CN114612655B - Vein recognition algorithm transplanting method and device - Google Patents

Vein recognition algorithm transplanting method and device Download PDF

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CN114612655B
CN114612655B CN202210504041.9A CN202210504041A CN114612655B CN 114612655 B CN114612655 B CN 114612655B CN 202210504041 A CN202210504041 A CN 202210504041A CN 114612655 B CN114612655 B CN 114612655B
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vein
image
recognition algorithm
feature
vein recognition
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CN114612655A (en
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李学双
高旭
赵国栋
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Beijing Shengdian Cloud Information Technology Co ltd
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/0059—Measuring for diagnostic purposes; Identification of persons using light, e.g. diagnosis by transillumination, diascopy, fluorescence
    • A61B5/0075—Measuring for diagnostic purposes; Identification of persons using light, e.g. diagnosis by transillumination, diascopy, fluorescence by spectroscopy, i.e. measuring spectra, e.g. Raman spectroscopy, infrared absorption spectroscopy
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/48—Other medical applications
    • A61B5/4887—Locating particular structures in or on the body
    • A61B5/489—Blood vessels
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/22—Matching criteria, e.g. proximity measures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F8/00—Arrangements for software engineering
    • G06F8/60—Software deployment
    • G06F8/61—Installation
    • G06F8/63—Image based installation; Cloning; Build to order
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T3/00—Geometric image transformation in the plane of the image
    • G06T3/40—Scaling the whole image or part thereof
    • G06T3/4007—Interpolation-based scaling, e.g. bilinear interpolation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/20—Image enhancement or restoration by the use of local operators
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/11—Region-based segmentation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20024—Filtering details
    • G06T2207/20028—Bilateral filtering
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30101—Blood vessel; Artery; Vein; Vascular

Abstract

The invention discloses a vein recognition algorithm transplanting method and a device, belonging to the technical field of vein recognition and processing, and the method comprises the following steps: 1) constructing a vein image preprocessing algorithm to be transplanted; 2) constructing a vein recognition algorithm model to be transplanted; 3) the method comprises the steps of generating a dynamic link library based on a vein image preprocessing algorithm and a vein recognition algorithm model, compiling the dynamic link library into a library file, compiling based on the library file and a logic code in the vein recognition algorithm to generate an executable program, and importing the executable program into vein equipment. The method has the advantages that the dynamic link library is used in the transplanting process, so that the system space is saved, and the condition that when the application program uses the static link library, each application program needs to link the library code as an independent copy to the executable mirror image is avoided; the constructed algorithm can enhance the contrast of each part of the original image and lays a good foundation for subsequent matching identification; the preprocessing algorithm and the vein recognition algorithm model occupy less system memory and have good portability.

Description

Vein recognition algorithm transplanting method and device
Technical Field
The invention relates to the technical field of vein recognition and processing, in particular to a vein recognition algorithm transplanting method and a vein recognition algorithm transplanting device.
Background
Vein recognition is a new infrared biological recognition technology, which is to use an infrared camera to shoot the distribution diagram of veins in vivo (back of the hand, back of the finger, abdomen of the finger, palm and wrist) according to the characteristic that hemoglobin in the blood of veins in the human body absorbs near infrared or radiates far infrared rays of the human body.
Before the vein identification device is used, an algorithm needs to be implanted into the vein identification device for identifying the running of a program, and a method for transplanting the algorithm is, for example, an algorithm transplanting system based on an embedded platform and an algorithm transplanting method thereof disclosed in chinese patent CN108614703B, wherein the algorithm transplanting system includes an acquisition and evaluation unit for acquiring an evaluation algorithm; an algorithm flow adjusting unit for adjusting the algorithm flow; a multi-core allocation unit, which is used for allocating multi-core to the algorithm process for processing; the framework integration unit is used for carrying out framework integration on the algorithm process subjected to multi-core processing; and the recording unit is used for recording the algorithm into the embedded platform, so that the algorithm designed based on the PC end is transplanted to the embedded platform.
However, when the conventional algorithm migration method is adopted, when an application uses a statically linked library, each application needs to link the library code as an independent copy into an executable image, which results in huge memory and finally reduced operation speed in the identification process.
Disclosure of Invention
The invention aims to provide a vein recognition algorithm transplanting method and a vein recognition algorithm transplanting device, and aims to solve the problems that in the prior art, the recognition success rate is reduced due to unobvious enhancement of a gray level area of a vein, and the operation speed is low and the equipment cost is high due to large system memory occupation of a vein image preprocessing algorithm and a vein recognition algorithm model.
In order to achieve the purpose, the technical scheme provided by the invention is as follows:
the invention relates to a vein recognition algorithm transplanting method, which comprises the following steps:
1) constructing a vein image preprocessing algorithm to be transplanted;
2) constructing a vein recognition algorithm model to be transplanted;
3) the method comprises the steps of generating a dynamic link library based on a vein image preprocessing algorithm and a vein recognition algorithm model, compiling the dynamic link library into a library file, compiling based on the library file and a logic code in the vein recognition algorithm to generate an executable program, and importing the executable program into vein equipment.
Preferably, the step 3) specifically comprises the following steps:
3.1) generating a dynamic link library based on a vein image preprocessing algorithm to be transplanted and a vein recognition algorithm model to be transplanted;
3.2) compiling the dynamic link library into a library file by using a cross compiler;
3.3) compiling the compiled library file and the logic code in the vein recognition algorithm by using a cross compiler to generate an executable program;
3.4) importing the executable program into vein equipment for execution, and debugging the vein equipment until a target effect is achieved;
and 3.5) putting the debugged executable program into a root file system, setting the system to start and execute, manufacturing the file system into a mirror image, and burning the mirror image into the vein equipment by using a burning tool of a corresponding chip.
Preferably, the step 1) is to construct a vein image preprocessing algorithm to be transplanted based on a gray scale non-uniform correction method combining regional linear transformation and gaussian homomorphic filtering, the constructed vein image preprocessing algorithm is used for processing the vein image into a format meeting the input requirement of a vein recognition algorithm model, and the calculation step of the vein image preprocessing algorithm comprises:
1.1) carrying out scale normalization processing on the vein image;
1.2) carrying out image enhancement processing on the vein image subjected to the normalization processing.
