CN105553657B - A kind of fingerprint fuzzy vault implementation method of feature-based fusion - Google Patents
A kind of fingerprint fuzzy vault implementation method of feature-based fusion Download PDFInfo
- Publication number
- CN105553657B CN105553657B CN201610038430.1A CN201610038430A CN105553657B CN 105553657 B CN105553657 B CN 105553657B CN 201610038430 A CN201610038430 A CN 201610038430A CN 105553657 B CN105553657 B CN 105553657B
- Authority
- CN
- China
- Prior art keywords
- fingerprint
- point
- key
- minutiae
- fuzzy vault
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Expired - Fee Related
Links
Classifications
-
- 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
-
- G—PHYSICS
- 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
- G06V40/1347—Preprocessing; Feature extraction
Landscapes
- Engineering & Computer Science (AREA)
- Computer Security & Cryptography (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Human Computer Interaction (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Multimedia (AREA)
- Theoretical Computer Science (AREA)
- Image Analysis (AREA)
- Collating Specific Patterns (AREA)
Abstract
The present invention relates to a kind of fingerprint fuzzy vault implementation methods of feature-based fusion.The present invention comprises the following steps that the directional field information of step 1, the minutiae point coordinate information of fusion fingerprint A and fingerprint B obtains a new feature information aggregate；Step 2, using locking algorithm, the new feature information aggregate for needing key to be protected to obtain with step 1 is bound, generation fingerprint fuzzy vault；Step 3 extracts key using unlocking algorithm from fingerprint fuzzy vault using inquiry fingerprint A ', the B ' characteristic information by verifying.The fingerprint fuzzy vault method that the present invention is generated using feature-based fusion has stronger safety, higher realization efficiency compared with the fingerprint fuzzy vault method that single features generate.This only can just extract the key with CRC check function under legitimate user and two pieces of legal fingerprints (two pieces of fingerprints when registering), to ensure that confidentiality required by key secure storage, integrality, availability.
Description
Technical field
The invention belongs to cryptographies and biometrics identification technology field, and in particular to a kind of fingerprint mould of feature-based fusion
Paste national treasury implementation method.
Background technique
" A Fuzzy Vault Scheme " is proposed in A.Juels and M.Sudan in 2002.In the fuzzy gold that it is proposed
In the algorithm of library, the key of the unique characteristic set of user or other attribute sets A hybrid subscriber is entered and is based on Reed-Solomn
National treasury in.User can use has the identical attribute set B of most elements to recover key with set A.U.Uludag
Et al. propose a kind of fuzzy vault algorithm based on fingerprint characteristic, the algorithm before generating fingerprint key first to template and
The fingerprint of input carries out calibration and eliminates the angle change due to caused by rotation etc., and participate in comparing is the plane seat of fingerprint minutiae
Scale value, the field of direction, details vertex type, if the plane coordinate value of two points of certain of former and later two fingerprints, the field of direction difference are one
Within a threshold value, details vertex type is identical, then it is assumed that is identical point.Fuzzy vault scheme based on fingerprint characteristic can be used for
Protect secure storage key.
It is the present invention of core using the fingerprint fuzzy vault implementation method based on feature-based fusion, security guarantee is by list
The uniqueness of the more fingerprint characteristic fusions of one user or the fingerprint characteristic fusion of multi-user provides, and that fuzzy vault includes
It is the partial information of every piece of fingerprint, even if leakage, it is also difficult to reconstruct.
Summary of the invention
The purpose of the present invention is in view of the deficiencies of the prior art, under true and reliable experiment condition, provide a kind of spy
Levy the fingerprint fuzzy vault implementation method of grade fusion.
The present invention solves the technical solution that its technical problem is adopted and includes the following steps:
The directional field information of step 1, the minutiae point coordinate information for merging fingerprint A and fingerprint B obtains a new feature information
Set；
Step 2, using locking algorithm, the new feature information aggregate for needing key to be protected to obtain with step 1 is tied up
It is fixed, generate fingerprint fuzzy vault；
Step 3 uses unlocking algorithm using inquiry fingerprint A ', the B ' characteristic information by verifying from fingerprint fuzzy vault
Extract key.
