CN109840487B - Private key generation method and system of block chain electronic wallet based on fingerprint information - Google Patents
Private key generation method and system of block chain electronic wallet based on fingerprint information Download PDFInfo
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Abstract
The application provides a private key generation method and a private key generation system of a block chain electronic wallet based on fingerprint information, wherein the private key generation method comprises the following steps: carrying out image acquisition on the user fingerprint; carrying out binarization processing on the fingerprint image to obtain a binarized fingerprint image; extracting fingerprint feature points from the binary fingerprint image, and constructing a feature point set according to all the extracted fingerprint feature points; obtaining a normalized matrix according to the feature point set; and generating a private key corresponding to the user fingerprint by using the normalized matrix. The private key is generated based on the strong authentication biological characteristic information, the correlation between the private key and the fingerprint characteristic information of the user is strong, the safety of the private key can be improved, and the private key cannot be easily calculated out to obtain a specific value. In addition, the private key no longer needs to be stored, and the risks of being stolen and illegally used can be avoided. When the private key is lost or the storage device is forgotten to be carried, the private key can be reproduced according to the fingerprint characteristic information of the user.
Description
Technical Field
The application belongs to the technical field of information security, and particularly relates to a private key generation method and system of a block chain electronic wallet based on fingerprint information.
Background
In the block chain-based electronic wallet technology, the asymmetric encryption technology has a crucial influence on the security and reliability of the transaction. In the asymmetric encryption technology, a public key and a private key are generated through an encryption algorithm, the public key is used for encryption, and the private key is used for decryption. Typically, the public and private keys of the e-wallet user are generated at registration time and remain unchanged. The private key is held by the user and cannot be disclosed to other people, otherwise the security of the transaction is greatly threatened.
In the prior art, a public key and a private key are randomly generated based on the condition of an encryption algorithm and are stored in a certain database or equipment. Although the storage environment for the private key may be secure and confidential, the risk of theft of the private key is still not completely avoided. If the private key is stored in a centralized database of a certain operator, there may also be a risk that the operator violates the viewing of the user's private key. The greatest advantage of the personal electronic wallet based on the blockchain is a decentralized storage mode, and if the private key is stored in a centralized manner by an operator, the characteristics and advantages of the blockchain cannot be fully exerted.
In the actual application process, the operator acquires the fingerprint of the user and compares the fingerprint with the fingerprint data recorded in the database or the equipment to generate a verification result. And directly associating the fingerprint information of the user with the corresponding private key after the user passes the verification. However, the correlation between the private key and the fingerprint feature information of the user is weak, and once the private key is lost or the user forgets to carry the storage terminal, the private key cannot be reproduced, and only the private key can be regenerated. In addition, when the user steals the mobile phone, the tablet computer and other devices storing the private key, the private key of the user is at risk of being stolen.
Therefore, no matter the client side stores or the operator stores, the private key cannot effectively avoid the risk of being stolen, and the threat on transaction safety cannot be avoided.
Disclosure of Invention
In order to overcome the problems in the related art at least to a certain extent, the application provides a private key generation method and system of a block chain electronic wallet based on fingerprint information.
According to a first aspect of embodiments of the present application, there is provided a method for generating a private key of a blockchain electronic wallet based on fingerprint information, including the following steps:
carrying out image acquisition on the user fingerprint;
carrying out binarization processing on the fingerprint image to obtain a binarized fingerprint image;
extracting fingerprint feature points from the binary fingerprint image, and constructing a feature point set according to all the extracted fingerprint feature points;
obtaining a normalized matrix according to the feature point set;
and generating a private key corresponding to the user fingerprint by using the normalized matrix.
In the private key generation method, the specific process of extracting the fingerprint feature points from the binary fingerprint image and constructing the feature point set according to all the extracted fingerprint feature points includes:
scanning each pixel point in the binary fingerprint image, and selecting a certain pixel point q;
calculating a classification parameter by using pixel values of eight pixel points in an eight-neighborhood pixel map of a certain pixel point q, wherein the classification parameter N is calculated by adopting the following formula:
in the formula, piExpressing the pixel value of the ith pixel point in the eight-neighborhood pixel image of q;
determining whether the pixel point is an end point or a bifurcation point according to the classification parameter N;
denoising the binary fingerprint image to obtain denoised end points and bifurcation points;
and constructing a feature point set by using the denoised end points and bifurcation points.
