CN103258156B - A kind of method generating key based on fingerprint characteristic - Google Patents
A kind of method generating key based on fingerprint characteristic Download PDFInfo
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- CN103258156B CN103258156B CN201310126451.5A CN201310126451A CN103258156B CN 103258156 B CN103258156 B CN 103258156B CN 201310126451 A CN201310126451 A CN 201310126451A CN 103258156 B CN103258156 B CN 103258156B
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- 238000000034 method Methods 0.000 title claims abstract description 16
- 239000000203 mixture Substances 0.000 claims abstract description 6
- 238000000605 extraction Methods 0.000 claims abstract description 4
- 229910052704 radon Inorganic materials 0.000 claims abstract description 4
- SYUHGPGVQRZVTB-UHFFFAOYSA-N radon atom Chemical compound [Rn] SYUHGPGVQRZVTB-UHFFFAOYSA-N 0.000 claims abstract description 4
- 238000001228 spectrum Methods 0.000 claims description 13
- 239000000284 extract Substances 0.000 claims description 9
- 238000010606 normalization Methods 0.000 claims description 9
- 239000011159 matrix material Substances 0.000 claims description 7
- 238000012549 training Methods 0.000 claims description 5
- 238000006243 chemical reaction Methods 0.000 claims description 4
- 238000007781 pre-processing Methods 0.000 claims description 4
- 230000010354 integration Effects 0.000 claims description 3
- 230000002427 irreversible effect Effects 0.000 claims description 3
- 238000005520 cutting process Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000012937 correction Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 238000003909 pattern recognition Methods 0.000 description 1
- 230000008672 reprogramming Effects 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
Abstract
The present invention relates to a kind of method generating key based on fingerprint characteristic.The present invention includes user fingerprints registration phase and fingerprint key is lived again the stage, user fingerprints registration phase comprises the fingerprint two dimensional image extracting user; Utilize Radon to convert and generate different one-dimensional vector; Carry out the extraction of N rank discrete Fourier transform (DFT) and binary fingerprints key.The fingerprint key stage of living again comprises the fingerprint two dimensional image extracting inquiring user, adopts some steps identical with registration phase to obtain the Bit String of N number of bit composition of the highest weight.Fingerprint characteristic of the present invention can use as key veritably, and by key together with user identity binding.And security is the uniqueness based on fingerprint, also just say that user fingerprints is not revealed.
Description
Technical field
The invention belongs to pattern-recognition and technical field of cryptology, be specifically related to a kind of method generating key based on fingerprint characteristic.
Background technology
Due to the Fog property of fingerprint characteristic, namely the replacing of fingerprint acquisition instrument or the physical change of user fingerprints cause the fingerprint characteristic of acquisition unstable, just can not directly use as key itself.But, utilize error correcting technique can generate a Bit String based on a definite sequence exactly for same fingerprint.
Summary of the invention
Under true and reliable experiment condition, the invention provides a set of practical a kind of key generation method based on fingerprint characteristic and error correcting technique.
A kind of key generation method based on fingerprint characteristic and error correcting technique comprises user fingerprints registration phase and fingerprint key is lived again the stage;
Described user fingerprints registration phase is specific as follows:
1. extract the fingerprint two dimensional image of user, image normalization, the image doing a series of fingerprint image process is denoted as M
t.
2. image M
twith the different anglecs of rotation, utilize Radon to convert and generate different one-dimensional vector.First one-dimensional vector is normalized with the maximum norm of its element, then removes average.One-dimensional vector after normalization is denoted as x
0(n), n=0,1,2, K, N-1.N is the bit length that requirement generates fingerprint key, and x
in () represents the input vector of i-th iteration.
3. calculate x
in the N rank discrete Fourier transform (DFT) of () obtains frequency spectrum X
i(k), frequency spectrum size is | X
i(k) |.K is the frequency values of frequency spectrum, 0≤k≤N-1.Because the result of Fourier transform is symmetrical, given up the part repeated, get that half amplitude above, so just do not have phase information, the half given up below is filled with 0.Such conversion just becomes irreversible, obtains real sequence:
4. couple y
in () calculates N rank discrete Fourier transform (DFT) and obtains frequency spectrum Y
ik (), then calculates bispectrum: B
i(k
1, k
2)=Y
i(k
1) Y
i(k
2) Y
i *(k
1+ k
2), * represents complex conjugate computing, and bispectrum retains phase information.K
1, k
2all refer to the frequency values of frequency spectrum, span is [0, N-1].Bispectrum is all have non-zero imaginary part complex vector located, and is asymmetrical.
