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FINGERPRINT IMAGE ENHANCEMENT, SEGMENTATION AND MINUTIAE DETECTION
Blekinge Institute of Technology, Faculty of Engineering, Department of Applied Signal Processing.
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Prior to 1960's, the fingerprint analysis was carried out manually by human experts and for forensic purposes only. Automated fingerprint identification systems (AFIS) have been developed during the last 50 years. The success of AFIS resulted in that its use expanded beyond forensic applications and became common also in civilian applications. Mobile phones and computers equipped with fingerprint sensing devices for fingerprint-based user identification are common today.

Despite the intense development efforts, a major problem in automatic fingerprint identification is to acquire reliable matching features from fingerprint images with poor quality. Images where the fingerprint pattern is heavily degraded usually inhibit the performance of an AFIS system. The performance of AFIS systems is also reduced when matching fingerprints of individuals with large age variations.

This doctoral thesis presents contributions within the field of fingerprint image enhancement, segmentation and minutiae detection. The reliability of the extracted fingerprint features is highly dependent on the quality of the obtained fingerprints. Unfortunately, it is not always possible to have access to high quality fingerprints. Therefore, prior to the feature extraction, an enhancement of the quality of fingerprints and a segmentation are performed. The segmentation separates the fingerprint pattern from the background and thus limits possible sources of error due to, for instance, feature outliers. Most enhancement and segmentation techniques are data-driven and therefore based on certain features extracted from the low quality fingerprints at hand. Hence, different types of processing, such as directional filtering, are employed for the enhancement. This thesis contributes by proposing new research both for improving fingerprint matching and for the required pre-processing that improves the extraction of features to be used in fingerprint matching systems.

In particular, the majority of enhancement and segmentation methods proposed herein are adaptive to the characteristics of each fingerprint image. Thus, the methods are insensitive towards sensor and fingerprint variability. Furthermore, introduction of the higher order statistics (kurtosis) for fingerprint segmentation is presented. Segmentation of the fingerprint image reduces the computational load by excluding background regions of the fingerprint image from being further processed. Also using a neural network to obtain a more robust minutiae detector with a patch rejection mechanism for speeding up the minutiae detection is presented in this thesis.

Place, publisher, year, edition, pages
Karlskrona: Blekinge Tekniska Högskola, 2016. , 168 p.
Series
Blekinge Institute of Technology Doctoral Dissertation Series, ISSN 1653-2090 ; 2016:01
Keyword [en]
adaptive fingerprint image enhancement, fingerprint segmentation, gray-scale image normalization, minutiae features, neural networks, frequency analysis, kurtosis
National Category
Engineering and Technology Signal Processing
Identifiers
URN: urn:nbn:se:bth-11149ISBN: 978-91-7295-321-5 (print)OAI: oai:DiVA.org:bth-11149DiVA: diva2:881313
Public defence
2016-02-18, J1620, Karlskrona, 13:00 (English)
Supervisors
Available from: 2015-12-11 Created: 2015-12-10 Last updated: 2016-04-13Bibliographically approved
List of papers
1. Adaptive Fingerprint Binarization by Frequency Domain Analysis
Open this publication in new window or tab >>Adaptive Fingerprint Binarization by Frequency Domain Analysis
2006 (English)Conference paper, (Other academic) Published
Alternative title[sv]
Adaptiv Fingeravtryck Binarization av Frekvens Domän Analys
Abstract [en]

This paper presents a new approach for fingerprint enhancement by using directional filters and binarization. A straightforward method for automatically tuning the size of local area is obtained by analyzing entire fingerprint image in the frequency domain. Hence, the algorithm will adjust adaptively to the local area of the fingerprint image, independent on the characteristics of the fingerprint sensor or the physical appearance of the fingerprints. Frequency analysis is carried out in the local areas to design directional filters. Experimental results are presented.

Place, publisher, year, edition, pages
Pacific Grove: , 2006
Keyword
biometrics, fingerprint, binarization
National Category
Signal Processing
Identifiers
urn:nbn:se:bth-8963 (URN)000246925201033 ()oai:bth.se:forskinfoBB66330D02D2801CC125733E008178D6 (Local ID)oai:bth.se:forskinfoBB66330D02D2801CC125733E008178D6 (Archive number)oai:bth.se:forskinfoBB66330D02D2801CC125733E008178D6 (OAI)
Conference
Fortieth Asilomar Conference on Signals, Systems and Computers
Note
Copyright © 19xx/20xx IEEE. Reprinted from (all relevant publication info). This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of BTH's products or services Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by sending a blank email message to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.Available from: 2012-09-18 Created: 2007-08-22 Last updated: 2015-12-11Bibliographically approved
2. Improved Adaptive Fingerprint Binarization
Open this publication in new window or tab >>Improved Adaptive Fingerprint Binarization
2008 (English)Conference paper, (Refereed) Published
Abstract [en]

In this paper improvements to a previous work are presented. Removing the redundant artifacts in the fingerprint mask is introduced enhancing the final result. The proposed method is entirely adaptive process adjusting to each fingerprint without any further supervision of the user. Hence, the algorithm is insensitive to the characteristics of the fingerprint sensor and the various physical appearances of the fingerprints. Further, a detailed description of fingerprint mask generation not fully described in the previous work is presented. The improved experimental results are presented.

