Towards Fingerprint Mosaicking Artifact Detection: A Self-Supervised Deep Learning Approach

TL;DR

This paper presents a self-supervised deep learning method for detecting and scoring fingerprint mosaicking artifacts, improving accuracy and robustness without requiring manual annotations across various fingerprint modalities.

Contribution

It introduces a novel self-supervised framework for artifact detection and a scoring system, enhancing fingerprint image quality assessment without manual labeling.

Findings

High accuracy in detecting mosaicking artifacts across modalities

Robust performance on diverse data sources

Effective quantification of artifact severity

Abstract

Fingerprint mosaicking, which is the process of combining multiple fingerprint images into a single master fingerprint, is an essential process in modern biometric systems. However, it is prone to errors that can significantly degrade fingerprint image quality. This paper proposes a novel deep learning-based approach to detect and score mosaicking artifacts in fingerprint images. Our method leverages a self-supervised learning framework to train a model on large-scale unlabeled fingerprint data, eliminating the need for manual artifact annotation. The proposed model effectively identifies mosaicking errors, achieving high accuracy on various fingerprint modalities, including contactless, rolled, and pressed fingerprints and furthermore proves to be robust to different data sources. Additionally, we introduce a novel mosaicking artifact score to quantify the severity of errors, enabling automated evaluation of fingerprint images. By addressing the challenges of mosaicking artifact detection, our work contributes to improving the accuracy and reliability of fingerprint-based biometric systems.