CN109598247B - Two-dimensional code identity authentication method based on vein image detail point and grain characteristics - Google Patents
Two-dimensional code identity authentication method based on vein image detail point and grain characteristics Download PDFInfo
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Abstract
A two-dimensional code identity recognition method based on vein image detail points and texture characteristics. The method of the invention comprises the following steps: firstly, preprocessing operations such as filtering enhancement, segmentation, thinning and the like are carried out on a read-in finger vein image; on the basis, extracting the minutiae characteristics and the grain characteristics of the finger vein image after thinning, connecting the minutiae characteristics and the grain characteristics in series, and encrypting the vein characteristics after connecting in series in a random orthogonal mode; and finally, generating a two-dimensional code image of the vein feature by using the QR code for the encrypted feature. The invention effectively combines the vein characteristics with the two-dimensional code and effectively combines random orthogonal encryption processing, and has the advantages of high confidentiality, high use value and the like.
Description
Technical Field
The invention belongs to the technical field of pattern recognition, and particularly relates to a finger vein recognition technology and a two-dimensional code technology.
Background
Compared with biological characteristic identification technologies such as palm print identification and fingerprint identification, the vein identification technology has the following advantages besides uniqueness, universality and collectability: 1) strong immunity: the vein is an internal biological characteristic, so that the vein is not easily influenced by the external environment, and the possibility of misreading is reduced; 2) high anti-counterfeiting performance: the vein features are distributed under the skin, and can be dynamically acquired in real time only through a specific vein acquisition device, so that the conditions that identity information (fingerprints, passwords, cards and the like) is stolen and counterfeited are fundamentally avoided; 3) non-contact property: when vein characteristics are collected, the skin does not need to be in contact with the collecting device, the characteristic information of vein blood vessels can be collected, and a collected person can not generate rejection psychology due to worry about data leaving. Therefore, vein recognition is widely used in more and more occasions. This is accompanied by a security problem of the vein recognition template feature, because once the biometric feature is stolen, sensitive biometric information of the user can be obtained, causing huge loss to the user. Aiming at the problem, the invention connects the detail point characteristic and the line characteristic of the finger vein image in series, encrypts the series characteristic in a random orthogonal mode, and generates a two-dimensional code image of the vein characteristic by using a QR code for the encrypted characteristic for the verification of identity information so as to obtain the identity identification method with reliable performance and strong confidentiality.
Disclosure of Invention
The invention aims to provide a two-dimensional code identity recognition method based on vein image minutiae and line characteristics, wherein the minutiae and line characteristics of a finger vein image are connected in series in a characteristic mode, the series connection characteristic is encrypted in a random orthogonal mode, and a QR code is used for generating a two-dimensional code image of the vein characteristic for the encrypted characteristic, so that the vein characteristic vector is protected, and the safety of biological characteristics is improved.
The purpose of the invention is realized as follows:
a two-dimensional code identity recognition method based on vein image minutiae features is characterized by comprising the following steps: firstly, preprocessing operations such as filtering enhancement, segmentation, thinning and the like are carried out on a read-in finger vein image; on the basis, extracting the minutiae characteristics and the grain characteristics of the finger vein image after thinning, and connecting the minutiae characteristics and the grain characteristics in series; encrypting the vein features after serial connection in a random orthogonal mode; and finally, generating a two-dimensional code image of the vein feature by using the QR code for the encrypted feature.
The preprocessing operation and detail point feature extraction method is characterized by comprising the following steps of: and preprocessing operations such as filtering enhancement, segmentation and refinement. The specific method comprises the following steps:
and (3) filtering enhancement: the invention adopts a Gabor filtering enhancement algorithm to carry out filtering enhancement on the vein image. And filtering the image by using a Gabor window function along the vein line direction to strengthen the line information. Because the filtering is along the direction of the grains, the smoothing effect is realized in the direction of the grains, and therefore, some broken grains can be repaired. In addition, the Gabor filter has good frequency selectivity, and can effectively remove grain noise and keep the structure of grains.
And (3) dividing: because the gradient change of the foreground area and the background area of the finger vein image is obvious, the invention adopts a finger vein image segmentation method based on a gradient field. The gradient field of the vein image is firstly calculated, and then the foreground and the background of the image are segmented by the difference displayed by the vein pattern part and the background part in the gradient field.
Thinning: adopting a serial thinning algorithm to thin the segmented vein lines, and mainly comprising the following steps:
1) starting from the first row and the first column of the finger vein binary image, detecting a target point, namely a point with a pixel value of 1, line by line;
2) respectively comparing 8 neighborhood pixel points of each target pixel point with the eight given elimination templates, and if the 8 neighborhood pixel points are not matched with the eight given elimination templates, reserving the target point; otherwise, comparing the pixel points in the neighborhood of 15 of the point with the six reserved templates respectively, if the pixel points accord with any one of the reserved templates, reserving the point, and if not, deleting the point.
