CN102043961A - Vein feature extraction method and method for carrying out identity authentication by utilizing double finger veins and finger-shape features - Google Patents
Vein feature extraction method and method for carrying out identity authentication by utilizing double finger veins and finger-shape features Download PDFInfo
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Abstract
The invention relates to a method for carrying out identity authentication by utilizing double finger veins and finger-shape features, comprising the following steps of: firstly registering and storing vein features and information of finger-shape features of double fingers of a user; collecting, extracting and authenticating the vine features and the information of the finger-shape features of double fingers of a customer; respectively comparing the extracted double finger vein features and finger-shape features with the stored double finger vein features and information of the finger-shape features, and carrying out decision-level fusion; collecting images of hand shapes and finger veins, establishing a rectangular coordinate system by taking an intersection point between the double fingers as an original point and determining a region of interest (ROI); and finally respectively extracting the vein features and the finger-shape features, comparing with the stored double finger veins and information of the finger-shape features, and carrying out decision-level fusion. In the invention, two kinds of information of veins and finger shapes can be respectively extracted from one image and the two kinds of information are fused; and by introducing the coordinate system with the intersection point of the two fingers as the original point, the method provided by the invention not only improves the positioning precision, but also is convenient for users to use and effectively improves the system performance through double feature fusion.
Description
Technical field
The present invention relates to a kind of vena characteristic extracting method and utilize biological characteristic to carry out authentication method, particularly a kind of vena characteristic extracting method and utilize finger vena and method that hand-shaped characteristic carries out authentication.
Background technology
Present social people are more and more higher to the security requirement of information, how accurately to identify a people's identity, protection information security, become a crucial social concern that must solve.Traditional authentication more and more is difficult to satisfy the demand of society owing to very easily forging and losing, and at present convenient with safe solution is exactly biometrics identification technology undoubtedly.Biological characteristic is the fake certification scheme that physical traits such as the figure and features, sound according to the people are carried out the science of authentication.Living things feature recognition is exactly according to unique biological characteristic between each individuality it to be discerned authentication with identity.
Biometrics identification technology more and more is applied to authentication and identification, as adopt the biological identification and the recognition system of features such as face picture, fingerprint, iris, but no matter be fingerprint or iris face picture, these features all are exposed to the surface, be easy to change, such as with advancing age, change in various degree can be arranged all.Finger vena is as a kind of biological characteristic that is positioned at body interior, many-sided superiority is arranged: 1) have very strong universality and uniqueness, the variation of essence, different people's vein image differences can not take place in most people's vein blood vessel with advancing age; 2) vein is positioned at body interior, is difficult to forge or change by operation; 3) the epidermis feature can not influence vein pattern; 4) contactless collection, minimizing people's resentment.
Though finger vein features has its superiority, but certain defective is also arranged simultaneously, vein pattern is by the feature of vein blood vessel from the tripleplane to the two dimension, and certain variation takes place the two dimensional character image that very little angle rotation all can make, influences its First Pass Yield.The finger vein features lines are less, are difficult for determining identical region of interest ROI (Region of Interest).These have brought difficulty all for single finger vena identification and identification.
Summary of the invention
The technical problem to be solved in the present invention provides a kind of method from vein gray level image extraction vein pattern, and this method can not be subjected to the influence of vein tendency to extract the vein network.Contrast some extracting method in the past do not need from different directions extract minutiae respectively, do stack or fusion again.This method can disposablely be extracted the good vein network of effect.
Another problem that the present invention solves is to provide a kind of two methods that refer to that vein and finger-type feature are carried out authentication of utilizing, this method had both added the advantage of vein pattern, reduced the reject rate of azygos vein feature again, solved simultaneously in the azygos vein feature verification process, finger is fixing bad, needs the problem of authentication repeatedly.
In order to address the above problem, the present invention by the following technical solutions:
A kind of method of extracting vein pattern from the vein gray level image, corresponding region of interest ROI is done N * N matrix diagram on the vein gray level image of gathering, it is that symmetric points divide into groups in twos that the outer ring is had a few with the central point, and being divided into is 2N-2 group point, and the gray-scale value of every group of point with central point compared; In 2N-2 group point, if the gray-scale value that M group point is arranged all greater than the gray-scale value of central point, then window center point is the vein pattern point, all unique points formation vein network images that obtain; Vein network image to gained is done skeletonizing, obtains single pixel network characterization, does two-dimensional dct transform again, extracts the feature of the low frequency part of two-dimensional dct transform as the vein network.