The directional field information of the minutiae point coordinate information and fingerprint B that merge fingerprint A described in step 1 obtains a new feature
Information aggregate, specific as follows:
1.1 by the training of fingerprint minutiae twice, extracts, processing finger print information obtains one group of metastable user fingerprints
A details point set；Fingerprint A minutiae point is registered into a Hash table first with Geometric active contours technologyThen every by what is obtained
The plane coordinates of a minutiae point quantifies respectively to 8 bits, and is concatenated by abscissa low four with high four exclusive or of ordinate
Obtain the Bit String that a length is 12；Then integer is converted by the data type of all fingerprint A minutiae points, obtains one group of model
It is trapped among [0,212- 1] the integer set F inA, the number of stable user fingerprints A minutiae point is denoted as NA。
1.2 by the training of fingerprint minutiae twice, extracts, processing finger print information obtains one group of metastable user fingerprints
B details point set；Fingerprint B minutiae point is registered into a Hash table first with Geometric active contours technologyThen every by what is obtained
The plane coordinates of a minutiae point quantifies respectively to 8 bits, and concatenates and obtain the Bit String that a length is 16, then will own
The data type of fingerprint B minutiae point is converted into integer, then available one group of range is [0,216- 1] the integer set F inB, surely
The number of fixed user fingerprints B minutiae point is denoted as NB。
1.3, by extracting, handling finger print information, obtain the field of direction of each pixel, range is in 0-255；It is corresponding
In the plane coordinates of fingerprint A minutiae point, the field of direction of fingerprint B is extracted, the obtained field of direction is quantified respectively to one
The bit that length is 4, then can obtain one group of range [0,24- 1] the integer set O inB, wherein the number of the field of direction is NO。
1.4 by integer set FAAnd OBThe value of middle respective coordinates is combined, specifically with FA×16+OBForm obtains one
Group range is [0,216- 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 successively subjected to feature registration,
With not distinguishing details vertex type on time；The minutiae point that two width fingerprints can be registrated is denoted as the true detail point of this piece of fingerprint, takes same
The mean value for two groups of coordinate values that a true detail point takes in two images, is recorded as the coordinate value of the true detail point；It is registrated
Minutiae point and third width sample fingerprint carry out feature registration again；Metastable true detail point set is obtained after the completion of registration
It closes.
Using locking algorithm described in step 2, by the new feature information aggregate for needing key to be protected and step 1 to obtain into
Row binding generates fingerprint fuzzy vault, specific as follows:
Key k to be bound is formatted processing by 2.1, and formatization rule is successively from left to right every 16 ratio in order
Bit length is one piece, and m block is obtained in note one；And integer is converted by every piece of data type, one group of range is obtained [0,216-1]
Interior integer set K.
2.2 handle the block number m generated according to key formatization, in finite fieldOne multinomial P (x) of upper construction
P (x)=amxm+…+a2x2+a1x+a0(mod p) (1)
Wherein, it is Big prime 65537 that modulus p, which rule of thumb recommends value, and multinomial highest number m is according to key length one
As value range from 9 to 16.
2.3 add the CRC cyclic redundancy check code of 16 bit lengths for key to be bound, close to increase present invention protection
The reliability of key；And integer is converted by the data type of the CRC cyclic redundancy check code, a range is obtained [0,216-1]
Interior integer.
2.4 using cyclic redundancy check obtained in the key to be bound mentioned in step 2.1 and step 2.3 as multinomial
(1) coefficient, wherein a0For cyclic redundancy check, a1,...,amFor key to be bound.Then step 1.4 is obtained stable
Each details point data in integer set F is brought into multinomial (1) as multinomial input value x, acquires point set { (x, P (x))
| x ∈ F } it is true point set in fuzzy vault.