Place, publisher, year, edition, pages
Sanya, China: IEEE, 2008
National Category
Signal Processing
Identifiers
urn:nbn:se:bth-8315 (URN)000258873900156 ()oai:bth.se:forskinfo83728357C8048F5BC125751900393345 (Local ID)oai:bth.se:forskinfo83728357C8048F5BC125751900393345 (Archive number)oai:bth.se:forskinfo83728357C8048F5BC125751900393345 (OAI)
Conference
CISP
Available from: 2012-09-18 Created: 2008-12-08 Last updated: 2015-12-11Bibliographically approved
3. Adaptive Fingerprint Image Enhancement With Emphasis on Preprocessing of Data
Open this publication in new window or tab >>Adaptive Fingerprint Image Enhancement With Emphasis on Preprocessing of Data
2013 (English)In: IEEE Transactions on Image Processing, ISSN 1057-7149, E-ISSN 1941-0042, Vol. 22, no 2, 644-656 p.Article in journal (Refereed) Published
Abstract [en]

This article proposes several improvements to an adaptive fingerprint enhancement method that is based on contextual filtering. The term adaptive implies that parameters of the method are automatically adjusted based on the input fingerprint image. Five processing blocks comprise the adaptive fingerprint enhancement method, where four of these blocks are updated in our proposed system. Hence, the proposed overall system is novel. The four updated processing blocks are: 1) preprocessing; 2) global analysis; 3) local analysis; and 4) matched filtering. In the preprocessing and local analysis blocks, a nonlinear dynamic range adjustment method is used. In the global analysis and matched filtering blocks, different forms of order statistical filters are applied. These processing blocks yield an improved and new adaptive fingerprint image processing method. The performance of the updated processing blocks is presented in the evaluation part of this paper. The algorithm is evaluated toward the NIST developed NBIS software for fingerprint recognition on FVC databases.

Place, publisher, year, edition, pages
IEEE, 2013
Keyword
Directional filtering, Fourier transform, image processing, spectral feature estimation, successive mean quantization transform
National Category
Signal Processing
Identifiers
urn:nbn:se:bth-7002 (URN)10.1109/TIP.2012.2220373 (DOI)000314717800019 ()oai:bth.se:forskinfoB04EDCB08DEC540DC1257B2F003ADC77 (Local ID)oai:bth.se:forskinfoB04EDCB08DEC540DC1257B2F003ADC77 (Archive number)oai:bth.se:forskinfoB04EDCB08DEC540DC1257B2F003ADC77 (OAI)
External cooperation:
Available from: 2013-03-18 Created: 2013-03-15 Last updated: 2016-09-07Bibliographically approved
4. Neural Network based Minutiae Extraction from Skeletonized Fingerprints
Open this publication in new window or tab >>Neural Network based Minutiae Extraction from Skeletonized Fingerprints
2006 (English)Conference paper, (Other academic) Published
Alternative title[sv]
Neurala Nät baserad Minutiae Extraktion från Skeletoniserade Fingeravtryck
Abstract [en]

Human fingerprints are rich in details denoted minutiae. In this paper a method of minutiae extraction from fingerprint skeletons is described. To identify the different shapes and types of minutiae a neural network is trained to work as a classifier. The proposed neural network is applied throughout the fingerprint skeleton to locate various minutiae. A scheme to speed up the process is also presented. Extracted minutiae can then be used as identification marks for automatic fingerprint matching.

Place, publisher, year, edition, pages
Hong Kong: , 2006
Keyword
biometrics, fingerprint, minutiae, neural networks
National Category
Signal Processing
Identifiers
urn:nbn:se:bth-8962 (URN)000246127500098 ()oai:bth.se:forskinfoD7E0C5D38A2002DBC125733E0082E5EF (Local ID)oai:bth.se:forskinfoD7E0C5D38A2002DBC125733E0082E5EF (Archive number)oai:bth.se:forskinfoD7E0C5D38A2002DBC125733E0082E5EF (OAI)
Conference
TENCON 2006 IEEE Region 10 Conference
Note
Copyright © 19xx/20xx IEEE. Reprinted from (all relevant publication info). This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of BTH's products or services Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by sending a blank email message to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.Available from: 2012-09-18 Created: 2007-08-22 Last updated: 2015-12-11Bibliographically approved

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