A kind of methods of utilizing two finger veins and finger-type feature to carry out authentication, at first the two vein patterns that refer to of user's V-arrangement placement and the information of finger-type feature are stored in registration, gather the two vein patterns that refer to of authentication client V-arrangement placement and the information of finger-type feature then, respectively the vein pattern gathered and the vein pattern of finger-type feature and storage and the information of finger-type feature are compared the level of a making decision fusion.Further, when storage, extraction, utilize the near infrared collector to gather two vein image and finger-type images that refer to that V-arrangement is placed, extract point of crossing between two fingers, determine vein image region of interest ROI and finger-type interesting image regions ROI respectively, extract vein network characterization and finger-type feature then, in Feature Conversion with after merging, again and the feature of having stored compare, obtain final result of determination.
Further, described two vein image and the finger-type images of referring to that collect are carried out pre-service, adopt medium filtering and histogram transformation to carry out pre-service the described two images that refer to that collect.
The image pre-service comprised for two steps, medium filtering and histogram transformation.Because the singularity of vein paddy shape feature, salt-pepper noise is bigger to its influence, tends to extract some isolated noise spots, so at first do medium filtering twice, reduce salt-pepper noise, but this process also has corresponding influence to vein pattern.Next image is done histogram transformation respectively, outstanding respectively profile and two kinds of features of vein.
Medium filtering is based on a kind of nonlinear signal processing technology that can effectively suppress noise of sequencing statistical theory, and two dimension median filter is output as:
g(x，y)＝med{f(x-k，y-l)，(k，l∈W)}，
Wherein, (x, y), (x y) is respectively original image and processing back image to g to f.W is 15 * 15 two dimension pattern plate.Here do medium filtering twice to gathering the image that comes, effectively removed the noise of image.
If image is not added processing, directly extract contour feature and can bring a lot of interference, the high light corrosion of finger edge and some textures in the background all can be taken as profile and extract, and real outline line so just is not easily distinguishable out.
Concrete steps are as follows:
(1) Probability p (x) of each gray level appearance of computed image.
(2)p(x)＝p(x)×(x/125)；
(3) 0～255 calculate the probability sum of each gray-scale value appearance that is lower than this gray-scale value successively.This sequence is q (x), x ∈ [0,255]
(4) histogram transformation.
k(m，n)＝q(x)×255，
(m is that horizontal ordinate is m on the image n) to k, and ordinate is the gray-scale value that n is ordered, and x is the preceding gray-scale value of pixel value conversion for this reason.
Further, in described two finger vein images that collect and finger-type image, extract vein pattern and two kinds of information of finger-type feature respectively:
To the later image of process pre-service, extract hand edge contour information with the Canny algorithm, selecting two to refer to that the extreme point at nest place refers to the point of crossing as two, is that true origin is set up rectangular coordinate system with the point of crossing, determines vein image region of interest ROI and contour images region of interest ROI;
After obtaining the contour images region of interest ROI, in contour images, pointing left hand edge with fitting a straight line, is that benchmark rotation translation image makes fitting a straight line rotate to the vertical direction parallel with the coordinate Y-axis with the fitting a straight line that obtains, and with the position of region of interest ROI left hand edge fixed range; Vein image follows that contour images is done same translational movement and the anglec of rotation is adjusted;
Further, in above-mentioned adjusted image, from profile interesting image regions ROI, extract the contour feature of the finger-type of finger-type transverse width sequence:
For the finger edge information in the contour images region of interest ROI, carry out Feature Conversion, become the characteristic information that is easy to comparison and storage.The present invention extracts the laterally orderly width information of finger.
From the vein image region of interest ROI, extract the vein pattern of DCT low frequency:
The gray level image of finger vena has following characteristics: the xsect intensity profile of finger vena is paddy shape, and more near the lowest point, gray-scale value is low more.
The window of being a N * N is like this begun by the upper left corner on gray level image, and a pixel of a pixel slides into the lower right corner line by line.When vein is arranged in window, some points of outmost turns around the window, its gray-scale value will be greater than the gray-scale value of central point.