Further, the step of determining whether the pixel point is an endpoint or a bifurcation point according to the classification parameter N comprises the following steps:
when N is equal to 1, the pixel point q is an end point;
and when N is equal to 3, the pixel point q is a bifurcation point.
Further, the steps of denoising the binary fingerprint image to obtain denoised end points and bifurcation points comprise the following specific processes:
taking a certain end point as a starting point, moving Z pixels along a ridge line where the end point is located, if a bifurcation point is met in the Z pixels, judging the bifurcation point to be a noise point, and rejecting the noise point;
traversing all end points and ridge lines in the binary fingerprint image, and eliminating all noise points;
and Z is an adjustable parameter and is adjusted according to the size and the resolution of the fingerprint image.
In the private key generation method, the specific process of obtaining the normalized matrix according to the feature point set in the step is as follows:
assuming that k elements exist in the feature point set Q, respectively searching two elements closest to the elements for each element in the k elements, and calculating Euclidean distance between any two elements in the three elements;
sorting three Euclidean distances corresponding to a certain element according to a preset sequence, wherein the three Euclidean distances obtained by sorting form a distance vector d;
forming a two-dimensional distance vector D0 by using the distance vectors corresponding to the elements in the k elements;
for each element in the two-dimensional distance vector D0, calculating a sum of three Euclidean distances contained in the element, sorting the sum according to a preset sequence, wherein the Euclidean distances contained in the elements corresponding to the sorted sum form a matrix D with k rows and 3 columns;
normalizing each row of elements in the matrix D with the k rows and 3 columns to obtain a normalized matrix
Further, the step of normalizing each column element in the matrix D with k rows and 3 columns includes:
wherein m is 1,2,3, …, k, i is 2 or 3;
denotes the normalized element, dm,iRepresenting the element of the m-th row and i-th column of the matrix D, Dm,1Representing the element in row m and column 1 of matrix D.
In the private key generating method, the specific process of generating the private key corresponding to the user fingerprint by using the normalized matrix in the step is as follows:
for each element in the normalized matrix
Calculating their corresponding rounded integers
In the formula, round represents rounding according to a rounding rule; λ represents an adjustable parameter, which is a non-negative integer;
and arranging and generating private keys corresponding to the user fingerprints according to a preset sequence.
According to a second aspect of embodiments of the present application, there is provided a private key generation system including:
the image acquisition module is used for acquiring fingerprint information of a user;
the binarization processing module is used for carrying out binarization processing on the fingerprint image to obtain a binarization fingerprint image;
the set construction module is used for extracting fingerprint feature points from the binary fingerprint image and constructing a feature point set according to all the extracted fingerprint feature points;
the normalization matrix generation module is used for generating a normalization matrix by using each element in the feature point set;
and the generating module is used for generating a private key corresponding to the user fingerprint by using the normalized matrix.
According to a third aspect of embodiments of the present application, there is provided a private key generation apparatus for a blockchain electronic wallet based on fingerprint information, including:
a processor for processing the received data, wherein the processor is used for processing the received data,
the processor is capable of invoking intelligent contracts from a blockchain,
the intelligent contract comprises a computer program that,
the computer program, when running on the processor, performs the steps of:
carrying out image acquisition on the user fingerprint;
carrying out binarization processing on the fingerprint image to obtain a binarized fingerprint image;
extracting fingerprint feature points from the binary fingerprint image, and constructing a feature point set according to all the extracted fingerprint feature points;
obtaining a normalized matrix according to the feature point set;
and generating a private key corresponding to the user fingerprint by using the normalized matrix.
According to a fourth aspect of embodiments of the present application, there is provided a blockchain-based personal electronic wallet comprising a private key generated by any one of the private key generation methods described above, the private key generation method being stored as a smart contract on the blockchain.
According to the above embodiments of the present application, at least the following advantages are obtained: the private key generation method generates the private key based on the strong authentication biological characteristic information, the private key is strong in correlation with the fingerprint characteristic information of the user, the safety of the private key can be improved, and the private key cannot be easily deduced to be a specific value. By adopting the block chain-based personal electronic wallet creating method, the private key does not need to be stored, and the risks of being stolen and illegally used can be avoided. The present application can enhance the decentralized nature of blockchain personal electronic wallets. The storage medium is not needed, and the private key can be reproduced according to the fingerprint characteristic information of the user when the private key is lost or the storage device is forgotten to be carried.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the scope of the invention, as claimed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of the specification of the application, illustrate embodiments of the application and together with the description, serve to explain the principles of the application.
