Personalized Fingerprint Segmentation

Yin, Yilong; Shi, Zhichen

doi:10.1007/978-3-642-10677-4_91

Cited by 14 publications

(10 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: 94%

“…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%

“…1 Various regions of fingerprint images: a high-quality ridge region b low-quality ridge region c unrecoverable ridge region d nonridge region e region with remaining ridges stage. A robust segmentation method has been devised by Guo et al [10] where a unique model is generated for each fingerprint and based on which a particular fingerprint is segmented. Wang et al [30] used energy of Gaussian Hermite moment for fingerprint segmentation.…”

Section: Introductionmentioning

confidence: 99%

“…Generally, it is executed in two ways: (i) block-wise processing and (ii) pixel-wise processing. 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].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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: 94%

Section: Experimental Results and Performance Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fingerprint Image Segmentation Using Block-Based Statistics and Morphological Filtering

Das

Mukhopadhyay

2015

Arab J Sci Eng

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“…The foreground is the region representing the area of fingertip touch on the sensor and, the background refers to the rest of the pixels falling at the noisy marginal sides of the image and do not contain ridge-valley information. Guo, Yin, & Shi (2009) proposed a personalized, so called, Automatic Labeling based Linear Neighborhood Propagation (ALLNP), fingerprint segmentation algorithm, in which the output only depends on the input image, instead of a set of images. The blocks are arranged in an ascending order by their contrast.…”

Section: Fingerprint Image Segmentationmentioning

confidence: 99%

A Novel Method for Orientation Estimation in Highly Corrupted Fingerprint Images

Bakhtiari

2014

International Journal of Intelligent Computing in Medical Scien

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New approaches are introduced for the fundamental components of a fingerprint reconstruction system, namely, fingerprint segmentation, scratch detection and, orientation smoothing in large degraded regions. The orientation information in corrupted regions is restored without relying on the singular points. The simulation results show that the error between the predicted orientation fields and the ground truth values is negligible in terms of Mean Absolute Error, and is comparable with the error produced by other competitive reconstruction methods. The computer simulations are conducted on 180 images of FVC2004 DB3_A and 80 images FVC2000 DB1_B databases of fingerprint images. The presented tools are tested using a Fingerprint Recognition System and demonstrated a great ability in improving the Receiver Operating Characteristic curve and Equal Error Rate.

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