CN110163119A - A kind of finger vein identification method and system - Google Patents
A kind of finger vein identification method and system Download PDFInfo
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- CN110163119A CN110163119A CN201910360334.2A CN201910360334A CN110163119A CN 110163119 A CN110163119 A CN 110163119A CN 201910360334 A CN201910360334 A CN 201910360334A CN 110163119 A CN110163119 A CN 110163119A
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- G06V10/20—Image preprocessing
- G06V10/24—Aligning, centring, orientation detection or correction of the image
- G06V10/243—Aligning, centring, orientation detection or correction of the image by compensating for image skew or non-uniform image deformations
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- G06V10/25—Determination of region of interest [ROI] or a volume of interest [VOI]
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- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/12—Fingerprints or palmprints
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- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
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Abstract
The invention discloses a kind of finger vein identification method and systems, comprising: determines the lower edges point set of finger areas, refinement edge a to pixel wide；It is concentrated from the marginal point after refinement and chooses suitable point progress border extended, obtain true edge point set；According to pixel coordinate is obtained, the rotation of finger is corrected, and background gray levels are set 0；ROI is obtained, width is selected as 0.73 times of original image, and top edge selects nethermost edge coordinate when intercepting, lower edge selects uppermost edge coordinate to carry out height interception when intercepting；Histogram equalization, Gabor filtering are carried out to ROI；Total 3816 is opened after 636 class vein images extract ROI and is saved；The method of one vs n is used when matching, the similarity score of ROI image is opened using the normalized direction calculating two of zero-mean, range is 0 to 1, and it is higher that numerical value closer to 1 represents similarity degree, is judged as image to be matched main body with the highest affiliated main body of that image of image to be matched similarity degree.
Description
Technical field
The present invention relates to image procossing and biometrics identification technology fields, and in particular to a kind of finger vena identification side
Method and system.
Background technique
Refer to that hand vein recognition is one kind of biometrics identification technology, refers to that vein identification technology can be inhaled according to blood flow
The characteristic of receipts feature wavelength relationship irradiates finger using near infrared light, can take the finger vein image of invasion.Due to referring to
Vein pattern is difficult to be replicated, and everyone finger vein pattern is different from, while increasing with the age and hardly occurring
Variation, therefore refer to that vein identification technology has the characteristics that vivo identification, highly-safe, uniqueness, it is managed in company gate inhibition, hotel
Reason, government organs, prison access control, medical verification etc. have huge application prospect.
In referring to hand vein recognition or verification process, since illumination is unstable when acquiring vein image, the rotation of finger can
The picture quality of acquisition can be caused irregular, so one kind is needed to rotate bring application condition Shandong for illumination and finger
The algorithm of stick, which enables, refers to that hand vein recognition is applied in real life.
Summary of the invention
The technical problem to be solved in the present invention is that providing a kind of hand for the deficiency for referring to hand vein recognition algorithm at present
Refer to vein identification method and system to solve the above problems.
A kind of finger vein identification method, comprising:
S1, the lower edges point set that finger areas is determined in original image, refinement edge are described to a pixel wide
Finger areas in original image is horizontal positioned；
S2, it is concentrated from the marginal point after refinement and chooses suitable point progress border extended, obtain true edge point set；
S3, it is rotated into capable correction to finger, and according to true edge point set, non-finger areas gray value is set 0；
S4, the image by S3 processing is cut, width is selected as 0.73 ± 5% times of original image, preferably
0.73 times, top edge selects to select uppermost edge coordinate to carry out when nethermost edge coordinate, lower edge interception when intercepting
Height intercepts, and obtains vein region of interest ROI；
S5, histogram equalization and Gabor filtering are carried out to vein region of interest ROI, it is quiet after obtaining image enhancement
Arteries and veins region of interest ROI, use to be matched；
S6, vein region of interest is extracted using processing the step of S1-S5 to multiple vein images of preset multiple classifications
Domain ROI is simultaneously saved；
The method that S7, hand vein recognition matching use one vs n, it is quiet using zero-mean normalized method calculating every two
The similarity score of arteries and veins region of interest ROI image identifies that belong to same category of vein interested according to similarity score
Region ROI image.
