CN118013494A - Identity verification method and system based on vectorization signature - Google Patents
Identity verification method and system based on vectorization signature Download PDFInfo
- Publication number
- CN118013494A CN118013494A CN202410412002.5A CN202410412002A CN118013494A CN 118013494 A CN118013494 A CN 118013494A CN 202410412002 A CN202410412002 A CN 202410412002A CN 118013494 A CN118013494 A CN 118013494A
- Authority
- CN
- China
- Prior art keywords
- signature
- image
- vectorization
- iris
- fingerprint
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 25
- 238000012795 verification Methods 0.000 title claims abstract description 22
- 238000012545 processing Methods 0.000 claims abstract description 18
- 239000000284 extract Substances 0.000 claims abstract description 4
- 210000000554 iris Anatomy 0.000 claims description 53
- 238000003708 edge detection Methods 0.000 claims description 21
- 230000006870 function Effects 0.000 claims description 20
- 230000007797 corrosion Effects 0.000 claims description 15
- 238000005260 corrosion Methods 0.000 claims description 15
- 238000000605 extraction Methods 0.000 claims description 13
- 238000004590 computer program Methods 0.000 claims description 11
- 238000001514 detection method Methods 0.000 claims description 6
- 230000004927 fusion Effects 0.000 claims description 4
- 230000002708 enhancing effect Effects 0.000 claims description 3
- 239000011159 matrix material Substances 0.000 claims description 3
- 239000012528 membrane Substances 0.000 claims description 3
- 230000010363 phase shift Effects 0.000 claims description 3
- 238000007781 pre-processing Methods 0.000 claims description 3
- 238000007670 refining Methods 0.000 claims description 3
- 230000004044 response Effects 0.000 claims description 3
- 230000009466 transformation Effects 0.000 claims description 3
- 238000010586 diagram Methods 0.000 description 6
- 238000005516 engineering process Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004075 alteration Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 230000000877 morphologic effect Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Landscapes
- Collating Specific Patterns (AREA)
Abstract
The invention relates to an identity verification method and system based on vectorization signature, comprising the following steps: step S1, acquiring an image of a signature, and identifying the edge and the characteristic of the signature image by utilizing an image processing and identifying algorithm, and step S2, converting the signature image into a vectorization path according to the identified signature characteristic to obtain a vectorization signature; step S3, respectively extracting fingerprint characteristic points and iris characteristic points of an authorizer; s4, encrypting the vectorization signature based on the fingerprint and the iris characteristic points; step S5, storing the encrypted vectorization signature in a data field on a blockchain; and S6, the authorized user realizes biological feature verification in the intelligent contract, acquires the encrypted vectorization signature, extracts fingerprint feature points and iris feature points of the authorizer, and performs decryption operation to obtain the vectorization signature. The invention realizes accurate verification of the user identity, protects the privacy information of the user and improves the security of signature use.
Description
Technical Field
The invention relates to the technical field of electronic signature management, in particular to an identity verification method and system based on vectorization signature.
Background
The electronic signature is a representation form of the electronic signature, the electronic signature operation is converted into the same visual effect as the paper file stamping operation by utilizing an image processing technology, and meanwhile, the authenticity and the integrity of the electronic information and the non-repudiation of a signer are ensured by utilizing the electronic signature technology. Along with the development of the power grid, the project is numerous, signature is needed, and currently, when the electronic signature is used and managed, encryption protection is generally carried out on the electronic signature by setting a secret key, so that the security is low.
Disclosure of Invention
In order to solve the problems, the invention aims to provide an identity verification method and system based on vectorization signature, which realize accurate verification of user identity, protect privacy information of users and improve security of signature use.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
an identity verification method based on vectorization signature comprises the following steps:
S1, acquiring a signature image, and identifying edges and features of the signature image by utilizing an image processing and identifying algorithm;
S2, converting the signature image into a vectorization path according to the identified signature characteristics to obtain vectorization signatures;
Step S3, respectively extracting fingerprint characteristic points and iris characteristic points of an authorizer by using a fingerprint image processing algorithm and an iris image processing algorithm;
Step S4, encrypting the vectorization signature by adopting an encryption algorithm based on the fingerprint and the iris characteristic points to obtain the encrypted vectorization signature;
s5, storing the encrypted vectorization signature in a data field on a blockchain, and realizing access control on encrypted data through an intelligent contract, wherein only authorized users can decrypt and access the data;
and S6, the authorized user realizes biological feature verification in the intelligent contract, acquires the encrypted vectorization signature, extracts fingerprint feature points and iris feature points of the authorizer, and performs decryption operation to obtain the vectorization signature.
