Data Uncertainty Guided Noise-aware Preprocessing Of Fingerprints
This work addresses the challenge of reliable fingerprint authentication in noisy conditions, which is crucial for security applications, though it appears incremental as it builds on existing preprocessing models.
The authors tackled the problem of poor performance of fingerprint matching systems on noisy and poor-quality fingerprints by proposing a data uncertainty-based framework that quantifies noise and identifies problematic regions, achieving robust performance across 13 publicly available databases and two fingerprint processing tasks.
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.