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

Abstract

Generating realistic palmprint (more generally biometric) images has always been an interesting and, at the same time, challenging problem. Classical statistical models fail to generate realistic-looking palmprint images, as they are not powerful enough to capture the complicated texture representation of palmprint images. In this work, we present a deep learning framework based on generative adversarial networks (GAN), which is able to generate realistic palmprint images. To help the model learn more realistic images, we proposed to add a suitable regularization to the loss function, which imposes the line connectivity of generated palmprint images. This is very desirable for palmprints, as the principal lines in palm are usually connected. We apply this framework to a popular palmprint databases, and generate images which look very realistic, and similar to the samples in this database. Through experimental results, we show that the generated palmprint images look very realistic, have a good diversity, and are able to capture different parts of the prior distribution. We also report the Frechet Inception distance (FID) of the proposed model, and show that our model is able to achieve really good quantitative performance in terms of FID score.

arXiv commentarXiv admin note: substantial text overlap with arXiv:1812.10482, arXiv:1812.04822

References in corpus (8)

• Conditional Generative Adversarial Nets
• Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
• GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
• Finger-GAN: Generating Realistic Fingerprint Images Using Connectivity Imposed GAN
• DeepIris: Iris Recognition Using A Deep Learning Approach
• FingerNet: Pushing The Limits of Fingerprint Recognition Using Convolutional Neural Network
• Iris-GAN: Learning to Generate Realistic Iris Images Using Convolutional GAN
• Iris Recognition Using Scattering Transform and Textural Features