CN110659535B - Private key generation method and system based on fingerprint identification - Google Patents
Private key generation method and system based on fingerprint identification Download PDFInfo
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/12—Fingerprints or palmprints
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T11/00—2D [Two Dimensional] image generation
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- G06V10/26—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion
- G06V10/267—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion by performing operations on regions, e.g. growing, shrinking or watersheds
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- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/12—Fingerprints or palmprints
- G06V40/1347—Preprocessing; Feature extraction
- G06V40/1359—Extracting features related to ridge properties; Determining the fingerprint type, e.g. whorl or loop
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Abstract
The invention discloses a private key generation method based on fingerprint identification, which comprises the steps of collecting and processing fingerprint information, extracting fingerprint ridges of an image, calculating the curvature of the fingerprint ridges, extracting fingerprint ridges of the image, calculating the frequency of the fingerprint ridges, generating fingerprint characteristic seeds, generating a password by using the fingerprint characteristic seeds, reserving a private key and a public key by a user, encrypting data by using the private key by using the user, and verifying the encrypted data by using the public key by using other users.
Description
Technical Field
The invention relates to the technical field of block chain encryption, in particular to a private key generation method and system based on fingerprint identification.
Background
Cryptography based on mathematics has been developed for a long time, and cryptographic applications are also widely used in social activities. The most widely applied asymmetric encryption algorithm is more widely applied, the more widely applied asymmetric encryption algorithm is more critical to key protection in asymmetric encryption, at present, a mnemonic symbol or a U shield mode is often adopted for key protection, the first mode enables the private key to be easy to remember by the brain, the second mode enables the private key to be difficult to crack, but the two private keys have the problems that the private keys are easy to lose, for example, the private keys are forgotten after being used for too long time, and the U shield is stolen by a thief in a wallet. The private key is almost the most important identification mark of the identity and the asset in digital life, and the loss or the theft of the private key means the loss or the theft of the digital identity and the asset. How to provide a private key management method which is not forgotten due to long-term non-use and is discarded when being carried around becomes a problem which needs to be solved urgently.
Disclosure of Invention
The invention aims to provide a private key generation method and a private key generation system based on fingerprint identification, which realize the function of using a fingerprint as a private key and solve the problems of forgetting and losing the private key.
A method for generating a private key based on fingerprint recognition, the method comprising:
s1, collecting fingerprint information and processing:
1.1, a user shoots fingerprint image data through a fingerprint touch device, and extracts and stores a fingerprint image;
1.2 prompting a user to switch a fingerprint placing position and repeating the fingerprint extracting step;
1.3, correcting, splicing and removing the duplicate of the collected image;
1.4, merging the collected images into an image, and segmenting the image;
s2, extracting fingerprint ridges of the image, calculating the curvature of the fingerprint ridges, extracting fingerprint ridges of the image, and calculating the frequency of the fingerprint ridges;
s3, generating fingerprint feature seeds:
3.1, converting the curvature of the fingerprint ridge line and the frequency of the fingerprint ridge line into character strings and then sequentially connecting the character strings;
3.2 converting each character of the character string into 8-bit binary system;
3.3 calculating the digit length converted into the binary system, and obtaining a remainder after modulus of 512 by using the digit length;
3.4 verifying whether the remainder is 448, if the remainder is 448, not using complementary bits, and if the remainder is 448, carrying out complementary bits;
3.5 obtaining the length of the binary digit after completion of bit padding, dividing the length of the digit by 512 to obtain a remainder, verifying whether the remainder is zero, if the remainder is zero, turning to 3.6, if the remainder is not zero, padding the binary digit with the digit zero, and then recalculating the remainder until the remainder is zero;
3.6 dividing the character string with 512 integral multiple length obtained by zero padding into multiple sections of binary system according to 512 length, 24, and putting each section of 512 binary system into five sections of buffer areas, which are respectively marked as A, B, C, D, E;
3.7 obtaining the operation result of each cache region according to the displacement calculation formula, and sequentially splicing the operation results to obtain fingerprint characteristic seeds;
and S4, generating a password by using the fingerprint feature seed, reserving a private key and disclosing a public key by the user, encrypting data by using the private key by the user, and verifying the encrypted data by using the public key by other users.
