The effectiveness of fingerprint-based authentication systems on good quality
fingerprints is established long back. However, the performance of standard
fingerprint matching systems on noisy and poor quality fingerprints is far from
satisfactory. Towards this, we propose a data uncertainty-based framework which
enables the state-of-the-art fingerprint preprocessing models to quantify noise
present in the input image and identify fingerprint regions with background
noise and poor ridge clarity. Quantification of noise helps the model two
folds: firstly, it makes the objective function adaptive to the noise in a
particular input fingerprint and consequently, helps to achieve robust
performance on noisy and distorted fingerprint regions. Secondly, it provides a
noise variance map which indicates noisy pixels in the input fingerprint image.
The predicted noise variance map enables the end-users to understand erroneous
predictions due to noise present in the input image. Extensive experimental
evaluation on 13 publicly available fingerprint databases, across different
architectural choices and two fingerprint processing tasks demonstrate
effectiveness of the proposed framework.