2.5 add the hash point set far more than true point set number, each group in hash point set for fuzzy vault
Element is randomly generated, and requires each group element and really put unequal, and each group element is unsatisfactory for multinomial (1).
2.6 disorderly set true point set and hash point set, ultimately generate one and include true point set, hash point set
With the fingerprint fuzzy vault including fuzzy vault essential information.
The fuzzy vault essential information includes multinomial highest number m and modulus p；
Solution is used from fingerprint fuzzy vault using inquiry fingerprint A ', the B ' characteristic information by verifying described in step 3
It locks algorithm and extracts key, specific as follows:
3.1 pass through the relevant treatment and Geometric active contours technology one Hash table of registration to inquiry fingerprint A 'Then with
The Hash table obtained in step 1.1It is matched, filters out the most benchmark point set H of matching numberA′, then pass through step
Integer set F is obtained in rapid 1.1AMethod obtain FA′；Range is [0,212- 1] in, the number of minutiae point is NA′.If NA′≥
9, enter step 3.2；Otherwise, fingerprint A ' is re-entered, the step is repeated.
3.2 pass through the relevant treatment and Geometric active contours technology one Hash table of registration to inquiry fingerprint B 'Then with
The Hash table obtained in step 1.2It is matched, filters out the most benchmark point set H of matching numberB′, then pass through step
Integer set F is obtained in rapid 1.2BMethod obtain FB′；Range is [0,216- 1] in, the number of minutiae point is NB′.If NB′≥
9, enter step 3.3；Otherwise, fingerprint B ' is re-entered, the step is repeated.
3.3 inquire inquiry fingerprint B ' carry out alignement using the difference value of inquiry fingerprint B ' image direction field
The field of direction of fingerprint B '；It then takes out and corresponds to details point set FA′In plane coordinates field of direction value；And the side that will be obtained
Quantify respectively to field value to 4 bits, obtains one group of range [0,24- 1] the integer set O inB′, field of direction number NO′。
3.4 by integer set FA′And OB′The value of middle respective coordinates is combined, specifically with FA′×16+OB′Form obtains
One group of range is [0,216- 1] the integer set F ' in, wherein the number of minutiae point is N '.
The each group of data inquired in fingerprint A ', B ' details point set F ' and fuzzy vault is carried out traversal comparison by 3.5, if phase
The number of the point of symbol is not less than the polynomial highest number m (i.e. block number) in fuzzy vault, enters step 3.6；Otherwise it extracts
Key failure.
3.6 pairs of points being consistent found are combined calculating, and every m point is one group, utilize Lagrange's interpolation to every group
Method is attempted to restore possible key, and obtained possibility key is carried out CRC check.If by CRC check, extract key at
Function；Otherwise continue next group of trial, until having attempted all combined situations, do not extract key yet, then extract key failure.
Beneficial effects of the present invention
Compared with the cryptographic key protection method of single fingerprint characteristic fuzzy vault, the present invention utilizes the fusion life of fingerprint characteristic grade
At fuzzy vault realize the protection of key, there is stronger safety, higher realization efficiency, to count in mobile terminal etc.
It calculates and uses offer possible under the equipment of resource anxiety, also ensure that only legitimate user can just extract under legal fingerprint and have
The key of CRC check function has ensured the confidentiality, integrality, availability of key secure storage.
Detailed description of the invention
The fingerprint fuzzy vault implementation method flow chart of Fig. 1 feature-based fusion.
The unlock flow chart of Fig. 2 fuzzy vault.
The fingerprint fuzzy vault of Fig. 3 feature-based fusion generates analogous diagram.
The unlock analogous diagram of Fig. 4 fuzzy vault.
Specific embodiment
Below in conjunction with attached drawing, the invention will be further described.