Further, the complementary bits in the 3.4 specifically include:
after the binary digits are complemented, the first complement is 1, the second complement is 0, and the third complement is 1, which are sequentially circulated.
Further, the displacement calculation formula is as follows:
F(t)＝(B AND C)or((NOT B)AND D)(0<＝t<＝19)
f is the data of the buffer area after displacement, F (t) indicates that the t bit is displaced according to the formula, namely F (t) is the displacement of the buffer area of the several bits.
A fingerprint identification based private key generation system, the system comprising:
a fingerprint recognizer for recognizing and collecting fingerprint data;
the fingerprint restorer is used for restoring the local fingerprint data into the original appearance of the whole fingerprint;
the fingerprint characteristic device is used for extracting the unique characteristics of the original appearance of the fingerprint;
a fingerprint private key generator for generating a key pair using the fingerprint unique feature as a seed,
in the system, the fingerprint recognizer collects images through a fingerprint touch device and searches fingerprint information in the images through analyzing the images, the unclear lines in the fingerprints are intelligently filled and restored into original continuous lines and stored, when the fingerprint information is restored through the fingerprint restorer, a plurality of fingerprint images are compared, common divisor among the images is searched and extracted, image areas where the common divisor is located are marked out, the common divisor is corrected and combined, the curvature of fingerprint ridges and the frequency of the fingerprint ridges are extracted and calculated through the fingerprint characteristic device, and finally, the fingerprint characteristic value is used as a seed through the fingerprint private key device to generate a password pair,
the fingerprint recognizer, the fingerprint restorer, the fingerprint characterizer and the fingerprint private key device are sequentially connected.
Further, the fingerprint recognizer comprises:
an image acquisition module: the fingerprint touch device is used for collecting images and comprises a fingerprint touch screen and a camera;
the fingerprint extraction module: the fingerprint information searching device is used for searching fingerprint information in an image by analyzing the image and intelligently filling and restoring unclear lines in the fingerprint into original continuous lines;
a data storage module: for storing the extracted fingerprint data to a storage device,
in the fingerprint recognizer, after the image acquisition module acquires image information through the fingerprint touch device, the fingerprint extraction module intelligently restores fingerprint lines, and fingerprint data after extraction processing is stored in the storage device through the data storage module.
Further, the fingerprint restorer includes:
fingerprint fragment comparison module: the fingerprint image recognition system is used for comparing a plurality of fingerprint images of the same finger, searching and extracting common divisor among the plurality of images and marking an image area where the common divisor is located;
fingerprint piece concatenation module: the fingerprint image extracting device is used for performing overlapping and splicing on the fingerprint image extracted with the common divisor to restore the most original fingerprint image;
fingerprint piece correction module: used for correcting inconsistent lines after image overlapping caused by image deformation and deformity correction in the fingerprint acquisition process, combining repeated but non-overlapping lines which appear in the overlapping process into one line, correcting and combining the lines with deformity and voucher failure problems caused by different fragment shooting angles,
in the fingerprint restorer, the common divisor of a plurality of fingerprint images is extracted through the fingerprint fragment comparison module, the fingerprint images extracted with the common divisor are overlapped and spliced under the action of the fingerprint fragment splicing module, the original fingerprint images are restored, and finally the problems of inconsistent lines and deformed lines are corrected through the fingerprint fragment correction module.
Further, the fingerprint characterizer comprises:
an image segmentation module: the fingerprint segmentation method comprises the steps of segmenting a foreground region and a background region of a fingerprint and removing an invalid region;
fingerprint curvature analysis module: the fingerprint ridge line curvature calculating device is used for calculating the fingerprint ridge line curvature at the intersection of fingerprint ridge lines at the fingerprint center and arranging the curvature according to the descending order;
fingerprint line frequency analysis module: for extracting and calculating the frequency of the gray distribution of fingerprint ridges and valleys,
in the fingerprint characteristic device, in order to calculate the curvature of the fingerprint and the frequency of fingerprint lines, a foreground area and a background area of the fingerprint are divided through an image dividing module, invalid areas influencing calculation results are removed, the curvature of the fingerprint is calculated through a fingerprint curvature analyzing module, and the frequency of the fingerprint is calculated through a fingerprint line frequency analyzing module.
