Synthetic Latent Fingerprint Generation Using Style Transfer
Amol S. Joshi Ali Dabouei Nasser Nasrabadi Jeremy Dawson
Limited data availability is a challenging problem in the latent fingerprint
domain. Synthetically generated fingerprints are vital for training data-hungry
neural network-based algorithms. Conventional methods distort clean
fingerprints to generate synthetic latent fingerprints. We propose a simple and
effective approach using style transfer and image blending to synthesize
realistic latent fingerprints. Our evaluation criteria and experiments
demonstrate that the generated synthetic latent fingerprints preserve the
identity information from the input contact-based fingerprints while possessing
similar characteristics as real latent fingerprints. Additionally, we show that
the generated fingerprints exhibit several qualities and styles, suggesting
that the proposed method can generate multiple samples from a single
fingerprint.