CN105553657A - Feature level fused fingerprint fuzzy vault realization method - Google Patents
Feature level fused fingerprint fuzzy vault realization method Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L9/00—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols
- H04L9/08—Key distribution or management, e.g. generation, sharing or updating, of cryptographic keys or passwords
- H04L9/0816—Key establishment, i.e. cryptographic processes or cryptographic protocols whereby a shared secret becomes available to two or more parties, for subsequent use
- H04L9/0838—Key agreement, i.e. key establishment technique in which a shared key is derived by parties as a function of information contributed by, or associated with, each of these
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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
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Abstract
The invention relates to a feature level fused fingerprint fuzzy vault realization method. The invention comprises following steps: step 1, fusing the detail point coordinate information of a fingerprint A and the direction field information of a fingerprint B to obtain a new feature information set; step 2, binding a secret key needed to be protected and the new feature information set obtained in the step 1 by using a locking algorithm, thus generating a fingerprint fuzzy vault; and step 3, extracting the secret key from the fingerprint fuzzy vault by using an unlocking algorithm according to the verified searching fingerprints A' and B' feature information. According to the invention, compared with the single feature generated fingerprint fuzzy vault method, the feature level fusion generated fingerprint fuzzy vault method has stronger security and higher realization efficiency. The secret key only can be extracted by a legal user and two legal fingerprints (namely the two fingerprints for register); therefore, the confidentiality, integrity and usability demanded by the secret key security storage are ensured.
Description
Technical field
The invention belongs to cryptography and biometrics identification technology field, be specifically related to a kind of fingerprint fuzzy vault implementation method of feature-based fusion.
Background technology
Propose " AFuzzyVaultScheme " at A.Juels and M.Sudan in 2002.In the fuzzy vault algorithm that it proposes, the key of characteristic set unique for user or other community sets A hybrid subscriber is entered in the national treasury based on Reed-Solomn.User can utilize the community set B having most element identical with set A to recover key.The people such as U.Uludag propose a kind of fuzzy vault algorithm based on fingerprint characteristic, first this algorithm carried out calibrating to the fingerprint of template and input the angle change eliminated because rotation etc. causes before generating fingerprint key, what participate in comparison is plane coordinates value, the field of direction, the minutiae point type of fingerprint minutiae, if the plane coordinates value of certain two point of former and later two fingerprints, field of direction difference is within a threshold value, minutiae point type is identical, then think identical point.Fuzzy vault scheme based on fingerprint characteristic may be used for protection safe storage key.
The present invention that the fingerprint fuzzy vault implementation method using feature based level to merge is core, the uniqueness that security guarantee is merged by the many fingerprint characteristic of sole user or the fingerprint characteristic of multi-user merges provides, and the partial information of just every piece of fingerprint that fuzzy vault comprises, even if reveal, be also difficult to reconstruct.
Summary of the invention
The object of the invention is for the deficiencies in the prior art, under true and reliable experiment condition, provide a kind of fingerprint fuzzy vault implementation method of feature-based fusion.
The present invention solves the technical scheme that its technical problem adopts and comprises the steps:
Step 1, the minutiae point coordinate information of fusion fingerprint A and the directional field information of fingerprint B obtain a new feature information aggregate;
Step 2, utilize and to lock algorithm, the new feature information aggregate needing the key of protection and step 1 to obtain is bound, generation fingerprint fuzzy vault;
Step 3, the query fingerprints A ' of utilization process checking, B ' characteristic information adopt unlock algorithm to extract key from fingerprint fuzzy vault.
The minutiae point coordinate information of fusion fingerprint A described in step 1 and the directional field information of fingerprint B obtain a new feature information aggregate, specific as follows:
1.1 by twice fingerprint minutiae training, and extraction, process finger print information obtain the set of one group of metastable user fingerprints A minutiae point; First utilize Geometric active contours technology that fingerprint A minutiae point is registered a Hash table
then the plane coordinates of each minutiae point obtained is quantized to 8 bits respectively, and to be connected in series with high four XORs of ordinate that to obtain a length be the Bit String of 12 by low four of abscissa; Then the data type of all fingerprint A minutiae point is converted into integer, obtains a class range [0,2
12-1] the integer set F in
a, the number of stable user fingerprints A minutiae point is designated as N
a.