A kind of fingerprint fuzzy vault implementation method of feature-based fusion mainly includes three parts: the first step, merges fingerprint A
Minutiae point coordinate information and the directional field information of fingerprint B obtain a new feature information aggregate；Second step is calculated using locking
Method binds the new feature information aggregate for needing key to be protected to obtain with step 1, generates fingerprint fuzzy vault；Third
Step extracts key using unlocking algorithm from fingerprint fuzzy vault using inquiry fingerprint A ', the B ' characteristic information by verifying.
The directional field information of the minutiae point coordinate information and fingerprint B that merge fingerprint A described in step 1 obtains a new feature
Information aggregate (flow chart is as shown in Figure 1), specific as follows:
1.1 by the training of fingerprint minutiae twice, extracts, processing finger print information obtains one group of metastable user fingerprints
A details point set；Fingerprint A minutiae point is registered into a Hash table first with Geometric active contours technologyThen every by what is obtained
The plane coordinates of a minutiae point quantifies respectively to 8 bits, and is concatenated by abscissa low four with high four exclusive or of ordinate
Obtain the Bit String that a length is 12；Then integer is converted by the data type of all fingerprint A minutiae points, obtains one group of model
It is trapped among [0,212- 1] the integer set F inA, the number of stable user fingerprints A minutiae point is denoted as NA。
1.2 by the training of fingerprint minutiae twice, extracts, processing finger print information obtains one group of metastable user fingerprints
B details point set；Fingerprint B minutiae point is registered into a Hash table first with Geometric active contours technologyThen every by what is obtained
The plane coordinates of a minutiae point quantifies respectively to 8 bits, and concatenates and obtain the Bit String that a length is 16, then will own
The data type of fingerprint B minutiae point is converted into integer, then available one group of range is [0,216- 1] the integer set F inB, surely
The number of fixed user fingerprints B minutiae point is denoted as NB。
1.3, by extracting, handling finger print information, obtain the field of direction of each pixel, range is in 0-255；It is corresponding
In the plane coordinates of fingerprint A minutiae point, the field of direction of fingerprint B is extracted, the obtained field of direction is quantified respectively to one
The bit that length is 4, then can obtain one group of range [0,24- 1] the integer set O inB, wherein the number of the field of direction is NO。
1.4 by integer set FAAnd OBThe value of middle respective coordinates is combined, specifically with FA×16+OBForm obtains one
Group range is [0,216- 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 successively subjected to feature registration,
With not distinguishing details vertex type on time；The minutiae point that two width fingerprints can be registrated is denoted as the true detail point of this piece of fingerprint, takes same
The mean value for two groups of coordinate values that a true detail point takes in two images, is recorded as the coordinate value of the true detail point；It is registrated
Minutiae point and third width sample fingerprint carry out feature registration again；Metastable true detail point set is obtained after the completion of registration
It closes.
Using locking algorithm described in step 2, by the new feature information aggregate for needing key to be protected and step 1 to obtain into
Row binding generates fingerprint fuzzy vault, specific as follows:
128 bit length keys to be bound are formatted processing by 2.1, and formatization rule is successively from left to right to press
Sequentially every 16 bit length is one piece, and note one is obtained 9 pieces；And integer is converted by every piece of data type, obtain one group of range
[0,216- 1] the integer set K in.
2.2 handle the block number 9 generated according to key formatization, in finite fieldOne multinomial P (x) of upper construction
P (x)=a9x9+…+a2x2+a1x+a0(mod 65537) (2)
2.3 add the CRC cyclic redundancy check code of 16 bit lengths for key to be bound, close to increase present invention protection
The reliability of key；And integer is converted by the data type of the CRC cyclic redundancy check code, a range is obtained [0,216-1]
Interior integer.
2.4 using cyclic redundancy check obtained in the key to be bound mentioned in step 2.1 and step 2.3 as multinomial
(2) coefficient, wherein a0For cyclic redundancy check, a1,...,amFor key to be bound.Then step 1.4 is obtained stable
Each details point data in integer set F is brought into multinomial (2) as multinomial input value x, acquires point set { (x, P (x))
| x ∈ F } it is true point set in fuzzy vault.
