CN102332156A - Fingerprint enhancement method based on time domain and frequency domain simultaneously for filtering - Google Patents

Abstract

The invention relates to the technical field of fingerprint image enhancement treatment and particularly relates to a fingerprint enhancement method based on a time domain and a frequency domain simultaneously for filtering, and a final enhanced image can be obtained by performing frequency domain filtering enhancement treatment while performing time domain filtering enhancement treatment and further performing fusion treatment on the enhanced image. By adopting the fingerprint enhancement method, the enhanced filtering can be respectively performed on a fingerprint image at the time domain and the frequency domain, thereby overcoming the deficiencies of the traditional method and greatly enhancing the treatment effect of the fingerprint image.

Description

A kind of while is based on the fingerprint Enhancement Method of time-domain and frequency-domain filtering

Technical field

The present invention relates to fingerprint image enhancement process technical field, particularly a kind of while is based on the fingerprint Enhancement Method of time-domain and frequency-domain filtering.

Background technology

Traditional fingerprint Enhancement Method is mainly two kinds of time-domain filtering and frequency filterings.Time-domain filtering is mainly through the structure suitable filters; In time domain fingerprint image is carried out convolution algorithm; Thereby reach the purpose of enhancing, like methods such as wave filter, nonlinear diffusion wave filter, context wave filter and scale-space filtering based on Gabor filter.The frequency domain Fourier transform can convert in frequency and does product calculation do convolution algorithm in time domain; Thereby fast and effeciently image is handled; Reach the purpose that strengthens image, as based on direction frequency domain filter, anisotropic wave filter, directly or methods such as Short Time Fourier Transform analysis and " Log-Gabor " wave filter.But; These fingerprint Enhancement Method all are to use single wave filter; And the parameter of selective filter is mostly based on the streakline structure of fingerprint; Because fingerprint image has the characteristic of non-stationary, the streakline structure of inferior quality fingerprint image is complicated especially, and the effect of such filtering reinforcement method often can not be satisfactory.

Summary of the invention

Technical matters to be solved by this invention is: the enhancement method of fingerprint image of a kind of while based on time-domain filtering and frequency domain filtering is provided; In time domain the streakline of fingerprint image is repaired effectively; Select fingerprint image is strengthened filtering from direction and frequency domain respectively at frequency domain, thereby strengthen fingerprint image.

The technical solution adopted for the present invention to solve the technical problems is: a kind of while is based on the enhancement method of fingerprint image of time-domain filtering and frequency domain filtering; After in the time-domain filtering enhancement process, carrying out the frequency domain filtering enhancement process, again the enhancing image is carried out fusion treatment and finally strengthened image.

Described time-domain filtering enhancement process comprises former schematic partial plan picture normalization, asks local streakline direction, compensation filter and smoothing processing.

Described frequency domain filtering enhancement process comprises former figure global image normalization, asks the adjustment of directional diagram, Gabor Filtering Processing and gradation of image.

Described enhancing image co-registration is handled and is comprised that corresponding pixel points adds up and the gray scale adjustment.

The invention has the beneficial effects as follows: this fingerprint Enhancement Method can strengthen filtering to fingerprint image respectively in time domain and frequency domain, has overcome the deficiency of classic method, has greatly strengthened the treatment effect of fingerprint image.

Description of drawings

Below in conjunction with accompanying drawing and embodiment the present invention is further specified;

Fig. 1 is the theoretical block diagram of implementation of the present invention;

Fig. 2 is the theoretical block diagram of time-domain filtering of the present invention;

Fig. 3 is the former figure of fingerprint of time domain fingerprint image enhancing step 1 and the image after the local normalization;

Fig. 4 is the theoretical block diagram of frequency domain filtering of the present invention;

Fig. 5 is that time-domain and frequency-domain of the present invention strengthens the image co-registration block diagram;

Fig. 6 is that fingerprint image 1 strengthens experimental result;

Fig. 7 is that fingerprint image 2 strengthens experimental result.

