Abstract
In this paper, we represent the fingerprint with a novel local feature descriptor, which is composed of minutia, the sample points on associated ridge and the adjacent orientation distribution. Then a novel fingerprint recognition method is proposed combining the orientation field and the local feature descriptor. We compare two descriptor lists from the input and template fingerprints to calculate a set of transformation vectors for fingerprint alignment. The similarity score is evaluated by fusing the orientation field and the local feature descriptor. The experiments have been conducted on three large-scale databases. The comparison results approve that our algorithm is more accurate and robust than previous methods based on the minutiae or ridge features, especially for those poor-quality and partial fingerprints.
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Zhang, Y., Yang, X., Su, Q., Tian, J. (2007). Fingerprint Recognition Based on Combined Features. In: Lee, SW., Li, S.Z. (eds) Advances in Biometrics. ICB 2007. Lecture Notes in Computer Science, vol 4642. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74549-5_30
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DOI: https://doi.org/10.1007/978-3-540-74549-5_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74548-8
Online ISBN: 978-3-540-74549-5
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