2.5 add the hash point set far more than true point set number, each group in hash point set for fuzzy vault
Element is randomly generated, and requires each group element and really put unequal, and each group element is unsatisfactory for multinomial (2).
2.6 disorderly set true point set and hash point set, ultimately generate one and include true point set, hash point set
With the fingerprint fuzzy vault including fuzzy vault essential information, as shown in Figure 3.
The fuzzy vault essential information includes highest number 9 and modulus 65537 in multinomial；
Solution is used from fingerprint fuzzy vault using inquiry fingerprint A ', the B ' characteristic information by verifying described in step 3
It locks algorithm and extracts key (flow chart is as shown in Figure 2), specific as follows:
3.1 pass through the relevant treatment and Geometric active contours technology one Hash table of registration to inquiry fingerprint A 'Then with
The Hash table obtained in step 1.1It is matched, filters out the most benchmark point set H of matching numberA′, then pass through step
Integer set F is obtained in rapid 1.1AMethod obtain FA′；Range is [0,212- 1] in, the number of minutiae point is NA′.If NA′≥
9, enter step 3.2；Otherwise, fingerprint A ' is re-entered, the step is repeated.
3.2 pass through the relevant treatment and Geometric active contours technology one Hash table of registration to inquiry fingerprint B 'Then with
The Hash table obtained in step 1.2It is matched, filters out the most benchmark point set H of matching numberB′, then pass through step
Integer set F is obtained in rapid 1.2BMethod obtain FB′；Range is [0,216- 1] in, the number of minutiae point is NB′.If NB′≥
9, enter step 3.3；Otherwise, fingerprint B ' is re-entered, the step is repeated.
3.3 inquire inquiry fingerprint B ' carry out alignement using the difference value of inquiry fingerprint B ' image direction field
The field of direction of fingerprint B '；It then takes out and corresponds to details point set FA′In plane coordinates field of direction value；And the side that will be obtained
Quantify respectively to field value to 4 bits, obtains one group of range [0,24- 1] the integer set O inB′, field of direction number NO′。
3.4 by integer set FA′And OB′The value of middle respective coordinates is combined, specifically with FA′×16+OB′Form obtains
One group of range is [0,216- 1] the integer set F ' in, wherein the number of minutiae point is N '.
The each group of data inquired in fingerprint A ', B ' details point set F ' and fuzzy vault is carried out traversal comparison by 3.5, if phase
The number of the point of symbol is not less than the polynomial highest number m (i.e. block number) in fuzzy vault, enters step 3.6；Otherwise it extracts
Key failure.
3.6 pairs of points being consistent found are combined calculating, and every 9 points are one group, utilize Lagrange's interpolation to every group
Method is attempted to restore possible key, and obtained possibility key is carried out CRC check.If by CRC check, extract key at
Function, as shown in Figure 4；Otherwise continue next group of trial, until having attempted all combined situations, do not extract key yet, then extract
Key failure.