Corresponding region of interest ROI is made the window of a N * N on the vein gray level image of gathering, the outer ring is had a few, and be that symmetric points divide into groups in twos with the central point, can be divided into 2N-2 group point altogether, the gray-scale value of every group of point with central point compared.In this 2N-2 group point, if the gray-scale value that M group point is arranged then is a vein edge feature point all greater than the gray-scale value of central point, all unique points that obtain constitute the vein network image at last, wherein, experiment draws: when M is that the vein that extracts of three of window width N/a period of time is more complete.
What more should emphasize is that the direction tendency of vein is to not influence of the present invention.
The invention concrete steps
Step?1：
One of the image outermost obtained punctuated to be changed to zero point, eliminates because the border is former thereby the pseudo-special card point of a circle that produces.
Step?2：
Establishment and previous step outer ring are changed to 0 the equal big or small storage matrix Z of image array, and each element value is initially 0.
Step?3：
Correspondence is made the window of a N * N on the vein gray level image of gathering, the outer ring is had a few, and be that symmetric points divide into groups in twos with the central point, can be divided into 2N-2 group point altogether, the gray-scale value of every group of point with central point compared.
Step?4：
In this 2N-2 group point, all greater than the gray-scale value of central point, then central point is a vein edge feature point as if the gray-scale value that M group point is arranged, and all unique points that so obtain constitute the vein network image at last.
Effect was better when the width of rectangular window was suitable with the blood vessel width, and noise is introduced in less meeting.
The vein network image that extracts is done skeletonizing, obtain single pixel network characterization; At last the single pixel network characterization that obtains is done two-dimensional dct transform, the feature after the last conversion of low frequency part conduct of extraction two-dimensional dct transform.
Further, the authentication contrast stage, respectively the two finger-type feature of vein patterns and finger-type transverse width sequence and two finger-type features of vein patterns and finger-type transverse width sequence that refer to of the DCT low frequency component of storage of referring to of the DCT low frequency component that extracts are carried out the Euclidean distance comparison, the level of a making decision fusion.Only when both all met the demands, authentication was passed through.
Beneficial effect of the present invention:
1) use finger vein features for convenience, overcome its defective simultaneously, the present invention has introduced the finger contours feature with the finger vena relation of having living space, collector once can be gathered the feature of forefinger and middle finger simultaneously, two fingers are placed with V-type, adopt two to refer to images acquired, not only can accurately determine comparison domain, the selection and the distortion that can reduce to point simultaneously can bring great convenience for the user; Simultaneously less with respect to azygos vein contrast characteristic authentication method, the fusion of finger vena and finger-type can increase characteristic information again, the elevator system performance.
2) region of interest ROI can be determined in the point of crossing of forefinger and middle finger, refers to that with two the point of crossing is that initial point is introduced coordinate system, comes accurate adjustment with finger contours, has not only improved bearing accuracy, is user-friendly to simultaneously, and bicharacteristic merges and improved system performance effectively.
3) the present invention can extract vein and two kinds of information of finger-type respectively from piece image, and it is convenient to extract.
Description of drawings
The image that Fig. 1 gathers for the present invention.
Fig. 2 determines the synoptic diagram of comparison domain for the present invention.
The vein network characteristic pattern that Fig. 3 tentatively extracts for the present invention.
Fig. 4 is the profile (a) of the last intercepting of the present invention, the comparison domain of vein (b).
Fig. 5 is N * N matrix diagram, the intermediate point pixel value be X (j, k).
Embodiment
Below in conjunction with accompanying drawing the present invention is further described.
Embodiment:
With reference to Fig. 1, Fig. 2, the present invention relates to a kind of two and refer to that finger-types combine with vein and carry out the method for biological identification, contain following steps:
In the image that the above this method is handled, forefinger 1 and middle finger 2 must V-type be put, as shown in Figure 1.Only in this way can extract the point of crossing in the middle of two fingers, and be initial point introducing coordinate system, determine area-of-interest R0I 4 with this point of crossing 3.
The above this method can be extracted finger-type profile and vein network respectively from the image that single width is gathered.The finger-type profile that extracts as shown in Figure 2, the vein network is as shown in Figure 3.
Will carry out histogram transformation earlier and handle before extracting the finger-type profile, concrete steps are as follows:
Concrete steps are as follows:
(1) Probability p (x) of each gray level appearance of computed image.
