  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.
