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Adaptive Fingerprint Binarization by Frequency Domain Analysis
Responsible organisation
2006 (English)Conference paper (Other academic) PublishedAlternative title
Adaptiv Fingeravtryck Binarization av Frekvens Domän Analys (Swedish)
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 [en]
biometrics, fingerprint, binarization
National Category
Signal Processing
URN: urn:nbn:se:bth-8963ISI: 000246925201033Local ID: diva2:836739
Fortieth Asilomar Conference on Signals, Systems and Computers
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 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
In thesis
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.
Blekinge Institute of Technology Doctoral Dissertation Series, ISSN 1653-2090 ; 2016:01
adaptive fingerprint image enhancement, fingerprint segmentation, gray-scale image normalization, minutiae features, neural networks, frequency analysis, kurtosis
National Category
Engineering and Technology Signal Processing
urn:nbn:se:bth-11149 (URN)978-91-7295-321-5 (ISBN)
Public defence
2016-02-18, J1620, Karlskrona, 13:00 (English)
Available from: 2015-12-11 Created: 2015-12-10 Last updated: 2016-04-13Bibliographically approved

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Nilsson, MikaelClaesson, Ingvar
Signal Processing

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