Fig. 1 is a flowchart of a private key generation method for a blockchain electronic wallet based on fingerprint information according to an embodiment of the present disclosure.
Fig. 2 is a schematic structural diagram of a private key generation system of a blockchain electronic wallet based on fingerprint information according to an embodiment of the present disclosure.
Fig. 3 is a schematic diagram illustrating an application state of a personal electronic wallet based on a blockchain according to an embodiment of the present invention.
Detailed Description
For the purpose of promoting a clear understanding of the objects, aspects and advantages of the embodiments of the present application, reference will now be made to the accompanying drawings and detailed description, wherein like reference numerals refer to like elements throughout.
The illustrative embodiments and descriptions of the present application are provided to explain the present application and not to limit the present application. Additionally, the same or similar numbered elements/components used in the drawings and the embodiments are used to represent the same or similar parts.
As used herein, "first," "second," …, etc., are not specifically intended to mean in a sequential or chronological order, nor are they intended to limit the application, but merely to distinguish between elements or operations described in the same technical language.
With respect to directional terminology used herein, for example: up, down, left, right, front or rear, etc., are simply directions with reference to the drawings. Accordingly, the directional terminology used is intended to be illustrative and is not intended to be limiting of the present teachings.
As used herein, the terms "comprising," "including," "having," "containing," and the like are open-ended terms that mean including, but not limited to.
As used herein, "and/or" includes any and all combinations of the described items.
References to "plurality" herein include "two" and "more than two"; reference to "multiple sets" herein includes "two sets" and "more than two sets".
As used herein, the terms "substantially", "about" and the like are used to modify any slight variation in quantity or error that does not alter the nature of the variation. In general, the range of slight variations or errors that such terms modify may be 20% in some embodiments, 10% in some embodiments, 5% in some embodiments, or other values. It should be understood by those skilled in the art that the aforementioned values can be adjusted according to actual needs, and are not limited thereto.
Certain words used to describe the present application are discussed below or elsewhere in this specification to provide additional guidance to those skilled in the art in describing the present application.
Example one
Fig. 1 is a flowchart of a private key generation method for a blockchain wallet based on fingerprint information according to an embodiment of the present application. As shown in fig. 1, the private key generation method of the blockchain electronic wallet based on fingerprint information includes the following steps:
and S1, acquiring the image of the user fingerprint.
And S2, performing binarization processing on the fingerprint image to obtain a binarized fingerprint image.
S3, extracting fingerprint feature points from the binary fingerprint image, and constructing a feature point set according to all the extracted fingerprint feature points, wherein the specific process is as follows:
note that the fingerprint feature points include end points and branch points of fingerprint ridges. Wherein, the end points refer to the ends of the ridge lines, and the bifurcation points refer to the intersection points of the three ridge lines.
And S31, scanning each pixel point in the binary fingerprint image and selecting a certain pixel point q.
S32, calculating a classification parameter N by using the pixel values of eight pixel points in the eight neighborhood pixel image of a certain pixel point q. The classification parameter N is calculated by adopting the following formula:
in the formula, piAnd expressing the pixel value of the ith pixel point in the eight-neighborhood pixel map of q.
S33, determining whether the pixel is an endpoint or a bifurcation point according to the classification parameter N, specifically, when N is equal to 1, the pixel q is an endpoint; and when N is equal to 3, the pixel point q is a bifurcation point.
S34, denoising the binary fingerprint image to obtain denoised end points and bifurcation points, wherein the specific process comprises the following steps:
and moving Z pixels along the ridge line where a certain end point is located by taking the end point as a starting point, if a bifurcation point is met in the Z pixels, judging the bifurcation point to be a noise point, and rejecting the noise point.
And traversing all end points and ridge lines in the binary fingerprint image, and eliminating all noise points.
Z is an adjustable parameter and can be adjusted according to the size and the resolution of the fingerprint image.
And S35, constructing a feature point set Q by using the denoised end points and bifurcation points.
S4, obtaining a normalized matrix according to the feature point set Q, wherein the specific process is as follows:
s41, assuming that there are k elements in the feature point set Q, for each of the k elements, respectively searching for two elements closest to the element, and calculating an euclidean distance between any two elements of the three elements.
