DiffFinger: Advancing Synthetic Fingerprint Generation through Denoising Diffusion Probabilistic Models

TL;DR

This paper introduces DiffFinger, a novel method using Denoising Diffusion Probabilistic Models to generate realistic and diverse synthetic fingerprint images, addressing privacy and data scarcity issues in biometric datasets.

Contribution

The study demonstrates that DDPMs can produce high-quality, diverse fingerprint images that rival real data, offering a promising alternative to GAN-based synthetic biometric generation.

Findings

DiffFinger produces fingerprint images with quality comparable to real data.

Synthetic fingerprints exhibit true-to-life variability and diversity.

DDPMs outperform GANs in generating realistic biometric data.

Abstract

This study explores the generation of synthesized fingerprint images using Denoising Diffusion Probabilistic Models (DDPMs). The significant obstacles in collecting real biometric data, such as privacy concerns and the demand for diverse datasets, underscore the imperative for synthetic biometric alternatives that are both realistic and varied. Despite the strides made with Generative Adversarial Networks (GANs) in producing realistic fingerprint images, their limitations prompt us to propose DDPMs as a promising alternative. DDPMs are capable of generating images with increasing clarity and realism while maintaining diversity. Our results reveal that DiffFinger not only competes with authentic training set data in quality but also provides a richer set of biometric data, reflecting true-to-life variability. These findings mark a promising stride in biometric synthesis, showcasing the potential of DDPMs to advance the landscape of fingerprint identification and authentication systems.