Further, the step S1 specifically includes:
step S11, edge detection is carried out on the signature image by using an edge detection algorithm to obtain an edge image Edges;
Step S12, thresholding is carried out on the edge image Edges and the edge image Edges, the image is converted into a binary image, the contour detection is carried out on the binary image by utilizing a contour detection function findContours function in an image processing library, the contour information Contours of the signature image is obtained, and the parameter information HoughParams of the special shape is further identified on the basis of a Hough transformation algorithm;
And S13, comprehensively considering the edge image Edges, the contour information Contours and the parameter information HoughParams, extracting information representing signature characteristics, and obtaining signature characteristic representations Features.
Further, the step S11 specifically includes:
(1) Converting the signature image into a gray scale image;
(2) Applying a Sobel operator to the gray level image to perform edge detection in the horizontal and vertical directions;
;
;
;
;
Wherein, Is Sobel operator in horizontal direction,/>For a Sobel operator in the vertical direction, i and j represent the row and column indices in the Sobel operator template,/>For the edge detection result in the horizontal direction,/>For the edge detection result in the vertical direction, I (x, y) represents the pixel value in the gray image, and x and y represent the coordinates of the pixel;
(3) Combining the edge detection results in the horizontal and vertical directions to obtain a final edge image Edges
。
Further, step S13 specifically includes:
Based on the acquired edge image Edges, contour information Contours, and parameter information HoughParams, the perimeter P and area A of the contour are calculated:
；
；
Where n is the number of contour midpoints, X-coordinate representing the a-th contour point,/>Representing the y-coordinate of the a-th contour point;)Representing the position of the a-th point in the contour, distance () represents the distance between two points;
and the number N lines of the straight lines and the radius R of the circle are counted according to the parameter information HoughParams to form the feature representation features= { P, A, N lines, R }.
Further, the step S2 specifically includes:
The contour information is fitted by using Bezier curves, and is converted into a smooth curve path:
;
Wherein, Is a point on the curve,/>Is/>Control point of subBezier curve,/>The order is that t is a parameter, and t is more than 0 and less than 1;
And then generating a straight line path according to the parameter information of the straight line, generating an arc path according to the parameter information of the circle, and converting the signature image into a vectorization path to obtain the vectorization signature.
Further, the step S3 specifically includes:
Acquiring a fingerprint image and an iris image of an authorizer;
feature points extracted by the fingerprint recognition algorithm are stored in FingerprintPoints, expressed as:
FingerprintPoints=ExtractFingerprintPoints(FingerprintImage);
ExtractFingerprintPoints is a fingerprint feature point extraction function in the OpenCV library, accepts the fingerprint image FINGERPRINTIMAGE as input, returns the extracted fingerprint feature points FingerprintPoints,
Feature points extracted by the iris recognition algorithm are stored in IrisPoints, expressed as: irisPoints = ExtractIrisPoints (IrisImage);
ExtractIrisPoints is an iris feature point extraction function in the OpenCV library, receives an iris image IRISIMAGE as input, and returns extracted iris feature points IrisPoints.