Preferably, in the step 1.1), the vein image is scaled by using an interpolation algorithm to further realize normalization processing;
in the step 1.2), the image enhancement processing is performed by using a gray scale non-uniformity correction method combining regional linear transformation and multi-scale homomorphic filtering, and the image enhancement processing specifically comprises the following steps:
1.2.1) setting the normalized vein image asf(x，y)，xAndyrespectively are the row coordinate and the column coordinate of the vein image matrix, and the interesting gray scale range of the image is[a，b]，aAndbrespectively taking the minimum gray value and the maximum gray value of the interested gray range interval of the vein image, carrying out regional linear transformation processing on the image after the scale normalization processing according to formulas (1) - (3), and expanding the interested gray range of the image to [ 2 ]c，d] ，cAnddrespectively carrying out minimum gray value and maximum gray value after expansion on the vein image interesting gray range interval to obtain the vein image after regional linear transformationg(x，y)：
Figure 631324DEST_PATH_IMAGE001
1.2.2) on the basis of the illumination-reflection model, on the processed vein image
Figure 275932DEST_PATH_IMAGE002
Carrying out logarithmic transformation to obtain a vein image after logarithmic transformation
Figure 529059DEST_PATH_IMAGE003
：
Figure 284525DEST_PATH_IMAGE004
1.2.3) based on
Figure 142760DEST_PATH_IMAGE003
Performing a Fourier transform to obtain:
Figure 958269DEST_PATH_IMAGE005
in the formula (I), the compound is shown in the specification,
Figure 636375DEST_PATH_IMAGE006
is composed of
Figure 696997DEST_PATH_IMAGE003
The fourier transform of (a) the signal,uandvrespectively obtaining variables of a row coordinate and a column coordinate x and y of the vein image matrix through Fourier transform;
1.2.4) use of filters
Figure 409738DEST_PATH_IMAGE007
Filtering the above formula to obtain a filtered frequency domain image
Figure 396149DEST_PATH_IMAGE008
：
Figure 827130DEST_PATH_IMAGE009
1.2.5) based on
Figure 924399DEST_PATH_IMAGE008
Performing inverse Fourier transform to obtain an image after frequency domain conversion to spatial domain
Figure 226067DEST_PATH_IMAGE010
：
Figure 383379DEST_PATH_IMAGE011
1.2.6) based on
Figure 36077DEST_PATH_IMAGE010
Is used for calculating a single-scale image
Figure 456080DEST_PATH_IMAGE012
：
Figure 143413DEST_PATH_IMAGE013
1.2.7) carrying out weighted summation on output results of homomorphic filtering of a plurality of scales to obtain a final image
Figure 471627DEST_PATH_IMAGE014
：
Figure 611621DEST_PATH_IMAGE015
In the formula (I), the compound is shown in the specification,N3-5 of the multi-scale number,
Figure 50693DEST_PATH_IMAGE016
for the different single-scale images, the images,
Figure 389270DEST_PATH_IMAGE017
is an image after the linear transformation of the region,
Figure 888384DEST_PATH_IMAGE018
is that
Figure 17140DEST_PATH_IMAGE017
The value range of the weight of (2) is more than 0 and less than 1,
Figure 259902DEST_PATH_IMAGE019
the weight of each single-scale image is required to satisfy the following conditions:
Figure 921828DEST_PATH_IMAGE020
。
preferably, the vein recognition algorithm model to be transplanted constructed in the step 2) is used for outputting a recognition result by using the processed vein image;
the vein identification calculation step of the constructed vein identification algorithm model to be transplanted comprises the following steps:
2.1) carrying out feature extraction on the preprocessed vein image;
2.2) carrying out feature comparison on the vein images after feature extraction.
Preferably, the specific step of performing feature extraction on the preprocessed vein image in the step 2.1) includes:
2.1.1) traversing the entire preprocessed vein imagef(x，y) Splitting the image according to the pixel points to obtain four split imagesF 1 (x，y)、F 2 (x，y)、F 3 (x，y)、F 4 (x，y)；
2.1.2) filtering the four split images by using bilateral filtering respectively;
2.1.3) respectively extracting the terrain characteristic lines of the four filtered vein images;
2.1.4) carrying out feature coding on the four topographic feature line images to obtain four vein feature images, and fusing the four vein features into a large feature image for subsequent comparison.
Preferably, in the step 2.1.3), the extracting of the feature lines of the terrain is to calculate first and second derivative values of each grid point by using concave-convex characteristics of the earth surface where the feature points are located and expressed in different directions through a 3 × 3 moving window, judge the type of the feature points according to the positive and negative of the derivatives, determine the feature points if the derivatives are positive, determine valley points if the derivatives are negative, connect all the ridge points to form ridge lines, and connect all the valley points to form valley lines, which are the feature lines of the terrain;
the feature coding of the topographic feature line image in the step 2.1.4) is to perform feature coding by using a positive and negative combination relation of a second derivative related to an elevation Z in an X direction and a Y direction, and the coding mode is as follows:
if the following conditions are satisfied:
Figure 591843DEST_PATH_IMAGE021
the gray value of the point is set to 0, i.e.
Figure 440851DEST_PATH_IMAGE022
；
If the following conditions are satisfied:
Figure 487304DEST_PATH_IMAGE023
the gray value of the point is set to 1, i.e.
Figure 3736DEST_PATH_IMAGE024
；
If the following conditions are satisfied:
Figure 844653DEST_PATH_IMAGE025
the gray value of the point is set to 2, i.e.
Figure 266987DEST_PATH_IMAGE027
；
If the following conditions are satisfied:
Figure 637925DEST_PATH_IMAGE028
the gray value of the point is set to 3, i.e.
Figure 649743DEST_PATH_IMAGE029
；
If the following conditions are satisfied:
Figure 738922DEST_PATH_IMAGE030
the gray value of the point is set to 4, i.e.
