CN108520211A - The extracting method of finger venous image feature based on finger folding line - Google Patents
The extracting method of finger venous image feature based on finger folding line Download PDFInfo
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- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
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Abstract
The invention belongs to image processing fields, to realize that the accurate positionin of finger target area, the quick enhancing and the accurate acquisition of finger vena topological structure of finger venous image provide clear reliable image for the match cognization of follow-up finger vena.For this purpose, the present invention is based on the extracting method of the finger venous image feature of finger folding line, steps are as follows：Step 1 extracts finger folding line information：Step 2 extracts finger venous image area-of-interest：Step 3 carries out Plane Rotation angle calibration system using finger contours：Step 4 finger venous image enhances：Step 5 finger vein grain --- framework characteristic extracts.Present invention is mainly applied to image procossing occasions.
Description
Technical field
The invention belongs to image processing fields, more particularly to method for preprocessing finger vein images.It concretely relates to be based on
The extracting method of the finger venous image feature of finger folding line.
Background technology
Finger vein identification technology is known as a kind of emerging bio-identification means with fingerprint recognition, iris recognition, palm shape
Not, other biological identification technologies such as recognition of face are compared and are had in uniqueness, stability, portability and In vivo detection etc.
More prominent feature, development space and application prospect are also relatively broad.
Finger vena is located at skin depths and relatively fine and closely woven, directly using the vein image of acquired original carry out matching and
Identification, it is difficult to reach higher accuracy of identification, thus the preprocess method of finger vein image directly affects finger vena
Recognition accuracy.
Since finger is not contacted with harvester, the difference of finger position can cause the finger venous image obtained to exist
Difference.Finger is inserted into influence of the depth to recognition result when to overcome acquisition, and the present invention is using finger folding line to finger vena figure
As being positioned, to obtain accurate target area, weak relevant portion is rejected.
The contrast of finger medium sized vein and non-vein region can be improved in image enhancement, is effectively improved picture quality, after being conducive to
Continuous identification.The problems such as time-consuming, enhancing effect is undesirable for image enhancement link, the present invention provides a kind of modularization is adaptive
Algorithm of histogram equalization is answered, arithmetic speed and picture quality have been considered, is effectively inhibited while reducing time loss
Ambient noise, prominent vein segment.
Since the fineness of vein varies with each individual, and when ambient temperature changes, the fineness of vein
It can change therewith, this undoubtedly can generate a degree of influence to the discrimination of finger vena.
Invention content
In order to overcome the deficiencies of the prior art, the present invention is directed to propose a kind of image preprocessing side for finger vena identification
Method.Using finger folding line image and finger venous image, realize the accurate positionin of finger target area, finger venous image it is fast
Speed enhancing and the accurate acquisition of finger vena topological structure provide clear reliable figure for the match cognization of follow-up finger vena
Picture.For this purpose, the technical solution adopted by the present invention is, and the extracting method of the finger venous image feature based on finger folding line, step
It is as follows：
Step 1 extracts finger folding line information：
Harvester acquires the finger folding line image and finger venous image of synchronization same root finger, ensures that it has
Target area in one-to-one relationship positioning finger venous image；
Step 2 extracts finger venous image area-of-interest：
By vein image binaryzation, finger precise boundary figure is obtained by Canny operators, binary map does algebraically with profile diagram
Subtraction, separation finger region and background area seek its largest connected domain, and then obtain finger areas, i.e. finger mask
Image, mask image do the finger venous image that multiplication of algebra operation obtains region of interest ROI with vein image；
Step 3 carries out Plane Rotation angle calibration system using finger contours：
Using least squares line fitting method calibration finger contours position；
Step 4 finger venous image enhances：
Image is enhanced using modular self-adaptive algorithm of histogram equalization, has considered arithmetic speed and figure
Image quality amount effectively inhibits picture noise while reducing time loss, enhances picture contrast；
Step 5 finger vein grain --- framework characteristic extracts：
When extracting veinprint feature, weaken the noise spot around vein using multiple dimensioned Gabor filter first,
And then complete veinprint structure is extracted using auto-thresholding algorithm, finally use the further mistake of morphological method
The noise spot and filling cavity left in filter lines image；When extracting vein framework characteristic, using morphological approach and most yeast
The vein skeletal extraction algorithm that rate method blends.
