The Quantum-Assisted Fingerprint Biometrics: a Novel Approach To Fast And Accurate Feature Extraction And Synthetic Generation

Abstract

This research explores the integration of artificial intelligence (AI) and quantum computing to enhance fingerprint biometrics through improved feature extraction and synthetic fingerprint generation. Traditional fingerprint biometrics face challenges related to processing speed and scalability, particularly when managing large datasets or creating synthetic fingerprints for testing and training purposes. We propose a dual approach: using convolutional neural networks (CNNs) to extract distinctive fingerprint features—such as loops, whorls, and minutiae points—and employing generative adversarial networks (GANs) for the synthesis of high-quality fingerprint images that preserve realistic patterns and variations. To address computational limitations in processing these data-intensive tasks, we explore the use of quantum computing algorithms. Specifically, we implement a hybrid quantum-classical model, using quantum support vector machines (QSVM) for feature classification and quantum-enhanced GANs (QGAN) to speed up synthetic fingerprint generation. Preliminary results indicate that quantum-assisted models demonstrate promising efficiency gains in both feature extraction and image synthesis, potentially enabling faster processing and improved scalability compared to classical models alone.This study contributes to biometric security by providing a framework for faster, more accurate fingerprint biometrics using cutting-edge AI and quantum methodologies. The findings hold potential applications in security systems, law enforcement, and digital identity management, where real-time analysis and synthetic data generation can strengthen verification and identification processes. Future work includes optimizing quantum components for larger datasets and further refining AI models to improve the realism of generated fingerprint images.