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Fingerprint matching by thin-plate spline modelling of elastic deformations
Open accessAbstract
This paper presents a novel minutiae matching method that describes elastic distortions in fingerprints by means of a thin-plate spline model, which is estimated using a local and a global matching stage. After registration of the fingerprints according to the estimated model, the number of matching minutiae can be counted using very tight matching thresholds. For deformed fingerprints, the algorithm gives considerably higher matching scores compared to rigid matching algorithms, while only taking 100ms on a 1GHz P-III machine. Furthermore, it is shown that the observed deformations are different from those described by theoretical models proposed in the literature.
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Citations
Cited by
Elastic Registration of Biological Images Using Vector-Spline Regularization
Sorzano, C. O. S., Thevenaz, P., & Unser, M. (2005) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2005.844030 · Cited by 323
Detection and Rectification of Distorted Fingerprints
Si, X., Feng, J., Zhou, J., & Luo, Y. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.2345403 · Cited by 79
Metadata
- Authors
- Bazen, A. M., & Gerez, S. H.
- Venue
- Pattern Recognition
- Publisher
- Elsevier BV
- Published
- 2003-08
- Volume
- 36
- Issue
- 8
- Pages
- 1859-1867
- ISSN
- 0031-3203 (ISSN-L)
- OpenAlex
- W2153717930
- Cited by
- 207
- References
- 0
- FWCI
- 15.93
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Bazen, A. M., & Gerez, S. H. (2003). Fingerprint matching by thin-plate spline modelling of elastic deformations. Pattern Recognition. https://doi.org/10.1016/s0031-3203(03)00036-0
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