Figure 861599DEST_PATH_IMAGE031
；
If the following conditions are satisfied:
Figure 352623DEST_PATH_IMAGE032
the gray value of the point is set to 5, i.e.
Figure 535343DEST_PATH_IMAGE033
；
If the following conditions are satisfied:
Figure 347703DEST_PATH_IMAGE034
the gray value of the point is set to 6, i.e.
Figure 274071DEST_PATH_IMAGE035
；
If the following conditions are satisfied:
Figure 619602DEST_PATH_IMAGE036
the gray value of the point is set to 7, i.e.
Figure 973223DEST_PATH_IMAGE037
；
In the formula (I), the compound is shown in the specification,
Figure 771414DEST_PATH_IMAGE038
are respectively shown inxDirection andyin direction with respect to elevationZThe second derivative of (a).
Preferably, the specific step of 2.2) performing feature comparison on the vein image after feature extraction includes:
2.2.1) carrying out similarity sequencing on the features to be matched, wherein the sequencing comprises the following specific steps:
2.2.1.1) if the number of the features to be matched is 1, skipping the step 2.2.1), directly entering the step 2.2.2), and if the number of the matched features is more than 1, entering the step 2.2.1.2);
2.2.1.2) scaling the image matrix to be matched and the template characteristics to one fourth of the original image by a bilinear interpolation method;
2.2.1.3) calculating a structural similarity coefficient of the image feature matrix after scaling to express vein image similarity:
Figure 501473DEST_PATH_IMAGE039
in the formula (I), the compound is shown in the specification,
Figure 435931DEST_PATH_IMAGE040
in order to calculate the value of the similarity,
Figure 960453DEST_PATH_IMAGE041
and
Figure 741546DEST_PATH_IMAGE042
two vein feature matrixes are respectively represented,
Figure 275296DEST_PATH_IMAGE043
and
Figure 329840DEST_PATH_IMAGE044
respectively representing feature matrices
Figure 25263DEST_PATH_IMAGE041
And
Figure 798047DEST_PATH_IMAGE042
the average value of the gray levels of (a),
Figure 666646DEST_PATH_IMAGE045
and
Figure 575696DEST_PATH_IMAGE046
respectively representing feature matrices
Figure 209065DEST_PATH_IMAGE041
And
Figure 203566DEST_PATH_IMAGE042
the standard deviation of the gray scale of (a),
Figure 79118DEST_PATH_IMAGE047
representing two feature matrices
Figure 842675DEST_PATH_IMAGE041
And
Figure 145480DEST_PATH_IMAGE042
the covariance of (a) of (b),
Figure 627277DEST_PATH_IMAGE048
and
Figure 40941DEST_PATH_IMAGE049
respectively are constants with the value range of more than 0 and less than or equal to 0.1;
2.2.1.4) sorting the features to be matched according to the size of the similarity value;
2.2.2) selecting top rankednAnd finally, accurately comparing the characteristics to be matched with the template characteristics, and specifically comprising the following steps:
2.2.2.1) carrying out sliding window similarity calculation on the characteristics to be matched and the template characteristics, wherein the calculation formula is as follows:
Figure 924583DEST_PATH_IMAGE050
in the formula:
Figure 899755DEST_PATH_IMAGE051
in order to calculate the comparison value after the calculation,
Figure 868848DEST_PATH_IMAGE052
in order to be the step size,
Figure 86202DEST_PATH_IMAGE053
for the maximum number of rows of the window,
Figure 824351DEST_PATH_IMAGE054
for the number of rows of the window,
Figure 468959DEST_PATH_IMAGE055
for the maximum number of columns of the window,
Figure 925348DEST_PATH_IMAGE056
for the number of window columns,
Figure 680815DEST_PATH_IMAGE057
is the step size in the direction of the window row,
Figure 539049DEST_PATH_IMAGE058
for the step size in the column direction of the window,
Figure 590444DEST_PATH_IMAGE059
for the features to be matched, the matching is carried out,
Figure 534129DEST_PATH_IMAGE060
is a row-column coordinate system, and is characterized in that,
Figure 93287DEST_PATH_IMAGE061
similarity calculation table for the feature to be matched and the template feature, similarity calculation table for the feature to be matched and the template feature
Figure 540449DEST_PATH_IMAGE061
Expressed as:
Figure 526859DEST_PATH_IMAGE062
wherein the content of the first and second substances,
Figure 692261DEST_PATH_IMAGE063
calculating tables for similarity
Figure 789530DEST_PATH_IMAGE061
Model parameter of (2), similarity calculation table
Figure 622357DEST_PATH_IMAGE061
Need to satisfy
Figure 275274DEST_PATH_IMAGE064
；
2.2.2.2) respectively rotating the vein features to be matched by 0 degree, plus or minus 2 degrees and plus or minus 4 degrees and then matching the vein features with the template features one by one to obtain a plurality of matching values, and selecting the minimum value from the plurality of matching values as a final matching result of the two features to be matched;
2.2.2.3) calculating the results of the vein images between and in classes according to the sliding window statistical calculation formula to obtain a data curve, determining a threshold value according to the curve, and judging whether the vein images belong to the same class or different classes according to the threshold value.
Preferably, the vein image preprocessing algorithm to be transplanted and the vein recognition algorithm model to be transplanted, which are constructed in the steps 1) and 2), are uploaded to a cloud, and the vein image preprocessing algorithm and the vein recognition algorithm model are extracted from the cloud in the step 3).
The invention also relates to a vein recognition algorithm transplantation device, which comprises:
the image preprocessing algorithm building module is used for building a vein image preprocessing algorithm to be transplanted;
the vein recognition algorithm model building module is used for building a vein recognition algorithm model to be transplanted;
and the transplantation module is used for generating a dynamic link library based on the vein image preprocessing algorithm and the vein recognition algorithm model, compiling the dynamic link library into a library file, compiling and generating an executable program based on the library file and a logic code in the vein recognition algorithm, and importing the executable program into the vein equipment.
