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|a 10.1109/TPAMI.2019.2949299
|2 doi
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|a pubmed24n1009.xml
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|a eng
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| 100 |
1 |
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|a Yin, Xuefei
|e verfasserin
|4 aut
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| 245 |
1 |
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|a 3D Fingerprint Recognition based on Ridge-Valley-Guided 3D Reconstruction and 3D Topology Polymer Feature Extraction
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|c 2021
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
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|2 rdamedia
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|a ƒa Online-Ressource
|b cr
|2 rdacarrier
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| 500 |
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|a Date Completed 29.09.2021
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|a Date Revised 29.09.2021
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a An automated fingerprint recognition system (AFRS) for 3D fingerprints is essential and highly promising for biometric security. Despite the progress in developing 3D AFRSs, achieving high-quality real-time reconstruction and high-accuracy recognition of 3D fingerprints remain two challenging issues. To address them, we propose a robust 3D AFRS based on ridge-valley (RV)-guided 3D fingerprint reconstruction and 3D topology polymer (TTP) feature extraction. The former considers the unique fingerprint characteristics of the RV and achieves real-time reconstruction. Unlike traditional triangulation-based methods that establish correspondences between points by cross-correlation-based searching, we propose to establish RV correspondences (RVCs) between ridges/valleys by defining and calculating a RVC matrix based on the topology of RV curves. To enhance depth reconstruction, curve-based smoothing is proposed to refine our novel RV disparity map. The TTP feature codes the 3D topology by projecting the 3D minutiae onto multiple planes and extracting their corresponding 2D topologies and has proven to be effective and efficient for 3D fingerprint recognition. Comprehensive experimental results demonstrate that our method outperforms the state-of-the-art methods in terms of both reconstruction and recognition accuracy. Also, due to its very short running time, it is appropriate for practical applications
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| 650 |
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|a Journal Article
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| 650 |
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|a Research Support, Non-U.S. Gov't
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| 700 |
1 |
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|a Zhu, Yanming
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Hu, Jiankun
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 43(2021), 3 vom: 29. März, Seite 1085-1091
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnns
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| 773 |
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|g volume:43
|g year:2021
|g number:3
|g day:29
|g month:03
|g pages:1085-1091
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|u http://dx.doi.org/10.1109/TPAMI.2019.2949299
|3 Volltext
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