  Fingerprint is a common biometric used for authentication and verification of
an individual. These images are degraded when fingers are wet, dirty, dry or
wounded and due to the failure of the sensors, etc. The extraction of the
fingerprint from a degraded image requires denoising and inpainting. We propose
to address these problems with an end-to-end trainable Convolutional Neural
Network based architecture called FPD-M-net, by posing the fingerprint
denoising and inpainting problem as a segmentation (foreground) task. Our
architecture is based on the M-net with a change: structure similarity loss
function, used for better extraction of the fingerprint from the noisy
background. Our method outperforms the baseline method and achieves an overall
3rd rank in the Chalearn LAP Inpainting Competition Track 3 - Fingerprint
Denoising and Inpainting, ECCV 2018