1.2 by twice fingerprint minutiae training, and extraction, process finger print information obtain the set of one group of metastable user fingerprints B minutiae point; First utilize Geometric active contours technology that fingerprint B minutiae point is registered a Hash table
then the plane coordinates of each minutiae point obtained is quantized to 8 bits respectively, and serial connection to obtain a length be the Bit String of 16, then the data type of all fingerprint B minutiae point is converted into integer, then can obtains a class range [0,2
16-1] the integer set F in
b, the number of stable user fingerprints B minutiae point is designated as N
b.
1.3 by extracting, processing finger print information, and obtain the field of direction of each pixel, its scope is in 0-255; Corresponding to the plane coordinates of fingerprint A minutiae point, the field of direction of fingerprint B extracted, the field of direction obtained being quantized to respectively a length is the bit of 4, then can obtain a class range [0,2
4-1] the integer set O in
b, wherein the number of the field of direction is N
o.
1.4 by integer set F
aand O
bthe value of middle respective coordinates combines, concrete with F
a× 16+O
bform obtains a class range [0,2
16-1] the integer set F in, wherein the number of minutiae point is N.
Fingerprint minutiae training method described in step 1.1 and 1.2 is as follows: sample fingerprint is carried out feature registration successively, does not distinguish minutiae point type during registration; Two width fingerprints the minutiae point of registration can be designated as the true detail point of this piece of fingerprint, get the average of two groups of coordinate figures that same true detail o'clock is got at two width images, are recorded as the coordinate figure of this true detail point; The minutiae point that registration is good and the 3rd width sample fingerprint carry out feature registration again; The set of metastable true detail point is obtained after registration completes.
Utilization described in step 2 is locked algorithm, is bound by the new feature information aggregate needing the key of protection and step 1 to obtain, generation fingerprint fuzzy vault, specific as follows:
Key k to be bound to be carried out format process by 2.1, format rule for successively from left to right in order every 16 bit lengths be one piece, note obtains altogether m block; And be integer by every blocks of data conversion in type, obtain a class range [0,2
16-1] the integer set K in.
The 2.2 block number m produced according to key format process, in finite field
upper structure multinomial P (x)
P(x)＝a
mx
m+…+a
2x
2+a
1x+a
0(modp)(1)
Wherein, modulus p rule of thumb recommends value to be Big prime 65537, the most high reps m of multinomial according to the general span of key length from 9 to 16.
2.3 add the CRC cyclic redundancy check (CRC) code of 16 bit lengths, to increase the reliability of Protective Key of the present invention for key to be bound; And the data type of this CRC cyclic redundancy check (CRC) code is converted into integer, obtain a scope [0,2
16-1] integer in.
2.4 using the CRC check code that obtains in the key to be bound mentioned in step 2.1 and step 2.3 coefficient as multinomial (1), wherein a
0for CRC check code, a
1..., a
mfor key to be bound.Then each minutiae point data in stable integer set F step 1.4 obtained are brought in multinomial (1) as multinomial input value x, try to achieve point set (x, P (x)) | x ∈ F} is the true set in fuzzy vault.
2.5 for fuzzy vault interpolation is far more than the hash point set of truly putting set number, and each group element in the set of hash point produces all at random, and requires each group element and truly put unequal, and each group element does not meet multinomial (1).
2.6 will truly put set and the set of hash point is disorderly put, and final generation one comprises the fingerprint fuzzy vault truly putting set, hash point set and fuzzy vault essential information.
Described fuzzy vault essential information comprises the most high reps m and modulus p of multinomial;
Query fingerprints A ', the B ' characteristic information of the utilization process checking described in step 3 adopt unlock algorithm to extract key from fingerprint fuzzy vault, specific as follows:
3.1 by registering a Hash table to the relevant treatment of query fingerprints A ' and Geometric active contours technology
then with the Hash table that obtains in step 1.1
mate, filter out the datum mark set H that matching number is maximum
a ', then by obtaining integer set F in step 1.1
amethod obtain F
a '; Scope is [0,2
12-1], in, the number of minutiae point is N
a '.If N
a '>=9, enter step 3.2; Otherwise, re-enter fingerprint A ', repeat this step.