Further, it in step S1, specifically includes:
S11, edge point set a, the pixel gray value phase of current pixel point gray value and its top 2 coordinate of distance are obtained
Difference assert that the pixel is marginal point more than 33；
S12, edge point set b is obtained, the gradient of entire image is calculated using Sobel operator, current pixel point gradient is more than
Then assert that the pixel is marginal point when the two neighboring pixel gradient value of gradient direction；
S13, intersection operation is done to edge point set a and edge point set b, obtains required edge point set, but only retained horizontal
15 pixel, refines edge before coordinate frequency, i.e., at most only one up contour point and one under each ordinate
Down contour point；
S14, edge point set are expressed as horizontal curve in uneven thickness in the picture, micronization processes are carried out to it, by side
Edge is refined to a pixel wide.
Further, it in step S2, is concentrated in lower edges point and an ordinate is selected to make closest to the point at center respectively
Edge is extended for starting point, if the point (x, y), if three coordinates (x-1, y-1) adjacent when being extended to the left side, (x-1,
Y), it is 255 that (x-1, y+1), which has a gray value, then this consecutive points is set as marginal point, continuation extends to the left, if not having
One gray value is 255, then taking these three coordinates, the calculated maximum point of gradient is as marginal point in S1, by constantly
Extension obtains complete finger contours.
Further, in step S3, four marginal points (x1, y1) are selected at 0.23 width and 0.77 width, (x2,
Y1), (x3, y2), (x4, y2) calculate the angle of finger rotation:According to calculated rotation angle
0 is set by image rotation to level, and background gray levels.
Further, it in step S4, specifically includes:
S41, the rectangular window for the use of width being 50, past since the middle coordinate of vein to move right, one coordinate of every movement
Calculation window average gray returns to maximum 5 window coordinates of average gray, and the smallest work of coordinate is selected from this 5
For ordinate baseline, turns left and intercept the vein region of interest ROI of 0.73 times of width of original image；
Height interception is carried out after the completion of S42, width interception, image top edge point set selects nethermost edge coordinate, under
Edge point set selects uppermost edge coordinate to carry out height interception, obtains vein region of interest ROI.
Further, it in step S5, specifically includes:
S51, a kind of limitation contrast self-adaptive direct specifically is used to the ROI progress histogram equalization that interception obtains
Square figure equalization algorithm；
S52, to after histogram equalization ROI carry out 0 °, 45 °, 90 °, 135 ° of tetra- trend pass filtering of Gabor.
Further, it in step S7, specifically includes:
S71, the coding that first all vein images are carried out with classification；
S72, one is chosen as image to be matched, image to be matched and candidate image are divided into 8 rectangular areas, calculated
The ZNCC value of the corresponding rectangular area in each pair of position, calculates the average ZNCC value of two images as measuring similarity value, ZNCC
Calculating need two pictures the sum of the difference of two squares, respective average gray, standard deviation, if the ruler of two corresponding rectangular areas
Very little is (2n+1) × (2n+1), and centre coordinate is respectively (u1,v1),(u2,v2), the sum of difference of two squares isGray scale is average
Value are as follows:Standard deviation are as follows: ZNCC are as follows: Take being averaged for 8 rectangular areas
For ZNCC value as measuring similarity standard, ZNCC value range is 0 to 1, and it is higher that numerical value closer to 1 represents similarity degree, with to
The highest affiliated class of that image of matching image similarity degree is judged as the affiliated class of image to be matched；
S73, the similarity of residual image and sequence in image to be matched and database are calculated, selects the maximum figure of similarity
Class is predicted as place class is used as, is compared with practical class, if equal, prediction is correct, otherwise prediction error；
S74: predict that correct number/matching total degree is the accuracy rate of algorithm when matching.
A kind of finger vein recognition system, comprising: processor and storage equipment；The processor loads and executes described deposit
Instruction and data in storage equipment is for realizing any one finger vein identification method described in claim 1~7.