Claims (3)
1. a kind of fingerprint fuzzy vault implementation method of feature-based fusion, it is characterised in that include the following steps:
The directional field information of step 1, the minutiae point coordinate information for merging fingerprint A and fingerprint B obtains a new feature information aggregate；
Step 2, using locking algorithm, the new feature information aggregate for needing key to be protected to obtain with step 1 is bound, is given birth to
At fingerprint fuzzy vault；
Step 3 is extracted from fingerprint fuzzy vault using unlocking algorithm using inquiry fingerprint A ', the B ' characteristic information by verifying
Key；
The directional field information of the minutiae point coordinate information and fingerprint B that merge fingerprint A described in step 1 obtains a new feature information
Set, specific as follows:
1.1 by the training of fingerprint minutiae twice, extracts, processing finger print information obtains one group of metastable fingerprint A minutiae point
Set；Fingerprint A minutiae point is registered into a Hash table first with Geometric active contours technologyThen each details that will be obtained
The plane coordinates of point quantifies respectively to 8 bits, and concatenates to obtain one with high four exclusive or of ordinate by abscissa low four
The Bit String that a length is 12；Then integer is converted by the data type of all fingerprint A minutiae points, obtains one group of range and exists
[0,212- 1] the integer set F inA, the number of stable user fingerprints A minutiae point is denoted as NA；
1.2 by the training of fingerprint minutiae twice, extracts, processing finger print information obtains one group of metastable fingerprint B minutiae point
Set；Fingerprint B minutiae point is registered into a Hash table first with Geometric active contours technologyThen each details that will be obtained
The plane coordinates of point quantifies respectively to 8 bits, and concatenates and obtain the Bit String that a length is 16, then by all fingerprint B
The data type of minutiae point is converted into integer, then available one group of range is [0,216- 1] the integer set F inB, stable
The number of user fingerprints B minutiae point is denoted as NB；
1.3, by extracting, handling finger print information, obtain the field of direction of each pixel, range is in 0-255；Corresponding to finger
The plane coordinates of line A minutiae point, the field of direction of fingerprint B is extracted, and the obtained field of direction is quantified respectively to a length
For 4 bit, then one group of range can be obtained [0,24- 1] the integer set O inB, wherein the number of the field of direction is NO；
1.4 by integer set FAAnd OBThe value of middle respective coordinates is combined, specifically with FA×16+OBForm obtains one group of range
[0,216- 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 successively being carried out feature registration, is registrated
When do not distinguish details vertex type；The minutiae point that two width fingerprints can be registrated is denoted as the true detail point of fingerprint, takes same true thin
The mean value for two groups of coordinate values that node takes in two images is recorded as the coordinate value of the true detail point；The minutiae point being registrated
Feature registration is carried out again with third width sample fingerprint；Metastable true detail point set is obtained after the completion of registration.
2. a kind of fingerprint fuzzy vault implementation method of feature-based fusion as described in claim 1, it is characterised in that step 2 institute
That states utilizes locking algorithm, and the new feature information aggregate for needing key to be protected to obtain with step 1 is bound, fingerprint is generated
Fuzzy vault, specific as follows:
Key k to be bound is formatted processing by 2.1, and formatization rule is successively from left to right every 16 bit long in order
Degree is one piece, and m block is obtained in note one；And integer is converted by every piece of data type, one group of range is obtained [0,216- 1] in
Integer set K；
2.2 handle the block number m generated according to key formatization, in finite fieldOne multinomial P (x) of upper construction
P (x)=amxm+...+a2x2+a1x+a0(mod p) (1)
Wherein, it is Big prime 65537 that modulus p, which rule of thumb recommends value, and multinomial highest number m generally takes according to key length
It is worth range from 9 to 16；
2.3 add the CRC cyclic redundancy check code of 16 bit lengths for key to be bound；And by the CRC cyclic redundancy check
The data type of code is converted into integer, obtains a range [0,216- 1] integer in；
2.4 using cyclic redundancy check obtained in the key to be bound mentioned in step 2.1 and step 2.3 as multinomial (1)
Coefficient, wherein a0For cyclic redundancy check, a1,...,amFor key to be bound；Then stable set of integers step 1.4 obtained
The each details point data closed in F is brought into multinomial (1) as multinomial input value x, acquires point set { (x, P (x)) | x ∈ F }
True point set as in fuzzy vault；
2.5 add the hash point set far more than true point set number, each group element in hash point set for fuzzy vault
It is randomly generated, and requires each group element and really put unequal, and each group element is unsatisfactory for multinomial (1)；
2.6 disorderly set true point set and hash point set, ultimately generate one and include true point set, hash point set and mould
Paste the fingerprint fuzzy vault including national treasury essential information；
The fuzzy vault essential information includes multinomial highest number m and modulus p.
