Global variational method for fingerprint segmentation by three-part decomposition

Thai, Duy Hoang; Gottschlich, Carsten

doi:10.1049/iet-bmt.2015.0010

Cited by 32 publications

(24 citation statements)

References 64 publications

(155 reference statements)

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“…a cartoon component, which contains piecewise constant or piecewise smooth parts, a texture component, which captures oscillating patterns, and a noise component, which contains small scale objects (corresponding to high frequency parts in the Fourier domain). After the decomposition step, the texture component can be utilized for applications such as fingerprint segmentation [28]. Image decompositions are obtained by formulating and solving minimization problems that impose suitable norms on the respective components.…”

Section: Discussionmentioning

confidence: 99%

DOTmark – A Benchmark for Discrete Optimal Transport

2017

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The Wasserstein metric or earth mover's distance (EMD) is a useful tool in statistics, machine learning and computer science with many applications to biological or medical imaging, among others. Especially in the light of increasingly complex data, the computation of these distances via optimal transport is often the limiting factor. Inspired by this challenge, a variety of new approaches to optimal transport has been proposed in recent years and along with these new methods comes the need for a meaningful comparison.In this paper, we introduce a benchmark for discrete optimal transport, called DOTmark, which is designed to serve as a neutral collection of problems, where discrete optimal transport methods can be tested, compared to one another, and brought to their limits on large-scale instances. It consists of a variety of grayscale images, in various resolutions and classes, such as several types of randomly generated images, classical test images and real data from microscopy.Along with the DOTmark we present a survey and a performance test for a cross section of established methods ranging from more traditional algorithms, such as the transportation simplex, to recently developed approaches, such as the shielding neighborhood method, and including also a comparison with commercial solvers.

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Section: Discussionmentioning

confidence: 99%

DOTmark – A Benchmark for Discrete Optimal Transport

2017

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“…Recently, this implementation of the FDB method has been applied to improve the performance of fingerprint liveness detection by the convolution comparison patterns [ 62 ] and fingerprint alteration detection [ 63 ]. The manually marked benchmark has been used by Thai and Gottschlich [ 64 ] and by Bartůněk [ 65 ] for evaluating a new fingerprint segmentation methods. The G3PD method [ 64 ] follows a variational approach to decompose a fingerprint image into three parts and obtains the ROI based on the texture component.…”

Section: Discussionmentioning

confidence: 99%

Filter Design and Performance Evaluation for Fingerprint Image Segmentation

Thai¹,

Huckemann²,

Gottschlich³

2016

PLoS ONE

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Fingerprint recognition plays an important role in many commercial applications and is used by millions of people every day, e.g. for unlocking mobile phones. Fingerprint image segmentation is typically the first processing step of most fingerprint algorithms and it divides an image into foreground, the region of interest, and background. Two types of error can occur during this step which both have a negative impact on the recognition performance: ‘true’ foreground can be labeled as background and features like minutiae can be lost, or conversely ‘true’ background can be misclassified as foreground and spurious features can be introduced. The contribution of this paper is threefold: firstly, we propose a novel factorized directional bandpass (FDB) segmentation method for texture extraction based on the directional Hilbert transform of a Butterworth bandpass (DHBB) filter interwoven with soft-thresholding. Secondly, we provide a manually marked ground truth segmentation for 10560 images as an evaluation benchmark. Thirdly, we conduct a systematic performance comparison between the FDB method and four of the most often cited fingerprint segmentation algorithms showing that the FDB segmentation method clearly outperforms these four widely used methods. The benchmark and the implementation of the FDB method are made publicly available.

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“…G3PD: A model for discrete three-part decomposition of fingerprint images has recently been proposed by Thai and Gottschlich in 2015 [25] with the aim of obtaining a texture image v which serves as a useful feature for estimating the region of interest (ROI). The G3PD model is included in the DG3PD model by choosing L = 2 and replacing the directional G-norm in the DG3PD model by the 1 -norm of curvelet coefficients (multi-scale and multi-orientation decomposition) to capture texture.…”

Section: Ono Et Almentioning

confidence: 99%

“…For the application of DG3PD to fingerprints, we are especially interested in the texture image v as a feature for subsequent processing steps like segmentation, orientation field estimation [32] and ridge frequency estimation [15], and fingerprint image enhancement [15,21]. The first of these processing steps is to separate the foreground from the background [25,57]. The foreground area (or region of interest) contains the relevant information for a fingerprint comparison.…”

Section: Feature Extractionmentioning

confidence: 99%

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Directional global three-part image decomposition

Thai

Gottschlich

2016

J Image Video Proc.

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We consider the task of image decomposition, and we introduce a new model coined directional global three-part decomposition (DG3PD) for solving it. As key ingredients of the DG3PD model, we introduce a discrete multi-directional total variation norm and a discrete multi-directional G-norm. Using these novel norms, the proposed discrete DG3PD model can decompose an image into two or three parts. Existing models for image decomposition Advantages of the DG3PD model over existing ones lie in the properties enforced on the cartoon and texture images. The geometric objects in the cartoon image have a very smooth surface and sharp edges. The texture image yields oscillating patterns on a defined scale which are both smooth and sparse. Moreover, the DG3PD method achieves the goal of perfect reconstruction by summation of all components better than the other considered methods. Relevant applications of DG3PD are a novel way of image compression as well as feature extraction for applications such as latent fingerprint processing and optical character recognition.

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Global variational method for fingerprint segmentation by three-part decomposition

Cited by 32 publications

References 64 publications

DOTmark – A Benchmark for Discrete Optimal Transport

DOTmark – A Benchmark for Discrete Optimal Transport

Filter Design and Performance Evaluation for Fingerprint Image Segmentation

Directional global three-part image decomposition

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