Further, the fingerprint private key device comprises:
the characteristic seed module: the fingerprint processing device is used for processing fingerprint features acquired by the fingerprint feature device into fixed values, wherein the fixed values directly express unique features of fingerprints and are used as feature seeds for generating private keys;
a private key generation module: the device is used for generating an asymmetric encryption key pair by taking the seed generated by the characteristic seed module as a random factor,
in the fingerprint private key device, fingerprint characteristics are processed through a characteristic seed module to obtain characteristic seeds, and an asymmetric encrypted key pair is generated through a private key generation module.
Compared with the prior art, the invention has the following beneficial effects:
the private key is generated by using the fingerprint, so that the private key is not memorized, not lost and different for everyone, and most importantly, the original encryption algorithm is not required to be modified and can be directly applied to all the encryption algorithms adopting asymmetric encryption;
the function of using the fingerprint as the private key is realized, and the problems of forgetting and losing the private key are solved;
the better than face recognition is that the face may be one touch long, but the fingerprint is not one touch, and the privacy of the private key is better.
Drawings
FIG. 1 is a general flow diagram of a private key generation method according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of collecting and processing fingerprint information according to the present invention;
FIG. 3 is a schematic flow chart of the scheme of the present invention for generating fingerprint feature seeds;
FIG. 4 is a displacement calculation formula in the solution of the present invention;
figure 5 is a schematic diagram of the components of the system in the solution of the invention,
100, a fingerprint recognizer 200, a fingerprint restorer 300, a fingerprint characterizer 400 and a fingerprint private key device.
Detailed Description
The invention is described in further detail below with reference to the figures and specific examples. It should be noted that the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
In order to solve the problem of easy memory, the invention adopts the idea that the private key does not need to be memorized; in order to solve the problem of discarding the private key, the idea is to make the private key not lost; the password is characterized in that everyone is required to be different, and the idea for solving the problem is to find out the different things of everyone; the key of the invention is to use the fingerprint to generate the private key, so the private key is not required to be memorized or lost, and is different for everyone, and most importantly, the original encryption algorithm is not required to be modified, and the method can be directly applied to all the encryption algorithms adopting asymmetric encryption.
The technical scheme relates to a private key generation method based on fingerprint identification, which comprises the following specific steps:
s1, collecting fingerprint information and processing:
1.1, a user shoots fingerprint image data through a fingerprint touch device, and extracts and stores a fingerprint image;
1.2 prompting the user to switch the fingerprint placing position to repeat the fingerprint extracting step, in this embodiment, prompting the user to switch the fingerprint placing position 5 times, the switching times of the fingerprint placing position only plays a role in getting data for the technical scheme, so that the number of times is within the protection range of the technical scheme;
1.3, correcting, splicing and removing the duplicate of the collected image;
1.4, merging the collected images into an image, and segmenting the image;
s2, extracting fingerprint ridges of the image, calculating the curvature of the fingerprint ridges, extracting fingerprint ridges of the image and calculating the frequency of the fingerprint ridges;
s3, generating fingerprint feature seeds:
3.1, converting the curvature of the fingerprint ridge line and the frequency of the fingerprint ridge line into character strings and then sequentially connecting the character strings;
3.2 converting each character of the character string into an 8-bit binary system, and obtaining a fingerprint feature seed through an SHA1 algorithm;
3.3 calculating the digit length after the conversion into the binary system, and obtaining a remainder after modulus of the digit length to 512;
3.4 verifying whether the remainder is 448, if the remainder is 448, not using complementary bits, if the remainder is 448, carrying out complementary bits, and after binary digits, carrying out complementary bits, namely, carrying out first complementary 1, second complementary 0 and third complementary 1, and sequentially circulating;
3.5 obtaining the length of the binary digit after completion of bit padding, dividing the length of the digit by 512 to obtain a remainder, verifying whether the remainder is zero, if the remainder is zero, turning to 3.6, if the remainder is not zero, padding the binary digit with the digit zero, and then recalculating the remainder until the remainder is zero;
3.6 dividing the character string with the length of 512 integral multiples obtained by zero padding into a plurality of sections of binary systems according to the length of 512, and putting the binary systems of each section of 512 into five sections of buffer areas which are respectively marked as A, B, C, D, E;
3.7 obtaining the operation result of each cache region according to the displacement calculation formula, and sequentially splicing the operation results to obtain fingerprint characteristic seeds;
the displacement calculation formula is as follows:
ft(X)＝(B AND C)or((NOT B)AND D)(0<＝t<＝19)
in this embodiment, t is an unknown number in the formula and represents 80 words of buffer identification.
