Synthetic Latent Fingerprint Generation Using Style Transfer
This addresses data scarcity for training neural networks in forensic fingerprint analysis, though it appears incremental as it builds on existing style transfer techniques.
The paper tackles the problem of limited training data for latent fingerprint recognition by proposing a style transfer and image blending method to generate realistic synthetic latent fingerprints, demonstrating that the generated samples preserve identity information while matching characteristics of real latent fingerprints.
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.