Compared with the prior art, the technical scheme provided by the invention has the following beneficial effects:
1. the vein recognition algorithm transplanting method generates a dynamic link library based on vein image preprocessing algorithm and vein recognition algorithm model transplanting, compiles the dynamic link library, compiles a logic code to generate an executable program, introduces the executable program into vein equipment, uses the dynamic link library, can save system space, can share a single dynamic link library by a plurality of application programs, and avoids that each application program links the library code as an independent copy into an executable mirror image when the application program uses the static link library.
2. The vein recognition algorithm transplanting method further guides the executable program into the vein equipment to be executed in the transplanting process of the vein image preprocessing algorithm and the vein recognition algorithm model, the vein equipment is debugged until the target effect is achieved, the debugged executable program is placed into the root file system, the system is set to start execution, the file system is made into a mirror image, the mirror image is burned into the vein equipment by using the burning tool of the corresponding chip, the executable program can be prevented from being directly placed into the vein equipment to be debugged, and the operation is simpler.
3. The vein image preprocessing algorithm in the vein recognition algorithm transplanting method is constructed based on a gray scale non-uniform correction method combining regional linear transformation and Gaussian homomorphic filtering, the method can enhance the contrast of each part of an original image, namely enhance the gray scale region of veins in an input image, relatively inhibit the non-vein gray scale regions, can enhance the preprocessing of the vein image, and can lay a good foundation for subsequent matching recognition.
4. The preprocessing algorithm and the vein recognition algorithm model used by the vein recognition algorithm transplanting method have the characteristics of small system memory occupation, no hardware platform, high landing speed and high transportability; can transplant to the volume after algorithm and the model conversion in the high in the clouds less, in the lower vein equipment of cost, can realize extensive volume production fast, also help improving vein recognition device's arithmetic speed.
Drawings
FIG. 1 is a flow chart of a vein recognition algorithm transplantation method;
fig. 2 is a structural framework diagram of a vein recognition algorithm transplantation device.
Detailed Description
For further understanding of the present invention, the present invention will be described in detail with reference to examples, which are provided for illustration of the present invention but are not intended to limit the scope of the present invention.
Example 1
Referring to fig. 1, the vein recognition algorithm transplantation method according to the present invention includes the following steps:
1) the vein image preprocessing algorithm to be transplanted is constructed based on a gray scale non-uniform correction method combining regional linear transformation and Gaussian homomorphic filtering, the vein image preprocessing algorithm is used for processing a vein image into a format meeting the input requirement of a vein recognition algorithm model, and the specific flow comprises the following steps:
1.1) utilizing an interpolation algorithm to zoom the vein image so as to realize normalization processing;
1.2) carrying out image enhancement processing on the vein image subjected to the normalization processing by utilizing a gray scale non-uniformity correction method combining regional linear transformation and multi-scale homomorphic filtering, namely, the method comprises the following steps:
1.2.1) setting the normalized vein image asf(x，y) The gray scale range of interest of the image is [ 2 ]a，b]Performing linear regional transformation processing on the image after the scale normalization processing according to the formulas (1) to (3), and expanding the interesting gray scale range of the image to [ 2 ]c，d]Obtaining vein image after linear regional transformationg(x，y)：
Figure 927973DEST_PATH_IMAGE065
After processing, because the gray levels of the transformed vein images between the interval [0, a ] and the interval [ b, L ] are compressed, the noise interference is weakened, the gray level range of the vein images shot by near infrared is 0 to 255, including the interested gray level range of the images and the noise gray level range, namely the noise gray level range is compressed as much as possible, and the interested gray level range is expanded; in the embodiment, the primary noise processing is carried out through the regional linear transformation, the noise range is compressed, and the influence on the processing result caused by the synchronous enhancement of the noise when the gray level of the region of interest is enhanced in the later period is avoided;
1.2.2) on the basis of the illumination-reflection model, on the processed vein image
Figure 828933DEST_PATH_IMAGE066
Carrying out logarithmic transformation to obtain a vein image after logarithmic transformation
Figure 781845DEST_PATH_IMAGE067
：
Figure 110058DEST_PATH_IMAGE068
1.2.3) based on
Figure 250053DEST_PATH_IMAGE067
Performing a Fourier transform to obtain:
Figure 423545DEST_PATH_IMAGE069
in the formula (I), the compound is shown in the specification,
Figure 965385DEST_PATH_IMAGE070
is composed of
Figure 965964DEST_PATH_IMAGE067
The fourier transform of (d).
1.2.4) use of filters
Figure 593254DEST_PATH_IMAGE071
Filtering the above formula to obtain a filtered frequency domain image
Figure 836017DEST_PATH_IMAGE072
：
Figure 232363DEST_PATH_IMAGE073
1.2.5) based on
Figure 902379DEST_PATH_IMAGE072
Performing inverse Fourier transform to obtain an image after frequency domain conversion to spatial domain
Figure 16965DEST_PATH_IMAGE074
：
Figure 63419DEST_PATH_IMAGE075
1.2.6) based on
Figure 314272DEST_PATH_IMAGE074
Is used for calculating a single-scale image
Figure 656654DEST_PATH_IMAGE076
：
Figure 258536DEST_PATH_IMAGE077
1.2.7) carrying out weighted summation on output results of homomorphic filtering of a plurality of scales to obtain a final image
Figure 843101DEST_PATH_IMAGE078
：
Figure 214040DEST_PATH_IMAGE079
In the formula (I), the compound is shown in the specification,Nis a multi-scale number, generally takes 3 to 5,
Figure 225858DEST_PATH_IMAGE080
for the different single-scale images, the images,
Figure 315037DEST_PATH_IMAGE081
is an image after the linear transformation of the region,
Figure 437714DEST_PATH_IMAGE018
is that
Figure 663159DEST_PATH_IMAGE081
The value range of the weight of (2) is more than 0 and less than 1,
Figure 612922DEST_PATH_IMAGE082
for each single-scale imageWeight, and need to satisfy:
Figure 923818DEST_PATH_IMAGE083
。
2) the method comprises the following steps of constructing a vein recognition algorithm model to be transplanted, wherein the vein recognition algorithm model is used for outputting a recognition result by utilizing a processed vein image, and the specific steps comprise:
2.1) carrying out feature extraction on the preprocessed vein image, namely, the method comprises the following steps:
2.1.1) traversing the entire preprocessed vein imagef(x，y) Splitting the image according to the pixel points to obtain four split imagesF 1 (x，y)、F 2 (x，y)、F 3 (x，y)、F 4 (x，y) The size of the split image is one fourth of that of the original image;
2.1.2) the four split images are respectively subjected to filtering processing by using bilateral filtering, and the vein image is split and then subjected to filtering processing, so that the size of the processed image can be reduced, and the operation efficiency is improved;
2.1.3) extracting terrain feature lines of the four filtered vein images respectively, namely, calculating first and second derivative values of each grid point by using concave-convex characteristics of the earth surface where the feature points are located and shown in different directions through a 3 x 3 moving window, judging the type of the feature points according to the positive and negative properties of the derivatives, judging ridge points if the derivatives are positive values, judging valley points if the derivatives are negative values, connecting all the ridge points to form ridge lines, and connecting all the valley points to form valley lines to form terrain feature lines;
in the embodiment, the terrain features are extracted by adopting a raster data image processing technology, regular grid DEM (digital elevation model) data is raster data, the elevation value corresponds to an image gray value, the DEM grid data can be regarded as a gray image, a vein gray image is regarded as DEM grid data, the image gray value corresponds to the elevation value, and subsequent operation is performed according to the theory.