It is as follows in an example of the present invention.
Step 1 extracts finger folding line information：
Finger folding line bianry image is obtained using dynamic threshold segmentation method, finger contours and folding line information are highlighted, using side
Edge detection removal finger contours, only retain finger folding line, utilize the finger folding line information of acquisition, positioning finger vena target area
Position, removal image both ends strong noise, Poor information part；
Step 2 extracts finger venous image area-of-interest：
(1) finger venous image binaryzation：The gray value of research object finger and its ambient background in finger venous image
There is notable difference, thus threshold value is obtained using Otsu algorithms automatically, binaryzation is carried out to image, to roughly obtain finger
Position and general shape；
(2) Canny operator extractions finger edge：Edge detection is carried out with Canny operators finger vein image, it is used
High and low threshold value is respectively 50 and 10, obtains the precise boundary figure of finger；
(3) error image：Finger vena bianry image subtracts contour images, thoroughly separates finger areas and background；
(4) mask images are obtained：The area for seeking (3) result each connected domain retains maximum connected domain part, removal
Other parts information, to obtain the region of finger, i.e. finger mask images；
(5) finger venous image of area-of-interest (ROI) is obtained：Utilize the mask images and finger vena that (4) obtain
Image is multiplied, to obtain the research object of subsequent image processing；
Step 3 carries out Plane Rotation angle calibration system using finger contours：
Fitting a straight line is carried out to the profile diagram of finger using least square method, fitting a straight line represents the direction of finger, if
It is reference direction to determine horizontal direction, and the angle in fitting a straight line same level direction represents the deviation angle of finger, according to the angle pair
Finger venous image is corrected, and then overcomes rational disturbance present in gatherer process；
Step 4 finger venous image enhances：
The sub-block that given block size is A × B, searching loop whole picture figure, each sub-block are distributed according to its gray probability
To corresponding cumulative distribution function, then the pixel of each sub-block central area a × b is equalized according to the function.It is false
If the grey level range of input picture f is [fmin, fmax], sub-block sum of all pixels is N, nkFor the pixel of gray level k in sub-block
Number, then probability density p (k) and corresponding cumulative distribution function C (k) are respectively such as following formula：
P (k)=nk/N (1)
The grey scale pixel value for then exporting sub-block is：
ga×b=int [gmin+(gmax-gmin)×C(fA×B)+0.5] (3)
Wherein, int [] is rounding symbol, [gmin, gmax] it is the grey level range for exporting image g, specific steps description
It is as follows：
(i) sub-block that size is A × B is chosen as mobile module, and X is put into the upper left corner of input picture；
(ii) region 1 will be named as by the regions covered X in input picture, the information in region 1 is copied in X；
(iii) histogram equalization is carried out to the image information in X；
(iv) region 2 corresponding with region in input picture 1, a × b picture at 2 center of region are found in the output image
A × b the pixel at the vegetarian refreshments centers X replaces；
(v) move right X a pixel every time, then repeats (ii)-(iv), until X right margins exceed input picture
Right margin after, X is moved to the leftmost side of input picture；
(vi) X is moved down to b pixel every time, then repeats (ii)-(v), until X lower boundaries exceed input picture
Lower boundary.