3. a kind of fingerprint fuzzy vault implementation method of feature-based fusion as claimed in claim 2, it is characterised in that step 3 institute
That states extracts key using unlocking algorithm from fingerprint fuzzy vault using inquiry fingerprint A ', the B ' characteristic information by verifying,
It is specific as follows:
3.1 pass through the relevant treatment and Geometric active contours technology one Hash table of registration to inquiry fingerprint A 'Then with step
The Hash table obtained in 1.1It is matched, filters out the most benchmark point set H of matching numberA′, then pass through step 1.1
Middle acquisition integer set FAMethod obtain FA′；Range is [0,212- 1] in, the number of minutiae point is NA′；If NA′>=9, into
Enter step 3.2；Otherwise, fingerprint A ' is re-entered, the step is repeated；
3.2 pass through the relevant treatment and Geometric active contours technology one Hash table of registration to inquiry fingerprint B 'Then with step
The Hash table obtained in 1.2It is matched, filters out the most benchmark point set H of matching numberB′, then pass through step 1.2
Middle acquisition integer set FBMethod obtain FB′；Range is [0,216- 1] in, the number of minutiae point is NB′；If NB′>=9, into
Enter step 3.3；Otherwise, fingerprint B ' is re-entered, the step is repeated；
3.3 obtain inquiry fingerprint to inquiry fingerprint B ' carry out alignement using the difference value of inquiry fingerprint B ' image direction field
The field of direction of B '；It then takes out and corresponds to details point set FA′In plane coordinates field of direction value；And the field of direction that will be obtained
Value quantifies respectively to 4 bits, obtains one group of range [0,24- 1] the integer set O inB′, field of direction number NO′；
3.4 by integer set FA′And OB′The value of middle respective coordinates is combined, specifically with FA′×16+OB′Form obtains one group
Range is [0,216- 1] the integer set F ' in, wherein the number of minutiae point is N '；
The each group of data inquired in fingerprint A ', B ' details point set F ' and fuzzy vault is carried out traversal comparison by 3.5, if be consistent
The number of point is not less than the polynomial highest number m in fuzzy vault, enters step 3.6；Otherwise key failure is extracted；
3.6 pairs of points being consistent found are combined calculating, and every m point is one group, are tasted to every group using Lagrange's interpolation
Examination restores possible key, and obtained possibility key is carried out CRC check；If extracting key success by CRC check；It is no
Then continue next group of trial, until having attempted all combined situations, does not extract key yet, then extract key failure.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610038430.1A CN105553657B (en) | 2016-01-19 | 2016-01-19 | A kind of fingerprint fuzzy vault implementation method of feature-based fusion |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610038430.1A CN105553657B (en) | 2016-01-19 | 2016-01-19 | A kind of fingerprint fuzzy vault implementation method of feature-based fusion |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN105553657A CN105553657A (en) | 2016-05-04 |
| CN105553657B true CN105553657B (en) | 2019-02-01 |
Family
ID=55832600
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610038430.1A Expired - Fee Related CN105553657B (en) | 2016-01-19 | 2016-01-19 | A kind of fingerprint fuzzy vault implementation method of feature-based fusion |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105553657B (en) |
Families Citing this family (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106169062B (en) * | 2016-06-08 | 2019-05-21 | 杭州电子科技大学 | A kind of implementation method referring to vein fusion 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 |
| EP3586257B1 (en) * | 2017-02-22 | 2022-10-26 | Fingerprint Cards Anacatum IP AB | Biometrics-based remote login |
| 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 |
| CN107181598B (en) * | 2017-07-05 | 2020-03-10 | 四川无声信息技术有限公司 | Fingerprint key processing method and device |
| 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 |