2.1.4) carrying out feature coding on the four topographic feature line images to obtain four vein feature images, namely carrying out feature coding by utilizing the positive and negative combination relation of second-order derivatives related to the elevation Z in the X direction and the Y direction, wherein the coding mode is as follows:
if the following conditions are satisfied:
Figure 850186DEST_PATH_IMAGE084
the gray value of the point is set to 0, i.e.
Figure 195716DEST_PATH_IMAGE085
；
If the following conditions are satisfied:
Figure 549337DEST_PATH_IMAGE086
the gray value of the point is set to 1, i.e.
Figure 347529DEST_PATH_IMAGE087
；
If the following conditions are satisfied:
Figure 77588DEST_PATH_IMAGE088
the gray value of the point is set to 2, i.e.
Figure 277625DEST_PATH_IMAGE027
；
If the following conditions are satisfied:
Figure 297752DEST_PATH_IMAGE028
the gray value of the point is set to 3, i.e.
Figure 583240DEST_PATH_IMAGE029
；
If the following conditions are satisfied:
Figure 851411DEST_PATH_IMAGE089
the gray value of the point is set to 4, i.e.
Figure 905954DEST_PATH_IMAGE031
；
If the following conditions are satisfied:
Figure 866957DEST_PATH_IMAGE090
the gray value of the point is set to 5, i.e.
Figure 639741DEST_PATH_IMAGE033
；
If the following conditions are satisfied:
Figure 446023DEST_PATH_IMAGE091
the gray value of the point is set to 6, i.e.
Figure 620652DEST_PATH_IMAGE035
；
If the following conditions are satisfied:
Figure 988442DEST_PATH_IMAGE092
the gray value of the point is set to 7, i.e.
Figure 248522DEST_PATH_IMAGE037
；
In the formula (I), the compound is shown in the specification,
Figure 858495DEST_PATH_IMAGE093
、
Figure 887631DEST_PATH_IMAGE038
are respectively shown inxSecond derivatives in direction and y-direction with respect to elevation Z;
fusing the four vein features into a large feature image for subsequent comparison;
2.2) comparing the features of the vein image (namely the large feature image) after feature extraction, specifically comprising the following steps:
2.2.1) carrying out similarity sequencing on the features to be matched, wherein the sequencing comprises the following specific steps:
2.2.1.1) if the number of the features to be matched is 1, skipping the step 2.2.1), directly entering the step 2.2.2), and if the number of the matched features is more than 1, entering the step 2.2.1.2);
2.2.1.2) scaling the image matrix to be matched and the template characteristics to one fourth of the original image by a bilinear interpolation method;
2.2.1.3) calculating a structural similarity coefficient of the image feature matrix after scaling to express vein image similarity:
Figure 190436DEST_PATH_IMAGE094
in the formula (I), the compound is shown in the specification,
Figure 672233DEST_PATH_IMAGE040
in order to calculate the value of the similarity,
Figure 85897DEST_PATH_IMAGE041
and
Figure 969539DEST_PATH_IMAGE042
two vein feature matrixes are respectively represented,
Figure 944711DEST_PATH_IMAGE043
and
Figure 710542DEST_PATH_IMAGE044
individual watchDisplay characteristic matrix
Figure 662317DEST_PATH_IMAGE041
And
Figure 666045DEST_PATH_IMAGE042
the average value of the gray levels of (a),
Figure 310653DEST_PATH_IMAGE045
and
Figure 767042DEST_PATH_IMAGE046
respectively representing feature matrices
Figure 522509DEST_PATH_IMAGE041
And
Figure 616629DEST_PATH_IMAGE042
the standard deviation of the gray scale of (a),
Figure 432138DEST_PATH_IMAGE047
representing two feature matrices
Figure 375823DEST_PATH_IMAGE041
And
Figure 934981DEST_PATH_IMAGE042
the covariance of (a) of (b),
Figure 647722DEST_PATH_IMAGE048
and
Figure 368553DEST_PATH_IMAGE049
respectively are constants with the value range of more than 0 and less than or equal to 0.1;
taking an NxN window (N is generally 3, 5 and 7) from the feature matrix during each calculation, then continuously sliding the window for calculation, and finally taking an average value as a global similarity value;
2.2.1.4) sorting the features to be matched according to the size of the similarity value;
2.2.2) selecting top rankednFinal accurate comparison between the features to be matched and the template features, (0)<n<10, n is an integer), the concrete steps are as follows:
2.2.2.1) carrying out sliding window similarity calculation on the characteristics to be matched and the template characteristics, wherein the calculation formula is as follows:
Figure 799534DEST_PATH_IMAGE050
in the formula:
Figure 896803DEST_PATH_IMAGE051
in order to calculate the comparison value after the calculation,
Figure 795594DEST_PATH_IMAGE052
in order to be the step size,
Figure 952906DEST_PATH_IMAGE053
for the maximum number of rows of the window,
Figure 605604DEST_PATH_IMAGE054
for the number of rows of the window,
Figure 506564DEST_PATH_IMAGE055
for the maximum number of columns of the window,
Figure 928318DEST_PATH_IMAGE056
for the number of window columns,
Figure 256531DEST_PATH_IMAGE057
is the step size in the direction of the window row,
Figure 396525DEST_PATH_IMAGE058
is the step size in the column direction of the window,
Figure 101176DEST_PATH_IMAGE059
for the features to be matched, the matching is carried out,
Figure 144481DEST_PATH_IMAGE095
the coordinates of the rows and the columns,
Figure 643595DEST_PATH_IMAGE061
