supervised learning on facial features.
• Monitored training and validation accuracy.

4. Overfitting Monitoring:

• Compared training vs validation performance.
• Identified signs of overfitting (high training accuracy but lower validation accuracy).
• Adjusted dataset size and preprocessing to mitigate.

5. Integration:

• Connected the trained model with the Tkinter banking interface.
• Facial recognition triggered for transactions ≥ 10M VND.
• Ensured secure flow before transaction approval.

📌 How to Run

1. Clone the repository:

   git clone https://github.com/DuyKhoiCoder30062004/Python-PyGroup3.git

2. Install required dependencies: pip install opencv-python pip install pyodbc
3. Configure the SQL Server connection string.
4. Run the application: python main.py

📌 Screenshots

📌 Contribution

Developed by a team of 4 students as part of academic coursework.

📌 License

This project is for educational purposes only.