Generating Realistic Fingerprint Biometrics with Attention-Guided Deep GANs

Abstract

Fingerprint biometrics have become an essential modality for identity verification systems due to their uniqueness, reliability, and permanence. However, the collection of large-scale, high-quality fingerprint datasets is hindered by privacy concerns, data imbalance, and acquisition noise. This has propelled research in synthetic fingerprint generation using generative models to augment existing datasets. In this study, we propose a novel method for generating high-fidelity synthetic fingerprint images using Attention-Guided Deep Generative Adversarial Networks (AG-DGANs). Our model integrates self-attention mechanisms into both the generator and discriminator architectures, allowing the system to capture long-range dependencies and intricate ridge details inherent in fingerprint patterns. We demonstrate that this approach significantly improves the visual realism, diversity, and discriminative value of generated fingerprints when compared to traditional GAN and convolutional GAN methods. Extensive experimental results validate the superior quality and utility of our synthetic data in biometric system training and evaluation.