3) And turning to the next target pixel point, and repeating the step 2) until all target pixel points of the whole finger vein image are traversed to obtain the finger vein refined image.
The vein feature extraction method is characterized by comprising the following steps:
1) minutiae feature extraction
The 3 × 3 template employed in the refined finger vein image detects the position of the minutiae point and the type of the minutiae point (end point and intersection point).
The number of intersections of points on the refined image is defined as:
wherein the content of the first and second substances,f(k) The eight neighborhood pixel values (the value of the binary image pixel point is 0 or 1) of the template center point of 3 × 3, and the k value is 1,2, …,8, which represents the pixel point of the corresponding position on the detail detection template, and the specific corresponding position is shown in fig. 3.
If it is
Then, thenNIs the end point of vein; if it is
Then, thenNThe bifurcation point of the vein line.
Recording all minutiae types and locations to features
In whicha i Is as followsiThe row number of each location of the detail point,b i is as followsiThe column number of the location of each detail point,c i is as followsiThe minutiae type is 0 for the endpoint value and 1 for the intersection value.
And (3) converting decimal digits of row numbers and column numbers of the position of the detail point into corresponding binary digits, and then, the vein feature data can be represented by binary.
2) Texture counting feature
Counting the length, type and number of vein lines in the refined vein image as part of the characteristicsF 2 Is shown to be
In whichd i Is a firstiThe length of the lines of each strip is,e i is as followsiThe type of each line, if the starting point of each line is an end point, a cross point is represented by 0, and the starting points are all cross points and are represented by 1,gthe total number of vein lines in the whole image.
3) Characteristic series connection
In order to fully utilize the identification information of different vein features, the extracted vein minutiae features and the vein features are connected in series and combined into a vector with larger dimension, so that a feature with better resolution capability to a biological individual can be obtained. I.e. the final venous characteristics
。
The vein feature encryption method is characterized by comprising the following steps: and adding BCH error correction codes into the extracted vein image characteristics to improve the error correction function of the identification system. The method for encrypting the characteristics added with the error correcting codes comprises the following steps:
1) first, a random vector is generated
The length of the random vector is less than or equal to the length of the vein feature vector;
2) calculating random vectorsOrthogonal matrix of
；
3) Solving orthogonal matrices
And vein feature matrixFInner product of (2)Y；
4) Will be provided withYAnd comparing each dimension of data with a preset threshold value, setting the dimension of data as 0 when the dimension of data is larger than the threshold value, and setting the dimension of data as 1 when the dimension of data is not larger than the threshold value, and finally obtaining a group of binary encrypted vein feature vectors.
The main contributions and characteristics of the invention are:
the invention provides a two-dimensional code identity authentication method based on finger vein features, aiming at the security problems that a personal finger vein feature template is possibly damaged and stolen, and the like.
Drawings
FIG. 1 is a main flow chart of the present invention.
Fig. 2 refines the method flowchart.
FIG. 3 details the detection template.
Detailed Description
The invention will now be described in more detail by way of example with reference to the accompanying drawings in which:
1 finger vein image preprocessing
Firstly, preprocessing operations such as filtering enhancement, image segmentation and thinning are carried out on the read finger vein image. The specific method comprises the following steps:
1.1 Filter enhancement
The invention adopts a Gabor filter enhancement algorithm to carry out filter enhancement on the vein image. The image is filtered using a Gabor window function in the direction along the vein lines, so that the line information is enhanced. Because the filtering is along the direction of the grains, the smoothing effect is realized in the direction of the grains, and therefore, some broken grains can be repaired. In addition, the Gabor filter has good frequency selectivity, and can effectively remove grain noise and keep the structure of grains.
1.2 segmentation
Because the gradient change of the foreground area and the background area of the finger vein image is obvious, the invention adopts a finger vein image segmentation method based on a gradient field. The gradient field of the vein image is firstly calculated, and then the foreground and the background of the image are segmented by the difference displayed by the vein pattern part and the background part in the gradient field.
1.3 refinement
Thinning the divided vein lines by adopting a serial thinning algorithm, and mainly comprising the following steps of:
1) starting from a first row and a first column position of a finger vein binary image, detecting a target point, namely a point with a pixel value of 1, line by line;
2) respectively comparing 8 neighborhood pixel points of each target pixel point with the eight given elimination templates, and if the 8 neighborhood pixel points are not matched with the eight given elimination templates, reserving the target point; otherwise, comparing the pixel points in the 15 neighborhoods of the point with the six reserved templates respectively, if the pixel points accord with any one of the reserved templates, reserving the point, and if the pixel points do not accord with any one of the reserved templates, deleting the point;
3) and (3) turning to the next target pixel point, and repeating the step (2) until all target pixel points of the whole finger vein image are traversed to obtain a finger vein refined image. The flow chart of the whole refining method is shown in figure 2.
