Wyniki wyszukiwania - BazTech

1

An improved algorithm for feature extraction from a fingerprint fuzzy image

Surmacz K., Saeed K., Rapta P.

Optica Applicata

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2013

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Vol. 43, nr 3

515--527

EN

Proper fingerprint feature extraction is crucial in fingerprint-matching algorithms. For good results, different pieces of information about a fingerprint image, such as ridge orientation and frequency, must be considered. It is often necessary to improve the quality of a fingerprint image in order for the feature extraction process to work correctly. In this paper we present a complete (fully implemented) improved algorithm for fingerprint feature extraction, based on numerous papers on this topic. The paper describes a fingerprint recognition system consisting of image preprocessing, filtration, feature extraction and matching for recognition. The image preprocessing includes normalization based on mean value and variation. The orientation field is extracted and Gabor filter is used to prepare the fingerprint image for further processing. For singular point detection, the Poincaré index with a partitioning method is used. The ridgeline thinning is presented and so is the minutia extraction by CN algorithm. The paper contains the comparison of obtained results to the other algorithms.

2

A new algorithm for fingerprint feature extraction without the necessity to improve its image

Rapta P., Saeed K.

Bio-Algorithms and Med-Systems

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2010

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Vol. 6, no. 12

25--29

EN

Based on Gabor filter, an algorithm is worked out and presented in this work. The algorithm uses ridge endings and ridge bifurcation to represent a fingerprint image. The experimental results have proven the algorithm completion in preparing the fingerprint image for simple classification and hence high success rate of recognition. Spurious features from detected set of minutiae are deleted by a postprocessing stage. The detected features are observed to be reliable and accurate. The algorithm was implemented in Matlab and therefore it is under steady modification and improvement as each step can be easily visualized graphically to check for further analysis. The best feature of the algorithm is the unnecessity for noise removal, brightness or contrast improvement, normalization or even histogram equalization.

3

Combined fingerprints recognition method basing on image filtering, minutiae analysis and reference point location

Porwik P.

Elektronika : konstrukcje, technologie, zastosowania

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2007

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Vol. 48, nr 4

31-39

EN

The method of fingerprint recognition is proposed. If such method the reference point is determined, appropriate bank of filters and minutiae-based representation has been used. That capturing fingerprints approach gives good results of fingerprint recognition. Proposed analysis of finger imprint allows to compute a unique fingerprint code. The proposed method by means of the NIST database was tested where different quality fingerprints are stored. Some elements of the method with other approaches was also compared.

PL

Przedstawiono metodę rozpoznawania linii papilarnych na podstawie wyznaczonego punktu referencyjnego, filtrowania obrazów oraz minucji. Otrzymane wyniki wykazują dobrą jakość rozpoznawania linii papilarnych. W wyniku odpowiedniej analizy można także otrzymać unikatowy kod linii papilarnych. Metoda była testowana za pomocą obrazów linii papilarnych zgromadzonych w bazie NIST. Niektóre jej elementy zostały porównane z innymi metodami.

4

Fingerprint identification using minutiae point pattern matching

Stąpor K.

Archiwum Informatyki Teoretycznej i Stosowanej

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2002

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T. 14, z. 1

17-31

EN

The 2-level fingerprint identification method is presented. The method starts with the extraction of structural features (minutiae points) which is based on the computed ridge orientation flow, and is composed of: division of an input image into blocks, computing directional image, smoothing directions, foreground/backround segmentation, directional filtering, binarization, thinning, minutiae detection and postprocessing. Classification (the 1-st level) is based on the number and locations of singular points (corel/delta points), witch are found based on Poincare index.At the 2-nd level, the matching between two sets of the minutiae points is performed: an unknown pattern, and those in the database.

PL

W artykule przedstawiono 2-stopniową metodę identyfikacji linii papilarnych. Zaproponowano nową metodę ekstrakcji cech (tzw. minutiae points), czyli punktów rozgałęzień linii oraz punktów końcowych, opartą na orientacji w blokach, wygładzanie kierunków w blokach, segmentacja, kierunkowa filtracja, binaryzacja, ścienianie, detekcja punktów charakterystycznych, przetwarzanie końcowe. Klasyfikacja (1-szy stopień) wykorzystuje liczbę oraz położenie tzw. punktów core i delta, które wyznacza się na podstawie indeksu Poincare. Identyfikacja na 2-gim stopniu polega na porównaniu dwóch zbiorów punktów charakterystycznych: nieznanej próbki oraz próbki wzorcowej.