Abstract
Generally, image quality has a significant effect on gray-level features. Structural features, such as Core and Delta (Figure 1.5), can only be used to classify fingerprints into four or five classes. The most commonly used features in fingerprint recognition are minutiae (Figure 1.5). This representation reduces fingerprint recognition problem to a point matching problem. The key problem with this representation is that there is no reliable minutiae-based feature extraction algorithm. Figure 2.1 shows a block diagram of a minutiae-based feature extraction procedure, which is widely used in most fingerprint recognition systems.
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© 2004 Springer Science+Business Media New York
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Bhanu, B., Tan, X. (2004). Learned Templates for Minutiae Extraction. In: Computational Algorithms for Fingerprint Recognition. Kluwer International Series on Biometrics, vol 1. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-0491-7_2
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DOI: https://doi.org/10.1007/978-1-4615-0491-7_2
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4613-5103-0
Online ISBN: 978-1-4615-0491-7
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