CN108416882A - Portable fingerprint identification device - Google Patents
Portable fingerprint identification device Download PDFInfo
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- CN108416882A CN108416882A CN201810272016.6A CN201810272016A CN108416882A CN 108416882 A CN108416882 A CN 108416882A CN 201810272016 A CN201810272016 A CN 201810272016A CN 108416882 A CN108416882 A CN 108416882A
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- G—PHYSICS
- G07—CHECKING-DEVICES
- G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
- G07C9/00—Individual registration on entry or exit
- G07C9/30—Individual registration on entry or exit not involving the use of a pass
- G07C9/32—Individual registration on entry or exit not involving the use of a pass in combination with an identity check
- G07C9/37—Individual registration on entry or exit not involving the use of a pass in combination with an identity check using biometric data, e.g. fingerprints, iris scans or voice recognition
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- G—PHYSICS
- G06—COMPUTING OR CALCULATING; 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
- G06V40/13—Sensors therefor
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- G—PHYSICS
- G06—COMPUTING OR CALCULATING; 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
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Abstract
In order to improve the recognition accuracy to real human body characteristic parameter, improve the identification safety of gate inhibition's unit, the present invention provides a kind of portable fingerprint identification devices, fingerprint recognition for gate inhibition's unit with identification sensor, including local, long-range two parts are identified respectively and comprehensive descision, the prompt of None- identified is provided for the false medium being identified, the prompt of identification success or failure is provided for the true medium that can be identified, the influence of the drift generated in temperature-rise period to gate inhibition's unit identification sensor of itself by way of heating in identification process, which is not given, to be eliminated, but it energetically pays attention to and has carried out the comparison that square root calculates this experience calculation in the calculating of testing result twice, to be omitted in analysis and calculating, square root when calculating v is eliminated in calculating.Through experiment, 43% or so is improved for the recognition accuracy of false body to be identified.
Description
Technical field
The invention belongs to security protections and technical field of image processing, and in particular to a kind of portable fingerprint identification device.
Background technology
Living things feature recognition is a kind of new identity identifying technology.In actual life, everyone has with other people not
Same unique biological characteristic.With the development of computer technology, people can extract the biological information of itself, than
Such as face, fingerprint refer to vein, iris, vocal print.The technology that this physical trait by people carries out identification is referred to as giving birth to
Object feature identification technique.
Recently, due to smart phone and the development of various movements and wearable device, it is used for the technology of safety certification
Importance is increasing.In the technology of this form, fingerprint identification technology is because of the convenience of height, safety and economic feasibility
And it is widely used.It, can be by obtaining the fingerprint image of user via identification sensor and will obtain in general fingerprint recognition
The fingerprint image taken is compared to execute user authentication or verification with pre-registered fingerprint image.When elaborate falseness
When fingerprint pattern is input into sensor, fingerprint identification device is possibly can not be by false fingerprint pattern and real fingerprint image
Case is distinguished, it is thus possible to which false fingerprint pattern is identified as biological fingerprint.For example, when engrave fingerprint material (such as rubber,
Silica gel, gelatin, epoxy resin and latex) with sensor contacted when, the fingerprint being engraved on this material may be identified as
Mankind's fingerprint.Therefore, identity is identified using only single biological characteristic (single mode biological characteristic) and is easy to be forged and cheats,
Such as single fingerprint picture is easy to be forged, and easily imitates and steals, and fingerprint recognition is initially used in crime field and makes
Some users, which exist, contradicts psychology, single fingerprint (referring to vein etc.) feature of some user cannot collect effectively clearly
Image, so that single mode biological characteristic system is had in practical applications must limitation.And multi-modal biological characteristic system
Safety coefficient is improved, the risk that identifying system is broken is reduced, more there is applicability.