The beneficial effects of the present invention are: this finger vein identification method, can be very quasi- by the way of border extended
The ROI of vein pattern really is extracted, while the issuable rotation of finger in image acquisition process is corrected, is used
A kind of histogram equalization method, so that image is more robust to illumination in the matching process, using the Gabor in four directions
Filter enhances the textural characteristics of vein, using the matched method of this figure of ZNCC, the finger vena disclosed in Shandong University
98.7% accuracy rate is reached on database.
Detailed description of the invention
Present invention will be further explained below with reference to the attached drawings and examples, in attached drawing:
Fig. 1 is that the ROI of finger vein identification method of the present invention extracts flow chart；
Fig. 2 is that the certification of finger vein identification method of the present invention matches flow chart；
Fig. 3 is the change procedure of vein image when the ROI of finger vein identification method of the present invention is extracted；
Fig. 4 is the equal error rate curves of finger vein identification method of the present invention；
Fig. 5 is the accuracy rate curve of finger vein identification method of the present invention.
Specific embodiment
To make the object, technical solutions and advantages of the present invention clearer, below in conjunction with attached drawing to embodiment party of the present invention
Formula is further described.
As shown in Figure 1 and Figure 2, a kind of finger vein identification method, includes the following steps:
S1, the lower edges point set that finger areas is determined in original image, refinement edge are described to a pixel wide
Finger areas in original image is horizontal positioned；
Detailed process is as follows:
S11, edge point set a is obtained, current pixel point gray value differs super with the gray value of its top 2 pixel of distance
It crosses 33 and then assert that the pixel is marginal point.
S12, edge point set b is obtained, the gradient of entire image is calculated using Sobel operator, current pixel point gradient is more than
Then assert that the pixel is marginal point when the two neighboring pixel gradient value of gradient direction.
S13, intersection operation is done to edge point set a and edge point set b, obtains required edge point set, but only retained horizontal
15 pixel, refines edge before coordinate frequency, i.e., at most only one up contour point and one under each ordinate
Down contour point.
S14, edge point set are expressed as horizontal curve in uneven thickness in the picture, micronization processes are carried out to it, by side
Edge is refined to a pixel wide.
S2, it is concentrated from the marginal point after refinement and chooses suitable point progress border extended, obtain true edge point set；
Detailed process is as follows:
The point for selecting an ordinate respectively closest to center is concentrated to extend edge as starting point in lower edges point, if
The point (x, y), if three coordinates (x-1, y-1) adjacent when extending to the left side, (x-1, y), (x-1, y+1) has one
Gray value is 255, then this consecutive points is set as marginal point, and continuation extends to the left, if none gray value is 255,
These three coordinates calculated maximum point of gradient in S1 is taken to obtain complete finger wheel by constantly extending as marginal point
It is wide.
S3, it is a possibility that finger rotates accuracy rate decline when leading to matching when reducing acquisition image, finger is rotated into
Row correction, and according to true edge point set, non-finger areas gray value is set 0；
Detailed process is as follows:
Four marginal points (x1, y1) of selection at 0.23 width and 0.77 width, (x2, y1), (x3, y2), (x4, y2),
Calculate the angle of finger rotation:According to calculated rotation angle by image rotation to level.And handle
Background gray levels set 0.
S4, the image by S3 processing is cut, width is selected as 0.73 ± 5% times of original image, preferably
0.73 times, top edge selects to select uppermost edge coordinate to carry out when nethermost edge coordinate, lower edge interception when intercepting
Height intercepts, and obtains vein region of interest ROI；
Detailed process is as follows:
S41, the rectangular window for the use of width being 50, past since the middle coordinate of vein to move right, one coordinate of every movement
Calculation window average gray returns to maximum 5 window coordinates of average gray, and the smallest work of coordinate is selected from this 5
For ordinate baseline, turns left and intercept the ROI of 0.73 times of width of original image.