similarity calculation table for the feature to be matched and the template feature, similarity calculation table for the feature to be matched and the template feature
Figure 270886DEST_PATH_IMAGE061
Expressed as:
Figure 513648DEST_PATH_IMAGE096
wherein, the first and the second end of the pipe are connected with each other,
Figure 909995DEST_PATH_IMAGE063
calculating tables for similarity
Figure 580010DEST_PATH_IMAGE061
The model parameters can be correspondingly set according to different vein image libraries, and a similarity calculation table
Figure 694597DEST_PATH_IMAGE061
Need to satisfy
Figure 475471DEST_PATH_IMAGE064
；
2.2.2.2) respectively rotating the vein features to be matched by 0 degree, plus or minus 2 degrees and plus or minus 4 degrees and then matching the vein features with the template features one by one to obtain a plurality of matching values, and selecting the minimum value from the plurality of matching values as a final matching result of the two features to be matched;
2.2.2.3) calculating the results of the vein images between and in classes according to the sliding window statistical calculation formula to obtain a data curve, determining a threshold value according to the curve, and judging whether the vein images belong to the same class or different classes according to the threshold value.
3) Transplanting a vein image pre-processing algorithm and a vein recognition algorithm model to a vein device, the vein device comprising: camera, light filter, reflector, infrared lamp plate, touch sensor touch, central processing unit CPU, its concrete step is:
3.1) under a windows system, generating a dynamic link library by a pretreatment algorithm to be transplanted and a vein recognition algorithm model under a release mode;
3.2) compiling the dynamic link library by using a cross compiler to generate a so library file under the linux system;
3.3) compiling the logic code in the so library file and the vein recognition algorithm to generate an executable program by using a cross compiler mips-linux-gnu-gcc; wherein the logic code is other programs in the vein recognition algorithm, such as programs for controlling the switch of equipment, prompting a user to place a finger and the like;
3.4) using the tool set lrzsz to introduce the executable program into the vein equipment through the serial port for execution, and debugging the vein equipment until the target effect is achieved;
3.5) putting the debugged executable program into a root file system, then setting the system to start execution, then making the file system into a mirror image, and burning the mirror image into vein equipment by using a burning tool of a corresponding chip;
the dynamic link library is used, the system space can be saved, a plurality of application programs can share the single dynamic link library, and the situation that when the application programs use the static link library, each application program needs to link the library codes into the executable mirror image as independent copies is avoided. The executable program is debugged in the equipment firstly and then put into the root file system, so that the problem that the operation is complex because the executable program is directly put into the vein equipment for debugging can be avoided.
The vein device executes the program after being powered on, and can perform a registration and verification process.
Example 2
Referring to fig. 2, the present invention also relates to a vein recognition algorithm transplantation device, which includes:
the image preprocessing algorithm building module builds a vein image preprocessing algorithm to be transplanted based on a gray scale non-uniform correction method combining regional linear transformation and Gaussian homomorphic filtering, and the vein image preprocessing algorithm is used for processing a vein image into a format meeting the input requirement of a vein recognition algorithm model; the image preprocessing algorithm building module is used for realizing the function of the step 1) in the embodiment 1;
the vein recognition algorithm model building module is used for building a vein recognition algorithm model to be transplanted, and the vein recognition algorithm model is used for outputting a recognition result by utilizing the processed vein image; the vein recognition algorithm model construction module is used for realizing the function of the step 2) of the embodiment 1;
the transplantation module is used for transplanting the vein image preprocessing algorithm and the vein recognition algorithm model to vein equipment; the transplanting module is used for realizing the function of the step 3) of the embodiment 1.
Obviously, the vein recognition algorithm transplantation device of the present embodiment can completely implement the vein recognition algorithm transplantation method described in embodiment 1.
The present invention has been described in detail with reference to the embodiments, but the description is only for the preferred embodiments of the present invention and should not be construed as limiting the scope of the present invention. All equivalent changes and modifications made within the scope of the present invention shall fall within the scope of the present invention.

