CN104504372A - Bionic texture feature extraction method for finger vein image - Google Patents

Abstract

The invention discloses a bionic texture feature extraction method for a finger vein image. The bionic texture feature extraction method has the advantages that the feature is extracted by a Gabor filter with good similarity of a mammal visual cortex simple cell receptive field model, when the method is used for performing feature identification, the effect is similar to identifying object by human eyes, and is good; the adverse influence on the identifying accuracy by the noise is overcome, and the identifying property and robustness of a finger vein identifying system are improved.

Description

A kind of bionical texture characteristic extracting method of finger venous image

Technical field

The present invention relates to finger vena identification field, specifically a kind of bionical texture characteristic extracting method of finger venous image.

Background technology

Finger vena identification is a kind of emerging biometrics identification technology with better development prospect, and the key of finger vena identification is how accurately to extract vein network, carries out feature extraction and matching on this basis.In order to overcome the impact of low-quality finger vein image on recognition result, the finger vein identification method in conjunction with bionical textural characteristics and wire textural characteristics is suggested.Its ultimate principle first does certain pre-service to the finger venous image gathered, and comprises image enhaucament, size normalization etc., then to the feature of pretreated image zooming-out in conjunction with bionical texture and wire texture, and merges coding and produce proper vector.Finally utilize the Hamming distances between proper vector to calculate the characteristic similarity of two width finger venous images, mate according to the feature power set and threshold value, whether both checkings are from same piece of finger.In order to meet fusion and the combination of two kinds of characteristic images, the bionical texture characteristic extracting method of the finger venous image being applicable to this combination is proposed hereby.

Summary of the invention

Be provide a kind of effect similar eye recognition object to solve above-mentioned the deficiencies in the prior art for the purpose of the present invention, there is the bionical texture characteristic extracting method of the finger venous image of good completeness

For achieving the above object, the present invention adopts following technical scheme: the two-dimensional Gabor filter using mammalian visual cortex simple cell to accept field model good approximation is extracted, and the functional form of two-dimensional Gabor filter can be expressed as formula (1):

$G\left(\stackrel{\→}{x}\right)=\frac{{\left|\right|\stackrel{\→}{k}\left|\right|}^2}{{\mathrm{\σ}}^2}{e}^{-\frac{{\left|\right|\stackrel{\→}{k}\left|\right|}^2{\left|\right|\stackrel{\→}{x}\left|\right|}^2}{2{\mathrm{\σ}}^2}}\left(e^{i(\stackrel{\→}{k}\·\stackrel{\→}{x})-e^{-\frac{{\mathrm{\σ}}^2}{2}}}\right)---\left(1\right)$

Wherein for the image coordinate of given position; $\stackrel{\→}{k}=\left(\begin{array}{c}{k}_1\\ k_2\end{array}\right)=\left(\begin{array}{c}{k}_v\cos \mathrm{\α}\\ k_v\sin \mathrm{\α}\end{array}\right)$ For the centre frequency of wave filter; α is the direction of wave filter texture feature extraction; σ ²for the variance of wave filter Gaussian envelope.In order to allow the bionical texture extracted not by the impact of gradation of image absolute figure, and insensitive to the illumination variation of image, deduct at the real part of two-dimensional Gabor filter according to finger venous image size and noise situations, select and fix suitable Gabor filter variance parameter σ ², regulate filter frequency parameter k _v, and direction parameter α, obtain one group of two-dimensional Gabor filter and with them to pretreated finger venous image carry out convolution, shown in (2):

$J_{i,j}\left(\stackrel{\→}{x}\right)=\∫I\left({\stackrel{\→}{x}}^{\′}\right)G_{k_i,{\mathrm{\α}}_j}(\stackrel{\→}{x}-{\stackrel{\→}{x}}^{\′})d^2{\stackrel{\→}{x}}^{\′}---\left(2\right)$

Each point in image the response of multiple Gabor filter can be obtained get and wherein respond the strongest frequency parameter k _vi, and direction parameter α _jas the bionical textural characteristics of this point, shown in (3):

$D_g\left({\stackrel{\→}{x}}_0\right)=\arg {\max }_{i,j}\left(J_{i,j}\left({\stackrel{\→}{x}}_0\right)\right)---\left(3\right)$

Then whole finger venous image bionical textural characteristics be

The invention has the beneficial effects as follows: by extracting the bionical textural characteristics of finger venous image, the Gabor filter using mammalian visual cortex simple cell to accept field model good approximation is extracted, carry out feature identification with it, the similar eye recognition object of effect, has good completeness; Improve recognition performance and the robustness of finger vein recognition system.