Step 5 finger vein grain --- framework characteristic extracts：
To interfere horizontal line quantity and effective information degree of loss as performance indicator, threshold adaptive is realized using iterative algorithm
It chooses, to image into row threshold division processing, extracts the lines information for referring to arteries and veins, the circular configuration element for being 1 with size is to it
Corrosion treatment is carried out, the noise with finger vena adhesion in image is removed.The characteristics of according to finger vein grain in being horizontally orientated to,
Closed operation processing repeatedly is carried out to image with linear structure element, vacancy is filled up, restores the integrality of information as possible；
The problems such as in order to make up effective information missing existing for single algorithm extraction vein skeleton and fracture, pass through morphology
The vein skeleton image that thinning algorithm obtains blends, and obtains final finger vena topological structure.
The specific refinement step of step 1 is as follows：
(1) the average distance d of finger folding line range image left end, correction folding line horizontal offset δ are calculated；
(2) the horizontal length of side D of finger venous image is obtained；
(3) in finger venous image, by α × (d+ δ), range image right side β × region (D-d- δ) on the left of range image
Interior pixel value is set to 0, and remainder is the target area paid close attention in image, and α and β are the coefficient for extracting target area.
The features of the present invention and advantageous effect are：
153 width finger venous images (3 width image of each finger collection) from 51 different fingers are tested.
Intensity of illumination is different, finger deflection angle is different and finger is inserted into harvester degree under different conditions to finger vena figure
As carrying out pretreatment operation, more clearly image enhancement effects are can get, and remain the big of original finger venous image
Partial key information.The experimental results showed that：This method has good generalization ability, is that a kind of effective finger venous image is pre-
Processing method.Partial test result is as shown in figure 12.In order to verify method for preprocessing finger vein images proposed by the present invention
Validity carries out 1 respectively using Hausdorff apart from 153 width finger venous image of mean algorithm pair:1 and 1:N is identified, is obtained
Its equal accidentally rate is 3.268%.
Description of the drawings：
The extraction result of Fig. 1 finger folding lines：(a) the finger folding line image of fingerprint image (b) extraction.
Fig. 2 utilizes folding line positioning extraction key message schematic diagram.
Fig. 3 extracts the handling result of key message.
Fig. 4 extracts ROI finger vena flow charts.
Fig. 5 extracts ROI finger venous image processes：(a) original image (b) binary image (c) Canny edge images
(d) finger venous image of error image (e) mask image (f) ROI.
The processing procedure of Fig. 6 anti-rotation interference：(a) finger contours image (b) fitting a straight line image (c) corrects image.
Fig. 7 modular self-adaptive algorithm of histogram equalization schematic diagrams.
Fig. 8 modular self-adaptive algorithm of histogram equalization handling results.
Fig. 9 finger vena cutting procedures：(a) Gabor filtering images (b) Threshold segmentation image (c) erosion operation denoising figure
As (d) closed operation bridges image.
Figure 10 finger vena skeletal extraction processes:(a) morphological method extraction skeleton image (b) method of maximum curvature extracts bone
Frame image.
Figure 11 finger venas original image is compared with vein skeleton fused image:(a) finger vena original image (b) bone
Frame blending image.
Figure 12 partial test result figures.
Specific implementation mode
In order to reduce the interference that vein thickness brings accuracy of identification, the present invention carries on the basis of obtaining veinprint
A kind of vein skeletal extraction algorithm blending Morphology Algorithm and maximum curvature algorithm is gone out.This algorithm can weaken vein
Marginal information, only retain vein single pixel geometry, be not only able to preferably inhibit veinprint fineness pair
The interference of recognition accuracy, and have stronger robustness to the variation of intensity of illumination during venous collection, it is sufficient to it is follow-up
Images match and identification provide and reliable support and ensure.
It is mainly included the following steps that in conjunction with the method for preprocessing finger vein images of finger folding line：
Step 1 extracts finger folding line information：
Harvester acquires the finger folding line image and finger venous image of synchronization same root finger, ensures that it has
One-to-one relationship, therefore, folding line information can clearly reflect that finger is inserted into the position of harvester, position finger vena figure
Target area as in.