(2)p(x)＝p(x)×(x/125)；
(3) 0～255 calculate the probability sum of each gray-scale value appearance that is lower than this gray-scale value successively.This sequence is q (x), x ∈ [0,255]
(4) histogram transformation.
k(m，n)＝q(x)×255，
(m is that horizontal ordinate is m on the image n) to k, and ordinate is the gray-scale value that n is ordered, and x is the preceding gray-scale value of pixel value conversion for this reason.
Before extracting the vein network, on the basis of Fig. 1, make 16 * 16 medium filtering.Extract the vein network with vein network extraction algorithm then, concrete steps are as follows:
Step?1：
One of the image outermost as shown in Figure 1 obtained punctuated to be changed to zero point, eliminates because the border is former thereby the pseudo-special card point of a circle that produces.
Step?2：
Establishment and previous step outer ring are changed to 0 the equal big or small storage matrix Z of image array, and each element value is initially 0.
Step?3：
Fig. 5 is the window that pointwise is slided on image, size is 17 * 17 (N=17 this moment), at first set count value counter=0, two sequence numbers in comparison diagram 5 rectangular box are the point of O, if P (k)＞P (X)+threshold and P (k)＞P (X)+threshold, then counter is from adding 1.Relatively sequence number is 2 point again, by that analogy, and up to 31.
Wherein:
Threshold is a threshold value.
P (k) is the some corresponding gray of O for sequence number among Fig. 5,0≤k≤31.
P (X) is the central point corresponding gray of X for sequence number among Fig. 5.
Step?4：
Judge that whether counter＞M is true (M=5 this moment), if true, then be changed to 255 with X, otherwise be 0.
Effect was better when the width of rectangular window was suitable with the blood vessel width, and noise is introduced in less meeting, and adding compare threshold threshold also is in order to suppress ground unrest.The vein network characterization that tentatively extracts as shown in Figure 3.
The above this method two refers to that point of crossing 3 are initial point with what obtain among Fig. 2 after obtaining as Fig. 2, finger contours and vein network image shown in Figure 3, set up coordinate system, intercepting region of interest ROI 4.The region of interest ROI size of intercepting is 170 * 256, and unit is a pixel.
The above this method is done skeletonizing to the vein network image that extracts and is handled, and obtains single pixel network characterization, as Fig. 4 (a) image.At last the single pixel network characterization that obtains is done two-dimensional dct transform, the vein pattern after the last conversion of low frequency part conduct of extraction two-dimensional dct transform.The width sequence of extracting Fig. 4 (b) finger-type region of interest ROI is as the finger-type feature, and the finger-type width sequence that obtains is an one-dimension array.
The above this method is taked the fusion certification policy of decision level in last fusion authentication; The Euclidean distance that compares vein pattern respectively, the Euclidean distance of finger-type width sequence.When the Euclidean distance of vein pattern less than 100, and the Euclidean distance of finger-type width sequence is less than 600 o'clock, authentication is passed through, otherwise refusal.
Obviously, the above embodiment of the present invention only is for example of the present invention clearly is described, and is not to be qualification to embodiments of the present invention.For those of ordinary skill in the field, can also make other changes in different forms on the basis of the above description.Here can't give exhaustive to all embodiments.Everyly belong to the row that conspicuous variation that technical scheme of the present invention extends out or change still are in protection scope of the present invention.
Claims (10)
1. method of extracting vein pattern from the vein gray level image, it is characterized in that, corresponding region of interest ROI is done N * N matrix diagram on the vein gray level image of gathering, it is that symmetric points divide into groups in twos that the outer ring is had a few with the central point, being divided into is 2N-2 group point, and the gray-scale value of every group of point with central point compared; In 2N-2 group point, if the gray-scale value that M group point is arranged all greater than the gray-scale value of central point, then window center point is the vein pattern point, all unique points formation vein network images that obtain; Vein network image to gained is done skeletonizing, obtains single pixel network characterization, does two-dimensional dct transform again, extracts the feature of the low frequency part of two-dimensional dct transform as the vein network.
2. one kind is utilized two methods that refer to that vein and finger-type feature are carried out authentication, it is characterized in that, at first the two vein pattern of interesting image regions ROI and the information of finger-type feature of referring to of user are stored in registration, gather the two vein pattern of interesting image regions ROI and information of finger-type feature of referring to of authentication client then, the vein pattern of gathering is compared grade fusion of making decision respectively with the finger-type feature of storage with the vein pattern of storage and the finger-type feature of collection.