| CN108960039B (en) * | 2018-05-07 | 2021-08-06 | 西安电子科技大学 | Irreversible fingerprint template encryption method based on symmetric hash |
| CN113254989B (en) * | 2021-04-27 | 2022-02-15 | 支付宝(杭州)信息技术有限公司 | Fusion method and device of target data and server |
| CN113507380B (en) * | 2021-09-10 | 2021-12-17 | 浙江大学 | Privacy protection remote unified biometric authentication method and device and electronic equipment |
| 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 |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102510330A (en) * | 2011-11-02 | 2012-06-20 | 杭州电子科技大学 | Novel fuzzy vault method based on fingerprint characteristic data and matching algorithm |
| CN102710417A (en) * | 2012-06-18 | 2012-10-03 | 杭州电子科技大学 | Fuzzy vault method based on fingerprint features and Internet key exchange protocol |
| 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 |
-
2016
- 2016-01-19 CN CN201610038430.1A patent/CN105553657B/en not_active Expired - Fee Related
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102510330A (en) * | 2011-11-02 | 2012-06-20 | 杭州电子科技大学 | Novel fuzzy vault method based on fingerprint characteristic data and matching algorithm |
| CN102710417A (en) * | 2012-06-18 | 2012-10-03 | 杭州电子科技大学 | Fuzzy vault method based on fingerprint features and Internet key exchange protocol |
| 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 |
Non-Patent Citations (2)
| Title |
|---|
| fingerprint combination for privacy protection;Sheng Li;《IEEE》;20130331;全文 * |
| 生物特征密码技术综述;游林;《杭州电子科技大学学报》;20150515;第35卷(第3期);第2-3节、图3、图5 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN105553657A (en) | 2016-05-04 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105553657B (en) | A kind of fingerprint fuzzy vault implementation method of feature-based fusion | |
| Uludag et al. | Securing fingerprint template: Fuzzy vault with helper data | |
| Arakala et al. | Fuzzy extractors for minutiae-based fingerprint authentication | |
| Li et al. | An effective biometric cryptosystem combining fingerprints with error correction codes | |
| CN106059753B (en) | A kind of fingerprint key generation new method for digital signature | |
| CN101674299B (en) | Method for generating key | |
| CN106936586A (en) | A kind of biological secret key extracting method based on fingerprint bit string and Error Correction of Coding | |
| Yang et al. | A delaunay triangle-based fuzzy extractor for fingerprint authentication | |
| Benhammadi et al. | Password hardened fuzzy vault for fingerprint authentication system | |
| Liu et al. | Encrypted domain matching of fingerprint minutia cylinder-code (MCC) with l1 minimization | |
| Sadhya et al. | Review of key‐binding‐based biometric data protection schemes | |
| CN102710417B (en) | Fuzzy vault method based on fingerprint features and Internet key exchange protocol | |
| Wu et al. | Fingerprint bio‐key generation based on a deep neural network | |
| Albakri et al. | Convolutional neural network biometric cryptosystem for the protection of the blockchain’s private key | |
| Yuan | Multimodal cryptosystem based on fuzzy commitment | |
| Arunachalam et al. | AES Based Multimodal Biometric Authentication using Cryptographic Level Fusion with Fingerprint and Finger Knuckle Print. | |
| Shukla et al. | Securing fingerprint templates by enhanced minutiae‐based encoding scheme in Fuzzy Commitment | |
| Yang et al. | A Delaunay triangle group based fuzzy vault with cancellability | |
| CN106169062B (en) | A kind of implementation method referring to vein fusion fuzzy vault | |
| CN104376307A (en) | Fingerprint image information coding method | |
| CN104320247A (en) | Shared key protection method based on elliptical curve and fingerprint fuzzy vault | |
| Nandini et al. | Efficient cryptographic key generation from fingerprint using symmetric hash functions | |
| Liu et al. | Palmprint based multidimensional fuzzy vault scheme | |
| CN106712957B (en) | A kind of double factor authentication method based on convolutional encoding | |
| Sandhya et al. | Cancelable fingerprint cryptosystem based on convolution coding |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20190201 Termination date: 20220119 |
|
| CF01 | Termination of patent right due to non-payment of annual fee |