2 finger vein image feature extraction
2.1 minutiae feature extraction
The 3 × 3 template employed in the refined finger vein image detects the position of the minutiae points and the type of the minutiae points (end points and intersections).
The number of intersections of points on the refined image is defined as:
wherein the content of the first and second substances,f(k) The eight neighborhood pixel values (the value of the binary image pixel point is 0 or 1) of the template center point of 3 × 3, and the k value is 1,2, …,8, which represents the pixel point of the corresponding position on the detail detection template, and the specific corresponding position is shown in fig. 3.
If it is
Then, thenNIs the end point of vein; if it is
Then, thenNThe bifurcation point of the vein line.
Recording all minutiae types and locations to features
In whicha i Is as followsiThe row number of the location of each detail point,b i is as followsiThe column number of the location of each detail point,c i is as followsiThe minutiae type is 0 for the endpoint value and 1 for the intersection value.
And (3) converting decimal digits of row numbers and column numbers of the position of the detail point into corresponding binary digits, and then, the vein feature data can be represented by binary.
2.2 line count feature
Counting the length, type and number of vein lines in the refined vein image as part of the characteristicsF 2 Is shown to be
Whereind i Is as followsiThe length of the lines of each strip is,e i is as followsiThe type of each line, if the starting point of each line is an end point, a cross point is represented by 0, and the starting points are all cross points and are represented by 1,gthe total number of vein lines in the whole image.
2.3 feature concatenation
To make full use of different staticsThe invention relates to identification information of vein features, which is characterized in that the extracted vein detail point features and the vein features are connected in series and combined into a vector with larger dimension, so that a feature with better resolution capability to a biological individual can be obtained. I.e. the final vein characteristics
。
3. Vein image feature encryption
And adding BCH error correction codes into the extracted vein image characteristics to improve the error correction function of the identification system. The method for encrypting the characteristics added with the error correcting codes comprises the following steps:
1) first, a random vector is generated
The length of the random vector is less than or equal to the length of the vein feature vector;
2) orthogonal matrix for solving random vector
；
3) Solving orthogonal matrices
And vein feature matrixFInner product of (2)Y；
4) Will be provided withYAnd comparing each dimension of data with a preset threshold, setting the dimension of data as 0 if the dimension of data is larger than the threshold, and setting the dimension of data as 1 if the dimension of data is not larger than the threshold, and finally obtaining a group of binary encrypted vein feature vectors.
Two-dimensional code generation and identity authentication based on finger vein characteristics
The invention adopts QR codes to generate vein two-dimensional code images by using the encrypted vein characteristics. The finger vein image feature two-dimensional code encoding method comprises the following steps:
1) analyzing vein characteristic data, and constructing a code word sequence;
2) setting a module in the code word sequence;
3) a mask;
4) determining the version information of the QR code;
5) and generating a two-dimensional code image of the vein feature.
5. Vein two-dimensional code authentication
The vein two-dimensional code identity verification process comprises the following steps:
1) carrying out QR code decoding operation on the generated vein two-dimensional code image;
2) carrying out decryption operation on the decoded feature sequence to obtain the features of the original image;
3) and finally matching and identifying by comparing the Euclidean distance between the vein image features to be identified and the feature vectors of the template samples, thereby completing identity authentication.
Claims (2)
1. A two-dimensional code identity recognition method based on vein image minutiae and grain characteristics is characterized by comprising the following steps: firstly, performing filtering enhancement, segmentation and refinement pretreatment on a read finger vein image; extracting the minutiae characteristics and the grain characteristics of the refined finger vein image, and connecting the minutiae characteristics and the grain characteristics in series; encrypting the vein features after the serial connection in a random orthogonal mode; finally, generating a two-dimensional code image of the vein feature by using a QR code for the encrypted feature;
the identity recognition method comprises the following steps:
1) carrying out QR code decoding operation on the generated vein two-dimensional code image;
2) carrying out decryption operation on the decoded feature sequence to obtain the features of the original image;
3) finally, matching and identifying are realized by comparing the Euclidean distance between the vein image features to be identified and the feature vectors of the template samples, and identity authentication is completed;
the method for extracting the feature and the grain feature of the minutiae comprises the following specific steps:
1) minutiae feature extraction
Detecting the positions of the minutiae points and the types of the minutiae points, namely end points and intersection points, by adopting a 3 x 3 template in the refined finger vein image;
the number of intersections of points on the refined image is defined as:
wherein, the first and the second end of the pipe are connected with each other,f (k) Is the eight neighborhood pixel value of the central point of the template of 3 multiplied by 3, the value of the binary image pixel point is 0 or 1,kthe values are 1,2, … and 8, and the values represent pixel points at corresponding positions on the detail detection template;
if it isC N =1, thenNIs the end point of vein; if it isC N =3, thenNA bifurcation point of vein lines;
recording all minutiae types and locations to featuresF 1 ={(a 1 ,b 1 ,c 1 ),(a 2 ,b 2 ,c 2 ),…,(a n ,b n ,c n ) In whicha i Is a firstiThe row number of each location of the detail point,b i is as followsiThe column number of the location of each detail point,c i is a firstiThe detail point type of each detail point is 0 at the end point value and 1 at the intersection point value;
decimal digits of row numbers and column numbers of the detail point positions are converted into corresponding binary digits, and the vein feature data can be represented by binary;
2) texture counting feature
Counting the length, type and number of vein lines in the refined vein image as part of the characteristicsF 2 Is shown to beF 2 ={(d 1 ,e 1 ), (d 2 ,e 2 ),…,(d m ,e m ), gTherein ofd i Is as followsiThe length of the lines of each strip is,e i is as followsiThe type of each line, if the starting point of the line is an end point and a cross point is represented by 0, the starting pointAll of which are cross-over points are denoted by 1,gthe total number of vein lines in the whole image;
3) characteristic series connection
Connecting the extracted vein detail point characteristics and the vein characteristics in series, and combining the vein detail point characteristics and the vein characteristics into a vector with larger dimension, namely the final vein characteristicsF={F 1 , F 2 }；
The vein feature encryption method comprises the following steps: adding BCH error correction codes into the extracted vein image features; the method for encrypting the characteristics added with the error correcting codes comprises the following steps:
1) first, a random vector is generated
The length of the random vector is less than or equal to the length of the vein feature vector;
2) orthogonal matrix for solving random vector
；
3) Solving orthogonal matrices
Inner product with vein feature matrix FY；
And comparing each dimension of data in the Y with a preset threshold, setting the dimension of data as 0 if the dimension of data is larger than the threshold, and setting the dimension of data as 1 if the dimension of data is not larger than the threshold, and finally obtaining a group of binary encrypted vein feature vectors.
2. The two-dimensional code identification method based on the vein image detail point and the line characteristic as claimed in claim 1, wherein the preprocessing operation is: the method comprises the following specific steps of carrying out pretreatment operations of filtering enhancement, segmentation and refinement:
and (3) filtering enhancement: filtering and enhancing the vein image by adopting a Gabor filtering and enhancing algorithm; filtering the image by using a Gabor window function along the vein line direction to strengthen the line information;
and (3) dividing: a finger vein image segmentation method based on a gradient field is adopted; firstly, calculating a gradient field of a vein image, and then segmenting the foreground and the background of the image according to the difference between the display of a vein pattern part and the display of a background part in the gradient field;
thinning: adopting a serial thinning algorithm to thin the segmented vein lines, and comprising the following steps:
1) starting from a first row and a first column position of a finger vein binary image, detecting a target point, namely a point with a pixel value of 1, line by line;
2) respectively comparing 8 neighborhood pixel points of each target pixel point with the eight given elimination templates, and if the eight elimination templates are not matched, reserving the target point; otherwise, comparing the pixel points in the 15 neighborhoods of the point with the six reserved templates respectively, if the pixel points accord with any one of the reserved templates, reserving the point, and if the pixel points do not accord with any one of the reserved templates, deleting the point;
3) and (3) turning to the next target pixel point, and repeating the step (2) until all target pixel points of the whole finger vein image are traversed to obtain a finger vein refined image.
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| CN109993142B (en) * | 2019-04-10 | 2023-09-12 | 黑龙江大学 | Two-dimensional code identity authentication method based on finger multi-mode biological characteristics |
| CN109993257A (en) * | 2019-04-10 | 2019-07-09 | 黑龙江大学 | A kind of two dimensional code based on vein pattern |
| CN110135142B (en) * | 2019-04-30 | 2021-03-05 | 成都甄识科技有限公司 | Geometric slope-based mesh physiological texture feature description method |
| CN111209851B (en) * | 2020-01-04 | 2023-06-20 | 圣点世纪科技股份有限公司 | Finger vein recognition method based on deep fusion of finger abdominal vein and finger dorsal vein |
| CN112102209B (en) * | 2020-11-17 | 2021-02-19 | 四川圣点世纪科技有限公司 | Abnormal vein image restoration method and device |
| CN112668412A (en) * | 2020-12-15 | 2021-04-16 | 深兰科技（上海）有限公司 | Two-dimensional code generation method and device, electronic equipment and storage medium |
| CN113052045B (en) * | 2021-03-17 | 2023-04-28 | 中国工商银行股份有限公司 | Method, apparatus, computing device and medium for identifying finger vein image |
| CN115223211B (en) * | 2022-09-20 | 2022-12-02 | 山东圣点世纪科技有限公司 | Identification method for converting vein image into fingerprint image |
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