CN109598247A - Two dimensional code identity identifying method based on vein image minutiae point and patterned feature - Google Patents
Two dimensional code identity identifying method based on vein image minutiae point and patterned feature Download PDFInfo
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Abstract
Two dimensional code personal identification method based on vein image minutiae point and patterned feature.The method comprise the steps that being filtered the pretreatment operations such as enhancing, segmentation, refinement to the finger venous image of reading first；The minutiae feature and patterned feature of the finger venous image after refinement are extracted on this basis, and both features are connected, and the vein pattern after series connection is encrypted by the way of random orthogonal；Finally, the image in 2 D code of vein pattern is generated using QR code to encrypted feature.Vein pattern and two dimensional code are effectively combined by the present invention, and combine effective random orthogonal encryption, have many advantages, such as that strong security use value is high.
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 subcutaneously and can be dynamically acquired in real time only by 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 a 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 minutiae feature extraction method is characterized by comprising the following steps: 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. 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 carried out along the direction of the grains, the filtering has the function of smoothing 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 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,f(k) An eight-neighborhood pixel value (the value of a binary image pixel point is 0 or 1) of the central point of the template of 3 × 3, and the value of k is 1,2, …,8, which represents a pixel point at a corresponding position on the detail detection template, for example, a specific corresponding position isAs shown in fig. 3.
If it isThen, thenNIs the end point of vein; if it isThen, thenNThe bifurcation point of the vein line.
Recording all minutiae types and locations to featuresIn 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) Line counting feature
Counting the length, type and number of vein lines in the refined vein image as part of the characteristicsF 2Is shown to beWhereind 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.
3) Characteristic series connection
In order to fully utilize the identification information of different vein characteristics, the invention adopts the stepsThe extracted vein minutiae features and the extracted vein minutiae features are connected in series and combined into a vector with larger dimension, so that a feature with better resolution capability on a biological individual can be obtained. I.e. the final vein characteristics。
The vein feature encryption method is characterized in that: 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 generatedThe 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 matricesAnd 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.
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 filtering enhancement algorithm to carry out filtering 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 carried out along the direction of the grains, the filtering has the function of smoothing 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
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 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 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 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,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 isThen, thenNIs the end point of vein; if it isThen, thenNThe bifurcation point of the vein line.
Recording all minutiae types and locations to featuresIn 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 2Is shown to beWhereind 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
In order to fully utilize the identification information of different vein features, 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 vector with better effect on biological individuals can be obtainedCharacterization of resolving power. 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 generatedThe 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 matricesAnd 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 encrypted vein features are used for generating a vein two-dimensional code image by adopting a QR code. 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 is as follows:
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 (4)
1. A two-dimensional code identity recognition method based on vein image minutiae and grain characteristics 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.
2. The preprocessing operation and minutiae feature extraction method of claim 1, wherein: the method comprises the following steps of preprocessing operations such as filtering enhancement, segmentation and refinement, and specifically comprises the following steps:
and (3) filtering enhancement: according to the method, a Gabor filtering enhancement algorithm is adopted to carry out filtering enhancement on the vein image; filtering the image by using a Gabor window function along the vein line direction to strengthen the line information; because the filtering is carried out along the direction of the grains, the filtering has the function of smoothing 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; 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 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 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 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.
3. The vein feature extraction method according to claim 1, wherein:
1) minutiae feature extraction
Detecting the positions of the minutiae points and the types of the minutiae points (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,f(k) An eight-neighborhood pixel value (a binary image pixel value is 0 or 1) of a template center point of 3 × 3, and a value of k is 1,2, …,8, which represents a pixel point at a corresponding position on the detail detection template, and the specific corresponding position is shown in fig. 3;
if it isThen, thenNIs the end point of vein; if it isThen, thenNA bifurcation point of vein lines;
recording all minutiae types and locations to featuresIn 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 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) line counting feature
Counting the length, type and number of vein lines in the refined vein image as part of the characteristicsF 2Is shown to beWhereind 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;
3) characteristic series connection
In order to fully utilize the identification information of different vein features, the vein minutiae features and the grain features extracted by the method are connected in series and combined into a vector with larger dimension, so that a feature with better resolution capability to a biological individual, namely a final vein feature can be obtained。
4. The vein feature encryption method according to claim 1, wherein: BCH error correcting codes are added into the extracted vein image characteristics, so that the error correcting function of the identification system is improved; the method for encrypting the characteristics added with the error correcting codes comprises the following steps:
1) first, a random vector is generatedThe 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 matricesAnd vein feature matrixFInner product of (2)Y；
4) Will be provided withYComparing the data of each dimension with a preset threshold valueIf the dimension data is larger than the threshold value, setting the dimension data to be 0, otherwise setting the dimension data to be 1, and finally obtaining a group of binary encrypted vein feature vectors.
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| CN109993257A (en) * | 2019-04-10 | 2019-07-09 | 黑龙江大学 | A kind of two dimensional code based on vein pattern |
| CN110135142A (en) * | 2019-04-30 | 2019-08-16 | 成都甄识科技有限公司 | A kind of netted physiology patterned feature based on geometry slope describes method |
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| CN112102209A (en) * | 2020-11-17 | 2020-12-18 | 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 |
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