Step 2 extracts finger venous image area-of-interest：
Due to the gray value gradual change of target background, subsequent target processing procedure is influenced, therefore extract finger vena first
Interesting image regions.By vein image binaryzation, finger precise boundary figure is obtained by Canny operators.Binary map and profile
Figure does algebraically subtraction, separation finger region and background area, seeks its largest connected domain, and then obtain finger areas, i.e.,
Finger mask image.Mask image does the finger vena that multiplication of algebra operation obtains area-of-interest (ROI) with vein image
Image.
Step 3 carries out Plane Rotation angle calibration system using finger contours：
In order to adapt to the finger of different thicknesses degree, the bore of venous collection device is generally wider, in gatherer process, hand
What is referred to puts the rotation being likely that there are in plane, and the processing and identification to later stage finger venous image cause very big interference.Cause
This, using least squares line fitting method calibration finger contours position.
Step 4 finger venous image enhances：
The contrast of vein segment and muscular tissue parts in finger venous image is smaller, quiet for the ease of follow-up finger
Arteries and veins feature extraction and identification need to enhance image.The present invention proposes modular self-adaptive algorithm of histogram equalization, comprehensive
Conjunction considers arithmetic speed and picture quality, effectively inhibits picture noise while reducing time loss, enhances image comparison
Degree.
Step 5 finger vein grain --- framework characteristic extracts：
When extracting veinprint feature, weaken the noise spot around vein using multiple dimensioned Gabor filter first,
And then complete veinprint structure is extracted using auto-thresholding algorithm, finally use the further mistake of morphological method
The noise spot and filling cavity left in filter lines image；When extracting vein framework characteristic, innovative use morphological approach
The vein skeletal extraction algorithm blended with method of maximum curvature, the larger robustness for enhancing feature extraction algorithm, Neng Gou
Clear consistent vein geometry is obtained under different conditions.
It is as follows in an example of the present invention.
Step 1 extracts finger folding line information：
Finger folding line bianry image is obtained using dynamic threshold segmentation method, finger contours and folding line information are highlighted, using side
Edge detection removal finger contours, only retain finger folding line, as shown in Fig. 1 (b).Largely refer to arteries and veins acquisition image hair by observing
It is existing：The topology information of finger vena is mainly distributed on finger middle section, and the effective information that image both ends include is less, root
The vein blood vessel that portion only has several roughs strong, end is only dispersed with some capillaries.In addition, finger vena harvester is close red
Outer light source is placed on the middle part of collector, closer away from finger middle section, and farther out from both ends, therefore both ends light is weak.For this
One feature, herein using the finger folding line information obtained, the position of positioning finger vena target area removes image both ends height and makes an uproar
The part of sound, Poor information.The schematic diagram of this method is as shown in Fig. 2, the specific method is as follows：
(1) the average distance d of finger folding line range image left end, correction folding line horizontal offset δ are calculated；
(2) the horizontal length of side D of finger venous image is obtained；
(3) in finger venous image, by α × (d+ δ), range image right side β × region (D-d- δ) on the left of range image
Interior pixel value is set to 0, and remainder is the target area paid close attention in image.Wherein, α and β is the coefficient for extracting target area.
It is found by many experiments, more suitable when α is 0.04 and β is 0.2, the finger vena in conjunction with used in the present invention is adopted
Packaging placement location situation, by many experiments and theoretically pixel position calculating, it is 65 to obtain folding line horizontal offset δ, place
The results are shown in Figure 3 for reason.
Step 2 extracts finger venous image area-of-interest：
Flow chart is as shown in figure 4, concrete processing procedure is as described below：
(1) finger venous image binaryzation：The gray scale of research object (finger) and its ambient background in finger venous image
Value has notable difference, thus obtains threshold value automatically using Otsu algorithms and carry out binaryzation to image, to roughly obtain finger
Position and general shape, as shown in Fig. 5 (b).