3.2 by registering a Hash table to the relevant treatment of query fingerprints B ' and Geometric active contours technology
then with the Hash table that obtains in step 1.2
mate, filter out the datum mark set H that matching number is maximum
b ', then by obtaining integer set F in step 1.2
bmethod obtain F
b '; Scope is [0,2
16-1], in, the number of minutiae point is N
b '.If N
b '>=9, enter step 3.3; Otherwise, re-enter fingerprint B ', repeat this step.
3.3 difference value utilizing query fingerprints B ' image direction field, carry out alignement to query fingerprints B ', obtain the field of direction of query fingerprints B '; Then take out and correspond to minutiae point set F
a 'in the field of direction value of plane coordinates; And the field of direction value obtained is quantized to 4 bits respectively, obtain a class range [0,2
4-1] the integer set O in
b ', its field of direction number N
o '.
3.4 by integer set F
a 'and O
b 'the value of middle respective coordinates combines, concrete with F
a '× 16+O
b 'form obtains a class range [0,2
16-1] the integer set F ' in, wherein the number of minutiae point is N '.
Query fingerprints A ', B ' minutiae point set F ' are carried out traversal with each group of data in fuzzy vault and contrast by 3.5, if the number of the point conformed to is not less than polynomial most high reps m (i.e. block number) in fuzzy vault, enter step 3.6; Otherwise extract key failure.
The 3.6 pairs of points conformed to found carry out combination and calculate, and every m point is one group, utilize Lagrange's interpolation to attempt recovering possibility key, and the possible key obtained is carried out CRC check to every group.If by CRC check, then extract key success; Otherwise continue next group to attempt, until attempted all combined situation, extract key not yet, then extract key failure.
Beneficial effect of the present invention
Compared with the cryptographic key protection method of single fingerprint characteristic fuzzy vault; the present invention utilizes fingerprint characteristic level to merge the fuzzy vault of generation to realize the protection of key; there is stronger fail safe, higher implementation efficiency; possibility is provided for using under the equipment of the computational resource anxieties such as mobile terminal; also ensure that and only have validated user under legal fingerprint, just can extract key with CRC check function, ensured confidentiality, integrality, availability that secret key safety stores.
Accompanying drawing explanation
The fingerprint fuzzy vault implementation method flow chart of Fig. 1 feature-based fusion.
The unblock flow chart of Fig. 2 fuzzy vault.
The fingerprint fuzzy vault of Fig. 3 feature-based fusion generates analogous diagram.
The unblock analogous diagram of Fig. 4 fuzzy vault.
Embodiment
Below in conjunction with accompanying drawing, the invention will be further described.
The fingerprint fuzzy vault implementation method of feature-based fusion mainly comprises three parts: a first step, and the directional field information of the minutiae point coordinate information and fingerprint B that merge fingerprint A obtains a new feature information aggregate; Second step, utilizes algorithm of locking, and is bound by the new feature information aggregate that the key and step 1 that need protection obtain, generates fingerprint fuzzy vault; 3rd step, query fingerprints A ', the B ' characteristic information of the checking of utilization process adopt unlock algorithm to extract key from fingerprint fuzzy vault.
The minutiae point coordinate information of fusion fingerprint A described in step 1 and the directional field information of fingerprint B obtain a new feature information aggregate (flow chart is as shown in Figure 1), specific as follows:
1.1 by twice fingerprint minutiae training, and extraction, process finger print information obtain the set of one group of metastable user fingerprints A minutiae point; First utilize Geometric active contours technology that fingerprint A minutiae point is registered a Hash table
then the plane coordinates of each minutiae point obtained is quantized to 8 bits respectively, and to be connected in series with high four XORs of ordinate that to obtain a length be the Bit String of 12 by low four of abscissa; Then the data type of all fingerprint A minutiae point is converted into integer, obtains a class range [0,2
12-1] the integer set F in
a, the number of stable user fingerprints A minutiae point is designated as N
a.
