1 research outputs found
FDeblur-GAN: Fingerprint Deblurring using Generative Adversarial Network
While working with fingerprint images acquired from crime scenes, mobile
cameras, or low-quality sensors, it becomes difficult for automated
identification systems to verify the identity due to image blur and distortion.
We propose a fingerprint deblurring model FDeblur-GAN, based on the conditional
Generative Adversarial Networks (cGANs) and multi-stage framework of the stack
GAN. Additionally, we integrate two auxiliary sub-networks into the model for
the deblurring task. The first sub-network is a ridge extractor model. It is
added to generate ridge maps to ensure that fingerprint information and
minutiae are preserved in the deblurring process and prevent the model from
generating erroneous minutiae. The second sub-network is a verifier that helps
the generator to preserve the ID information during the generation process.
Using a database of blurred fingerprints and corresponding ridge maps, the deep
network learns to deblur from the input blurry samples. We evaluate the
proposed method in combination with two different fingerprint matching
algorithms. We achieved an accuracy of 95.18% on our fingerprint database for
the task of matching deblurred and ground truth fingerprints.Comment: 8 Pages, Accepted in IJCB Conferenc