Height interception is carried out after the completion of S42, width interception, image top edge point set selects nethermost edge coordinate, under
Edge point set selects uppermost edge coordinate to carry out height interception, obtains vein area-of-interest (ROI).
S5, histogram equalization and Gabor filtering are carried out to vein region of interest ROI, it is quiet after obtaining image enhancement
Arteries and veins region of interest ROI, use to be matched；
Detailed process is as follows:
S51, a kind of limitation contrast self-adaptive direct specifically is used to the ROI progress histogram equalization that interception obtains
Square figure equalization algorithm (CLAHE).
S52, to after histogram equalization ROI carry out 0 °, 45 °, 90 °, 135 ° of tetra- trend pass filtering of Gabor.
S6, it is saved after opening 636 class vein images extraction ROI to total 3816；
Using the method for one vs n when S7, S7, matching, (Zero Mean Normalized is normalized using zero-mean
Cross- Correlation, ZNCC) direction calculating two open the similarity score of ROI image, range is 0 to 1, and numerical value more connects
Nearly 1 to represent similarity degree higher, is judged as to be matched with the highest affiliated main body of that image of image to be matched similarity degree
Image subject.
Detailed process is as follows:
S71, the coding that first all vein images are carried out with classification.
S72, one is chosen as image to be matched, image to be matched and candidate image are divided into 8 rectangular areas, calculated
The ZNCC value of the corresponding rectangular area in each pair of position, calculates the average ZNCC value of two images as measuring similarity value, ZNCC
Calculating need two pictures the sum of the difference of two squares, respective average gray, standard deviation, if the ruler of two corresponding rectangular areas
Very little is (2n+1) × (2n+1), and centre coordinate is respectively (u1,v1),(u2,v2), the sum of difference of two squares isGray scale is average
Value are as follows:Standard deviation are as follows: ZNCC are as follows: Take being averaged for 8 rectangular areas
For ZNCC value as measuring similarity standard, ZNCC value range is 0 to 1, and it is higher that numerical value closer to 1 represents similarity degree, with to
The highest affiliated class of that image of matching image similarity degree is judged as the affiliated class of image to be matched.
S73, the similarity of residual image and sequence in image to be matched and database are calculated, selects the maximum figure of similarity
Class is predicted as place class is used as, is compared with practical class, if equal, prediction is correct, otherwise prediction error.
S74: predict that correct number/matching total degree is the accuracy rate of algorithm when matching.
The image transform processes that ROI of the invention is extracted indicate that 3.1 represent original image, what 3.2 representatives extracted in Fig. 3
Point set a, 3.3 represent the point set b extracted, and 3.4 represent the intersection of point set a and point set b, and 3.5 represent the edge of extension, 3.6 generations
Background gray levels are set 0 vein image by table, and 3.7 represent the correction postrotational vein image of finger, and 3.8 represent histogram equalization
The ROI image of change, 3.9 represent the ROI image that four direction Gabor filters are used on the basis of 3.8.
As shown in figure 4, abscissa represents accuracy of system identification, ordinate representative refuse it is sincere, as the similarity threshold of setting changes
And change, prediction is correct but when similarity is less than threshold value calculates, prediction error but when similarity be greater than threshold value count sincere into refusing
Accuracy of system identification, the 20*40 in the lower right corner, 40*80 are counted, 80*160 represents the resolution ratio after ROI scaling, and dotted line and solid line intersection point represent
Under different ROI resolution ratio etc. error rates, it can be seen that effect is best when ROI zooms to 40*80.
As shown in figure 5, abscissa represents the similarity threshold of setting, ordinate represents accuracy rate, it can be seen that ROI is differentiated
For rate in 40*80, similarity threshold is set as accuracy rate highest when 0.68.