Among Fig. 3, (a) be the former figure of fingerprint of time domain fingerprint image enhancing step 1; (b) be image after time domain fingerprint image enhancing step 1 local normalization; Among Fig. 6, (a) be the former figure of fingerprint that fingerprint image 1 strengthens experiment; (b) be that the fingerprint that fingerprint image 1 strengthens behind the time-domain filtering of experiment strengthens image; (c) be that the fingerprint that fingerprint image 1 strengthens behind the frequency domain filtering of experiment strengthens image; (d) the fingerprint enhancing image after the time-domain filtering that strengthens experiment for fingerprint image 1 strengthens with frequency domain filtering simultaneously; Among Fig. 7, (a) be the former figure of fingerprint that fingerprint image 2 strengthens experiment; (b) be that the fingerprint that fingerprint image 2 strengthens behind the time-domain filtering of experiment strengthens image; (c) be that the fingerprint that fingerprint image 2 strengthens behind the experiment frequency domain filterings strengthens image; (d) be the fingerprint image 2 fingerprint enhancing image after strengthening the experiment time-domain filterings and strengthening simultaneously with frequency domain filtering.

Embodiment

The time domain fingerprint image strengthens

Streakline fracture and streakline fuzzy region to fingerprint can be extracted the fact that exerts an influence to fingerprint characteristic; Construct the convolution template of suitable time domain filtering and choose reasonable wave filter; Combing and repairing are carried out in these streakline zones; Can reach the purpose that strengthens fingerprint image, the selection of filter parameter mainly is a structure convolution template parameter, and concrete steps as shown in Figure 2 are following:

Step 1: topography's normalization

Consider that each local intensity profile of image is unbalanced, thus adopt local normalization to handle to image, definition expectation average M among this paper ₀=128, expectation variance V ₀=128 * 128, local window size W * W is (8 * 8).Topography normalization be to each pixel img of image (i j) averages and variance for the part at center, again to central point img (i j) standardizes, thereby obtains better effect, and key step is following:

(1) obtains that (i j) is the average M and the variance V of the topography at center with img;

$M(i,j)=\frac{1}{R*L}\underset{j-w/2}{\overset{j+w/2}{\mathrm{\Σ}}}\underset{i-w/2}{\overset{i+w/2}{\mathrm{\Σ}}}\mathrm{img}(i,j),(i,j)\∈\mathrm{img}$

$V(i,j)=\underset{j-w/2}{\overset{j+w/2}{\mathrm{\Σ}}}\underset{i-w/2}{\overset{i+w/2}{\mathrm{\Σ}}}{\left(\mathrm{img}(i,j)-M(i,j)\right)}^2,(i,j)\∈\mathrm{img}---\left(1\right)$

(2) by following formula to central point img (i, j) processing of standardizing;

$\mathrm{norimg}(i,j)=M0+\frac{V0(\mathrm{img}(i,j)-M(i,j))}{V(i,j)},(i,j)\∈\mathrm{img}\∩V(i,j)\≠0$

norimg(i，j)＝M0+100*(img(i，j)-M(i，j)，(i，j)∈img?∩V(i，j)＝0(2)

(3) (i, j) edge value is handled to norimg.

if?norimg(i，j)＜0，norimg(i，j)＝0，

esle?if?norimg(i，j)＞255，norimg(i，j)＝255(3)

Result after normalization is handled as stated above is shown in Fig. 3 (b).

Step 2: ask local streakline direction

Fingerprint image adopts gradient algorithm that the fingerprint local direction is estimated after normalization is handled.Fingerprint image after the normalization is divided into non-overlapping, the size of piece is W * W, and (i j) for center and mask operator carry out convolution, like the sobel operator, obtains the horizontal gradient component G of each pixel in the piece with the pixel of piece central point _X(u, v), VG (vertical gradient) component G _Y(u, v) and whole horizontal gradient component G _XX(i, j), VG (vertical gradient) component G _XY(i, j), through the upwards horizontal gradient component and (4)～(6) the formula acquisition by formula of VG (vertical gradient) component of point of counterparty.

