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Improved Adaptive Fingerprint Binarization
Ansvarlig organisasjon
2008 (engelsk)Konferansepaper, Publicerat paper (Fagfellevurdert) 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.

sted, utgiver, år, opplag, sider
Sanya, China: IEEE , 2008.
HSV kategori
Identifikatorer
URN: urn:nbn:se:bth-8315ISI: 000258873900156Lokal ID: oai:bth.se:forskinfo83728357C8048F5BC125751900393345OAI: oai:DiVA.org:bth-8315DiVA, id: diva2:836022
Konferanse
CISP
Tilgjengelig fra: 2012-09-18 Laget: 2008-12-08 Sist oppdatert: 2015-12-11bibliografisk kontrollert
Inngår i avhandling
1. FINGERPRINT IMAGE ENHANCEMENT, SEGMENTATION AND MINUTIAE DETECTION
Åpne denne publikasjonen i ny fane eller vindu >>FINGERPRINT IMAGE ENHANCEMENT, SEGMENTATION AND MINUTIAE DETECTION
2016 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
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.

sted, utgiver, år, opplag, sider
Karlskrona: Blekinge Tekniska Högskola, 2016. s. 168
Serie
Blekinge Institute of Technology Doctoral Dissertation Series, ISSN 1653-2090 ; 2016:01
Emneord
adaptive fingerprint image enhancement, fingerprint segmentation, gray-scale image normalization, minutiae features, neural networks, frequency analysis, kurtosis
HSV kategori
Identifikatorer
urn:nbn:se:bth-11149 (URN)978-91-7295-321-5 (ISBN)
Disputas
2016-02-18, J1620, Karlskrona, 13:00 (engelsk)
Veileder
Tilgjengelig fra: 2015-12-11 Laget: 2015-12-10 Sist oppdatert: 2016-04-13bibliografisk kontrollert

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Nilsson, MikaelClaesson, Ingvar

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