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Fingerprint singularity and core point detection

Fingerprint has been widely applied as a personal identification. Due to reliability and uniqueness features. In fingerprint, there are two kinds of features: the global feature and local feature. The global feature includes the ridge orientation map, core and delta locations, while the local f...

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Main Authors: Ahmad, Fadzilah, Mohamad, Dzulkifli
Format: Book Section
Published: Penerbit UTM 2007
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://eprints.utm.my/id/eprint/13468/
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spelling my.utm.134682011-08-15T05:30:10Z http://eprints.utm.my/id/eprint/13468/ Fingerprint singularity and core point detection Ahmad, Fadzilah Mohamad, Dzulkifli QA75 Electronic computers. Computer science Fingerprint has been widely applied as a personal identification. Due to reliability and uniqueness features. In fingerprint, there are two kinds of features: the global feature and local feature. The global feature includes the ridge orientation map, core and delta locations, while the local feature form by minutiae points. Singular points are the most important global features that contain the significant global information which play an important role in fingerprint pattern classification (Hong and Jain, 1999) and fingerprint matching (Sharath et al., 2000). One of the features of fingerprint identification and verification is singularity (see Figure 1.0). The accuracy of singularity extraction basically depends on the quality of images. Therefore, in order to improve the identification and verification process, we need to enhance the fingerprint image. The poor quality of fingerprint image makes efficient singularity extraction algorithm degrades rapidly and we cannot identify the singular points area efficiently. A majority of techniques that used to enhance the fingerprint images are based on the use of contextual filters whose parameters depend on the local ridge frequency and orientation. The filters themselves may be spatial (Gorman and Nickerson, 1989; Jain et al. 1998) or based on Fourier domain analysis (Sherlock et al. 1994; Watson et al. 1994). Penerbit UTM 2007 Book Section PeerReviewed Ahmad, Fadzilah and Mohamad, Dzulkifli (2007) Fingerprint singularity and core point detection. In: Advances in Image Processing and Pattern Recognition: Algorithms & Practice. Penerbit UTM , Johor, pp. 195-210. ISBN 978-983-52-0621-4
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Ahmad, Fadzilah
Mohamad, Dzulkifli
Fingerprint singularity and core point detection
description Fingerprint has been widely applied as a personal identification. Due to reliability and uniqueness features. In fingerprint, there are two kinds of features: the global feature and local feature. The global feature includes the ridge orientation map, core and delta locations, while the local feature form by minutiae points. Singular points are the most important global features that contain the significant global information which play an important role in fingerprint pattern classification (Hong and Jain, 1999) and fingerprint matching (Sharath et al., 2000). One of the features of fingerprint identification and verification is singularity (see Figure 1.0). The accuracy of singularity extraction basically depends on the quality of images. Therefore, in order to improve the identification and verification process, we need to enhance the fingerprint image. The poor quality of fingerprint image makes efficient singularity extraction algorithm degrades rapidly and we cannot identify the singular points area efficiently. A majority of techniques that used to enhance the fingerprint images are based on the use of contextual filters whose parameters depend on the local ridge frequency and orientation. The filters themselves may be spatial (Gorman and Nickerson, 1989; Jain et al. 1998) or based on Fourier domain analysis (Sherlock et al. 1994; Watson et al. 1994).
format Book Section
author Ahmad, Fadzilah
Mohamad, Dzulkifli
author_facet Ahmad, Fadzilah
Mohamad, Dzulkifli
author_sort Ahmad, Fadzilah
title Fingerprint singularity and core point detection
title_short Fingerprint singularity and core point detection
title_full Fingerprint singularity and core point detection
title_fullStr Fingerprint singularity and core point detection
title_full_unstemmed Fingerprint singularity and core point detection
title_sort fingerprint singularity and core point detection
publisher Penerbit UTM
publishDate 2007
url http://eprints.utm.my/id/eprint/13468/
_version_ 1643646197176467456
score 13.211869

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