Developed Fingerprint Segmentation Technique based on Mean and Variance Intensity Thresholding

Bahgat, G.A.; Hefnawy, Alaa A.; Khalil, A. H.; Kader, N.S. Abdel; Mashali, Samia A.

doi:10.12792/iciae2016.047

Cited by 1 publication

(2 citation statements)

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“…If a pixel is in a region where the low luminance (lower luminance to a set ready level value), then it is replaced by the value 0 (black color). However, if a pixel is in a region where the luminance is clearly defined, it is replaced by the value 1 (white color) [10][11].…”

Section: Pretreatment Of Fingerprint Imagesmentioning

confidence: 99%

“…Some pretreatment and enhancement steps are frequently performed to simplify the minutiae extraction task [4][5][6][7][8]. The first step of the algorithm concerns the fingerprint image segmentation; this phase requires the conversion of the grayscale fingerprints image into a binary image [9][10][11]. The binary images obtained by the binarization process are generally subjected to a thinning step [12][13] which makes it possible to reduce the thickness of the peak line to one pixel [14][15].…”

Section: Introductionmentioning

confidence: 99%

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Reconstruction of Fingerprint Shape using Fractal Interpolation

Bajahzar¹,

Guedri²

2019

IJACSA

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One of the severe problems in a fingerprint-based system is retaining the fingerprint images. In this paper, we propose a method to minimize the fingerprint images size and retain the reference points. The method is divided into three parts, the first part is about digital image preprocessing that allows us to eliminate the noise, improve the image, convert it into a binary image, detect the skeleton and locate the reference point. The second part concerns the detection of critical points by the Douglas-Peucker method. The final part presents the methodology for the fingerprint curves reconstruction using the fractal interpolation curves. The experimental result shows the accuracy of this reconstruction method. The relative error (ER) is between 2.007% and 5.627% and the mean squared error (MSE) is between 0.126 and 0.009 at a small iterations number. On the other hand, for a greater number of iterations, the ER is between 0.415% and 1.64% and MSE is between 0.000124 and 0.0167. This clearly indicates that the interpolated curves and the original curves are virtually identical and exceedingly close.

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Section: Pretreatment Of Fingerprint Imagesmentioning

confidence: 99%