Assuming that any element in the k elements is Q0, two elements Q1 and Q2 which are closest to the element Q0 are searched in the set Q, and the euclidean distance d1 between the elements Q0 and Q1, the euclidean distance d2 between Q1 and Q2, and the euclidean distance d2 between Q2 and Q0 are respectively calculated.
S42, sorting the three Euclidean distances corresponding to the element q0 according to a preset sequence, and forming a distance vector d by the three Euclidean distances obtained through sorting.
Specifically, d1, d2 and d3 may be arranged in order from small to large. Assume that the Euclidean distances d1, d2 and d3 are ordered from small to large to obtain distance vectors: d ═ d (d1, d2, d3)
S43, a two-dimensional distance vector D0 is formed using the distance vectors corresponding to the respective k elements.
S44, for each element in the two-dimensional distance vector D0, calculating a sum of three Euclidean distances contained in the element, sorting the sum according to a preset sequence, and forming a matrix D with k rows and 3 columns by the Euclidean distances contained in the elements corresponding to the sorted sum.
Therein, for example, the matrix D of k rows and 3 columns may be in the form of:
s45, carrying out normalization processing on each row element in the matrix D with k rows and 3 columns to obtain a normalized matrix
The process of normalizing each column element in the matrix D with k rows and 3 columns is as follows:
wherein m is 1,2,3, …, k, i is 2 or 3;
denotes the normalized element, dm,iRepresenting the element of the m-th row and i-th column of the matrix D, Dm,1Representing the element in row m and column 1 of matrix D.
For example, the normalized matrix of the matrix D of k rows and 3 columns in step S44
Can be as follows:
s5, generating a private key corresponding to the user fingerprint by using the normalized matrix, wherein the specific process is as follows:
s51, normalizing each element in the matrix
Calculating their corresponding rounded integers
Specifically, the following formula can be used to obtain:
in the formula, round represents rounding according to a rounding rule; λ represents an adjustable parameter, which is a non-negative integer and can take the value of 1,2, … ….
And S52, arranging and generating private keys corresponding to the user fingerprints according to a preset sequence.
For example, using the normalized matrix obtained in step S45
A private key of the following form may be generated when λ ═ 1:
1252611316……11416。
the private key generation method generates the private key based on the strong authentication biological characteristic information, the private key is strong in correlation with the fingerprint characteristic information of the user, the safety of the private key can be improved, and the private key cannot be easily deduced to be a specific value.
Example two
As shown in fig. 2, on the basis of the private key generation method for the blockchain electronic wallet based on the fingerprint information, the application also provides a private key generation system for the blockchain electronic wallet based on the fingerprint information, which includes an image acquisition module 1, a binarization processing module 2, a set construction module 3, a normalization matrix module 4 and a generation module 5.
The image acquisition module 1 is used for acquiring fingerprint information of a user.
The binarization processing module 2 is used for carrying out binarization processing on the fingerprint image to obtain a binarization fingerprint image.
The set construction module 3 is used for extracting fingerprint feature points from the binary fingerprint image and constructing a feature point set according to all the extracted fingerprint feature points.
The normalization matrix module 4 generates a normalization matrix using each element in the feature point set.
The generating module 5 generates a private key corresponding to the user fingerprint by using the normalized matrix
Specifically, the set building module 3 includes a pixel point scanning module, a classification parameter calculation module, a feature point determination module, a denoising module, and a set building module.
The pixel scanning module is used for scanning each pixel in the binary fingerprint image and determining a certain pixel.
The classification parameter calculation module calculates classification parameters by using pixel values of eight pixel points in an eight-neighborhood pixel map of a certain pixel point
And the characteristic point determining module is used for determining whether the pixel point is an endpoint or a bifurcation point according to the classification parameters.
And the denoising module is used for denoising the binary fingerprint image to obtain denoised end points and bifurcation points.
And the set establishing module establishes a feature point set by using the denoised end points and bifurcation points.
Specifically, the normalization matrix module 4 includes a euclidean distance calculation module, a sorting module, a two-dimensional distance vector establishing module, a matrix establishing module, and a normalization module.
The Euclidean distance calculation module is used for calculating the Euclidean distance between each element in the feature point set and two elements which are closest to the element in the feature point set.