(2) Canny operator extractions finger edge：Illumination is uneven when Image Acquisition, after image binaryzation, part background area
There are still adhesions for domain and finger areas.Therefore edge detection (institute in of the invention is carried out with Canny operators finger vein image
High and low threshold value is respectively 50 and 10), the precise boundary figure of finger is obtained, as shown in Fig. 5 (c).
(3) error image：Finger vena bianry image subtracts contour images, thoroughly separates finger areas and background, such as Fig. 5
(d) shown in.
(4) mask images are obtained：The area for seeking (3) result each connected domain retains maximum connected domain part, removal
Other parts information, to obtain the region of finger, i.e. finger mask images, as shown in Fig. 5 (e).
(5) finger venous image of area-of-interest (ROI) is obtained：Utilize the mask images and finger vena that (4) obtain
Image is multiplied, to obtain the research object of subsequent image processing.Fig. 5 (e) is multiplied with Fig. 5 (a), obtains the ROI of Fig. 5 (a), such as
Shown in Fig. 5 (f).
Step 3 carries out Plane Rotation angle calibration system using finger contours：
Fitting a straight line, handling result such as Fig. 6 (b) institutes are carried out to the profile diagram (Fig. 6 (a)) of finger using least square method
Show.The fitting a straight line can represent the direction of finger, set horizontal direction as reference direction, the folder in fitting a straight line same level direction
Angle represents the deviation angle of finger, is corrected according to the angle finger vein image, and then overcome in gatherer process and exist
Rational disturbance, shown in finger venous image such as Fig. 6 (c) after correction.
Step 4 finger venous image enhances：
The present invention proposes a kind of modular self-adaptive algorithm of histogram equalization.Its thought：Given block size be A ×
The sub-block of B, searching loop whole picture figure, each sub-block are distributed to obtain corresponding cumulative distribution function according to its gray probability, then
The pixel of each sub-block central area a × b is equalized according to the function.Assuming that the grey level range of input picture f is
[fmin, fmax], sub-block sum of all pixels is N, nkFor the number of pixels of gray level k in sub-block, then probability density p (k) and corresponding
Cumulative distribution function C (k) is respectively such as following formula：
P (k)=nk/N (1)
The grey scale pixel value for then exporting sub-block is：
ga×b=int [gmin+(gmax-gmin)×C(fA×B)+0.5] (3)
Wherein, int [] is rounding symbol, [gmin, gmax] it is the grey level range for exporting image g.The principle of the algorithm
Figure is as shown in Figure 7.Specific steps are described as follows：
(i) sub-block that size is A × B is chosen as mobile module, and X is put into the upper left corner of input picture；
(ii) region 1 will be named as by the regions covered X in input picture, the information in region 1 is copied in X；
(iii) histogram equalization is carried out to the image information in X；
(iv) region 2 corresponding with region in input picture 1, a × b picture at 2 center of region are found in the output image
A × b the pixel at the vegetarian refreshments centers X replaces；
(v) move right X a pixel every time, then repeats (ii)-(iv), until X right margins exceed input picture
Right margin after, X is moved to the leftmost side of input picture；
(vi) X is moved down to b pixel every time, then repeats (ii)-(v), until X lower boundaries exceed input picture
Lower boundary.
Using the handling result of the algorithm as shown in figure 8, it is only 1.229s (530 double-cores four of Intel Core i3 to take
Thread processor, speed 2.93GHz), vein segment clarity is higher, and ambient noise is inhibited well, improves image pair
Than degree, though there are still blocky effect, degree very little influences subsequent processing smaller.The algorithm synthesis considers image quality
With the factors such as time cost, the requirement of finger venous image enhancing is met.
Step 5 finger vein grain --- framework characteristic extracts：
Gabor filtering operations are carried out to weaken the adverse effect that blocky effect is brought to enhanced finger venous image.