Further, the fingerprint identification algorithm specifically comprises the following steps:
(1) Preprocessing the fingerprint image of the authorizer, including denoising, enhancing and refining operations;
(2) Extracting feature points from the preprocessed fingerprint image by using a fingerprint extraction algorithm, wherein the feature points comprise minutiae points and bifurcation points:
Calculating gradient field information of each pixel point for the preprocessed fingerprint image, and primarily extracting minutiae and bifurcation points according to the calculated gradient field information;
According to the preliminarily extracted minutiae, defining a corrosion kernel K F around the minutiae, performing corrosion operation on pixels around the minutiae, and repeating the corrosion operation for a plurality of times until the pixels around the minutiae cannot be corroded any more, thereby obtaining enhanced minutiae characteristics:
Let the preliminary extracted minutiae point locations be (x 0,y0), the corrosion operation be expressed as:
;
Wherein, Representing the image at coordinates/>Gray value at/>Is the relative position of K F in the corrosion core;
Defining an expansion kernel K P around the bifurcation point according to the bifurcation point which is preliminarily extracted, performing expansion operation on pixels around the bifurcation point, and repeating the expansion operation for a plurality of times until the pixels around the bifurcation point can not be expanded any more, so as to obtain the characteristics of the bifurcation point after enhancement;
let the position of the branch point of the preliminary extraction be The expansion operation is expressed as:
;
Wherein, Is the relative position of K P in the expansion core;
(3) The extracted and bifurcation point features are expressed in the form of a coordinate table, and all feature points are expressed by a matrix:
。
Further, the feature points extracted by the iris recognition algorithm are specifically as follows:
Firstly, edge detection and positioning are carried out on iris images of authorizers, and boundaries of irises are determined;
Normalizing the iris boundary to a fixed-size membrane region;
within the normalized iris region, texture features are extracted by Gabor filter method, the Gabor filter response being expressed as:
；
Wherein, Is the image coordinates/>At the rotation angle/>Lower coordinates,/>Is wavelength,/>For the direction/>For phase shift,/>Is standard deviation/>Is ellipticity;
And encoding the extracted texture features to obtain iris feature points.
Further, the vectorization signature is encrypted by adopting an encryption algorithm, which specifically comprises the following steps:
converting the vectorized signature into a digital form as data to be encrypted;
Based on the fingerprint and iris characteristic points of the authorizer, constructing a biological characteristic point set:
BiometricPoints=MergeBiometricPoints(FingerprintPoints,IrisPoints)
here MergeBiometricPoints is a biometric point fusion function, accepting fingerprint feature points FingerprintPoints, iris feature points IrisPoints as input, returning to the comprehensive biometric point set
BiometricPoints；
Using the biometric point set BiometricPoints as a key, and performing encryption operation on the vectorized signature based on the AES symmetric encryption algorithm to obtain an encrypted vectorized signature:
C=AES(PL,BiometricPoints);
where PL represents the vectorized signature to be encrypted, C represents the vectorized signature after encryption, AES represents the AES encryption algorithm.
An identity verification system based on vectorized signature comprises a processor, a memory and a computer program stored on the memory, wherein the processor specifically executes the steps in an identity verification method based on vectorized signature when executing the computer program.
The invention has the following beneficial effects:
1. the invention can realize accurate verification of the user identity, protect the privacy information of the user and improve the security of signature use;
2. The accuracy of minutiae and bifurcation points can be further improved by combining neighborhood information and morphological operation of the pixel points, so that the accuracy and robustness of fingerprint image feature point extraction are improved, and the vectorization signature is encrypted and protected by utilizing a biological feature point set extracted from biological features such as fingerprints and irises as an encryption key, so that the security and privacy of signature data are ensured, and the accuracy and reliability of verification are improved;
3. The encrypted vectorization signature is stored on the blockchain, access control is realized through the intelligent contract, the safety and the non-falsification of the data are ensured, the authorized user performs biological feature verification through the intelligent contract, the authorized user can decrypt and access the data only, and the reliability of identity verification is enhanced.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
The invention is described in further detail below with reference to the attached drawings and specific examples:
referring to fig. 1, in this embodiment, there is provided an identity verification method based on vectorized signature, including the steps of:
S1, acquiring a signature image, and identifying edges and features of the signature image by utilizing an image processing and identifying algorithm;
S2, converting the signature image into a vectorization path according to the identified signature characteristics to obtain vectorization signatures;
Step S3, respectively extracting fingerprint characteristic points and iris characteristic points of an authorizer by using a fingerprint image processing algorithm and an iris image processing algorithm;
Step S4, encrypting the vectorization signature by adopting an encryption algorithm based on the fingerprint and the iris characteristic points to obtain the encrypted vectorization signature;
s5, storing the encrypted vectorization signature in a data field on a blockchain, and realizing access control on encrypted data through an intelligent contract, wherein only authorized users can decrypt and access the data;
and S6, the authorized user realizes biological feature verification in the intelligent contract, acquires the encrypted vectorization signature, extracts fingerprint feature points and iris feature points of the authorizer, and performs decryption operation to obtain the vectorization signature.