1.2 by twice fingerprint minutiae training, and extraction, process finger print information obtain the set of one group of metastable user fingerprints B minutiae point; First utilize Geometric active contours technology that fingerprint B minutiae point is registered a Hash table
then the plane coordinates of each minutiae point obtained is quantized to 8 bits respectively, and serial connection to obtain a length be the Bit String of 16, then the data type of all fingerprint B minutiae point is converted into integer, then can obtains a class range [0,2
16-1] the integer set F in
b, the number of stable user fingerprints B minutiae point is designated as N
b.
1.3 by extracting, processing finger print information, and obtain the field of direction of each pixel, its scope is in 0-255; Corresponding to the plane coordinates of fingerprint A minutiae point, the field of direction of fingerprint B extracted, the field of direction obtained being quantized to respectively a length is the bit of 4, then can obtain a class range [0,2
4-1] the integer set O in
b, wherein the number of the field of direction is N
o.
1.4 by integer set F
aand O
bthe value of middle respective coordinates combines, concrete with F
a× 16+O
bform obtains a class range [0,2
16-1] the integer set F in, wherein the number of minutiae point is N.
Fingerprint minutiae training method described in step 1.1 and 1.2 is as follows: sample fingerprint is carried out feature registration successively, does not distinguish minutiae point type during registration; Two width fingerprints the minutiae point of registration can be designated as the true detail point of this piece of fingerprint, get the average of two groups of coordinate figures that same true detail o'clock is got at two width images, are recorded as the coordinate figure of this true detail point; The minutiae point that registration is good and the 3rd width sample fingerprint carry out feature registration again; The set of metastable true detail point is obtained after registration completes.
Utilization described in step 2 is locked algorithm, is bound by the new feature information aggregate needing the key of protection and step 1 to obtain, generation fingerprint fuzzy vault, specific as follows:
128 bit length keys to be bound to be carried out format process by 2.1, format rule for successively from left to right in order every 16 bit lengths be one piece, note obtains altogether 9 pieces; And be integer by every blocks of data conversion in type, obtain a class range [0,2
16-1] the integer set K in.
The 2.2 block numbers 9 produced according to key format process, in finite field
upper structure multinomial P (x)
P(x)＝a
9x
9+…+a
2x
2+a
1x+a
0(mod65537)(2)
2.3 add the CRC cyclic redundancy check (CRC) code of 16 bit lengths, to increase the reliability of Protective Key of the present invention for key to be bound; And the data type of this CRC cyclic redundancy check (CRC) code is converted into integer, obtain a scope [0,2
16-1] integer in.
2.4 using the CRC check code that obtains in the key to be bound mentioned in step 2.1 and step 2.3 coefficient as multinomial (2), wherein a
0for CRC check code, a
1..., a
mfor key to be bound.Then each minutiae point data in stable integer set F step 1.4 obtained are brought in multinomial (2) as multinomial input value x, try to achieve point set (x, P (x)) | x ∈ F} is the true set in fuzzy vault.
2.5 for fuzzy vault interpolation is far more than the hash point set of truly putting set number, and each group element in the set of hash point produces all at random, and requires each group element and truly put unequal, and each group element does not meet multinomial (2).
2.6 will truly put set and the set of hash point is disorderly put, and final generation one comprises the fingerprint fuzzy vault truly putting set, hash point set and fuzzy vault essential information, as shown in Figure 3.
Described fuzzy vault essential information comprises most high reps 9 and modulus 65537 in multinomial;
Query fingerprints A ', the B ' characteristic information of the utilization process checking described in step 3 adopt unlock algorithm to extract key (flow chart is as shown in Figure 2) from fingerprint fuzzy vault, specific as follows:
3.1 by registering a Hash table to the relevant treatment of query fingerprints A ' and Geometric active contours technology
then with the Hash table that obtains in step 1.1
mate, filter out the datum mark set H that matching number is maximum
a ', then by obtaining integer set F in step 1.1
amethod obtain F
a '; Scope is [0,2
12-1], in, the number of minutiae point is N
a '.If N
a '>=9, enter step 3.2; Otherwise, re-enter fingerprint A ', repeat this step.