A kind of finger vein identification method proposed by the present invention and system, at present refer to hand vein recognition algorithm deficiency,
Using the ROI extracting mode according to finger contours, while the rotational correction operation of finger is increased, when enhancing Image Acquisition
To the robustness of finger offset rotation, CLAHE histogram equalization is used, the contrast of image under different illumination is drawn
It stretches, enhances the robustness different to illumination condition, using the Gabor filter in four directions, enhance veinprint, use
The figure matching process of ZNCC, while ROI is divided into eight parts, similarity is sought respectively and is averaged.The present invention is in Shandong
It is verified on finger vena database disclosed in university, obtains 98.7% or so accuracy rate, it is quiet due to the database
Arteries and veins picture quality is lower, therefore in the preferable situation of vein image acquisition equipment, higher accuracy rate can be reached, in identity
The available extensive use of certification identification aspect.
The embodiment of the present invention is described with above attached drawing, but the invention is not limited to above-mentioned specific
Embodiment, the above mentioned embodiment is only schematical, rather than restrictive, those skilled in the art
Under the inspiration of the present invention, without breaking away from the scope protected by the purposes and claims of the present invention, it can also make very much
Form, all of these belong to the protection of the present invention.
Claims (8)
1. a kind of finger vein identification method characterized by comprising
S1, the lower edges point set that finger areas is determined in original image, refinement edge are described original to a pixel wide
Finger areas in image is horizontal positioned；
S2, it is concentrated from the marginal point after refinement and chooses suitable point progress border extended, obtain true edge point set；
S3, it is rotated into capable correction to finger, and according to true edge point set, non-finger areas gray value is set 0；
S4, the image by S3 processing being cut, width is selected as 0.73 ± 5% times of original image, and preferably 0.73
Times, top edge selects to select uppermost edge coordinate to carry out height when nethermost edge coordinate, lower edge interception when intercepting
Interception, obtains vein region of interest ROI；
S5, histogram equalization and Gabor filtering, the vein sense after obtaining image enhancement are carried out to vein region of interest ROI
Interest region ROI, use to be matched；
S6, vein region of interest ROI is extracted using processing the step of S1-S5 to multiple vein images of preset multiple classifications
And it is saved；
The method that S7, hand vein recognition matching use one vs n calculates every two vein senses using the normalized method of zero-mean
The similarity score of interest region ROI image identifies according to similarity score and belongs to same category of vein area-of-interest
ROI image.
2. a kind of finger vein identification method according to claim 1, which is characterized in that in step S1, specifically include:
S11, edge point set a is obtained, current pixel point gray value differs super with the pixel gray value of its top 2 coordinate of distance
It crosses 33 and then assert that the pixel is marginal point；
S12, edge point set b is obtained, the gradient of entire image is calculated using Sobel operator, current pixel point gradient is more than gradient
Then assert that the pixel is marginal point when the two neighboring pixel gradient value in direction；
S13, intersection operation is done to edge point set a and edge point set b, obtains required edge point set, but only retains abscissa
15 pixel, refines edge before frequency, i.e., at most only one up contour point and one are following under each ordinate
Edge point；
S14, edge point set are expressed as horizontal curve in uneven thickness in the picture, and micronization processes are carried out to it, and edge is thin
Change to a pixel wide.
3. a kind of finger vein identification method according to claim 1, which is characterized in that in step S2, in lower edges
Point concentrates the point for selecting an ordinate respectively closest to center to extend edge as starting point, if the point (x, y), to a point left side
If side three coordinates (x-1, y-1) adjacent when extending, (x-1, y), it is 255 that (x-1, y+1), which has a gray value, then this
Consecutive points are set as marginal point, and continuation extends to the left, if none gray value is 255, these three coordinates are taken to fall into a trap in S1
The maximum point of the gradient of calculating is used as marginal point, obtains complete finger contours by constantly extending.
4. a kind of finger vein identification method according to claim 1, which is characterized in that in step S3, in 0.23 width
With four marginal points (x1, y1) of selection at 0.77 width, (x2, y1), (x3, y2), (x4, y2) calculates the angle of finger rotation:0 is set by image rotation to level, and background gray levels according to calculated rotation angle.