Invention content
In view of the above analysis knowledge of gate inhibition's unit is improved in order to improve the recognition accuracy to real human body characteristic parameter
Other safety, the present invention provides a kind of portable fingerprint identification device, the finger for gate inhibition's unit with identification sensor
Line identification, including：
First fingerprint image obtains and storage unit, for by identification sensor the first fingerprint image of acquisition, described the
One fingerprint image includes first part and second part；
First local analytic unit carries out the first local analytics for the first part to the first fingerprint image, obtains
To the first local analysis result；
First remote analysis unit carries out the first remote analysis for the second part to the first fingerprint image, obtains
To the first remote analysis result；
First judging unit, for when the first local analysis result meets less than the First Eigenvalue and is more than Second Eigenvalue
When, fingerprint is detected after heating up by gate inhibition's unit again and obtains the second fingerprint image, while detecting finger to be identified and knowing
The polar coordinates angle theta of the identification plane of individual sensormn∈ [0,1], second fingerprint image include Part III and the 4th
Point；Otherwise prompt None- identified is without obtaining the second fingerprint image；
Second local analytics unit carries out the second local analytics for the Part III to the second fingerprint image, obtains
To the second local analytics result；
Second remote analysis unit carries out the second remote analysis for the Part IV to the second fingerprint image, obtains
To the second remote analysis result；According to the first local analysis result, the second local analytics result, the first remote analysis result and the
Two remote analysis are as a result, determine the fingerprint recognition result of gate inhibition's unit.
Further, the described first local analytic unit includes：
First neighborhood determination unit is used for centered on the image geometry center of first part, preset length is radius
Data are as pending first part's image data in neighborhood；
First pretreatment unit is obtained for carrying out binaryzation and noise reduction process to pending first part's image data
To data set O；
Encryption unit obtains data set O ' for the data set O to be carried out symmetry encryption；
The First Eigenvalue acquiring unit, for data set O ' and preset reference finger print data M to be handled as follows：To pre-
If reference fingerprint data M carries out binary conversion treatment and obtains M '；The diagonal matrix of data set O ' is calculated；According to the diagonal matrix
Exponent number intercepts the intermediary matrix P of same exponent number since the value of first, the upper left corners the data set M '；Calculate intermediary matrix P with
The characteristic value K1 for the matrix that data set O ' multiplication crosses obtain.
Further, first remote analysis unit includes：
First transmission unit is transferred to remote server for the second part to first fingerprint image；
First hash value determination unit, for carrying out Hash fortune to the data of the second part received in remote server
Calculation obtains the first hash value corresponding with the second part；
Second pretreatment unit, for M pairs of matrix data K corresponding to the second part and preset reference finger print data
The matrix S answered is multiplied, and one that wherein exponent number is smaller in the two matrixes obtains matrix K with diagonal matrix polishing after multiplication '；
Third pretreatment unit, for centered on the gray scale barycenter of the image of the second part, the preset length
For radius, the image data matrix L of second part is obtained, matrix L is projected to obtain matrix L ', by matrix L ' and matrix K '
Multiplication cross is carried out, wherein terminates smaller one in the two matrixes with diagonal matrix polishing, matrix Q is obtained after multiplication cross；
Second Eigenvalue acquiring unit is used for the characteristic value F of calculating matrix Q.
Further, the second local analytics unit includes：
Second neighborhood determination unit is used for centered on the image geometry center of Part III, preset length is radius
Data are as pending Part III image data in neighborhood；
Third pretreatment unit is obtained for carrying out binaryzation and noise reduction process to pending Part III image data
To data set R；
Second encryption unit, for the data set R to be carried out symmetry encryption by key of the average value of data set O,
Obtain data set R '；
Third feature value acquiring unit, for data set R ' and preset reference finger print data M to be handled as follows：To pre-
If reference fingerprint data M carries out binary conversion treatment and obtains M '；The diagonal matrix of data set R ' is calculated；According to the diagonal matrix
Exponent number intercepts the intermediary matrix T of same exponent number since the value of first, the upper left corners the data set M '；Calculate intermediary matrix T with
The characteristic value K2 for the matrix that data set R ' multiplication crosses obtain.
Further, second remote analysis unit includes：
Second transmission unit is transferred to remote server for the Part IV to second fingerprint image；
Fourth feature value acquiring unit, for setting
Wherein, kmnIndicate the gray value of the image pixel (m, n) of Part IV；
Greyscale transformation Tr () is carried out to the image of Part IV and obtains θ 'mn：
N, N are the natural number more than 2；
Wherein
Wherein θcFor Boundary Recognition threshold value, is determined by fingerprint Boundary Recognition empirical value, then calculated as follows again：
Transformation coefficient k 'mn=(K-1) θmn
Image boundary is extracted, the image boundary matrix extracted is
Edges=[k 'mn]
Calculate the characteristic value E of the image boundary matrix；
Second hash value determination unit, for carrying out Hash fortune to the data of the Part IV received in remote server
Calculation obtains the second hash value corresponding with the Part IV；
Third hash value determination unit obtains third hash value for carrying out Hash operations to preset reference finger print data.