3.2 by registering a Hash table to the relevant treatment of query fingerprints B ' and Geometric active contours technology
then with the Hash table that obtains in step 1.2
mate, filter out the datum mark set H that matching number is maximum
b ', then by obtaining integer set F in step 1.2
bmethod obtain F
b '; Scope is [0,2
16-1], in, the number of minutiae point is N
b '.If N
b '>=9, enter step 3.3; Otherwise, re-enter fingerprint B ', repeat this step.
3.3 difference value utilizing query fingerprints B ' image direction field, carry out alignement to query fingerprints B ', obtain the field of direction of query fingerprints B '; Then take out and correspond to minutiae point set F
a 'in the field of direction value of plane coordinates; And the field of direction value obtained is quantized to 4 bits respectively, obtain a class range [0,2
4-1] the integer set O in
b ', its field of direction number N
o '.
3.4 by integer set F
a 'and O
b 'the value of middle respective coordinates combines, concrete with F
a '× 16+O
b 'form obtains a class range [0,2
16-1] the integer set F ' in, wherein the number of minutiae point is N '.
Query fingerprints A ', B ' minutiae point set F ' are carried out traversal with each group of data in fuzzy vault and contrast by 3.5, if the number of the point conformed to is not less than polynomial most high reps m (i.e. block number) in fuzzy vault, enter step 3.6; Otherwise extract key failure.
The 3.6 pairs of points conformed to found carry out combination and calculate, and every 9 points are one group, and utilizing Lagrange's interpolation to attempt recovering to every group may key, and the possible key obtained is carried out CRC check.If by CRC check, then extract key success, as shown in Figure 4; Otherwise continue next group to attempt, until attempted all combined situation, extract key not yet, then extract key failure.
Claims (4)
1. a fingerprint fuzzy vault implementation method for feature-based fusion, is characterized in that comprising the steps:
Step 1, the minutiae point coordinate information of fusion fingerprint A and the directional field information of fingerprint B obtain a new feature information aggregate;
Step 2, utilize and to lock algorithm, the new feature information aggregate needing the key of protection and step 1 to obtain is bound, generation fingerprint fuzzy vault;
Step 3, the query fingerprints A ' of utilization process checking, B ' characteristic information adopt unlock algorithm to extract key from fingerprint fuzzy vault.
2. the fingerprint fuzzy vault implementation method of a kind of feature-based fusion as claimed in claim 1, is characterized in that the minutiae point coordinate information of fusion fingerprint A described in step 1 and the directional field information of fingerprint B obtain a new feature information aggregate, specific as follows:
1.1 by twice fingerprint minutiae training, and extraction, process finger print information obtain the set of one group of metastable fingerprint A minutiae point; First utilize Geometric active contours technology that fingerprint A minutiae point is registered a Hash table
then the plane coordinates of each minutiae point obtained is quantized to 8 bits respectively, and to be connected in series with high four XORs of ordinate that to obtain a length be the Bit String of 12 by low four of abscissa; Then the data type of all fingerprint A minutiae point is converted into integer, obtains a class range [0,2
12-1] the integer set F in
a, the number of stable user fingerprints A minutiae point is designated as N
a;
1.2 by twice fingerprint minutiae training, and extraction, process finger print information obtain the set of one group of metastable fingerprint B minutiae point; First utilize Geometric active contours technology that fingerprint B minutiae point is registered a Hash table
then the plane coordinates of each minutiae point obtained is quantized to 8 bits respectively, and serial connection to obtain a length be the Bit String of 16, then the data type of all fingerprint B minutiae point is converted into integer, then can obtains a class range [0,2
16-1] the integer set F in
b, the number of stable user fingerprints B minutiae point is designated as N
b;
1.3 by extracting, processing finger print information, and obtain the field of direction of each pixel, its scope is in 0-255; Corresponding to the plane coordinates of fingerprint A minutiae point, the field of direction of fingerprint B extracted, the field of direction obtained being quantized to respectively a length is the bit of 4, then can obtain a class range [0,2
4-1] the integer set O in
b, wherein the number of the field of direction is N
o;
1.4 by integer set F
aand O
bthe value of middle respective coordinates combines, concrete with F
a× 16+O
bform obtains a class range [0,2
16-1] the integer set F in, wherein the number of minutiae point is N.
