Towards Fingerprint Mosaicking Artifact Detection: A Self-Supervised Deep Learning Approach
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915095674617856 |
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| author | Ruzicka, Laurenz Spenke, Alexander Bergmann, Stephan Nolden, Gerd Kohn, Bernhard Heitzinger, Clemens |
| author_facet | Ruzicka, Laurenz Spenke, Alexander Bergmann, Stephan Nolden, Gerd Kohn, Bernhard Heitzinger, Clemens |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_05034 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards Fingerprint Mosaicking Artifact Detection: A Self-Supervised Deep Learning Approach Ruzicka, Laurenz Spenke, Alexander Bergmann, Stephan Nolden, Gerd Kohn, Bernhard Heitzinger, Clemens Computer Vision and Pattern Recognition Machine Learning Image and Video Processing 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. |
| title | Towards Fingerprint Mosaicking Artifact Detection: A Self-Supervised Deep Learning Approach |
| topic | Computer Vision and Pattern Recognition Machine Learning Image and Video Processing |
| url | https://arxiv.org/abs/2501.05034 |