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Adaptive Fingerprint Image Enhancement With Emphasis on Preprocessing of Data
Blekinge Institute of Technology, School of Engineering, Department of Electrical Engineering.
Blekinge Institute of Technology, School of Engineering, Department of Electrical Engineering.
Blekinge Institute of Technology, School of Engineering, Department of Electrical Engineering.
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. Vol. 22, no 2, 644-656 p.
Keyword [en]
Directional filtering, Fourier transform, image processing, spectral feature estimation, successive mean quantization transform
National Category
Signal Processing
URN: urn:nbn:se:bth-7002DOI: 10.1109/TIP.2012.2220373ISI: 000314717800019Local ID: diva2:834571
Available from: 2013-03-18 Created: 2013-03-15 Last updated: 2016-09-07Bibliographically 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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