The sorting module is used for sorting the three Euclidean distances corresponding to each element in the feature point set respectively and forming a distance vector by using the three Euclidean distances obtained after sorting.
The two-dimensional distance vector establishing module utilizes the distance vectors corresponding to the elements to form the two-dimensional distance vector.
The matrix establishing module is used for calculating sum values of three Euclidean distances contained in each element in the two-dimensional distance vector, sorting is carried out according to the sum values, and the Euclidean distances contained in the elements corresponding to the sorted sum values form a matrix with a plurality of rows and three columns.
The normalization module is used for performing normalization processing on each row of elements in the matrix with the rows and the columns to obtain a normalized matrix.
Specifically, the generation module 5 includes a rounding module and a ranking generation module.
And the rounding module is used for calculating a rounded integer of each element in the normalized matrix.
The sorting generation module is used for arranging and generating private keys corresponding to the user fingerprints according to a preset sequence.
It should be noted that: the private key generation system provided in the foregoing embodiment is only illustrated by dividing each program module, and in practical applications, the processing distribution may be completed by different program modules according to needs, that is, the internal structure of the private key generation system is divided into different program modules, so as to complete all or part of the processing described above. In addition, the private key generation system and the private key generation method provided by the above embodiments belong to the same concept, and specific implementation processes thereof are detailed in the method embodiments and are not described herein again.
The private key generation system generates the private key based on the strong authentication biological characteristic information, the private key is strong in correlation with the fingerprint characteristic information of the user, the safety of the private key can be improved, and the private key cannot be easily deduced to be a specific value.
Based on the hardware implementation of each module in the private key generation system, in order to implement the private key generation method for the fingerprint information-based blockchain electronic wallet provided in the embodiment of the present application, an embodiment of the present application further provides a private key generation apparatus for a blockchain electronic wallet based on fingerprint information, including: a processor and a memory for storing a computer program capable of running on the processor. Wherein the processor, when executing the computer program, performs the steps of:
and carrying out image acquisition on the user fingerprint.
And carrying out binarization processing on the fingerprint image to obtain a binarization fingerprint image.
And extracting fingerprint feature points from the binary fingerprint image, and constructing a feature point set according to all the extracted fingerprint feature points.
And obtaining a normalized matrix according to the feature point set.
And generating a private key corresponding to the user fingerprint by using the normalized matrix.
In an exemplary embodiment, the present application further provides a computer storage medium, which is a computer readable storage medium, for example, a memory including a computer program, which is executable by a processor in a private key generation system to perform the steps in the private key generation method. The computer-readable storage medium may be a magnetic random access Memory (FRAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Programmable Read-Only Memory (EPROM), an Electrically Erasable Programmable Read-Only Memory (EEPROM), a Flash Memory (Flash Memory), a magnetic surface Memory, an optical disk, or a Compact Disc Read-Only Memory (CD-ROM), among other memories.
EXAMPLE III
As shown in fig. 3, the present application also provides a block chain-based personal electronic wallet, which includes a private key generated by any one of the above private key generation methods, and the private key generation method is stored on the block chain as an intelligent contract. When a user needs to obtain the private key, the private key corresponding to the user fingerprint can be regenerated by only calling the intelligent contract from the block chain and using the private key generation method.
By adopting the private key generation method of the block chain electronic wallet based on the fingerprint information, the private key does not need to be recorded any more, and the risks of being stolen and being illegally used can be avoided. The method can enhance the decentralized nature of blockchain personal electronic wallets. The storage medium is not needed, and the private key can be reproduced according to the fingerprint characteristic information of the user when the private key is lost or the storage device is forgotten to be carried. In addition, the private key generation method based on the strong authentication biological characteristics can effectively improve the safety of the private key, so that the private key cannot be easily deduced to be a specific value.
The foregoing is merely an illustrative embodiment of the present application, and any equivalent changes and modifications made by those skilled in the art without departing from the spirit and principles of the present application shall fall within the protection scope of the present application.