$\mathrm{Gxx}=\underset{u=i-w/2}{\overset{i+w/2}{\mathrm{\Σ}}}\underset{v=j-w/2}{\overset{j+w/2}{\mathrm{\Σ}}}({\mathrm{Gx}}^2(u,v)-{\mathrm{Gy}}^2(u,v))---\left(4\right)$

$\mathrm{Gxy}=\underset{u=i-w/2}{\overset{i+w/2}{\mathrm{\Σ}}}\underset{v=j-w/2}{\overset{j+w/2}{\mathrm{\Σ}}}2\mathrm{Gx}(u,v)\mathrm{Gy}(u,v)---\left(5\right)$

$O(x,y)=\frac{1}{2}\arctan \left(\frac{\mathrm{Gxy}}{\mathrm{Gxx}}\right)---\left(6\right)$

In order accurately to estimate the streakline direction; After to the horizontal gradient component and VG (vertical gradient) component of obtaining pixel; It is carried out gaussian filtering handle, then directional diagram is handled, then directional diagram is carried out smoothly with Gaussian filter; Carry out gaussian filtering at last again and handle, for next step compensation filter is got ready.

Step 3: compensation filter and smoothing processing

Repair to cut off crestal line and suppress non-crestal line information according to the half-tone information of the direction of crestal line and neighborhood and compensate filtering, smothing filtering is that low frequency strengthens the spatial domain filtering technique, and its purpose is a Fuzzy Processing and abate the noise.The smothing filtering of spatial domain adopts simple average method, promptly asks the average gray value of this pixel neighborhood pixels point.The size of neighborhood is directly related with level and smooth effect, and neighborhood is big more, and smooth effect is good more, but neighborhood is excessive, marginal information is lost in a large number, thereby output image is thickened, and therefore needs choose reasonable neighborhood size.By formula (7～9) compensate directional diagram:

$\mathrm{enhimg}(i,j)=\underset{m=-w/2}{\overset{w/2}{\mathrm{\Σ}}}\underset{n=-k/2}{\overset{k/2}{\mathrm{\Σ}}}I(i^{\′},j^{\′})/[(w+1)*(h+1)]---\left(7\right)$

i′＝i+mcos(oimg(i，j))+n*sin(oimg(i，j)) (8)

j′＝j-msin(oimg(i，j))+n*cos(oimg(i，j)) (9)

Wherein oimg (i is the local direction obtained in second step j), and wherein window size is w * h, w=4, h=14, the selection of these values is to come according to the empirical value of repeatedly experiment.

The frequency domain fingerprint image strengthens

After time domain compensation filtering enhancing, the fingerprint image lines is comparatively clear, but in order to make fingerprint image more effectively extract characteristic, can take to strengthen based on the frequency domain of Gabor wave filter, and promptly the frequency domain fingerprint image strengthens.The frequency domain Fourier transform can convert in frequency and does product calculation do convolution algorithm in time domain, thereby fast and effeciently image is handled, and reaches the purpose that strengthens image.The frequency filter based on the Gabor wave filter of structure has good direction and frequency selectivity, can on direction and frequency domain selectivity, strengthen filtering to fingerprint image respectively at frequency domain, and its concrete performing step as shown in Figure 4 is following:

Step 1: global image normalization

After the process enhancement process first time; Each local intensity profile of fingerprint image is relatively more balanced; Can adopt the global image normalization that each gray values of pixel points on the image is adjusted; Adjust to one to the contrast of different original images and average gray fixedly on the rank, reduce the influence that different fingerprint image contrast differences are brought, definition expectation gray average mean ₀=180, expectation gray variance var ₀=3600, key step is following:

(1) gray-scale value of calculated fingerprint image and variance

$M=\frac{\underset{1}{\overset{R}{\mathrm{\Σ}}}\underset{1}{\overset{L}{\mathrm{\Σ}}}\mathrm{enhimg}(i,j)}{R*L},(i,j)\∈\mathrm{enhimg}---\left(10\right)$

$V=\frac{\underset{1}{\overset{R}{\mathrm{\Σ}}}\underset{1}{\overset{L}{\mathrm{\Σ}}}{\left(\mathrm{enhimg}(i,j)-M\right)}^2}{R*L},(i,j)\∈\mathrm{enhimg}---\left(11\right)$

R, L be the row value and the train value of presentation video respectively, and enhimg is the image information after strengthening for the first time.