According to feature of image, to interfere horizontal line quantity and effective information degree of loss as performance indicator, threshold value is realized using iterative algorithm
It is adaptive to choose, to image into row threshold division processing, extract the lines information for referring to arteries and veins.The circular configuration member for being 1 with size
Element carries out it corrosion treatment, removes the noise with finger vena adhesion in image.It is in be horizontally orientated to according to finger vein grain
The characteristics of, closed operation processing repeatedly is carried out to image with linear structure element, vacancy is filled up, restores the complete of information as possible
Property, processing procedure is as shown in Figure 9.
The problems such as in order to make up effective information missing existing for single algorithm extraction vein skeleton and fracture, pass through morphology
The vein skeleton image that thinning algorithm (Figure 10 (a)) is obtained with method of maximum curvature (Figure 10 (b)) blends, and obtains final finger
Vein topological structure, more to extract finger vena information, to improve subsequent match recognition accuracy.Two methods
Shown in fusion results such as Figure 11 (b).
The present invention utilizes the target area in finger folding line positioning finger venous image；It is proposed modular self-adaptive histogram
Equalization algorithm enhances picture contrast；Mutually melted by the vein skeleton image for obtaining morphological method with maximum curvature method
It closes, obtains relatively sharp complete vein topological structure.By 51 different fingers, 153 test sample verifications are of the invention in total
Algorithm has good generalization ability, and accidentally rate is only 3.268% for identification etc., and partial test result is as shown in figure 12.
Claims (3)
1. a kind of extracting method of the finger venous image feature based on finger folding line, characterized in that steps are as follows：
Step 1 extracts finger folding line information：
Harvester acquires the finger folding line image and finger venous image of synchronization same root finger, ensures that it has one by one
Target area in corresponding relationship positioning finger venous image；
Step 2 extracts finger venous image area-of-interest：
By vein image binaryzation, finger precise boundary figure is obtained by Canny operators, binary map does algebraically subtraction with profile diagram
Operation, separation finger region and background area seek its largest connected domain, and then obtain finger areas, i.e. finger mask image,
Mask image does the finger venous image that multiplication of algebra operation obtains region of interest ROI with vein image；
Step 3 carries out Plane Rotation angle calibration system using finger contours：
Using least squares line fitting method calibration finger contours position；
Step 4 finger venous image enhances：
Image is enhanced using modular self-adaptive algorithm of histogram equalization, has considered arithmetic speed and image matter
Amount effectively inhibits picture noise while reducing time loss, enhances picture contrast；
Step 5 finger vein grain --- framework characteristic extracts：
When extracting veinprint feature, weaken the noise spot around vein using multiple dimensioned Gabor filter first, in turn
Complete veinprint structure is extracted using auto-thresholding algorithm, line is further finally filtered using morphological method
The noise spot and filling cavity left in the image of road；When extracting vein framework characteristic, using morphological approach and method of maximum curvature
The vein skeletal extraction algorithm blended.
