Finger-GAN: Generating Realistic Fingerprint Images Using Connectivity Imposed GAN

by   Shervin Minaee, et al.

Generating realistic biometric images has been an interesting and, at the same time, challenging problem. Classical statistical models fail to generate realistic-looking fingerprint images, as they are not powerful enough to capture the complicated texture representation in fingerprint images. In this work, we present a machine learning framework based on generative adversarial networks (GAN), which is able to generate fingerprint images sampled from a prior distribution (learned from a set of training images). We also add a suitable regularization term to the loss function, to impose the connectivity of generated fingerprint images. This is highly desirable for fingerprints, as the lines in each finger are usually connected. We apply this framework to two popular fingerprint databases, and generate images which look very realistic, and similar to the samples in those databases. Through experimental results, we show that the generated fingerprint images have a good diversity, and are able to capture different parts of the prior distribution. We also evaluate the Frechet Inception distance (FID) of our proposed model, and show that our model is able to achieve good quantitative performance in terms of this score.


page 1

page 3

page 4

page 5


Palm-GAN: Generating Realistic Palmprint Images Using Total-Variation Regularized GAN

Generating realistic palmprint (more generally biometric) images has alw...

Iris-GAN: Learning to Generate Realistic Iris Images Using Convolutional GAN

Generating iris images which look realistic is both an interesting and c...

SynCoLFinGer: Synthetic Contactless Fingerprint Generator

We present the first method for synthetic generation of contactless fing...

Comparative analysis of segmentation and generative models for fingerprint retrieval task

Biometric Authentication like Fingerprints has become an integral part o...

Reverse Engineering of Generative Models: Inferring Model Hyperparameters from Generated Images

State-of-the-art (SOTA) Generative Models (GMs) can synthesize photo-rea...

NaturalFinger: Generating Natural Fingerprint with Generative Adversarial Networks

Deep neural network (DNN) models have become a critical asset of the mod...

DeepMasterPrint: Generating Fingerprints for Presentation Attacks

We present two related methods for creating MasterPrints, synthetic fing...

Please sign up or login with your details

Forgot password? Click here to reset