In this embodiment, step S1 specifically includes:
step S11, edge detection is carried out on the signature image by using an edge detection algorithm to obtain an edge image Edges;
Step S12, thresholding is carried out on the edge image Edges and the edge image Edges, the image is converted into a binary image, the contour detection is carried out on the binary image by utilizing a contour detection function findContours function in an image processing library, the contour information Contours of the signature image is obtained, and the parameter information HoughParams of the special shape is further identified on the basis of a Hough transformation algorithm;
And S13, comprehensively considering the edge image Edges, the contour information Contours and the parameter information HoughParams, extracting information representing signature characteristics, and obtaining signature characteristic representations Features.
In this embodiment, step S11 is specifically:
(1) Converting the signature image into a gray scale image;
(2) Applying a Sobel operator to the gray level image to perform edge detection in the horizontal and vertical directions;
;
;
;
;
Wherein, Is Sobel operator in horizontal direction,/>For a Sobel operator in the vertical direction, i and j represent the row and column indices in the Sobel operator template,/>For the edge detection result in the horizontal direction,/>For the edge detection result in the vertical direction, I (x, y) represents the pixel value in the gray image, and x and y represent the coordinates of the pixel;
(3) Combining the edge detection results in the horizontal and vertical directions to obtain a final edge image Edges
。
In this embodiment, step S13 specifically includes:
Based on the acquired edge image Edges, contour information Contours, and parameter information HoughParams, the perimeter P and area A of the contour are calculated:
；
；
Where n is the number of contour midpoints, X-coordinate representing the a-th contour point,/>Representing the y-coordinate of the a-th contour point;)Representing the position of the a-th point in the contour, distance () represents the distance between two points;
and the number N lines of the straight lines and the radius R of the circle are counted according to the parameter information HoughParams to form the feature representation features= { P, A, N lines, R }.
In this embodiment, step S2 specifically includes:
The contour information is fitted by using Bezier curves, and is converted into a smooth curve path:
;
Wherein, Is a point on the curve,/>Is/>Control point of subBezier curve,/>The order is that t is a parameter, and t is more than 0 and less than 1;
And then generating a straight line path according to the parameter information of the straight line, generating an arc path according to the parameter information of the circle, and converting the signature image into a vectorization path to obtain the vectorization signature.
In this embodiment, step S3 specifically includes:
Acquiring a fingerprint image and an iris image of an authorizer;
feature points extracted by the fingerprint recognition algorithm are stored in FingerprintPoints, expressed as:
FingerprintPoints=ExtractFingerprintPoints(FingerprintImage);
ExtractFingerprintPoints is a fingerprint feature point extraction function in the OpenCV library, accepts the fingerprint image FINGERPRINTIMAGE as input, returns the extracted fingerprint feature points FingerprintPoints,
Feature points extracted by the iris recognition algorithm are stored in IrisPoints, expressed as: irisPoints = ExtractIrisPoints (IrisImage);
ExtractIrisPoints is an iris feature point extraction function in the OpenCV library, receives an iris image IRISIMAGE as input, and returns extracted iris feature points IrisPoints.