Further, second remote analysis unit further includes recognition result determination unit, for determining the gate inhibition
The fingerprint recognition of unit is as a result, include：
Similarity calculated, for the first hash value to be added to the second hash value and carried out consistency Hash operations,
Operation result and third hash value are calculated into degree a similar to each other using MinHash algorithms；
Recognition result determination unit, for determiningWhether preset knowledge is less than
Other threshold value, when less than when prompt identify successfully, otherwise prompt recognition failures.
Technical scheme of the present invention has the following advantages：
The portable fingerprint identification device of the present invention can be based on long-range and factor calculating, identification are locally identified respectively
The influence of the drift generated in temperature-rise period to gate inhibition's unit identification sensor of itself by way of heating in the process is simultaneously
It does not give and eliminates, but energetically pay attention to and carried out square root in the calculating of testing result twice and calculate this experience
The comparison of calculation, to be omitted (square root when calculating v is eliminated in calculating) warp in analysis and calculating
Experiment improves 43% or so for the recognition accuracy of false body to be identified.
Description of the drawings
Fig. 1 shows the composition frame chart of the apparatus according to the invention.
Specific implementation mode
As shown in Figure 1, portable fingerprint identification device according to a preferred embodiment of the invention, for having identification sensing
The fingerprint recognition of gate inhibition's unit of device, including：
First fingerprint image obtains and storage unit, for by identification sensor the first fingerprint image of acquisition, described the
One fingerprint image includes first part and second part；
First local analytic unit carries out the first local analytics for the first part to the first fingerprint image, obtains
To the first local analysis result；
First remote analysis unit carries out the first remote analysis for the second part to the first fingerprint image, obtains
To the first remote analysis result；
First judging unit, for when the first local analysis result meets less than the First Eigenvalue and is more than Second Eigenvalue
When, fingerprint is detected after heating up by gate inhibition's unit again and obtains the second fingerprint image, while detecting finger to be identified and knowing
The polar coordinates angle theta of the identification plane of individual sensormn∈ [0,1], second fingerprint image include Part III and the 4th
Point；Otherwise prompt None- identified is without obtaining the second fingerprint image；
Second local analytics unit carries out the second local analytics for the Part III to the second fingerprint image, obtains
To the second local analytics result；
Second remote analysis unit carries out the second remote analysis for the Part IV to the second fingerprint image, obtains
To the second remote analysis result；According to the first local analysis result, the second local analytics result, the first remote analysis result and the
Two remote analysis are as a result, determine the fingerprint recognition result of gate inhibition's unit.
Further, the described first local analytic unit includes：
First neighborhood determination unit is used for centered on the image geometry center of first part, preset length is radius
Data are as pending first part's image data in neighborhood；
First pretreatment unit is obtained for carrying out binaryzation and noise reduction process to pending first part's image data
To data set O；
Encryption unit obtains data set O ' for the data set O to be carried out symmetry encryption；
The First Eigenvalue acquiring unit, for data set O ' and preset reference finger print data M to be handled as follows：To pre-
If reference fingerprint data M carries out binary conversion treatment and obtains M '；The diagonal matrix of data set O ' is calculated；According to the diagonal matrix
Exponent number intercepts the intermediary matrix P of same exponent number since the value of first, the upper left corners the data set M '；Calculate intermediary matrix P with
The characteristic value K1 for the matrix that data set O ' multiplication crosses obtain.