Embodiment

Below by embodiment, the present invention will be further described.

The bionical texture characteristic extracting method of a kind of finger venous image that the present embodiment provides, the Gabor filter using mammalian visual cortex simple cell to accept field model good approximation is extracted, carry out feature identification with it, the similar eye recognition object of effect, has good completeness; Meanwhile, in finger venous image, vein texture is generally the feature of wire texture, also proposed the wire textural characteristics based on Radon conversion, it is fused in the proper vector of finger venous image, further increases the completeness of proper vector.

The Gabor filter using mammalian visual cortex simple cell to accept field model good approximation extracts bionical textural characteristics, and the functional form of two-dimensional Gabor filter can be expressed as formula (1):

$G\left(\stackrel{\→}{x}\right)=\frac{{\left|\right|\stackrel{\→}{k}\left|\right|}^2}{{\mathrm{\σ}}^2}{e}^{-\frac{{\left|\right|\stackrel{\→}{k}\left|\right|}^2{\left|\right|\stackrel{\→}{x}\left|\right|}^2}{2{\mathrm{\σ}}^2}}\left(e^{i(\stackrel{\→}{k}\·\stackrel{\→}{x})-e^{-\frac{{\mathrm{\σ}}^2}{2}}}\right)---\left(1\right)$

Wherein for the image coordinate of given position; $\stackrel{\→}{k}=\left(\begin{array}{c}{k}_1\\ k_2\end{array}\right)=\left(\begin{array}{c}{k}_v\cos \mathrm{\α}\\ k_v\sin \mathrm{\α}\end{array}\right)$ For the centre frequency of wave filter; α is the direction of wave filter texture feature extraction; σ ²for the variance of wave filter Gaussian envelope.In order to allow the bionical texture extracted not by the impact of gradation of image absolute figure, and insensitive to the illumination variation of image.Deduct at the real part of two-dimensional Gabor filter shown in (1).

According to finger venous image size and noise situations, select and fix suitable Gabor filter variance parameter σ ², regulate filter frequency parameter k _v, and direction parameter α, obtain one group of two-dimensional Gabor filter and with them to pretreated finger venous image carry out convolution, shown in (2):

$J_{i,j}\left(\stackrel{\→}{x}\right)=\∫I\left({\stackrel{\→}{x}}^{\′}\right)G_{k_i,{\mathrm{\α}}_j}(\stackrel{\→}{x}-{\stackrel{\→}{x}}^{\′})d^2{\stackrel{\→}{x}}^{\′}---\left(2\right)$

Each point in image the response of multiple Gabor filter can be obtained get and wherein respond the strongest frequency parameter k _vi, and direction parameter α _jas the bionical textural characteristics of this point, shown in (3):

$D_g\left({\stackrel{\→}{x}}_0\right)=\arg {\max }_{i,j}\left(J_{i,j}\left({\stackrel{\→}{x}}_0\right)\right)---\left(3\right)$

Then whole finger venous image bionical textural characteristics be frequency parameter k _viget 8 kinds of possibilities (1,1/2,1/4,1/6,1/8,1/10,1/12,1/14; Unit: 1/ pixel), direction parameter α _jgetting 6 kinds may (0 °, 60 °, 120 °, 180 °, 240 °, 300 °).

Claims (2)

1. the bionical texture characteristic extracting method of a finger venous image, it comprises employing and uses mammalian visual cortex simple cell to accept the approximate two-dimensional Gabor filter extraction of field model, it is characterized in that the functional form of two-dimensional Gabor filter can be expressed as formula (1):

Wherein for the image coordinate of given position; for the centre frequency of wave filter; α is the direction of wave filter texture feature extraction; σ ²for the variance of wave filter Gaussian envelope.

2. the bionical texture characteristic extracting method of a kind of finger venous image according to claim 1, is characterized in that according to finger venous image size and noise situations, selects and fixes suitable Gabor filter variance parameter σ ², regulate filter frequency parameter k _v, and direction parameter α, obtain one group of two-dimensional Gabor filter and with them to pretreated finger venous image carry out convolution, shown in (2):

Each point in image the response of multiple Gabor filter can be obtained get and wherein respond the strongest frequency parameter k _vi, and direction parameter α _jas the bionical textural characteristics of this point, shown in (3):

Then whole finger venous image bionical textural characteristics be frequency parameter k _viget 8 kinds of possibilities, direction parameter α _jgetting 6 kinds may.