(2) by following formula image is standardized

$G(i,j)=\left\{\begin{array}{c}\mathrm{mean}0+\sqrt{\mathrm{var}0*{(\mathrm{enhimg}(i,j)-M)}^2/V},(i,j)\∈\mathrm{enhimg}>M\\ \mathrm{mean}0-\sqrt{\mathrm{var}0*{(\mathrm{enhimg}(i,j)-M)}^2/V},(i,j)\∈\mathrm{enhimg},\mathrm{otherwise}\end{array}\right.---\left(12\right)$

Step 2: ask directional diagram

In the image on the different directions point corresponding gray scale be vicissitudinous, the gray scale in crestal line differs very little, and is maximum but the gray scale on the vertical direction differs, and can obtain directional diagram through compute gradient, key step is following:

(1) utilizes sobel to calculate each pixel and do two convolution, obtain the VG (vertical gradient) component G of each pixel _Y(i is j) with horizontal gradient component G _X(i, j);

(2) the direction expansion with each pixel gradient vector is twice;

Gsx(i，j)＝Gx(i，j) ²-Gy(i，j) ²

Gsy(i，j)＝2*Gx(i，j)*Gy(i，j) (13)

(3) ask the average gradient at each pixel place vectorial, local field size is W ₁* W ₁, (W ₁=15), formula is:

$A\_\mathrm{Gsx}(i,j)=\underset{u=-W1/2+i}{\overset{u=W1/2+i}{\mathrm{\Σ}}}\underset{v=-W1/2+j}{\overset{v=-W1/2+j}{\mathrm{\Σ}}}\mathrm{Gsx}(u,v)$

$A\_\mathrm{Gsy}(i,j)=\underset{u=-W1/2+i}{\overset{u=W1/2+i}{\mathrm{\Σ}}}\underset{v=-W1/2+j}{\overset{v=-W1/2+j}{\mathrm{\Σ}}}\mathrm{Gsyy}(u,v)---\left(14\right)$

Then

$Q(i,j)=\left\{\begin{array}{c}0.5*\arctan (A\_\mathrm{Gsy}(i,j)/A\_\mathrm{Gsx}(i,j)),A\_\mathrm{Gsx}(i,j)\≠0\\ 0,\mathrm{otherwise}\end{array}\right.---\left(15\right)$

that obtains can not express all directions of fingerprint, and the angle of direction by formula (16) is adjusted:

$O(i,j)=\left\{\begin{array}{c}Q(i,j)+\frac{\mathrm{\&pi;}}{2},A\_\mathrm{Gsy}>0\\ Q(i,j)+\mathrm{\&pi;},A\_\mathrm{Gsx}(i,j)<0\&cap;A\_\mathrm{Gsy}(i,j)>0\\ Q(i,j),\mathrm{otherwise}\\ \end{array}\right.---\left(16\right)$

Wherein (i j) is adjusted angle to O.

Step 3:Gabor Filtering Processing

The Gabor wave filter has good direction and frequency selectivity, can when preserving correct crestal line and valley line structure, remove noise with it as frequency filter, and this paper adopts the Gabor wave filter to realize the enhancing of fingerprint image.Definition W ₂=10 are filtering mask size, O _X=3 are the logical size of frequency band, O _YBe the logical size of direction band, f=0.1 is the streakline average frequency, and key step is following:

(1) utilizes the gabor wave filter to be used in reference to print image and handle, strengthen template antithetical phrase piece with formula (17) as sub-piece and strengthen;

