Abstract
Latent fingerprints are one of the most valuable and unique biometric attributes that are extensively used in forensic and law enforcement applications. Compared to rolled/plain fingerprint, latent fingerprint is of poor quality in term of friction ridge patterns, hence a more challenging for automatic fingerprint recognition systems. Considering the difficulties of dusting, lifting, and recovery of latent fingerprint, this type of fingerprints remain expensive to develop and collect. In this paper, we present a novel approach for synthetic latent fingerprint generation using Generative Adversarial Network (GAN). Our proposed framework, named mask to latent fingerprint (Mask2LFP), uses binary mask of distorted fingerprint-like shapes as input, and outputs a realistic latent fingerprint. This work focuses on the generation of synthetic latent fingerprints. The aim is to alleviate the scarcity issue of latent fingerprint data and serve the increasing need for developing, evaluating, and enhancing fingerprint-based identification systems, especially in forensic applications.
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