Synthesis and Reconstruction of Fingerprints using Generative Adversarial Networks
This addresses privacy concerns in biometrics by enabling synthetic data generation for fingerprint recognition, though it is incremental as it builds on existing GAN architectures.
The paper tackled the need for large-scale fingerprint datasets by proposing a StyleGAN2-based framework for fingerprint synthesis and reconstruction, which outperformed state-of-the-art methods and improved realism, as demonstrated by spoofing verification systems.
Deep learning-based models have been shown to improve the accuracy of fingerprint recognition. While these algorithms show exceptional performance, they require large-scale fingerprint datasets for training and evaluation. In this work, we propose a novel fingerprint synthesis and reconstruction framework based on the StyleGan2 architecture, to address the privacy issues related to the acquisition of such large-scale datasets. We also derive a computational approach to modify the attributes of the generated fingerprint while preserving their identity. This allows synthesizing multiple different fingerprint images per finger. In particular, we introduce the SynFing synthetic fingerprints dataset consisting of 100K image pairs, each pair corresponding to the same identity. The proposed framework was experimentally shown to outperform contemporary state-of-the-art approaches for both fingerprint synthesis and reconstruction. It significantly improved the realism of the generated fingerprints, both visually and in terms of their ability to spoof fingerprint-based verification systems. The code and fingerprints dataset are publicly available: https://github.com/rafaelbou/fingerprint_generator.