Computer Science > Computer Vision and Pattern Recognition
arXiv:2311.15163 (cs)
[Submitted on 26 Nov 2023]
Title:Deep Learning-Based Approaches for Contactless Fingerprints Segmentation and Extraction
View a PDF of the paper titled Deep Learning-Based Approaches for Contactless Fingerprints Segmentation and Extraction, by M.G. Sarwar Murshed and 3 other authors
View PDFAbstract:Fingerprints are widely recognized as one of the most unique and reliable characteristics of human identity. Most modern fingerprint authentication systems rely on contact-based fingerprints, which require the use of fingerprint scanners or fingerprint sensors for capturing fingerprints during the authentication process. Various types of fingerprint sensors, such as optical, capacitive, and ultrasonic sensors, employ distinct techniques to gather and analyze fingerprint data. This dependency on specific hardware or sensors creates a barrier or challenge for the broader adoption of fingerprint based biometric systems. This limitation hinders the widespread adoption of fingerprint authentication in various applications and scenarios. Border control, healthcare systems, educational institutions, financial transactions, and airport security face challenges when fingerprint sensors are not universally available. To mitigate the dependence on additional hardware, the use of contactless fingerprints has emerged as an alternative. Developing precise fingerprint segmentation methods, accurate fingerprint extraction tools, and reliable fingerprint matchers are crucial for the successful implementation of a robust contactless fingerprint authentication system. This paper focuses on the development of a deep learning-based segmentation tool for contactless fingerprint localization and segmentation. Our system leverages deep learning techniques to achieve high segmentation accuracy and reliable extraction of fingerprints from contactless fingerprint images. In our evaluation, our segmentation method demonstrated an average mean absolute error (MAE) of 30 pixels, an error in angle prediction (EAP) of 5.92 degrees, and a labeling accuracy of 97.46%. These results demonstrate the effectiveness of our novel contactless fingerprint segmentation and extraction tools.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2311.15163 [cs.CV] |
| (orarXiv:2311.15163v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2311.15163 arXiv-issued DOI via DataCite |
Submission history
From: Syed Konain Abbas [view email][v1] Sun, 26 Nov 2023 01:56:10 UTC (6,504 KB)
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View a PDF of the paper titled Deep Learning-Based Approaches for Contactless Fingerprints Segmentation and Extraction, by M.G. Sarwar Murshed and 3 other authors
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