Further, first remote analysis unit includes：
First transmission unit is transferred to remote server for the second part to first fingerprint image；
First hash value determination unit, for carrying out Hash fortune to the data of the second part received in remote server
Calculation obtains the first hash value corresponding with the second part；
Second pretreatment unit, for M pairs of matrix data K corresponding to the second part and preset reference finger print data
The matrix S answered is multiplied, and one that wherein exponent number is smaller in the two matrixes obtains matrix K with diagonal matrix polishing after multiplication '；
Third pretreatment unit, for centered on the gray scale barycenter of the image of the second part, the preset length
For radius, the image data matrix L of second part is obtained, matrix L is projected to obtain matrix L ', by matrix L ' and matrix K '
Multiplication cross is carried out, wherein terminates smaller one in the two matrixes with diagonal matrix polishing, matrix Q is obtained after multiplication cross；
Second Eigenvalue acquiring unit is used for the characteristic value F of calculating matrix Q.
Further, the second local analytics unit includes：
Second neighborhood determination unit is used for centered on the image geometry center of Part III, preset length is radius
Data are as pending Part III image data in neighborhood；
Third pretreatment unit is obtained for carrying out binaryzation and noise reduction process to pending Part III image data
To data set R；
Second encryption unit, for the data set R to be carried out symmetry encryption by key of the average value of data set O,
Obtain data set R '；
Third feature value acquiring unit, for data set R ' and preset reference finger print data M to be handled as follows：To pre-
If reference fingerprint data M carries out binary conversion treatment and obtains M '；The diagonal matrix of data set R ' is calculated；According to the diagonal matrix
Exponent number intercepts the intermediary matrix T of same exponent number since the value of first, the upper left corners the data set M '；Calculate intermediary matrix T with
The characteristic value K2 for the matrix that data set R ' multiplication crosses obtain.
Further, second remote analysis unit includes：
Second transmission unit is transferred to remote server for the Part IV to second fingerprint image；
Fourth feature value acquiring unit, for setting
Wherein, kmnIndicate the gray value of the image pixel (m, n) of Part IV；
Greyscale transformation Tr () is carried out to the image of Part IV and obtains θ 'mn：
N is the natural number more than 2；
Wherein
Wherein θcFor Boundary Recognition threshold value, is determined by fingerprint Boundary Recognition empirical value, then calculated as follows again：
Transformation coefficient k 'mn=(K-1) θmn
Image boundary is extracted, the image boundary matrix extracted is
Edges=[k 'mn]
Calculate the characteristic value E of the image boundary matrix；
Second hash value determination unit, for carrying out Hash fortune to the data of the Part IV received in remote server
Calculation obtains the second hash value corresponding with the Part IV；
Third hash value determination unit obtains third hash value for carrying out Hash operations to preset reference finger print data.
Further, second remote analysis unit further includes recognition result determination unit, for determining the gate inhibition
The fingerprint recognition of unit is as a result, include：
Similarity calculated, for the first hash value to be added to the second hash value and carried out consistency Hash operations,
Operation result and third hash value are calculated into degree a similar to each other using MinHash algorithms；
Recognition result determination unit, for determiningWhether preset knowledge is less than
Other threshold value, when less than when prompt identify successfully, otherwise prompt recognition failures.
The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the invention, all essences in the present invention
All any modification, equivalent and improvement etc., should all be included in the protection scope of the present invention made by within refreshing and principle.
Claims (6)
1. a kind of portable fingerprint identification device, for the fingerprint recognition of gate inhibition's unit with identification sensor, including：
First fingerprint image obtains and storage unit, and for obtaining the first fingerprint image by identification sensor, described first refers to
Print image includes first part and second part；
First local analytic unit carries out the first local analytics for the first part to the first fingerprint image, obtains the
One local analytics result；
First remote analysis unit obtains for carrying out the first remote analysis to the second part of the first fingerprint image
One remote analysis result；
First judging unit is used for when the first local analysis result meets less than the First Eigenvalue and is more than Second Eigenvalue,
Again fingerprint is detected after heating up by gate inhibition's unit and obtains the second fingerprint image, while being detected finger to be identified and being passed with identification
The polar coordinates angle theta of the identification plane of sensormn∈ [0,1], second fingerprint image includes Part III and Part IV；It is no
Then prompt None- identified without obtaining the second fingerprint image；
Second local analytics unit obtains for carrying out the second local analytics to the Part III of the second fingerprint image
Two local analytics results；
Second remote analysis unit obtains for carrying out the second remote analysis to the Part IV of the second fingerprint image
Two remote analysis results；It is remote according to the first local analysis result, the second local analytics result, the first remote analysis result and second
Journey analysis result determines the fingerprint recognition result of gate inhibition's unit.
