The Gabor filter for biometric images has been investigated. Introduced a new Ateb-Gabor filter to improve the quality of fingerprint images. The sequence of filtration and recognition of biometric data is developed. For reliable fingerprint recognition, image correction is required, since interference caused by scanning may distort the lines of the imprints, which creates errors in recognition. The mathematical apparatus of the Ateb-functions provides additional functions for controlling Gabor's filtration, since it has a wider range of filtering options. It has been shown that the use of the Ateb-Gabor filter has a more controlling influence on the image, because in addition there are two parameters of rational numbers that considerably extends the filtration process. The method of filtering images based on the Gabor filter using Ateb-functions is developed. At present, work is being done on the application of a new filter to biometric images, bringing it to a finished software product. A two-dimensional Gabor filter is also developed, which in the future will allow people to recognize faces.
With the development of information technology, biometrics has become an essential
part of our everyday lives. Biometric identification systems require constant
improvement since they work fairly slowly and often give the wrong results. new
methods have been developed for fingerprint analysis, which scans them without any
contact [
One of the most popular methods for selecting the edges in the image is the use of
Gabor filters [
The filtration process involves the convolution of the filter and the input signal in the
spatial domain. In this paper, it was done in the Wolfram Mathematica 11 computing
environment [
The width and height of the filter are determined by the width and height of the
Gaussian component. Determining the correct width and height for the Gabor filter is
essential while designing new filters. Experimentally established [
We construct a generalized one-dimensional Gabor filter basing on the Ateb-functions
[
2 ( , , ) = − 2 2 ca ( , , 2 , , ), where - is the standard deviation of the Gaussian nucleus, which determines the amplitude of the function, - is the frequency of oscillations, which is defined as = 1/ , де T(m, n) – is the period of Ateb function ca ( , , 2 , , ), 2 − period of Ateb-function.
The experimental results of the filtration are shown in the figure below. In fig.1 a is filtered by an ordinary Gabor filter, and fig.1 b filter is Ateb-Gabor with m=n=3 with the best results of filtration.
The optimal correlation between frequency and width of the Gabor filter has been determined, which allowed performing filters automatically with the purpose of finding the edges of objects with different frequencies, sizes and directions. The optimal correlation is in each specific image of its own. The method of removing the average component of the Gabor filter is proposed, which allows reducing the value of the average filter to zero without deforming the filter. The results of numerous experiments demonstrate the successful selection of edges in the image based on the results obtained in the work of the Gabor filter parameters. 4
In each fingerprint, you can identify two types of attributes - global and local. Global signs are those that are visible to the naked eye. Another type of attributes is local.
They are selected because lines of the fingerprints are not straight. These points
provide unique fingerprint information in the process of identifying a person. Each
printout contains up to 70 minutia points [
The implementation of the Gabor filter for images takes place in five steps. Step1. Image normalization. Normalization of the image is necessary in order to set the previous mean values and deviations.
Step2. Calculation of the orientation.The orientation image Img represents the matrix N × N, in which each component Img (i, j) shows the local orientation, angle of inclination at a given point of the line with coordinates (i, j).
N × N, in which each component Img (i, j) shows the local frequency of the lines at a
given point, which is defined as the frequency of the crests directed along the
orientation of the protrusion. On the next step, the skeletelization of the image is based on
the wave method [
the local orientation of the speeches, applied to the pixels of the projections and vices of the image.
The Gabor filter for biometric images has been developed. A new filter of AtebGabor has been investigated and its efficiency in application to biometrics has been proved. Work is carried out to filter images and study their characteristics based on one-dimensional and two-dimensional Gabor filter. We think that we will achieve significant results in the near future. The use of a generalized Gabor filter will allow for better filtration and have a large number of parameters to choose from for the best filtering options. The change of the parameters m and n provides different values of the period, which makes it possible to expand the number of filter options. To solve the problem of fingerprint identification, the Ateb-Gabor function allows you to improve identification, and, based on it, filtration of images with a large number of ridges. This provides better characteristics than the usual one-dimensional Gabor filter.