5. in frequency planar, i-th time by bispectrum radially integral operation obtain one group of discrete series and be designated as:
Wherein
A is bifrequency (k
1, k
2) slope in space.By after integration
as the input of next iteration, namely
6. the inner product that the difference inputted before and after calculating iteration and a front iteration input, will obtain complex value:
Wherein D represents difference, and the phase place of M to be the amplitude of D and Φ be D, puts together for two and can generate an amplitude/phasing matrix [M Φ].
7. the extraction of binary fingerprints key.M, Φ binarization utilizes the statistical properties to determine whether required bit, and according to sorting to the bit of least wishing to obtain of wishing most to obtain, is stored as M respectively
a, A
g.And statistical application needs one to train set.The fingerprint image of multiple user in fingerprint base used when training set refers to that the present invention tests.For each user, training set is divided into two parts: the matrix (inner set) that registered user's fingerprint image generates and the matrix (outside set) that other user fingerprint image generates.The probability 0 or 1 of bit represents changeless level.Probability is 0 indicate without information (fixed bit), and probability is 1 expression bit information (completely random).But the probability perfection of little bit is exactly 0 or 1.So we, by calculating the weight w of each bit, comprise weight inside w
1=1-η
intrawith external weights w
2=1-η
extra, and w=w
1× w
2.η
intra, η
extrarepresent the inside entropy of bit and outside entropy respectively.
8. the Bit String of N number of bit composition of the highest weight is as fingerprint key (bio-key) B that will extract
orig.In order to identical fingerprint key can be reconstructed, the positional information of N number of bit of the highest weight is stored, and be used as the mask B of the fingerprint key of registered user
k-mask.
9. the encryption key k of λ-bits
origby obtaining a N number of Bit String K after RS algorithm for encryption
orig.By K
origwith B
origin conjunction with generation question blank Lookup
table.Storing queries table, deletes K
origwith B
orig.RS algorithm is the chnnel coding of the forward error correction of special nonbinary code that a class error correcting capability is very strong and low rate.
Described fingerprint key stage of living again is specific as follows:
1. extract the fingerprint two dimensional image of inquiring user, fingerprint image normalization, after doing a series of fingerprint image process, the image of inquiring user is denoted as M
q.
2. authentication of users takes the second step of registration phase to the 7th step, obtains the Bit String of N number of bit composition of the highest weight.In conjunction with the mask B of the fingerprint key of registered user
k-maskgenerate a Bit String, it can be used as the fingerprint key B of authentication of users
mod.
3.B
modutilize Lookup
tableobtain the Bit String K of N-bits
mod.Utilize RS algorithm to K
moddeciphering obtains the Bit String k' of λ-bits
orig.If k'
origwith k
origequal, think that authentication of users and registered user are same, the fingerprint key of registered user is successfully lived again.Otherwise, just think that authentication of users is illegal invader.
The fingerprint key length that this method generates can change by wish, and key can be changed by reprogramming.Feature of the present invention is that fingerprint characteristic can use as key veritably, and by key together with user identity binding.And security is the uniqueness based on fingerprint, also just say that user fingerprints is not revealed.
Accompanying drawing explanation
Fig. 1 is whole process flow diagram of the present invention;
Fig. 2 is registered user's fingerprint image;
Fig. 3 is training set;
Fig. 4 is query fingerprints figure.
Embodiment
Below in conjunction with accompanying drawing, the invention will be further described.Whole process flow diagram of the present invention is Fig. 1.
Described user fingerprints registration phase is specific as follows:
1. extract the fingerprint two dimensional image of user, image normalization.Subsequently, carry out cutting operation to this fingerprint image, the calculating of the field of direction and gradient, balanced, convergence, smoothly, strengthens, binaryzation, and the series of preprocessing operations such as refinement obtain a width and maintain fingerprint feature information bianry image clearly and be denoted as M
t.Registered fingerprint image is one in Fig. 2.
2. image M
twith the different anglecs of rotation, utilize Radon to convert and generate different one-dimensional vector.First one-dimensional vector is normalized with the maximum norm of its element, then removes average.That is, exactly the average of vector become 0 or vectorial each element deduct average.One-dimensional vector after normalization is denoted as x
0(n), n=0,1,2, K, N-1.N is the bit length that requirement generates fingerprint key.And x
in () represents the input vector of i-th iteration.