S4, generating a password pair by using the fingerprint characteristic seed, reserving a private key and disclosing a public key by the user, encrypting data by using the private key by the user, and verifying the encrypted data by using the public key by other users.
At present, a mnemonic symbol or U shield mode is usually adopted for protecting a secret key, the first mode enables the secret key to be easy to remember by the brain, the second mode enables the secret key to be difficult to crack, but the two kinds of secret keys are easy to lose, for example, the secret keys are not used for a long time and are forgotten, the secret keys are stolen by a thief in a U shield wallet, but the uniqueness of the secret keys is ensured from the beginning based on the technical scheme of generating the secret keys through fingerprint identification.
In addition, in order to solve the problems that the memory is easy, the private key cannot be lost, and the private key cannot be obtained and used instantly, because the private key cannot obtain original data information except the user, the private key cannot be obtained and used, and most importantly, the original encryption algorithm is not required to be modified, and the method can be directly applied to all encryption algorithms adopting asymmetric encryption.
In this technical solution, a private key generation system based on fingerprint identification is also provided, including:
a fingerprint recognizer 100 for recognizing and collecting fingerprint data;
a fingerprint restorer 200 for restoring the local fingerprint data into the original appearance of the whole fingerprint;
a fingerprint characteristic device 300 for extracting the unique characteristics of the original appearance of the fingerprint;
a fingerprint privacy key generator 400 for generating a key pair using the fingerprint unique feature as a seed,
in the system, a fingerprint recognizer 100 collects images through a fingerprint touch device and searches fingerprint information in the images through analyzing the images, the unclear lines in the fingerprints are intelligently filled and restored into original continuous lines and stored, when the fingerprint information is restored through a fingerprint restorer 200, a plurality of fingerprint images are compared, common divisor among the images is searched and extracted, image areas where the common divisor is located are marked out, the common divisor is corrected and combined, fingerprint ridge curvature and fingerprint ridge frequency are extracted and calculated through a fingerprint characteristic device 300, finally a fingerprint characteristic value is used as a seed through a fingerprint private key device 400 to generate a password pair,
in this embodiment, the fingerprint identifier 100, the fingerprint restorer 200, the fingerprint characterizer 300 and the fingerprint private key device 400 are sequentially connected.
Preferably, the fingerprint recognizer 100 includes:
an image acquisition module: the fingerprint touch equipment is used for acquiring images and comprises a fingerprint touch screen and a camera;
the fingerprint extraction module: the fingerprint information searching device is used for searching fingerprint information in an image by analyzing the image and intelligently filling and restoring unclear lines in the fingerprint into original continuous lines;
a data storage module: for storing the extracted fingerprint data to a storage device,
in the fingerprint recognizer 100, after the image acquisition module acquires image information through the fingerprint touch device, the fingerprint extraction module intelligently restores fingerprint lines, and stores the extracted and processed fingerprint data into the storage device through the data storage module.
Preferably, the fingerprint restorer 200 includes:
fingerprint fragment comparison module: the fingerprint image recognition system is used for comparing a plurality of fingerprint images of the same finger, searching and extracting common divisor among the plurality of images and marking an image area where the common divisor is located;
fingerprint piece concatenation module: the fingerprint image extracting device is used for performing overlapping and splicing on the fingerprint image extracted with the common divisor to restore the most original fingerprint image;
fingerprint piece correction module: used for correcting inconsistent lines after image overlapping caused by image deformation and deformity correction in the fingerprint acquisition process, combining repeated but non-overlapping lines which appear in the overlapping process into one line, correcting and combining the lines with deformity and voucher failure problems caused by different fragment shooting angles,
in the fingerprint restorer 200, the common divisor of a plurality of fingerprint images is extracted through the fingerprint fragment comparison module, the fingerprint images extracted with the common divisor are overlapped and spliced under the action of the fingerprint fragment splicing module, the original fingerprint images are restored, and finally the problems of inconsistent lines and deformed lines are corrected through the fingerprint fragment correction module.