Claims (6)

1. A vein recognition algorithm transplanting method is characterized in that: which comprises the following steps:
1) the method comprises the following steps of constructing a vein image preprocessing algorithm to be transplanted based on a gray scale non-uniformity correction method combining regional linear transformation and Gaussian homomorphic filtering, wherein the constructed vein image preprocessing algorithm is used for processing a vein image into a format meeting the input requirement of a vein recognition algorithm model, and the calculation step of the vein image preprocessing algorithm comprises the following steps:
1.1) carrying out scale normalization processing on the vein image;
1.2) carrying out image enhancement processing by utilizing a gray scale non-uniformity correction method combining regional linear transformation and multi-scale homomorphic filtering, wherein the image enhancement processing comprises the following specific steps:
1.2.1) setting the normalized vein image asf(x，y)，xAndyrespectively is the row coordinate and the column coordinate of the vein image matrix, and the interesting gray scale range of the vein image is [ 2 ]a，b]，aAndbrespectively a minimum gray value and a maximum gray value of a gray scale range of interest of the vein image, performing regional linear transformation processing on the image subjected to the scale normalization processing according to formulas (1) - (3), and expanding the gray scale range of interest of the vein image to [ 2 ]c，d] ，cAnddrespectively carrying out minimum gray value and maximum gray value after expansion on the vein image interesting gray range interval to obtain the vein image after regional linear transformationg(x，y)：
Figure DEST_PATH_IMAGE002
1.2.2) on the basis of the illumination-reflection model, on the processed vein image
Figure DEST_PATH_IMAGE004
Carrying out logarithmic transformation to obtain a vein image after logarithmic transformation
Figure DEST_PATH_IMAGE006
：
Figure DEST_PATH_IMAGE008
1.2.3) based on
Figure 645123DEST_PATH_IMAGE006
Performing a Fourier transform to obtain:
Figure DEST_PATH_IMAGE010
in the formula (I), the compound is shown in the specification,
Figure DEST_PATH_IMAGE012
is composed of
Figure 952476DEST_PATH_IMAGE006
The fourier transform of (a) the signal,uandvrespectively obtaining variables of a row coordinate and a column coordinate x and y of the vein image matrix through Fourier transform;
1.2.4) use of filters
Figure DEST_PATH_IMAGE014
Filtering the above formula to obtain a filtered frequency domain image
Figure DEST_PATH_IMAGE016
：
Figure DEST_PATH_IMAGE018
1.2.5) based on
Figure 388006DEST_PATH_IMAGE016
Performing inverse Fourier transform to obtain an image after frequency domain conversion to spatial domain
Figure DEST_PATH_IMAGE020
：
Figure DEST_PATH_IMAGE022
1.2.6) based on
Figure 901989DEST_PATH_IMAGE020
Is used for calculating a single-scale image
Figure DEST_PATH_IMAGE024
：
Figure DEST_PATH_IMAGE026
1.2.7) entering the output results of homomorphic filtering of multiple scalesLine weighted summation to obtain the final image
Figure DEST_PATH_IMAGE028
：
Figure DEST_PATH_IMAGE030
In the formula (I), the compound is shown in the specification,N3-5 of the multi-scale number,
Figure DEST_PATH_IMAGE032
for the different single-scale images, the images,
Figure DEST_PATH_IMAGE034
is an image after the linear transformation of the region,
Figure DEST_PATH_IMAGE036
is that
Figure 152580DEST_PATH_IMAGE034
The value range of the weight of (2) is more than 0 and less than 1,
Figure DEST_PATH_IMAGE038
the weight of each single-scale image is required to satisfy the following conditions:
Figure DEST_PATH_IMAGE040
；
2) the vein recognition method comprises the following steps of constructing a vein recognition algorithm model to be transplanted, outputting a recognition result by utilizing a processed vein image, and calculating the vein recognition of the constructed vein recognition algorithm model to be transplanted, wherein the vein recognition calculation step comprises the following steps:
2.1) carrying out feature extraction on the preprocessed vein image, wherein the specific steps comprise:
2.1.1) traversing the entire preprocessed vein imagef(x，y) Splitting the image according to the pixel points to obtain four split imagesImage of (2)F 1 (x，y)、F 2 (x，y)、F 3 (x，y)、F 4 (x，y)；
2.1.2) filtering the four split images by using bilateral filtering respectively;
2.1.3) respectively extracting the terrain characteristic lines of the four filtered vein images;
2.1.4) carrying out feature coding on the four topographic feature line images to obtain four vein feature images, and fusing the four vein features into a large feature image for subsequent comparison;
2.2) comparing the characteristics of the vein images after the characteristic extraction;
3) the method comprises the steps of generating a dynamic link library based on a vein image preprocessing algorithm and a vein recognition algorithm model, compiling the dynamic link library into a library file, compiling based on the library file and a logic code in the vein recognition algorithm to generate an executable program, and importing the executable program into vein equipment.
2. The vein recognition algorithm transplantation method of claim 1, wherein: the step 3) specifically comprises the following steps:
3.1) generating a dynamic link library based on a vein image preprocessing algorithm to be transplanted and a vein recognition algorithm model to be transplanted;
3.2) compiling the dynamic link library into a library file by using a cross compiler;
3.3) compiling the compiled library file and the logic code in the vein recognition algorithm by using a cross compiler to generate an executable program;
3.4) importing the executable program into the vein equipment for execution, and debugging the vein equipment until the target effect is achieved;
and 3.5) putting the debugged executable program into a root file system, setting the system to start and execute, manufacturing the file system into a mirror image, and burning the mirror image into the vein equipment by using a burning tool of a corresponding chip.
3. The vein recognition algorithm transplantation method of claim 1, wherein: in the step 2.1.3), extracting the terrain feature line is to calculate first and second derivative values of each grid point by using concave-convex characteristics of the earth surface where the feature points are located and showing in different directions through a 3 x 3 moving window, judge the type of the feature points according to the positive and negative of the derivatives, judge the feature points as ridge points if the derivatives are positive, judge valley points if the derivatives are negative, connect all ridge points to form ridge lines, connect all valley points to form valley lines, and obtain the terrain feature line;
the feature coding of the topographic feature line image in the step 2.1.4) is to perform feature coding by using a positive and negative combination relation of a second derivative related to an elevation Z in an X direction and a Y direction, and the coding mode is as follows:
if the following conditions are satisfied:
Figure DEST_PATH_IMAGE042
then the gray value of the pixel point is set to 0, that is
Figure DEST_PATH_IMAGE044
；
If the following conditions are satisfied:
Figure DEST_PATH_IMAGE046
the gray value of the pixel point is set to 1, that is
Figure DEST_PATH_IMAGE048
；
If the following conditions are satisfied:
Figure DEST_PATH_IMAGE050
then is provided withSet the gray value of the pixel to 2, i.e.
Figure DEST_PATH_IMAGE052
；
If the following conditions are satisfied:
Figure DEST_PATH_IMAGE054
then set the gray value of the pixel to 3, i.e.