Claims (9)
1. A private key generation method of a block chain electronic wallet based on fingerprint information is characterized by comprising the following steps:
carrying out image acquisition on the user fingerprint;
carrying out binarization processing on the fingerprint image to obtain a binarized fingerprint image;
extracting fingerprint feature points from the binary fingerprint image, and constructing a feature point set according to all the extracted fingerprint feature points;
obtaining a normalized matrix according to the feature point set, wherein the specific process is as follows:
assuming that k elements exist in the feature point set Q, respectively searching two elements which are closest to the element for each element in the k elements, and calculating Euclidean distance between the element and any two elements in the two elements which are closest to the element;
sorting three Euclidean distances corresponding to a certain element according to a preset sequence, wherein the three Euclidean distances obtained by sorting form a distance vector d;
forming a two-dimensional distance vector D0 by using the distance vectors corresponding to the elements in the k elements;
for each element in the two-dimensional distance vector D0, calculating a sum of three Euclidean distances contained in the element, sorting the sum according to a preset sequence, wherein the Euclidean distances contained in the elements corresponding to the sorted sum form a matrix D with k rows and 3 columns;
normalizing each row of elements in the matrix D with the k rows and 3 columns to obtain a normalized matrix
And generating a private key corresponding to the user fingerprint by using the normalized matrix.
2. The private key generation method according to claim 1, wherein the steps of extracting the fingerprint feature points from the binarized fingerprint image and constructing the feature point set according to all the extracted fingerprint feature points comprise:
scanning each pixel point in the binary fingerprint image, and selecting a certain pixel point q;
calculating a classification parameter by using pixel values of eight pixel points in an eight-neighborhood pixel map of a certain pixel point q, wherein the classification parameter N is calculated by adopting the following formula:
in the formula, piExpressing the pixel value of the ith pixel point in the eight-neighborhood pixel image of q;
determining whether the pixel point is an end point or a bifurcation point according to the classification parameter N;
denoising the binary fingerprint image to obtain denoised end points and bifurcation points;
and constructing a feature point set by using the denoised end points and bifurcation points.
3. The method of claim 2, wherein the step of determining whether the pixel point is an endpoint or a bifurcation point according to the classification parameter N comprises:
when N is equal to 1, the pixel point q is an end point;
and when N is equal to 3, the pixel point q is a bifurcation point.
4. The private key generation method according to claim 2, wherein the denoising processing is performed on the binarized fingerprint image in the step to obtain denoised end points and bifurcation points by:
taking a certain end point as a starting point, moving Z pixels along a ridge line where the end point is located, if a bifurcation point is met in the Z pixels, judging the bifurcation point to be a noise point, and rejecting the noise point;
traversing all end points and ridge lines in the binary fingerprint image, and eliminating all noise points;
and Z is an adjustable parameter and is adjusted according to the size and the resolution of the fingerprint image.
5. The method for generating a private key according to claim 1, wherein the step of normalizing each column element in the k row by 3 column matrix D comprises:
wherein m is 1,2,3, …, k, i is 2 or 3;
denotes the normalized element, dm,iRepresenting the element of the m-th row and i-th column of the matrix D, Dm,1Representing the element in row m and column 1 of matrix D.
6. The method for generating a private key according to claim 1, wherein the specific process of generating the private key corresponding to the user fingerprint by using the normalized matrix in the step is as follows:
for each element in the normalized matrix
Calculating their corresponding rounded integers
In the formula, round represents rounding according to a rounding rule; λ represents an adjustable parameter, which is a non-negative integer;
and arranging and generating private keys corresponding to the user fingerprints according to a preset sequence.
7. A private key generation system for a blockchain electronic wallet based on fingerprint information, comprising:
the image acquisition module is used for acquiring fingerprint information of a user;
the binarization processing module is used for carrying out binarization processing on the fingerprint image to obtain a binarization fingerprint image;
the set construction module is used for extracting fingerprint feature points from the binary fingerprint image and constructing a feature point set according to all the extracted fingerprint feature points;
and the normalization matrix generation module is used for generating a normalization matrix by using each element in the feature point set, wherein the generation process of the normalization matrix is as follows: assuming that k elements exist in the feature point set Q, respectively searching two elements which are closest to the element for each element in the k elements, and calculating Euclidean distance between the element and any two elements in the two elements which are closest to the element;
sorting three Euclidean distances corresponding to a certain element according to a preset sequence, wherein the three Euclidean distances obtained by sorting form a distance vector d;
forming a two-dimensional distance vector D0 by using the distance vectors corresponding to the elements in the k elements;
for each element in the two-dimensional distance vector D0, calculating a sum of three Euclidean distances contained in the element, sorting the sum according to a preset sequence, wherein the Euclidean distances contained in the elements corresponding to the sorted sum form a matrix D with k rows and 3 columns;
normalizing each row of elements in the matrix D with the k rows and 3 columns to obtain a normalized matrix
And the generating module is used for generating a private key corresponding to the user fingerprint by using the normalized matrix.