5. a kind of finger vein identification method according to claim 1, which is characterized in that in step S4, specifically include:
S41, the rectangular window for the use of width being 50, toward moving right since the middle coordinate of vein, one coordinate of every movement is calculated
Window average gray, returns to maximum 5 window coordinates of average gray, selects coordinate the smallest from this 5 as vertical
Coordinate baseline turns left and intercepts the vein region of interest ROI of 0.73 times of width of original image；
Height interception is carried out after the completion of S42, width interception, image top edge point set selects nethermost edge coordinate, lower edge
Point set selects uppermost edge coordinate to carry out height interception, obtains vein region of interest ROI.
6. a kind of finger vein identification method according to claim 1, which is characterized in that in step S5, specifically include:
S51, a kind of limitation contrast self-adapting histogram specifically is used to the ROI progress histogram equalization that interception obtains
Equalization algorithm；
S52, to after histogram equalization ROI carry out 0 °, 45 °, 90 °, 135 ° of tetra- trend pass filtering of Gabor.
7. a kind of finger vein identification method according to claim 1, which is characterized in that in step S7, specifically include:
S71, the coding that first all vein images are carried out with classification；
S72, one is chosen as image to be matched, image to be matched and candidate image are divided into 8 rectangular areas, calculated each pair of
The ZNCC value of the corresponding rectangular area in position calculates the average ZNCC value of two images as measuring similarity value, the meter of ZNCC
Calculate the sum of the difference of two squares for needing two pictures, respective average gray, standard deviation, if the size of two corresponding rectangular areas is
(2n+1) × (2n+1), centre coordinate are respectively (u1,v1),(u2,v2), the sum of difference of two squares isAverage gray
Are as follows:Standard deviation are as follows: ZNCC are as follows: Take being averaged for 8 rectangular areas
For ZNCC value as measuring similarity standard, ZNCC value range is 0 to 1, and it is higher that numerical value closer to 1 represents similarity degree, with to
The highest affiliated class of that image of matching image similarity degree is judged as the affiliated class of image to be matched；
S73, the similarity of residual image and sequence in image to be matched and database are calculated, selects the maximum image institute of similarity
It in class as prediction class, is compared with practical class, if equal, prediction is correct, otherwise prediction error；
S74: predict that correct number/matching total degree is the accuracy rate of algorithm when matching.
8. a kind of finger vein recognition system characterized by comprising processor and storage equipment；The processor load is simultaneously
The instruction and data executed in the storage equipment identifies for realizing any one finger vena described in claim 1~7
Method.
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| CN110751059A (en) * | 2019-09-27 | 2020-02-04 | 五邑大学 | Least square method-based finger vein ROI extraction method, device and storage medium |
| CN110826513A (en) * | 2019-11-13 | 2020-02-21 | 圣点世纪科技股份有限公司 | Calibration algorithm for consistency of finger vein equipment |
| CN110909631A (en) * | 2019-11-07 | 2020-03-24 | 黑龙江大学 | Finger vein image ROI extraction and enhancement method |
| CN111797828A (en) * | 2020-06-19 | 2020-10-20 | 智慧眼科技股份有限公司 | Method and device for acquiring ROI (region of interest) of finger vein image and related equipment |
| CN111898527A (en) * | 2020-07-29 | 2020-11-06 | 南京邮电大学 | Feature extraction method for low-quality finger vein image |
| CN111914786A (en) * | 2020-08-11 | 2020-11-10 | 重庆文理学院 | Finger vein identification method and system |
| CN112395988A (en) * | 2020-11-18 | 2021-02-23 | 深圳市威富视界有限公司 | Finger vein recognition method and device, computer equipment and storage medium |
| CN112926516A (en) * | 2021-03-26 | 2021-06-08 | 长春工业大学 | Robust finger vein image region-of-interest extraction method |
| CN113269080A (en) * | 2021-05-20 | 2021-08-17 | 南京邮电大学 | Palm vein identification method based on multi-channel convolutional neural network |
| CN113344964A (en) * | 2021-06-23 | 2021-09-03 | 江苏三恒科技股份有限公司 | Image processing-based mine robot rockfall monitoring and early warning method |
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