3. calculate x
in the N rank discrete Fourier transform (DFT) of () obtains frequency spectrum X
i(k), frequency spectrum size is | X
i(k) |.Because the result of Fourier transform is symmetrical, given up the part repeated, before that half get amplitude, so just do not have phase information, the half given up below is filled with 0.So just can obtain irreversible conversion, conversion obtains real sequence:
4. couple y
in () calculates N rank discrete Fourier transform (DFT) and obtains frequency spectrum Y
ik (), then calculates bispectrum: B
i(k
1, k
2)=Y
i(k
1) Y
i(k
2) Y
i *(k
1+ k
2), bispectrum retains phase information.Bispectrum is have non-zero imaginary part complex vector located, and is asymmetrical.
5. in frequency planar, i-th time by bispectrum radially integral operation obtain one group of discrete series and be designated as:
Wherein
A is bifrequency (k
1, k
2) slope in space.Just after integration
as the input of next iteration, namely
6. the inner product that the difference inputted before and after calculating iteration and a front iteration input, will obtain plural number:
Wherein D represents difference, and the phase place of M to be the amplitude of D and Φ be D, puts together for two and can generate an amplitude/phasing matrix.
7. the extraction of binary fingerprints key.M, Φ binarization utilizes the statistical properties to determine whether required bit, and according to sorting to the bit of least wishing to obtain of wishing most to obtain, is stored as M respectively
a, A
g.As shown in Figure 2, outside set as shown in Figure 3 in inner set.We, by calculating the weight w of each bit, comprise weight inside w
1=1-η
intrawith external weights w
2=1-η
extra, and w=w
1× w
2.
8. fingerprint key (bio-key) B that the Bit String that N number of bit of the highest weight forms will extract as us
orig.In order to identical fingerprint key can be reconstructed, the positional information of N number of bit of the highest weight is stored, and be used as the mask B of the fingerprint key of registered user
k-mask.
9. the encryption key k of λ-bits
origby obtaining a N number of Bit String K after RS algorithm for encryption
orig.By K
origwith B
origin conjunction with generation question blank Lookup
table.Storing queries table, deletes K
origwith B
orig.
Described fingerprint key stage of living again is specific as follows:
1. extract the fingerprint two dimensional image of inquiring user, image normalization.Subsequently, carry out cutting operation to this fingerprint image, the calculating of the field of direction and gradient, balanced, convergence, smoothly, strengthens, binaryzation, and the series of preprocessing operations such as refinement obtain a width and maintain fingerprint feature information bianry image clearly and be denoted as M
q.Query fingerprints image is one in Fig. 4.
2. authentication of users takes the second step of registration phase to the 7th step, obtains the Bit String of N number of bit composition of the highest weight.In conjunction with the mask B of the fingerprint key of registered user
k-maskgenerate a Bit String, it can be used as the fingerprint key B of authentication of users
mod.
3.B
modutilize Lookup
tableobtain the Bit String K of N-bits
mod.Utilize RS algorithm to K
moddeciphering obtains the Bit String k' of λ-bits
orig.If k'
origwith k
origequal, we think that authentication of users and registered user are same, and the fingerprint key of registered user is successfully lived again.Otherwise we just think that authentication of users is illegal invader.
Claims (1)
1. generate a method for key based on fingerprint characteristic, it is characterized in that the method comprises user fingerprints registration phase and biological secret key is lived again the stage;
Described user fingerprints registration phase is specific as follows:
Step 1. extracts the fingerprint two dimensional image of user, and image normalization, does fingerprint image preprocessing, the bianry image of the fingerprint feature information that is maintained, and this bianry image is denoted as M
t;
Step 2. image M
twith the different anglecs of rotation, utilize Radon to convert and generate different one-dimensional vector; First one-dimensional vector is normalized with the maximum norm of its element, then removes average; One-dimensional vector after normalization is denoted as x
0(n), n=0,1,2, K, N-1; N is the bit length that requirement generates biological secret key; And x
in () represents the input vector of i-th iteration;
Step 3. calculates x
in the N rank discrete Fourier transform (DFT) of () obtains frequency spectrum X
i(k), frequency spectrum size is | X
i(k) |; K is the frequency values of frequency spectrum, 0≤k≤N-1; The result of Fourier transform is symmetrical, gives up the part of repetition, and get that half amplitude above, the half given up below is filled with 0; Such conversion just becomes irreversible, obtains real sequence:
Step 4. couple y
in () calculates N rank discrete Fourier transform (DFT) and obtains frequency spectrum Y
ik (), then calculates bispectrum: B
i(k
1, k
2)=Y
i(k
1) Y
i(k
2) Y
i *(k
1+ k
