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Systematic methods for the computation of the directional fields and singular points of fingerprints

Bazen, A.M., Gerez, S.H.·2002·IEEE Transactions on Pattern Analysis and Machine Intelligence

Abstract

The first subject of the paper is the estimation of a high resolution directional field of fingerprints. Traditional methods are discussed and a method, based on principal component analysis, is proposed. The method not only computes the direction in any pixel location, but its coherence as well. It is proven that this method provides exactly the same results as the "averaged square-gradient method" that is known from literature. Undoubtedly, the existence of a completely different equivalent solution increases the insight into the problem's nature. The second subject of the paper is singular point detection. A very efficient algorithm is proposed that extracts singular points from the high-resolution directional field. The algorithm is based on the Poincare index and provides a consistent binary decision that is not based on postprocessing steps like applying a threshold on a continuous resemblance measure for singular points. Furthermore, a method is presented to estimate the orientation of the extracted singular points. The accuracy of the methods is illustrated by experiments on a live-scanned fingerprint database.

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Metadata

Authors
Bazen, A. M., & Gerez, S. H.
Venue
IEEE Transactions on Pattern Analysis and Machine Intelligence
Published
2002-07
Volume
24
Issue
7
Pages
905-919
ISSN
0162-8828 (ISSN-L)
OpenAlex
W2144127923
Cited by
601
References
14
FWCI
40.72
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Systematic methods for the computation of the directional fields and singular points of fingerprints. https://app.cochat.ai/papers/10.1109/tpami.2002.1017618
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Bazen, A. M., & Gerez, S. H. (2002). Systematic methods for the computation of the directional fields and singular points of fingerprints. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/tpami.2002.1017618

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

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