Segmentation of low quality fingerprint images

Fleyeh, Hasan; Jomaa, Diala; Dougherty, Mark

doi:10.1109/mcit.2010.5444844

Cited by 18 publications

(14 citation statements)

References 8 publications

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“…8, 9, 10, 11b, the outputs produced by FDB method and the proposed method are shown in Figs. 8,9,10,11c and Figs. 8,9,10,11d respectively.…”

Section: Experimental Results and Performance Analysismentioning

confidence: 98%

“…Table 1 shows the segmentation error of Type-I and Type-II for the outputs shown in Figs. 8,9,10,11 where Table 2 shows the average error for all the four databases with comparison between FDB and the proposed method.…”

Section: Experimental Results and Performance Analysismentioning

confidence: 99%

“…In block-wise segmentation methods, the entire fingerprint image is divided into several non-overlapping blocks, and some statistical measures are adopted to make the decision whether an image sub-block belongs to the foreground or background region [10,14,23,30,33,35]. To the contrary, features of each pixel are determined (e.g., intensity value, pixel orientation, gradient of direction, frequency) in pixel-wise segmentation [3,9,12,17,34]. Combination of both the approaches (i.e., block-wise and pixel-wise) can also be applied for fingerprint image segmentation [21,25].…”

Section: Introductionmentioning

confidence: 99%

“…Bazen and Gerez [3] developed a segmentation scheme based on pixel-wise coherence, mean, and variance. Fleyeh et al [9] proposed a segmentation technique for low-quality fingerprint images using pixel intensity-based feature selection. Another method for lowquality image segmentation has been suggested by Ma [17] using gradient-based features.…”

Section: Introductionmentioning

confidence: 99%

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Fingerprint Image Segmentation Using Block-Based Statistics and Morphological Filtering

Das

Mukhopadhyay

2015

Arab J Sci Eng

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Fingerprint segmentation is meant to separate the foreground region of a fingerprint image from its background region. This paper presents a block-based segmentation scheme which is executed in two passes. In the first pass, two sets of regions of interest (ROI) are identified separately using (i) morphological open-close filters and (ii) a statistical measure namely coefficient of variation (CV). These sets of ROIs are combined together to identify the overall ROI. In the second pass, a block-wise region shrink-merge technique, which employs a sequential combination of parameters like CV and average gray value, is applied to construct the final segmented image. The proposed method has been implemented and tested on a set of real fingerprint images and the experimental results visually establish the effectiveness of the proposed method. Besides, a comparative study based on some quantitative measures is furnished to verify the accuracy of the proposed segmentation algorithm.

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“…8, 9, 10, 11b, the outputs produced by FDB method and the proposed method are shown in Figs. 8,9,10,11c and Figs. 8,9,10,11d respectively.…”

Section: Experimental Results and Performance Analysismentioning

confidence: 98%

Section: Experimental Results and Performance Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Fingerprint Image Segmentation Using Block-Based Statistics and Morphological Filtering

Das

Mukhopadhyay

2015

Arab J Sci Eng

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“…Some segmentation methods were especially designed for segmenting low quality fingerprint images. These methods rely on gradient-based features [18], intensity-based features [13], [37] and structure-based features [21].…”

Section: Related Workmentioning

confidence: 99%

Automatic segmentation of latent fingerprints

Choi

Boaventura

et al. 2012

2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS)

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Abstract-Latent fingerprints are routinely found at crime scenes due to the inadvertent contact of the criminals' finger tips with various objects. As such, they have been used as crucial evidence for identifying and convicting criminals by law enforcement agencies. However, compared to plain and rolled prints, latent fingerprints usually have poor quality of ridge impressions with small fingerprint area, and contain large overlap between the foreground area (friction ridge pattern) and structured or random noise in the background. Accordingly, latent fingerprint segmentation is a difficult problem. In this paper, we propose a latent fingerprint segmentation algorithm whose goal is to separate the fingerprint region (region of interest) from background. Our algorithm utilizes both ridge orientation and frequency features. The orientation tensor is used to obtain the symmetric patterns of fingerprint ridge orientation, and local Fourier analysis method is used to estimate the local ridge frequency of the latent fingerprint. Candidate fingerprint (foreground) regions are obtained for each feature type; an intersection of regions from orientation and frequency features localizes the true latent fingerprint regions. To verify the viability of the proposed segmentation algorithm, we evaluated the segmentation results in two aspects: a comparison with the ground truth foreground and matching performance based on segmented region.

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Segmentation and Enhancement of Low Quality Fingerprint Images

Fleyeh

2016

Web Information Systems Engineering – WISE 2016

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Segmentation of low quality fingerprint images

Cited by 18 publications

References 8 publications

Fingerprint Image Segmentation Using Block-Based Statistics and Morphological Filtering

Fingerprint Image Segmentation Using Block-Based Statistics and Morphological Filtering

Automatic segmentation of latent fingerprints

Segmentation and Enhancement of Low Quality Fingerprint Images

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