Fingerprint Matching System

This repository contains a collection of deep learning models for fingerprint matching, along with a simple web application for deployment and demonstration.

Table of Contents

• Overview
• Features
• Project Structure
• Models Implemented
• Setup and Installation
• Running the Application
• License

Overview

The project focuses on developing and evaluating various deep learning architectures for accurate fingerprint identification and verification. It includes implementations of several attention mechanisms and established CNN backbones.

Features

• Multiple fingerprint matching models:
  • VGG-based
  • SENet (Squeeze-and-Excitation Networks)
  • CBAM (Convolutional Block Attention Module)
  • Self-Attention
  • Dual-Attention
• Pre-trained models:
  • MobileNet + Self-Attention
  • MobileNet + SENet
• A web application (app.py) for demonstrating model predictions.
• Supporting documentation (Report and Slides).

Project Structure

.
├── LICENSE
├── README.md
├── deploy/
│   ├── app.py                           # Flask application for deployment
│   ├── mobilenet_plus+Self-Attention.h5 # Trained MobileNet + Self-Attention model
│   ├── mobilenet_plus+Snet.h5           # Trained MobileNet + SENet model
│   └── requirements.txt                 # Python dependencies for deployment
├── docs/
│   ├── Report.pdf                       # Project report
│   └── Slide.pdf                        # Project presentation slides
└── models/
    ├── fingerprint-cbam.py              # Fingerprint model with CBAM
    ├── fingerprint-dual.py              # Fingerprint model with Dual-Attention
    ├── fingerprint-self.py              # Fingerprint model with Self-Attention
    ├── fingerprint-senet.py             # Fingerprint model with SENet
    └── vgg-fingerprint.py               # VGG-based fingerprint model

Models Implemented

The models/ directory contains the Python scripts for the following architectures:

• vgg-fingerprint.py: A fingerprint recognition model based on the VGG architecture.
• fingerprint-senet.py: Implements Squeeze-and-Excitation Networks to improve channel interdependencies.
• fingerprint-cbam.py: Integrates the Convolutional Block Attention Module, which applies both spatial and channel-wise attention.
• fingerprint-self.py: Utilizes Self-Attention mechanisms to capture global dependencies within the fingerprint images.
• fingerprint-dual.py: Implements a Dual-Attention mechanism.

The deploy/ directory contains pre-trained model weights:

• mobilenet_plus+Self-Attention.h5: A MobileNet-based architecture enhanced with a Self-Attention layer.
• mobilenet_plus+Snet.h5: A MobileNet-based architecture enhanced with an SENet layer. (Note: "Snet" likely refers to SENet).

Setup and Installation

To set up the project and run the deployment application, follow these steps:

1. Clone the repository:

   git clone <repository-url>
   cd <repository-folder-name>

   (Replace <repository-url> and <repository-folder-name> accordingly)

2. Navigate to the deployment directory:

   cd deploy

3. Install dependencies: It's recommended to use a virtual environment.

   python -m venv venv
   # Activate the virtual environment
   # On Windows (PowerShell):
   # .\venv\Scripts\Activate.ps1
   # On macOS/Linux:
   # source venv/bin/activate
   
   pip install -r requirements.txt

Running the Application

The app.py in the deploy directory is a web application (likely Flask or Streamlit) to test the trained models.

1. Ensure you are in the deploy directory and have installed the requirements.
2. Run the application:

   python app.py

3. Open your web browser and navigate to the URL provided by the application (e.g., http://127.0.0.1:5000 or http://localhost:8501).

License

This project is licensed under the terms of the LICENSE file. Please see the LICENSE file for more details.