2), wherein * represents complex conjugate computing, and bispectrum retains phase information; k
1, k
2all refer to the frequency values of frequency spectrum, span is [0, N-1]; Bispectrum is all have non-zero imaginary part complex vector located, and is asymmetrical;
Step 5. in frequency planar, i-th time by bispectrum radially integral operation obtain one group of discrete series and be designated as:
wherein
a is bifrequency (k
1, k
2) slope in space; By after integration
as the input of next iteration, namely
Step 6. calculates the inner product that the difference that inputs before and after iteration and a front iteration input, and will obtain complex value:
Wherein D represents difference, and the phase place of M to be the amplitude of D and Φ be D, puts together for two and can generate an amplitude/phasing matrix [M Φ];
The extraction of step 7. scale-of-two biological secret key; M, Φ binarization utilizes the statistical properties to determine whether required bit, and according to sorting to the bit of least wishing to obtain of wishing most to obtain, is stored as M respectively
a, A
g; And statistical application needs one to train set; Training set refers to the fingerprint image of multiple user in fingerprint base used; For each user, training set is divided into two parts: the matrix that registered user's fingerprint image generates, i.e. inner set; The matrix that other user fingerprint image generates, i.e. outside set; The probability 0 or 1 of bit represents changeless level; Probability is 0 indicate without information, and probability is 1 expression bit information; But the probability perfection of little bit is exactly 0 or 1; So by the weight w calculating each bit, comprise weight inside w
1=1-η
intrawith external weights w
2=1-η
extra, and w=w
1× w
2; η
intra, η
extrarepresent the inside entropy of bit and outside entropy respectively;
The Bit String of N number of bit composition of the highest weight of step 8. is as the biological secret key B that will extract
orig; In order to identical biological secret key can be reconstructed, the positional information of N number of bit of the highest weight is stored, and be used as the mask B of the biological secret key of registered user
k-mask;
The encryption key k of step 9. λ-bits
origby obtaining a N number of Bit String K after RS algorithm for encryption
orig; By K
origwith B
origin conjunction with generation question blank Lookup
table; Storing queries table, deletes K
origwith B
orig;
Described biological secret key stage of living again is specific as follows:
Steps A. extract the fingerprint two dimensional image of inquiring user, fingerprint image normalization, does fingerprint image preprocessing, the bianry image of the fingerprint feature information that is maintained, and this bianry image is denoted as M
q;
Step B. authentication of users takes the step 1-step 7 of registration phase, obtains the Bit String of N number of bit composition of the highest weight; In conjunction with the mask B of the biological secret key of registered user
k-maskgenerate a Bit String, it can be used as the biological secret key B of authentication of users
mod;
Step C.B
modutilize Lookup
tableobtain the Bit String K of N-bits
mod; Utilize RS algorithm to K
moddeciphering obtains the Bit String k' of λ-bits
orig; If k'
origwith k
origequal, think that authentication of users and registered user are same, the biological secret key of registered user is successfully lived again; Otherwise, just think that authentication of users is illegal invader.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN103870810B (en) * | 2014-03-03 | 2017-02-08 | 杭州电子科技大学 | Method for generating human digital vein biometric key |
| US10069627B2 (en) * | 2015-07-02 | 2018-09-04 | Qualcomm Incorporated | Devices and methods for facilitating generation of cryptographic keys from a biometric |
| CN105844214B (en) * | 2016-03-02 | 2019-06-21 | 华南理工大学 | A kind of information fingerprint extracting method of the multipath depth coding based on bit space |
| SE1650416A1 (en) * | 2016-03-31 | 2017-10-01 | Fingerprint Cards Ab | Secure storage of fingerprint related elements |
| TW201822709A (en) * | 2016-12-30 | 2018-07-01 | 曦威科技股份有限公司 | Real-time heart rate detection method and real-time heart rate detection system therefor |
| 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 |
| CN110659535B (en) * | 2018-06-28 | 2022-05-31 | 本无链科技(深圳)有限公司 | Private key generation method and system based on fingerprint identification |
| CN109344593B (en) * | 2018-10-24 | 2021-01-26 | 三星（中国）半导体有限公司 | Biological information verification method, verification server and entry and verification client |
| CN110601853B (en) * | 2019-09-17 | 2021-05-11 | 腾讯科技（深圳）有限公司 | Block chain private key generation method and equipment |
| CN117176344B (en) * | 2023-10-30 | 2024-01-12 | 北京亚大通讯网络有限责任公司 | Fingerprint key generation method based on digital information acquisition |
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| US20090319772A1 (en) * | 2008-04-25 | 2009-12-24 | Netapp, Inc. | In-line content based security for data at rest in a network storage system |
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