In this embodiment, the fingerprint identification algorithm specifically includes:
(1) Preprocessing the fingerprint image of the authorizer, including denoising, enhancing and refining operations;
(2) Extracting feature points from the preprocessed fingerprint image by using a fingerprint extraction algorithm, wherein the feature points comprise minutiae points and bifurcation points:
Calculating gradient field information of each pixel point for the preprocessed fingerprint image, and primarily extracting minutiae and bifurcation points according to the calculated gradient field information;
According to the preliminarily extracted minutiae, defining a corrosion kernel K F around the minutiae, performing corrosion operation on pixels around the minutiae, and repeating the corrosion operation for a plurality of times until the pixels around the minutiae cannot be corroded any more, thereby obtaining enhanced minutiae characteristics:
Let the preliminary extracted minutiae point locations be (x 0,y0), the corrosion operation be expressed as:
;
Wherein, Representing the image at coordinates/>Gray value at/>Is the relative position of K F in the corrosion core;
Defining an expansion kernel K P around the bifurcation point according to the bifurcation point which is preliminarily extracted, performing expansion operation on pixels around the bifurcation point, and repeating the expansion operation for a plurality of times until the pixels around the bifurcation point can not be expanded any more, so as to obtain the characteristics of the bifurcation point after enhancement;
let the position of the branch point of the preliminary extraction be The expansion operation is expressed as:
;
Wherein, Is the relative position of K P in the expansion core;
(3) The extracted and bifurcation point features are expressed in the form of a coordinate table, and all feature points are expressed by a matrix:
。
in this embodiment, the feature points extracted by the iris recognition algorithm are specifically as follows:
Firstly, edge detection and positioning are carried out on iris images of authorizers, and boundaries of irises are determined;
Normalizing the iris boundary to a fixed-size membrane region;
within the normalized iris region, texture features are extracted by Gabor filter method, the Gabor filter response being expressed as:
；
Wherein, Is the image coordinates/>At the rotation angle/>Lower coordinates,/>Is wavelength,/>For the direction/>For phase shift,/>Is standard deviation/>Is ellipticity;
And encoding the extracted texture features to obtain iris feature points.
In this embodiment, an encryption algorithm is used to encrypt the vectorized signature, specifically:
converting the vectorized signature into a digital form as data to be encrypted;
Based on the fingerprint and iris characteristic points of the authorizer, constructing a biological characteristic point set:
BiometricPoints=MergeBiometricPoints(FingerprintPoints,IrisPoints)
here MergeBiometricPoints is a biometric point fusion function, accepting fingerprint feature points FingerprintPoints, iris feature points IrisPoints as input, returning to the comprehensive biometric point set
BiometricPoints；
In this embodiment, the biometric point fusion function may be adjusted according to the actual requirement, and in this embodiment, it is preferable to first create an empty biometric point set BiometricPoints. Then, all the feature points in the fingerprint feature point set FingerprintPoints are added one by one to BiometricPoints, then all the feature points in the iris feature point set IrisPoints are added one by one to BiometricPoints, and finally the combined biometric feature point set BiometricPoints is returned.
Using the biometric point set BiometricPoints as a key, and performing encryption operation on the vectorized signature based on the AES symmetric encryption algorithm to obtain an encrypted vectorized signature:
C=AES(PL,BiometricPoints);
where PL represents the vectorized signature to be encrypted, C represents the vectorized signature after encryption, AES represents the AES encryption algorithm.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the invention in any way, and any person skilled in the art may make modifications or alterations to the disclosed technical content to the equivalent embodiments. However, any simple modification, equivalent variation and variation of the above embodiments according to the technical substance of the present invention still fall within the protection scope of the technical solution of the present invention.
Claims (10)
1. An identity verification method based on vectorization signature is characterized by comprising the following steps:
S1, acquiring a signature image, and identifying edges and features of the signature image by utilizing an image processing and identifying algorithm;
S2, converting the signature image into a vectorization path according to the identified signature characteristics to obtain vectorization signatures;
Step S3, respectively extracting fingerprint characteristic points and iris characteristic points of an authorizer by using a fingerprint image processing algorithm and an iris image processing algorithm;
Step S4, encrypting the vectorization signature by adopting an encryption algorithm based on the fingerprint and the iris characteristic points to obtain the encrypted vectorization signature;
s5, storing the encrypted vectorization signature in a data field on a blockchain, and realizing access control on encrypted data through an intelligent contract, wherein only authorized users can decrypt and access the data;
and S6, the authorized user realizes biological feature verification in the intelligent contract, acquires the encrypted vectorization signature, extracts fingerprint feature points and iris feature points of the authorizer, and performs decryption operation to obtain the vectorization signature.
