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An automatic fingerprint classification technique based on singular points and structure shape of orientation fields

Generally, an automatic fingerprint classification system aims to classify the fingerprints into several categories based on global features such as ridge structure and singular points. Its process basically covers: segmentation, enhancement, orientation field estimation, singular point detection, a...

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Main Author: Saparudin, Saparudin
Format: Thesis
Language:English
Published: 2012
Subjects:
QA Mathematics
Online Access:http://eprints.utm.my/id/eprint/30643/5/SaparudinPFSKSM2012.pdf
http://eprints.utm.my/id/eprint/30643/
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spelling my.utm.306432017-09-29T07:42:49Z http://eprints.utm.my/id/eprint/30643/ An automatic fingerprint classification technique based on singular points and structure shape of orientation fields Saparudin, Saparudin QA Mathematics Generally, an automatic fingerprint classification system aims to classify the fingerprints into several categories based on global features such as ridge structure and singular points. Its process basically covers: segmentation, enhancement, orientation field estimation, singular point detection, and classification. However, its performance is heavily relied on image quality that comes in various forms such as low contrast, wet, dry, bruise, cuts, stains, etc. Although a great effort has been made by previous studies to come out with various methods, their performances especially in terms of accuracy are fallen short, and room for improvements is still wide open. Thus, this thesis proposes an automatic fingerprint classification scheme based on singular points and structural shape of orientation fields. This method begins with foreground extractions using a composite method which combines local mean values of the grey-levels with local variances of the gradient magnitudes. Then, noise patches in the foreground are detected using coherence, and are enhanced using minimum variance of gradient magnitude. Next, Least Mean Square algorithm is applied to estimate the orientation fields, and a corrective procedure is performed on the false ones using minimum variance of the orientation fields. Later, an orientation image is created, and then partitioned into several distinct regions of homogenous orientation fields. The convergence point of these regions implicitly reveals an area that most likely contains a singular point. Subsequently, core and delta in this localized area are then detected using the Poincaré index. Finally, based on the number of core and delta and their locations, an exclusive membership of the fingerprint can be ascertained. Should it fail, the structure shape of the orientation fields neighbouring the core or delta is analysed. The performance of the proposed method is evaluated and tested using 27,000 fingerprints of NIST Special Database 14, which is considered de facto standard dataset for development and testing of fingerprint classification systems. The results obtained are very encouraging with accuracy rate of 89.31% that markedly outperformed the latest work of the renowned researchers. 2012-07 Thesis NonPeerReviewed application/pdf en http://eprints.utm.my/id/eprint/30643/5/SaparudinPFSKSM2012.pdf Saparudin, Saparudin (2012) An automatic fingerprint classification technique based on singular points and structure shape of orientation fields. PhD thesis, Universiti Teknologi Malaysia, Faculty of Civil Engineering.
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/
language English
topic QA Mathematics
spellingShingle QA Mathematics
Saparudin, Saparudin
An automatic fingerprint classification technique based on singular points and structure shape of orientation fields
description Generally, an automatic fingerprint classification system aims to classify the fingerprints into several categories based on global features such as ridge structure and singular points. Its process basically covers: segmentation, enhancement, orientation field estimation, singular point detection, and classification. However, its performance is heavily relied on image quality that comes in various forms such as low contrast, wet, dry, bruise, cuts, stains, etc. Although a great effort has been made by previous studies to come out with various methods, their performances especially in terms of accuracy are fallen short, and room for improvements is still wide open. Thus, this thesis proposes an automatic fingerprint classification scheme based on singular points and structural shape of orientation fields. This method begins with foreground extractions using a composite method which combines local mean values of the grey-levels with local variances of the gradient magnitudes. Then, noise patches in the foreground are detected using coherence, and are enhanced using minimum variance of gradient magnitude. Next, Least Mean Square algorithm is applied to estimate the orientation fields, and a corrective procedure is performed on the false ones using minimum variance of the orientation fields. Later, an orientation image is created, and then partitioned into several distinct regions of homogenous orientation fields. The convergence point of these regions implicitly reveals an area that most likely contains a singular point. Subsequently, core and delta in this localized area are then detected using the Poincaré index. Finally, based on the number of core and delta and their locations, an exclusive membership of the fingerprint can be ascertained. Should it fail, the structure shape of the orientation fields neighbouring the core or delta is analysed. The performance of the proposed method is evaluated and tested using 27,000 fingerprints of NIST Special Database 14, which is considered de facto standard dataset for development and testing of fingerprint classification systems. The results obtained are very encouraging with accuracy rate of 89.31% that markedly outperformed the latest work of the renowned researchers.
format Thesis
author Saparudin, Saparudin
author_facet Saparudin, Saparudin
author_sort Saparudin, Saparudin
title An automatic fingerprint classification technique based on singular points and structure shape of orientation fields
title_short An automatic fingerprint classification technique based on singular points and structure shape of orientation fields
title_full An automatic fingerprint classification technique based on singular points and structure shape of orientation fields
title_fullStr An automatic fingerprint classification technique based on singular points and structure shape of orientation fields
title_full_unstemmed An automatic fingerprint classification technique based on singular points and structure shape of orientation fields
title_sort automatic fingerprint classification technique based on singular points and structure shape of orientation fields
publishDate 2012
url http://eprints.utm.my/id/eprint/30643/5/SaparudinPFSKSM2012.pdf
http://eprints.utm.my/id/eprint/30643/
_version_ 1643648595938770944
score 13.211869

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