Data Uncertainty Guided Noise-aware Preprocessing Of Fingerprints

TL;DR

This paper introduces a data uncertainty-based framework for fingerprint preprocessing that quantifies noise and identifies poor-quality regions, significantly improving robustness on noisy and distorted fingerprints.

Contribution

It presents a novel noise-aware preprocessing model that quantifies noise and adapts to it, enhancing fingerprint matching performance on low-quality images.

Findings

Effective noise quantification improves robustness on noisy fingerprints.

The noise variance map aids in understanding and diagnosing errors.

Demonstrated superior performance across 13 fingerprint datasets.

Abstract

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.