Attention-Guided Deep GANs for Realistic Fingerprint Image Synthesis

Abstract

In biometric authentication systems, the availability of high-quality fingerprint images is critical for training, testing, and validating deep learning models. Traditional fingerprint datasets, however, often suffer from limitations such as class imbalance, privacy concerns, and scarcity in edge cases. This research proposes a novel method for synthesizing realistic fingerprint images using Attention-Guided Deep Generative Adversarial Networks (AGD-GANs). The model leverages an attention mechanism within the GAN architecture to focus on critical fingerprint features, such as ridge patterns and minutiae points, thus improving the fidelity and diversity of the synthesized samples. A thorough experimental analysis demonstrates that AGD-GAN outperforms baseline models in terms of visual realism, quality metrics, and downstream performance in biometric systems. The results are validated using the FVC2004 and LivDet datasets, and assessed via both quantitative metrics such as FID and SSIM, and qualitative human evaluation.