Fingerprint minutiae training method described in step 1.1 and 1.2 is as follows: sample fingerprint is carried out feature registration successively, does not distinguish minutiae point type during registration; Two width fingerprints the minutiae point of registration can be designated as the true detail point of this piece of fingerprint, get the average of two groups of coordinate figures that same true detail o'clock is got at two width images, are recorded as the coordinate figure of this true detail point; The minutiae point that registration is good and the 3rd width sample fingerprint carry out feature registration again; The set of metastable true detail point is obtained after registration completes.
3. the fingerprint fuzzy vault implementation method of a kind of feature-based fusion as claimed in claim 2; the utilization that it is characterized in that described in step 2 is locked algorithm; the new feature information aggregate that the key and step 1 that need protection obtain is bound, generates fingerprint fuzzy vault, specific as follows:
Key k to be bound to be carried out format process by 2.1, format rule for successively from left to right in order every 16 bit lengths be one piece, note obtains altogether m block; And be integer by every blocks of data conversion in type, obtain a class range [0,2
16-1] the integer set K in;
The 2.2 block number m produced according to key format process, in finite field
upper structure multinomial P (x)
P(x)＝a
mx
m+…+a
2x
2+a
1x+a
0(modp)(1)
Wherein, modulus p rule of thumb recommends value to be Big prime 65537, the most high reps m of multinomial according to the general span of key length from 9 to 16;
2.3 add the CRC cyclic redundancy check (CRC) code of 16 bit lengths for key to be bound; And the data type of this CRC cyclic redundancy check (CRC) code is converted into integer, obtain a scope [0,2
16-1] integer in;
2.4 using the CRC check code that obtains in the key to be bound mentioned in step 2.1 and step 2.3 coefficient as multinomial (1), wherein a
0for CRC check code, a
1..., a
mfor key to be bound; Then each minutiae point data in stable integer set F step 1.4 obtained are brought in multinomial (1) as multinomial input value x, try to achieve point set (x, P (x)) | x ∈ F} is the true set in fuzzy vault;
2.5 for fuzzy vault interpolation is far more than the hash point set of truly putting set number, and each group element in the set of hash point produces all at random, and requires each group element and truly put unequal, and each group element does not meet multinomial (1);
2.6 will truly put set and the set of hash point is disorderly put, and final generation one comprises the fingerprint fuzzy vault truly putting set, hash point set and fuzzy vault essential information;
Described fuzzy vault essential information comprises the most high reps m and modulus p of multinomial.
4. the fingerprint fuzzy vault implementation method of a kind of feature-based fusion as claimed in claim 3, it is characterized in that query fingerprints A ', the B ' characteristic information of the utilization process checking described in step 3 adopt unlock algorithm to extract key from fingerprint fuzzy vault, specific as follows:
3.1 by registering a Hash table to the relevant treatment of query fingerprints A ' and Geometric active contours technology
then with the Hash table that obtains in step 1.1
mate, filter out the datum mark set H that matching number is maximum
a ', then by obtaining integer set F in step 1.1
amethod obtain F
a '; Scope is [0,2
12-1], in, the number of minutiae point is N
a '; If N
a '>=9, enter step 3.2; Otherwise, re-enter fingerprint A ', repeat this step;
3.2 by registering a Hash table to the relevant treatment of query fingerprints B ' and Geometric active contours technology
then with the Hash table that obtains in step 1.2
mate, filter out the datum mark set H that matching number is maximum
b ', then by obtaining integer set F in step 1.2
bmethod obtain F
b '; Scope is [0,2
16-1], in, the number of minutiae point is N
b '; If N
b '>=9, enter step 3.3; Otherwise, re-enter fingerprint B ', repeat this step;
3.3 difference value utilizing query fingerprints B ' image direction field, carry out alignement to query fingerprints B ', obtain the field of direction of query fingerprints B '; Then take out and correspond to minutiae point set F
a 'in the field of direction value of plane coordinates; And the field of direction value obtained is quantized to 4 bits respectively, obtain a class range [0,2
4-1] the integer set O in
b ', its field of direction number N
o ';
3.4 by integer set F
a 'and O
b 'the value of middle respective coordinates combines, concrete with F
a '× 16+O
b 'form obtains a class range [0,2
16-1] the integer set F ' in, wherein the number of minutiae point is N ';
Query fingerprints A ', B ' minutiae point set F ' are carried out traversal with each group of data in fuzzy vault and contrast by 3.5, if the number of the point conformed to is not less than polynomial most high reps m in fuzzy vault, enter step 3.6; Otherwise extract key failure;
The 3.6 pairs of points conformed to found carry out combination and calculate, and every m point is one group, utilize Lagrange's interpolation to attempt recovering possibility key, and the possible key obtained is carried out CRC check to every group; If by CRC check, then extract key success; Otherwise continue next group to attempt, until attempted all combined situation, extract key not yet, then extract key failure.