Figure DEST_PATH_IMAGE056
；
If the following conditions are satisfied:
Figure DEST_PATH_IMAGE058
then set the gray value of the pixel to 4, i.e.
Figure DEST_PATH_IMAGE060
；
If the following conditions are satisfied:
Figure DEST_PATH_IMAGE062
then set the gray value of the pixel to 5, i.e.
Figure DEST_PATH_IMAGE064
；
If the following conditions are satisfied:
Figure DEST_PATH_IMAGE066
the gray value of the pixel point is set to 6, that is
Figure DEST_PATH_IMAGE068
；
If the following conditions are satisfied:
Figure DEST_PATH_IMAGE070
the gray value of the pixel is set to 7, that is
Figure DEST_PATH_IMAGE072
；
In the formula (I), the compound is shown in the specification,
Figure DEST_PATH_IMAGE074
、
Figure DEST_PATH_IMAGE076
are respectively shown inxDirection andyin direction with respect to elevationZThe second derivative of (a).
4. The vein recognition algorithm transplantation method of claim 1, wherein: the 2.2) the specific steps of comparing the features of the vein image after feature extraction comprise:
2.2.1) carrying out similarity sequencing on the features to be matched, wherein the sequencing comprises the following specific steps:
2.2.1.1) if the number of the features to be matched is 1, skipping the step 2.2.1), and directly entering the step 2.2.2), and if the number of the matched features is more than 1, entering the step 2.2.1.2);
2.2.1.2) scaling the image matrix to be matched and the template characteristics to one fourth of the original image by a bilinear interpolation method;
2.2.1.3) calculating a structural similarity coefficient of the image feature matrix after scaling to express vein image similarity:
Figure DEST_PATH_IMAGE078
in the formula (I), the compound is shown in the specification,
Figure DEST_PATH_IMAGE080
in order to calculate the value of the similarity,
Figure DEST_PATH_IMAGE082
and
Figure DEST_PATH_IMAGE084
two vein feature matrixes are respectively represented,
Figure DEST_PATH_IMAGE086
and
Figure DEST_PATH_IMAGE088
respectively representing feature matrices
Figure 915655DEST_PATH_IMAGE082
And
Figure 572901DEST_PATH_IMAGE084
the average value of the gray levels of (a),
Figure DEST_PATH_IMAGE090
and
Figure DEST_PATH_IMAGE092
respectively representing feature matrices
Figure 896435DEST_PATH_IMAGE082
And
Figure 768576DEST_PATH_IMAGE084
the standard deviation of the gray scale of (a),
Figure DEST_PATH_IMAGE094
representing two feature matrices
Figure 683312DEST_PATH_IMAGE082
And
Figure 968799DEST_PATH_IMAGE084
the covariance of (a) of (b),
Figure DEST_PATH_IMAGE096
and
Figure DEST_PATH_IMAGE098
respectively are constants with the value range of more than 0 and less than or equal to 0.1;
2.2.1.4) sorting the features to be matched according to the size of the similarity value;
2.2.2) selecting top rankednAnd finally, accurately comparing the characteristics to be matched with the template characteristics, and specifically comprising the following steps:
2.2.2.1) carrying out sliding window similarity calculation on the characteristics to be matched and the template characteristics, wherein the calculation formula is as follows:
Figure DEST_PATH_IMAGE100
in the formula:
Figure DEST_PATH_IMAGE102
in order to calculate the comparison value after the calculation,
Figure DEST_PATH_IMAGE104
in order to be the step size,
Figure DEST_PATH_IMAGE106
for the maximum number of rows of the window,
Figure DEST_PATH_IMAGE108
for the number of rows of the window,
Figure DEST_PATH_IMAGE110
for the maximum number of columns of the window,
Figure DEST_PATH_IMAGE112
the number of columns of the window is,
Figure DEST_PATH_IMAGE114
is the step size in the direction of the window row,
Figure DEST_PATH_IMAGE116
for the step size in the column direction of the window,
Figure DEST_PATH_IMAGE118
for the features to be matched, the matching is carried out,
Figure DEST_PATH_IMAGE120
is a row-column coordinate system, and is characterized in that,
Figure DEST_PATH_IMAGE122
similarity calculation table for the feature to be matched and the template feature, similarity calculation table for the feature to be matched and the template feature
Figure 815400DEST_PATH_IMAGE122
Expressed as:
Figure DEST_PATH_IMAGE124
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE126
calculating tables for similarity
Figure 463419DEST_PATH_IMAGE122
Model parameter of (2), similarity calculation table
Figure 424422DEST_PATH_IMAGE122
Need to satisfy
Figure DEST_PATH_IMAGE128
；
2.2.2.2) respectively rotating the vein features to be matched by 0 degree, plus or minus 2 degrees and plus or minus 4 degrees and then matching the vein features with the template features one by one to obtain a plurality of matching values, and selecting the minimum value from the plurality of matching values as a final matching result of the two features to be matched;
2.2.2.3) calculating the results of the vein images between and in classes according to the sliding window statistical calculation formula to obtain a data curve, determining a threshold value according to the curve, and judging whether the vein images belong to the same class or different classes according to the threshold value.
5. The vein recognition algorithm transplantation method of claim 1, wherein: uploading the vein image preprocessing algorithm to be transplanted and the vein recognition algorithm model to be transplanted, which are constructed in the steps 1) and 2), to a cloud, and extracting the vein image preprocessing algorithm and the vein recognition algorithm model from the cloud in the step 3).
6. A vein recognition algorithm transplantation device using the vein recognition algorithm transplantation method according to claim 1, characterized in that: it includes:
the image preprocessing algorithm building module is used for building a vein image preprocessing algorithm to be transplanted;
the vein recognition algorithm model building module is used for building a vein recognition algorithm model to be transplanted;
and the transplantation module is used for generating a dynamic link library based on the vein image preprocessing algorithm and the vein recognition algorithm model, compiling the dynamic link library into a library file, compiling and generating an executable program based on the library file and a logic code in the vein recognition algorithm, and importing the executable program into the vein equipment.
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