2. the extracting method of the finger venous image feature based on finger folding line as described in claim 1, characterized in that one
It is as follows in example：
Step 1 extracts finger folding line information：
Finger folding line bianry image is obtained using dynamic threshold segmentation method, finger contours and folding line information is highlighted, is examined using edge
Removal finger contours are surveyed, finger folding line is only retained, utilize the finger folding line information of acquisition, the position of positioning finger vena target area
Set, removal image both ends strong noise, Poor information part；
Step 2 extracts finger venous image area-of-interest：
(1) finger venous image binaryzation：There is bright the gray value of research object finger and its ambient background in finger venous image
Significant difference is different, thus obtains threshold value automatically using Otsu algorithms and carry out binaryzation to image, to roughly obtain the position of finger
And general shape；
(2) Canny operator extractions finger edge：Edge detection is carried out with Canny operators finger vein image, height used,
Low threshold is respectively 50 and 10, obtains the precise boundary figure of finger；
(3) error image：Finger vena bianry image subtracts contour images, thoroughly separates finger areas and background；
(4) mask images are obtained：The area for seeking (3) result each connected domain retains maximum connected domain part, removes other
Partial information, to obtain the region of finger, i.e. finger mask images；
(5) finger venous image of area-of-interest (ROI) is obtained：Utilize the mask images and finger venous image that (4) obtain
It is multiplied, to obtain the research object of subsequent image processing；
Step 3 carries out Plane Rotation angle calibration system using finger contours：
Fitting a straight line is carried out to the profile diagram of finger using least square method, fitting a straight line is to represent the direction of finger, sets water
The deviation angle that finger square is represented to the angle for reference direction, fitting a straight line same level direction, according to the angle to finger
Vein image is corrected, and then overcomes rational disturbance present in gatherer process；
Step 4 finger venous image enhances：
Given block size is the sub-block of A × B, searching loop whole picture figure, and each sub-block is distributed to obtain pair according to its gray probability
Then the cumulative distribution function answered equalizes the pixel of each sub-block central area a × b according to the function.Assuming that defeated
The grey level range for entering image f is [fmin, fmax], sub-block sum of all pixels is N, nkFor the number of pixels of gray level k in sub-block, then
Probability density p (k) and corresponding cumulative distribution function C (k) are respectively such as following formula：
P (k)=nk/N (1)
The grey scale pixel value for then exporting sub-block is：
ga×b=int [gmin+(gmax-gmin)×C(fA×B)+0.5] (3)
Wherein, int [] is rounding symbol, [gmin, gmax] it is the grey level range for exporting image g, specific steps are described as follows：
(i) sub-block that size is A × B is chosen as mobile module, and X is put into the upper left corner of input picture；
(ii) region 1 will be named as by the regions covered X in input picture, the information in region 1 is copied in X；
(iii) histogram equalization is carried out to the image information in X；
(iv) region 2 corresponding with region in input picture 1, a × b pixel at 2 center of region are found in the output image
It is replaced with a × b pixel at the centers X；
(v) move right X a pixel every time, then repeats (ii)-(iv), until X right margins exceed the right side of input picture
Behind boundary, X is moved to the leftmost side of input picture；
(vi) X is moved down to b pixel every time, then repeats (ii)-(v), until X lower boundaries are beyond under input picture
Boundary.
Step 5 finger vein grain --- framework characteristic extracts：
To interfere horizontal line quantity and effective information degree of loss as performance indicator, threshold adaptive choosing is realized using iterative algorithm
Take, to image into row threshold division processing, extract refer to arteries and veins lines information, with size be 1 circular configuration element to its into
Row corrosion treatment removes the noise with finger vena adhesion in image.It is the characteristics of according to finger vein grain in being horizontally orientated to, more
It is secondary that closed operation processing is carried out to image with linear structure element, vacancy is filled up, restores the integrality of information as possible；
The problems such as in order to make up effective information missing existing for single algorithm extraction vein skeleton and fracture, pass through morphologic thinning
The vein skeleton image that algorithm obtains blends, and obtains final finger vena topological structure.
3. the extracting method of the finger venous image feature based on finger folding line as described in claim 1, characterized in that step
1 specific refinement step is as follows：
(1) the average distance d of finger folding line range image left end, correction folding line horizontal offset δ are calculated；
(2) the horizontal length of side D of finger venous image is obtained；
It (3), will be in β × region (D-d- δ) on the right side of range image left side α × (d+ δ), range image in finger venous image
Pixel value is set to 0, and remainder is the target area paid close attention in image, and α and β are the coefficient for extracting target area.
Priority Applications (1)
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| CN109522842A (en) * | 2018-11-16 | 2019-03-26 | 中国民航大学 | A kind of blood vessel network restorative procedure based on finger venous image |
| CN110147769A (en) * | 2019-05-22 | 2019-08-20 | 成都艾希维智能科技有限公司 | A kind of finger venous image matching process |
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