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

Laurenz Ruzicka1, Alexander Spenke2, Stephan Bergmann2

  • 1Department of Digital Safety and Security, Austrian Institute of Technology, 1210 Vienna, Austria.

Sensors (Basel, Switzerland)
|June 26, 2026
PubMed
Summary

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This summary is machine-generated.

This study introduces a deep learning method to detect and score fingerprint mosaicking errors, enhancing biometric system reliability. The self-supervised approach works on unlabeled data, improving image quality assessment.

Area of Science:

  • Biometrics
  • Computer Vision
  • Machine Learning

Background:

  • Fingerprint mosaicking combines multiple impressions but is prone to errors degrading image quality.
  • Accurate detection of these hard mosaicking artifacts is crucial for reliable biometric systems.

Purpose of the Study:

  • To develop a deep learning-based method for detecting and scoring hard mosaicking artifacts in fingerprint images.
  • To enable automated evaluation of fingerprint image quality at scale.

Main Methods:

  • A self-supervised learning framework was used to train a segmentation model on large-scale unlabeled fingerprint data.
  • The model was evaluated across various fingerprint modalities (contactless, rolled, pressed) and data sources.
  • A novel mosaicking artifact score was introduced to quantify error severity.
Keywords:
contactless fingerprintdetectionmosaicking artifacts

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Main Results:

  • The proposed model achieved high segmentation performance in identifying mosaicking errors.
  • The method demonstrated robustness across different fingerprint types and data sources.
  • The mosaicking artifact score enables scalable, automated quality assessment.

Conclusions:

  • The deep learning approach effectively addresses the challenge of reference-free hard artifact detection in fingerprint mosaicking.
  • This work contributes to improving the accuracy and reliability of fingerprint-based biometric systems.