Data Uncertainty Guided Noise-aware Preprocessing Of Fingerprints

by   Indu Joshi, et al.

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.



There are no comments yet.


page 1

page 3

page 5

page 6

page 7


Two-stage quality adaptive fingerprint image enhancement using Fuzzy c-means clustering based fingerprint quality analysis

Fingerprint recognition techniques are immensely dependent on quality of...

A Collaborative Approach using Ridge-Valley Minutiae for More Accurate Contactless Fingerprint Identification

Contactless fingerprint identification has emerged as an reliable and us...

Robust Wireless Fingerprinting via Complex-Valued Neural Networks

A "wireless fingerprint" which exploits hardware imperfections unique to...

Sensor-invariant Fingerprint ROI Segmentation Using Recurrent Adversarial Learning

A fingerprint region of interest (roi) segmentation algorithm is designe...

An Efficient Method for Recognizing the Low Quality Fingerprint Verification by Means of Cross Correlation

In this paper, we propose an efficient method to provide personal identi...

Fingerprint Distortion Rectification using Deep Convolutional Neural Networks

Elastic distortion of fingerprints has a negative effect on the performa...

Unsupervised Data Uncertainty Learning in Visual Retrieval Systems

We introduce an unsupervised formulation to estimate heteroscedastic unc...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.