3. a kind of two methods that refer to that vein and finger-type feature are carried out authentication of utilizing according to claim 2 is characterized in that when storage, collection, two images that refer to that the described V-arrangement that collects is placed carry out pre-service.
4. a kind of two methods that refer to that vein and finger-type feature are carried out authentication of utilizing according to claim 3 is characterized in that, adopt medium filtering and histogram transformation to carry out pre-service to the described two images that refer to that collect.
5. according to claim 2 or 4 described a kind of two methods that refer to that vein and finger-type feature are carried out authentication of utilizing, it is characterized in that, when storage, collection, adopt the method for claim 1 to extract vein pattern.
6. a kind of two methods that refer to that vein and finger-type feature are carried out authentication of utilizing according to claim 5 is characterized in that, extract vein pattern and two kinds of information of finger-type feature in described two finger that collects respectively in the image, comprise the steps:
1) extracts finger contours with the edge extracting algorithm;
2) referring to point of crossing with extreme point between two fingers as two, is that true origin is set up rectangular coordinate system with this point of crossing, determines the region of interest ROI of vein image and contour images;
3) in contour images, finger left hand edge fitting a straight line is that benchmark rotation translation image is to the fixed position with the fitting a straight line that obtains; Vein image follows that contour images is done same translational movement and the anglec of rotation is adjusted;
4) in above-mentioned adjusted image, utilize the described method of claim 1 from the vein image region of interest ROI, to extract the vein pattern of DCT low frequency, from profile interesting image regions ROI, extract the finger-type feature of finger-type transverse width sequence.
7. a kind of two methods that refer to that vein and finger-type feature are carried out authentication of utilizing according to claim 6 is characterized in that, extract the edge with the Canny algorithm in the step 1).
8. a kind of two methods that refer to that vein and finger-type feature are carried out authentication of utilizing according to claim 6, it is characterized in that, the translation of vein described in the step 3), contour images and rotation are adjusted: the left profile with two outline lines of finger edge is a benchmark, carries out match with straight line; Again image is rotated to an angle, the fitting a straight line of gained is rotated to the vertical direction parallel with the coordinate Y-axis, and move to the position with the left hand edge fixed range of region of interest ROI.
9. according to claim 2 or 6 described a kind of two methods that refer to that vein and finger-type feature are carried out authentication of utilizing, it is characterized in that described vein pattern is the DCT low frequency component; Described finger-type is characterized as finger-type transverse width sequence.
10. a kind of two methods that refer to that vein and finger-type feature are carried out authentication of utilizing according to claim 9 is characterized in that it contains following steps:
1) two information that refer to vein and finger-type feature of registration storage user,
(1) points the hand shape placed apart and the image of finger vena with adjacent two of near infrared collector collection, do the pre-service of medium filtering and histogram transformation;
(2) to through the later image of pre-service, extract the edge with the Canny algorithm; Extreme point refers to the point of crossing as two between referring to two then, is that initial point is set up rectangular coordinate system with the point of crossing coordinate, determines region of interest ROI;
(3) after the region of interest ROI that obtains vein image and contour images, in contour images, point left hand edge with fitting a straight line, with the fitting a straight line that obtains is that benchmark rotation translation image makes fitting a straight line rotate to the vertical direction parallel with the coordinate Y-axis, and moves to the position with region of interest ROI left hand edge fixed range; Vein image is done same translation and rotation with contour images;
(4) from adjusted vein image region of interest ROI and contour images region of interest ROI, extract the vein pattern of low frequency component of two-dimensional dct transform and the finger-type feature of finger-type transverse width sequence component respectively;
(5) with above-mentioned two refer to vein pattern and finger-type feature registration storages;
2) according to 1) in the step of (1)-(4), gather, extract two information that refer to veins and finger-type feature of authentication client, extract the low frequency component and the finger-type transverse width sequence of two-dimensional dct transform respectively;
3) with 2) low frequency component and 1 of the two-dimensional dct transform that extracts) Euclidean distance of the DCT low frequency component stored compares, and does decision level fusion; With 2) the finger-type transverse width sequence and 1 extracted) Euclidean distance of the finger-type transverse width sequence of storing does decision level fusion.
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