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| CN106169062A (en) * | 2016-06-08 | 2016-11-30 | 杭州电子科技大学 | A kind of implementation method referring to that vein merges fuzzy vault |
| CN106778520A (en) * | 2016-11-25 | 2017-05-31 | 哈尔滨工程大学 | A kind of fuzzy safety box encryption method of finger vena |
| CN106936586A (en) * | 2016-12-07 | 2017-07-07 | 中国电子科技集团公司第三十研究所 | A kind of biological secret key extracting method based on fingerprint bit string and Error Correction of Coding |
| CN107181598A (en) * | 2017-07-05 | 2017-09-19 | 四川无声信息技术有限公司 | Fingerprint key processing method and processing device |
| CN107181592A (en) * | 2017-05-12 | 2017-09-19 | 西安电子科技大学 | The key bindings method of adaptive fingerprint minutiae quantity |
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| CN106778520A (en) * | 2016-11-25 | 2017-05-31 | 哈尔滨工程大学 | A kind of fuzzy safety box encryption method of finger vena |
| CN106936586A (en) * | 2016-12-07 | 2017-07-07 | 中国电子科技集团公司第三十研究所 | A kind of biological secret key extracting method based on fingerprint bit string and Error Correction of Coding |
| CN110300971B (en) * | 2017-02-22 | 2022-07-15 | 指纹卡安娜卡敦知识产权有限公司 | Method, network node, client device and storage medium for biometric-based authentication |
| CN110300971A (en) * | 2017-02-22 | 2019-10-01 | 指纹卡有限公司 | Telnet based on biological characteristic |
| CN107181592A (en) * | 2017-05-12 | 2017-09-19 | 西安电子科技大学 | The key bindings method of adaptive fingerprint minutiae quantity |
| CN107181592B (en) * | 2017-05-12 | 2019-11-22 | 西安电子科技大学 | The key bindings method of adaptive fingerprint minutiae quantity |
| CN107181598A (en) * | 2017-07-05 | 2017-09-19 | 四川无声信息技术有限公司 | Fingerprint key processing method and processing device |
| CN108429614A (en) * | 2018-01-05 | 2018-08-21 | 杭州电子科技大学 | A kind of fuzzy vault implementation method merged with face characteristic grade based on fingerprint |
| CN108429614B (en) * | 2018-01-05 | 2020-10-30 | 杭州电子科技大学 | Fuzzy vault realization method based on fingerprint and face feature level fusion |
| CN108960039A (en) * | 2018-05-07 | 2018-12-07 | 西安电子科技大学 | A kind of irreversible fingerprint template encryption method based on symmetrical hash |
| CN113254989A (en) * | 2021-04-27 | 2021-08-13 | 支付宝(杭州)信息技术有限公司 | Fusion method and device of target data and server |
| CN113507380A (en) * | 2021-09-10 | 2021-10-15 | 浙江大学 | Privacy protection remote unified biometric authentication method and device and electronic equipment |
| CN116861489A (en) * | 2023-02-23 | 2023-10-10 | 重庆市规划和自然资源信息中心 | Serialization security management method for map two-dimensional data |
| CN116861489B (en) * | 2023-02-23 | 2024-03-08 | 重庆市规划和自然资源信息中心 | Serialization security management method for map two-dimensional data |
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