  Biometric Authentication like Fingerprints has become an integral part of the
modern technology for authentication and verification of users. It is pervasive
in more ways than most of us are aware of. However, these fingerprint images
deteriorate in quality if the fingers are dirty, wet, injured or when sensors
malfunction. Therefore, extricating the original fingerprint by removing the
noise and inpainting it to restructure the image is crucial for its
authentication. Hence, this paper proposes a deep learning approach to address
these issues using Generative (GAN) and Segmentation models. Qualitative and
Quantitative comparison has been done between pix2pixGAN and cycleGAN
(generative models) as well as U-net (segmentation model). To train the model,
we created our own dataset NFD - Noisy Fingerprint Dataset meticulously with
different backgrounds along with scratches in some images to make it more
realistic and robust. In our research, the u-net model performed better than
the GAN networks
