Fingerprint Generation and Authentication using ADCGAN

This project leverages Adaptive Deep Convolutional Generative Adversarial Networks (ADCGAN) for generating and authenticating fingerprint images, crucial for applications like mobile security, biometrics, and airport systems.

Overview

Fingerprints are unique and widely used for secure authentication. This project employs deep learning with ADCGAN to generate realistic fingerprints and authenticate them with a high degree of accuracy, achieving 92% authentication accuracy.

Key Features

• Fingerprint Synthesis: Generates realistic fingerprints using ADCGAN.
• Fingerprint Authentication: Authenticates fingerprints generated with ADCGAN.
• High Accuracy: 92% accuracy on the Socofing fingerprint dataset.
• Application Areas: Useful for secure access in mobile, biometric systems, and more.

Getting Started

Prerequisites

• Python 3.x
• Required Libraries: tensorflow, keras, torch, opencv-python, numpy, matplotlib

Installation

1. Clone the repository:

   git clone https://github.com/username/Fingerprint-Generation-Authentication-ADCGAN.git

Results

Accuracy: The ADCGAN model achieved 92% accuracy on the test set. Generated Samples: Sample generated fingerprints can be found in the results/ directory.

Demostration

Ecommerce.Website.1.mp4

Applications

This project has potential applications in: Mobile Security: Fingerprint-based access control Biometric Systems: Secure identity verification Airport & Public Safety: Reliable biometric identification systems

Reference

For more details, refer to my full paper: Fingerprint Generation and Authentication using ADCGAN

Additionally, you can find more information here: Semantic Scholar