$E(i,j)=\exp (-\frac{1}{2}(\frac{x^2}{{\mathrm{Ox}}^2}+\frac{y^2}{{\mathrm{Oy}}^2}))*\cos (2*\mathrm{\&pi;}*f*x)$

x＝v*sin(O(i，j))+u*cos(O(i，j))

y＝v*cos(O(i，j))-u*sin(O(i，j)) (17)

(2) the sub-piece after will strengthening by formula (18) be merged into the complete finger print image;

$E(i,j)=\underset{u=-W2/2}{\overset{W2/2}{\mathrm{\&Sigma;}}}\underset{v=-W2/2}{\overset{W2}{\mathrm{\&Sigma;}}}{E(u,v)}^{+}G(i-u,j-v)---\left(18\right)$

Wherein E is the image after strengthening, and the Gabor wave filter can reasonably carry out filtering to fingerprint image at each topography's piece, improves signal noise ratio (snr) of image, thereby can preserve comparatively complete sum fingerprint ridge information clearly.

Strengthen image co-registration

In conjunction with like Fig. 5, strengthen image co-registration and may further comprise the steps:

Step 1: the fusion of pixel;

Time domain enhancing fingerprint image and frequency domain enhancing fingerprint image are added up according to each corresponding pixel,

Realize the fusion of pixel.

Step 2: gradation of image adjustment;

The enhancing image that merges is at last carried out the gray scale adjustment, and key step is following:

(1) obtains enhancing back fingerprint image maximum gradation value ma and minimum gradation value mi, get difference val=ma-mi then;

(2) each pixel with E deducts minimum value;

E(i，j)＝E(i，j)-mi (19)

(3) utilize formula (20) that each gray values of pixel points is adjusted.

E2(i，j)＝E(i，j)*256/val (20)

Specific embodiment

Present embodiment adopts the Matlab programming language on the platform of matlab7.0, to realize; Chosen two fingerprint images and done experiment test, experimental result such as Fig. 6 and shown in Figure 7 can find out from experimental result; For different images; The effect that different filtering modes are obtained is different, Fig. 6 and Fig. 7 former figure before the effect after out-of-date, frequency domain strengthen is superior to strengthening, but two pieces of fingerprint images are best passing through the reinforced effects that obtains after time-domain filtering and the frequency domain filtering enhancement process.Reason is: time-domain filtering is handled the contrast of the crestal line and the valley line that can effectively improve fingerprint, and along the combing of fingerprint local direction, the crestal line of compensation fingerprint image; Particularly streakline ruptures and the streakline fuzzy region; Frequency domain filtering utilizes frequency bandpass filter on frequency domain, to carry out the fingerprint image enhancing, has good direction and selects and frequency selective characteristic, can make full use of the frequency and the directional information of streakline in the regional area; Outstanding streakline inherent structure; Remove picture noise, can fingerprint ridge line and the valley line structure is distortionless remain, reach the purpose of enhancing fingerprint image.Fig. 6 (d) and Fig. 7 (d) are the images of handling through time-domain and frequency-domain, combine the advantage of time-domain filtering and frequency domain filtering, and therefore, the reinforced effects of two images is best.

Claims (4)

1. while is characterized in that based on the enhancement method of fingerprint image of time-domain filtering and frequency domain filtering: in the time-domain filtering enhancement process, carry out the frequency domain filtering enhancement process, carry out fusion treatment and finally strengthened image strengthening image again.

2. a kind of while according to claim 1 is characterized in that based on the enhancement method of fingerprint image of time-domain filtering and frequency domain filtering: described time-domain filtering enhancement process comprises former schematic partial plan picture normalization, asks local streakline direction, compensation filter and smoothing processing.

3. a kind of while according to claim 1 is characterized in that based on the enhancement method of fingerprint image of time-domain filtering and frequency domain filtering: described frequency domain filtering enhancement process comprises former figure global image normalization, asks the adjustment of directional diagram, Gabor Filtering Processing and gradation of image.

4. a kind of while according to claim 1 is characterized in that based on the enhancement method of fingerprint image of time-domain filtering and frequency domain filtering: described enhancing image co-registration is handled and is comprised that corresponding pixel points adds up and the gray scale adjustment.

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