2. the apparatus according to claim 1, which is characterized in that the first local analytic unit includes：
First neighborhood determination unit, for centered on the image geometry center of first part, preset length for radius neighborhood
Interior data are as pending first part's image data；
First pretreatment unit is counted for carrying out binaryzation and noise reduction process to pending first part's image data
According to collection O；
Encryption unit obtains data set O ' for the data set O to be carried out symmetry encryption；
The First Eigenvalue acquiring unit, for data set O ' and preset reference finger print data M to be handled as follows：To default ginseng
It examines finger print data M progress binary conversion treatment and obtains M '；The diagonal matrix of data set O ' is calculated；According to the rank of the diagonal matrix
Number, intercepts the intermediary matrix P of same exponent number since the value of first, the upper left corners the data set M '；Calculate intermediary matrix P and number
According to the characteristic value K1 for the matrix that collection O ' multiplication crosses obtain.
3. the apparatus of claim 2, which is characterized in that first remote analysis unit includes：
First transmission unit is transferred to remote server for the second part to first fingerprint image；
First hash value determination unit is obtained for carrying out Hash operations to the data of the second part received in remote server
To the first hash value corresponding with the second part；
Second pretreatment unit, it is corresponding with preset reference finger print data M for matrix data K corresponding to the second part
Matrix S is multiplied, and one that wherein exponent number is smaller in the two matrixes obtains matrix K with diagonal matrix polishing after multiplication '；
Third pretreatment unit is used for centered on the gray scale barycenter of the image of the second part, the preset length is half
Diameter obtains the image data matrix L of second part, is projected to obtain matrix L to matrix L ', by matrix L ' and matrix K ' carry out
Multiplication cross wherein terminates smaller one with diagonal matrix polishing, matrix Q is obtained after multiplication cross in the two matrixes；
Second Eigenvalue acquiring unit is used for the characteristic value F of calculating matrix Q.
4. device according to claim 3, which is characterized in that the second local analytics unit includes：
Second neighborhood determination unit, for centered on the image geometry center of Part III, preset length for radius neighborhood
Interior data are as pending Part III image data；
Third pretreatment unit is counted for carrying out binaryzation and noise reduction process to pending Part III image data
According to collection R；
Second encryption unit is obtained for the data set R to be carried out symmetry encryption by key of the average value of data set O
Data set R '；
Third feature value acquiring unit, for data set R ' and preset reference finger print data M to be handled as follows：To default ginseng
It examines finger print data M progress binary conversion treatment and obtains M '；The diagonal matrix of data set R ' is calculated；According to the rank of the diagonal matrix
Number, intercepts the intermediary matrix T of same exponent number since the value of first, the upper left corners the data set M '；Calculate intermediary matrix T and number
According to the characteristic value K2 for the matrix that collection R ' multiplication crosses obtain.
5. device according to claim 4, which is characterized in that second remote analysis unit includes：
Second transmission unit is transferred to remote server for the Part IV to second fingerprint image；
Fourth feature value acquiring unit, for setting
Wherein, kmnIndicate the gray value of the image pixel (m, n) of Part IV；
Greyscale transformation Tr () is carried out to the image of Part IV and obtains θ 'mn：
R=2 ..., N, N are the natural number more than 2；
Wherein
Wherein θcFor Boundary Recognition threshold value, is determined by fingerprint Boundary Recognition empirical value, then calculated as follows again：
Transformation coefficient k 'mn=(K-1) θmn
Image boundary is extracted, the image boundary matrix extracted is
Edges=[k 'mn]
Calculate the characteristic value E of the image boundary matrix；
Second hash value determination unit is obtained for carrying out Hash operations to the data of the Part IV received in remote server
To the second hash value corresponding with the Part IV；
Third hash value determination unit obtains third hash value for carrying out Hash operations to preset reference finger print data.