2. The method for verifying identity based on vectorized signature as in claim 1, wherein the step S1 is specifically:
step S11, edge detection is carried out on the signature image by using an edge detection algorithm to obtain an edge image Edges;
Step S12, thresholding is carried out on the edge image Edges and the edge image Edges, the image is converted into a binary image, the contour detection is carried out on the binary image by utilizing a contour detection function findContours function in an image processing library, the contour information Contours of the signature image is obtained, and the parameter information HoughParams of the special shape is further identified on the basis of a Hough transformation algorithm;
And S13, comprehensively considering the edge image Edges, the contour information Contours and the parameter information HoughParams, extracting information representing signature characteristics, and obtaining signature characteristic representations Features.
3. The method for verifying identity based on vectorized signature as in claim 2, wherein the step S11 is specifically:
(1) Converting the signature image into a gray scale image;
(2) Applying a Sobel operator to the gray level image to perform edge detection in the horizontal and vertical directions;
;
;
;
;
Wherein, Is Sobel operator in horizontal direction,/>For a Sobel operator in the vertical direction, i and j represent the row and column indices in the Sobel operator template,/>For the edge detection result in the horizontal direction,/>For the edge detection result in the vertical direction, I (x, y) represents the pixel value in the gray image, and x and y represent the coordinates of the pixel;
(3) Combining the edge detection results in the horizontal and vertical directions to obtain a final edge image Edges;
。
4. The method for verifying identity based on vectorized signature as in claim 2, wherein the step S13 is specifically:
Based on the acquired edge image Edges, contour information Contours, and parameter information HoughParams, the perimeter P and area A of the contour are calculated:
；
；
Where n is the number of contour midpoints, X-coordinate representing the a-th contour point,/>Representing the y-coordinate of the a-th contour point;)Representing the position of the a-th point in the contour, distance () represents the distance between two points;
and the number N lines of the straight lines and the radius R of the circle are counted according to the parameter information HoughParams to form the feature representation features= { P, A, N lines, R }.
5. The method for authenticating an identity based on vectorized signature as set forth in claim 4, wherein said step S2 is specifically:
The contour information is fitted by using Bezier curves, and is converted into a smooth curve path:
;
Wherein, Is a point on the curve,/>Is/>Control point of subBezier curve,/>The order is that t is a parameter, and t is more than 0 and less than 1;
And then generating a straight line path according to the parameter information of the straight line, generating an arc path according to the parameter information of the circle, and converting the signature image into a vectorization path to obtain the vectorization signature.
6. The method for authenticating an identity based on vectorized signature as set forth in claim 1, wherein said step S3 is specifically:
Acquiring a fingerprint image and an iris image of an authorizer;
feature points extracted by the fingerprint recognition algorithm are stored in FingerprintPoints, expressed as:
FingerprintPoints=ExtractFingerprintPoints(FingerprintImage);
ExtractFingerprintPoints is a fingerprint feature point extraction function in the OpenCV library, accepts the fingerprint image FINGERPRINTIMAGE as input, returns the extracted fingerprint feature points FingerprintPoints,
Feature points extracted by the iris recognition algorithm are stored in IrisPoints, expressed as: irisPoints = ExtractIrisPoints (IrisImage);
ExtractIrisPoints is an iris feature point extraction function in the OpenCV library, receives an iris image IRISIMAGE as input, and returns extracted iris feature points IrisPoints.
