  Performing recognition tasks using latent fingerprint samples is often
challenging for automated identification systems due to poor quality,
distortion, and partially missing information from the input samples. We
propose a direct latent fingerprint reconstruction model based on conditional
generative adversarial networks (cGANs). Two modifications are applied to the
cGAN to adapt it for the task of latent fingerprint reconstruction. First, the
model is forced to generate three additional maps to the ridge map to ensure
that the orientation and frequency information is considered in the generation
process, and prevent the model from filling large missing areas and generating
erroneous minutiae. Second, a perceptual ID preservation approach is developed
to force the generator to preserve the ID information during the reconstruction
process. Using a synthetically generated database of latent fingerprints, the
deep network learns to predict missing information from the input latent
samples. We evaluate the proposed method in combination with two different
fingerprint matching algorithms on several publicly available latent
fingerprint datasets. We achieved the rank-10 accuracy of 88.02\% on the
IIIT-Delhi latent fingerprint database for the task of latent-to-latent
matching and rank-50 accuracy of 70.89\% on the IIIT-Delhi MOLF database for
the task of latent-to-sensor matching. Experimental results of matching
reconstructed samples in both latent-to-sensor and latent-to-latent frameworks
indicate that the proposed method significantly increases the matching accuracy
of the fingerprint recognition systems for the latent samples.