6. device according to claim 5, which is characterized in that second remote analysis unit further includes that recognition result is true
Order member, for determining the fingerprint recognition of gate inhibition's unit as a result, including：
Similarity calculated will be transported for the first hash value to be added to the second hash value and carried out consistency Hash operations
It calculates result and calculates degree a similar to each other using MinHash algorithms with third hash value；
Recognition result determination unit, for determiningWhether preset identification threshold is less than
Value, when less than when prompt identify successfully, otherwise prompt recognition failures.
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| CN201810272016.6A CN108416882B (en) | 2018-03-29 | 2018-03-29 | Portable fingerprint identification device |
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| CN201810272016.6A CN108416882B (en) | 2018-03-29 | 2018-03-29 | Portable fingerprint identification device |
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| CN108416882B CN108416882B (en) | 2021-05-18 |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109376721A (en) * | 2018-12-19 | 2019-02-22 | 江苏恒宝智能系统技术有限公司 | Method for extracting fingerprint feature, fingerprint register method, fingerprint identification method and device |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7039224B2 (en) * | 2002-04-29 | 2006-05-02 | Activcard Ireland Limited | Method and device for preventing false acceptance of latent fingerprint images |
| US7333638B2 (en) * | 2005-01-18 | 2008-02-19 | Lenovo (Singapore) Pte Ltd. | Minutiae fingerprint transactions |
| CN101149788A (en) * | 2006-09-18 | 2008-03-26 | 陈亘朝 | Double window fingerprint identification technology |
| CN101557428A (en) * | 2008-04-08 | 2009-10-14 | 中兴通讯股份有限公司 | Data card |
| CN103400436A (en) * | 2013-08-17 | 2013-11-20 | 慈溪市科创电子科技有限公司 | Access control system and control method thereof |
| CN105408821A (en) * | 2013-07-25 | 2016-03-16 | 尤尼卡技术股份有限公司 | Method and device for verifying diffractive elements |
| CN107316358A (en) * | 2017-06-21 | 2017-11-03 | 合肥市旺友门窗有限公司 | A kind of intelligent access control system |
| CN107481381A (en) * | 2017-09-13 | 2017-12-15 | 罗洪翠 | The method of work of the fingerprint control circuit of remote anti-theft door |
| CN107680210A (en) * | 2017-09-13 | 2018-02-09 | 罗洪翠 | The method of work of the fingerprint recognition circuit of remote anti-theft door |
-
2018
- 2018-03-29 CN CN201810272016.6A patent/CN108416882B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7039224B2 (en) * | 2002-04-29 | 2006-05-02 | Activcard Ireland Limited | Method and device for preventing false acceptance of latent fingerprint images |
| US7333638B2 (en) * | 2005-01-18 | 2008-02-19 | Lenovo (Singapore) Pte Ltd. | Minutiae fingerprint transactions |
| CN101149788A (en) * | 2006-09-18 | 2008-03-26 | 陈亘朝 | Double window fingerprint identification technology |
| CN101557428A (en) * | 2008-04-08 | 2009-10-14 | 中兴通讯股份有限公司 | Data card |
| CN105408821A (en) * | 2013-07-25 | 2016-03-16 | 尤尼卡技术股份有限公司 | Method and device for verifying diffractive elements |
| CN103400436A (en) * | 2013-08-17 | 2013-11-20 | 慈溪市科创电子科技有限公司 | Access control system and control method thereof |
| CN107316358A (en) * | 2017-06-21 | 2017-11-03 | 合肥市旺友门窗有限公司 | A kind of intelligent access control system |
| CN107481381A (en) * | 2017-09-13 | 2017-12-15 | 罗洪翠 | The method of work of the fingerprint control circuit of remote anti-theft door |
| CN107680210A (en) * | 2017-09-13 | 2018-02-09 | 罗洪翠 | The method of work of the fingerprint recognition circuit of remote anti-theft door |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109376721A (en) * | 2018-12-19 | 2019-02-22 | 江苏恒宝智能系统技术有限公司 | Method for extracting fingerprint feature, fingerprint register method, fingerprint identification method and device |
| CN109376721B (en) * | 2018-12-19 | 2020-01-24 | 江苏恒宝智能系统技术有限公司 | Fingerprint feature extraction method, fingerprint registration method, fingerprint identification method and device |
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| Publication number | Publication date |
|---|---|
| CN108416882B (en) | 2021-05-18 |
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