8. A private key generation apparatus for a blockchain electronic wallet based on fingerprint information, comprising:
a processor for processing the received data, wherein the processor is used for processing the received data,
the processor is capable of invoking intelligent contracts from a blockchain,
the intelligent contract comprises a computer program that,
the computer program, when running on the processor, performs the steps of:
carrying out image acquisition on the user fingerprint;
carrying out binarization processing on the fingerprint image to obtain a binarized fingerprint image;
extracting fingerprint feature points from the binary fingerprint image, and constructing a feature point set according to all the extracted fingerprint feature points;
obtaining a normalized matrix according to the feature point set, wherein the generation process of the normalized matrix is as follows: assuming that k elements exist in the feature point set Q, respectively searching two elements which are closest to the element for each element in the k elements, and calculating Euclidean distance between the element and any two elements in the two elements which are closest to the element;
sorting three Euclidean distances corresponding to a certain element according to a preset sequence, wherein the three Euclidean distances obtained by sorting form a distance vector d;
forming a two-dimensional distance vector D0 by using the distance vectors corresponding to the elements in the k elements;
for each element in the two-dimensional distance vector D0, calculating a sum of three Euclidean distances contained in the element, sorting the sum according to a preset sequence, wherein the Euclidean distances contained in the elements corresponding to the sorted sum form a matrix D with k rows and 3 columns;
normalizing each row of elements in the matrix D with the k rows and 3 columns to obtain a normalized matrix
And generating a private key corresponding to the user fingerprint by using the normalized matrix.
9. A blockchain-based personal electronic wallet comprising a private key generated by the private key generation method of any one of claims 1 to 6, the private key generation method being stored as a smart contract on a blockchain.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101452526A (en) * | 2008-10-31 | 2009-06-10 | 电子科技大学 | Two-dimension bar-code type identity authentication method based on finger print |
| CN102946310A (en) * | 2012-09-03 | 2013-02-27 | 杭州电子科技大学 | Fingerprint fuzzy vault method based on (k, w) threshold secret sharing scheme |
| CN103873253A (en) * | 2014-03-03 | 2014-06-18 | 杭州电子科技大学 | Method for generating human fingerprint biometric key |
| CN105701462A (en) * | 2016-01-11 | 2016-06-22 | 成都布林特信息技术有限公司 | Identity identification method |
| CN105975838A (en) * | 2016-06-12 | 2016-09-28 | 北京集创北方科技股份有限公司 | Secure chip, biological feature identification method and biological feature template registration method |
| CN106529407A (en) * | 2016-10-09 | 2017-03-22 | 北京电子科技职业学院 | Vehicle-mounted fingerprint recognition method |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2006245949A (en) * | 2005-03-02 | 2006-09-14 | Fuji Xerox Co Ltd | Image forming device |
| US20180114226A1 (en) * | 2016-10-24 | 2018-04-26 | Paypal, Inc. | Unified login biometric authentication support |
-
2019
- 2019-01-24 CN CN201910066429.3A patent/CN109840487B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101452526A (en) * | 2008-10-31 | 2009-06-10 | 电子科技大学 | Two-dimension bar-code type identity authentication method based on finger print |
| CN102946310A (en) * | 2012-09-03 | 2013-02-27 | 杭州电子科技大学 | Fingerprint fuzzy vault method based on (k, w) threshold secret sharing scheme |
| CN103873253A (en) * | 2014-03-03 | 2014-06-18 | 杭州电子科技大学 | Method for generating human fingerprint biometric key |
| CN105701462A (en) * | 2016-01-11 | 2016-06-22 | 成都布林特信息技术有限公司 | Identity identification method |
| CN105975838A (en) * | 2016-06-12 | 2016-09-28 | 北京集创北方科技股份有限公司 | Secure chip, biological feature identification method and biological feature template registration method |
| CN106529407A (en) * | 2016-10-09 | 2017-03-22 | 北京电子科技职业学院 | Vehicle-mounted fingerprint recognition method |
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