7. The method for authenticating an identity based on vectorized signature as set forth in claim 6, wherein said fingerprint recognition algorithm is specifically:
(1) Preprocessing the fingerprint image of the authorizer, including denoising, enhancing and refining operations;
(2) Extracting feature points from the preprocessed fingerprint image by using a fingerprint extraction algorithm, wherein the feature points comprise minutiae points and bifurcation points:
Calculating gradient field information of each pixel point for the preprocessed fingerprint image, and primarily extracting minutiae and bifurcation points according to the calculated gradient field information;
According to the preliminarily extracted minutiae, defining a corrosion kernel K F around the minutiae, performing corrosion operation on pixels around the minutiae, and repeating the corrosion operation for a plurality of times until the pixels around the minutiae cannot be corroded any more, thereby obtaining enhanced minutiae characteristics:
Let the preliminary extracted minutiae point locations be (x 0,y0), the corrosion operation be expressed as:
;
Wherein, Representing the image at coordinates/>Gray value at/>Is the relative position of K F in the corrosion core;
Defining an expansion kernel K P around the bifurcation point according to the bifurcation point which is preliminarily extracted, performing expansion operation on pixels around the bifurcation point, and repeating the expansion operation for a plurality of times until the pixels around the bifurcation point can not be expanded any more, so as to obtain the characteristics of the bifurcation point after enhancement;
let the position of the branch point of the preliminary extraction be The expansion operation is expressed as:
;
Wherein, Is the relative position of K P in the expansion core;
(3) The extracted and bifurcation point features are expressed in the form of a coordinate table, and all feature points are expressed by a matrix:
。
8. the identity verification method based on vectorization signature as claimed in claim 6, wherein the feature points extracted by the iris recognition algorithm are as follows:
Firstly, edge detection and positioning are carried out on iris images of authorizers, and boundaries of irises are determined;
Normalizing the iris boundary to a fixed-size membrane region;
within the normalized iris region, texture features are extracted by Gabor filter method, the Gabor filter response being expressed as:
；
Wherein, Is the image coordinates/>At the rotation angle/>Lower coordinates,/>Is wavelength,/>For the direction/>For phase shift,/>Is standard deviation/>Is ellipticity;
And encoding the extracted texture features to obtain iris feature points.
9. The identity verification method based on vectorization signature as claimed in claim 1, wherein said encrypting the vectorization signature by encryption algorithm comprises:
converting the vectorized signature into a digital form as data to be encrypted;
Based on the fingerprint and iris characteristic points of the authorizer, constructing a biological characteristic point set:
BiometricPoints=MergeBiometricPoints(FingerprintPoints,IrisPoints)
here MergeBiometricPoints is a biometric point fusion function, accepting fingerprint feature points FingerprintPoints, iris feature points IrisPoints as input, returning to the comprehensive biometric point set
BiometricPoints；
Using the biometric point set BiometricPoints as a key, and performing encryption operation on the vectorized signature based on the AES symmetric encryption algorithm to obtain an encrypted vectorized signature:
C=AES(PL,BiometricPoints);
where PL represents the vectorized signature to be encrypted, C represents the vectorized signature after encryption, AES represents the AES encryption algorithm.
10. An authentication system based on vectorized signatures, comprising a processor, a memory and a computer program stored on said memory, said processor, when executing said computer program, performing in particular the steps of an authentication method based on vectorized signatures as claimed in any one of claims 1-9.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410412002.5A CN118013494A (en) | 2024-04-08 | 2024-04-08 | Identity verification method and system based on vectorization signature |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410412002.5A CN118013494A (en) | 2024-04-08 | 2024-04-08 | Identity verification method and system based on vectorization signature |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN118013494A true CN118013494A (en) | 2024-05-10 |
Family
ID=90954935
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410412002.5A Pending CN118013494A (en) | 2024-04-08 | 2024-04-08 | Identity verification method and system based on vectorization signature |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN118013494A (en) |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6850252B1 (en) * | 1999-10-05 | 2005-02-01 | Steven M. Hoffberg | Intelligent electronic appliance system and method |