Preferably, the fingerprint characterizer 300 includes:
an image segmentation module: the fingerprint segmentation method comprises the steps of segmenting a foreground region and a background region of a fingerprint and removing an invalid region;
fingerprint curvature analysis module: the fingerprint ridge line curvature calculating device is used for calculating the fingerprint ridge line curvature at the intersection of fingerprint ridge lines at the fingerprint center and arranging the curvature according to the descending order;
fingerprint line frequency analysis module: for extracting and calculating the frequency of the gray distribution of fingerprint ridges and valleys,
in the fingerprint characterizer 300, in order to calculate the curvature of the fingerprint and the frequency of the fingerprint lines, the foreground region and the background region of the fingerprint are segmented by the image segmentation module, the invalid region affecting the calculation result is removed, the curvature of the fingerprint is calculated by the fingerprint curvature analysis module, and the frequency of the fingerprint is calculated by the fingerprint line frequency analysis module.
Preferably, fingerprint privacy key generator 400 includes:
a characteristic seed module: the fingerprint processing device is used for processing the fingerprint features acquired by the fingerprint feature device 300 into fixed values, wherein the fixed values directly express the unique features of the fingerprints and are used as feature seeds for generating private keys;
a private key generation module: the device is used for generating an asymmetric encryption key pair by taking the seed generated by the characteristic seed module as a random factor,
in the fingerprint private key device 400, the fingerprint features are processed by the feature seed module to obtain feature seeds, and the asymmetric encryption key pair is generated by the private key generation module.
The user keeps the private key and opens the public key, the user uses the private key to encrypt data, and other users use the public key to verify the encrypted data.
The above-mentioned embodiments are only preferred embodiments of the present invention, and do not limit the technical scope of the present invention, so that the changes and modifications made by the claims and the specification of the present invention should fall within the scope of the present invention.
Claims (3)
1. A private key generation method based on fingerprint identification is characterized by comprising the following steps:
s1, collecting fingerprint information and processing:
1.1, a user shoots fingerprint image data through a fingerprint touch device, and extracts and stores a fingerprint image;
1.2 prompting a user to switch a fingerprint placing position and repeating the fingerprint extracting step;
1.3, correcting, splicing and removing the duplicate of the collected image;
1.4, merging the collected images into an image, and segmenting the image;
s2, extracting fingerprint ridges of the image, calculating the curvature of the fingerprint ridges, extracting fingerprint ridges of the image, and calculating the frequency of the fingerprint ridges;
s3, generating fingerprint feature seeds:
3.1, converting the curvature of the fingerprint ridge line and the frequency of the fingerprint ridge line into character strings and then sequentially connecting the character strings;
3.2 converting each character of the character string into 8-bit binary system;
3.3 calculating the digit length converted into the binary system, and obtaining a remainder after modulus of 512 by using the digit length;
3.4 verifying whether the remainder is 448, if the remainder is 448, not using complementary bits, and if the remainder is 448, carrying out complementary bits;
3.5 obtaining the length of the binary digit after completion of bit padding, dividing the length of the digit by 512 to obtain a remainder, verifying whether the remainder is zero, if the remainder is zero, turning to 3.6, if the remainder is not zero, padding the binary digit with the digit zero, and then recalculating the remainder until the remainder is zero;
3.6 dividing the character string with the length of 512 integral multiples obtained by zero padding into a plurality of sections of binary systems according to the length of 512, and putting the binary systems of each section of 512 into five sections of buffer areas which are respectively marked as A, B, C, D, E;
3.7 obtaining the operation result of each cache region according to the displacement calculation formula, and sequentially splicing the operation results to obtain fingerprint characteristic seeds;
and S4, generating a password by using the fingerprint feature seed, reserving a private key and disclosing a public key by the user, encrypting data by using the private key by the user, and verifying the encrypted data by using the public key by other users.
2. The method of claim 1, wherein the complementary bits of 3.4 comprise:
after the binary digits are complemented, the first complement is 1, the second complement is 0, and the third complement is 1, which are sequentially circulated.
3. The method of claim 1, wherein the displacement calculation formula is:
F(t)＝(B AND C)or((NOT B)AND D)(0<＝t<＝19)
f is the data of the buffer area after displacement, F (t) indicates that the t bit is displaced according to the formula, namely F (t) is the displacement of the buffer area of the several bits.
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