  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.