| KR20110121874A (en) * | 2010-05-03 | 2011-11-09 | 남궁종 | Method and apparatus for iris recognition and wireless communications devic security system using it |
| US20200314094A1 (en) * | 2017-10-11 | 2020-10-01 | Samsung Electronics Co., Ltd. | Server, method for controlling server, and terminal device |
| CN112434319A (en) * | 2020-11-27 | 2021-03-02 | 无锡卡尔曼导航技术有限公司 | Data encryption method and device for electronic file |
| CN114978772A (en) * | 2022-07-27 | 2022-08-30 | 北京惠朗时代科技有限公司 | Separated storage electronic signature encryption protection system based on Internet |
| CN116192399A (en) * | 2023-01-09 | 2023-05-30 | 福建亿榕信息技术有限公司 | Electronic signature method and system based on fingerprint and digital certificate dual authentication |
| CN116368355A (en) * | 2021-09-05 | 2023-06-30 | 汉熵通信有限公司 | Internet of things system |
| CN117688533A (en) * | 2023-12-12 | 2024-03-12 | 确信信息股份有限公司 | Electronic signature method, electronic signature verification method and system based on artificial intelligence |
-
2024
- 2024-04-08 CN CN202410412002.5A patent/CN118013494A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6850252B1 (en) * | 1999-10-05 | 2005-02-01 | Steven M. Hoffberg | Intelligent electronic appliance system and method |
| KR20110121874A (en) * | 2010-05-03 | 2011-11-09 | 남궁종 | Method and apparatus for iris recognition and wireless communications devic security system using it |
| US20200314094A1 (en) * | 2017-10-11 | 2020-10-01 | Samsung Electronics Co., Ltd. | Server, method for controlling server, and terminal device |
| CN112434319A (en) * | 2020-11-27 | 2021-03-02 | 无锡卡尔曼导航技术有限公司 | Data encryption method and device for electronic file |
| CN116368355A (en) * | 2021-09-05 | 2023-06-30 | 汉熵通信有限公司 | Internet of things system |
| CN114978772A (en) * | 2022-07-27 | 2022-08-30 | 北京惠朗时代科技有限公司 | Separated storage electronic signature encryption protection system based on Internet |
| CN116192399A (en) * | 2023-01-09 | 2023-05-30 | 福建亿榕信息技术有限公司 | Electronic signature method and system based on fingerprint and digital certificate dual authentication |
| CN117688533A (en) * | 2023-12-12 | 2024-03-12 | 确信信息股份有限公司 | Electronic signature method, electronic signature verification method and system based on artificial intelligence |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| TWI678638B (en) | Method, device and system for identity verification using card characteristics | |
| Galbally et al. | Iris image reconstruction from binary templates: An efficient probabilistic approach based on genetic algorithms | |
| US9152869B2 (en) | Biometric authentication systems and methods | |
| KR102434562B1 (en) | Method and apparatus for detecting fake fingerprint, method and apparatus for recognizing fingerprint | |
| US20160085958A1 (en) | Methods and apparatus for multi-factor user authentication with two dimensional cameras | |
| CN109598247B (en) | Two-dimensional code identity authentication method based on vein image detail point and grain characteristics | |
| JP2014219967A (en) | Method and system for authenticating biometric data obtained from fingerprint of user, and biometric authentication system | |
| KR101853270B1 (en) | Authentication method for portable secure authentication apparatus using fingerprint | |
| CN109635756A (en) | It merges fingerprint and refers to the two dimensional code identity identifying method of vein | |
| Chavez-Galaviz et al. | Embedded biometric cryptosystem based on finger vein patterns | |
| KR20160133991A (en) | Fingerprint enrollment method and fingerprint verification method | |
| CN116778562B (en) | Face verification method, device, electronic equipment and readable storage medium | |
| Balakumar et al. | Secure biometric key generation scheme for cryptography using combined biometric features of fingerprint and iris | |
| Sujitha et al. | Highly secure palmprint based biometric template using fuzzy vault | |
| Tyagi et al. | Security enhancement through iris and biometric recognition in atm | |
| CN118013494A (en) | Identity verification method and system based on vectorization signature | |
| KR101270348B1 (en) | Apparatus and method for fingerprint verification | |
| JP6927611B1 (en) | Feature extraction device, feature extraction method, and program | |
| Tantubay et al. | A Survey of Biometric Key-Binding Biocrypto-System Using Different Techniques | |
| KR20040028210A (en) | Apparatus for Identifying a Person through Recognizing a Face and Method thereof | |
| KR20160101248A (en) | Portable secure authentication apparatus using fingerprint | |
| Szczepanik et al. | Security lock system for mobile devices based on fingerprint recognition algorithm | |
| JP4623053B2 (en) | Portable electronic device and personal authentication method using biometric information | |
| CN117150465A (en) | Composite encryption method, device, equipment and storage medium | |
| CN118138329A (en) | Device password transmission method, device and readable storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |