CN110348840A - It is a kind of to exempt from close payment system using the improved small amount of biological identification technology - Google Patents
It is a kind of to exempt from close payment system using the improved small amount of biological identification technology Download PDFInfo
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- CN110348840A CN110348840A CN201910467491.3A CN201910467491A CN110348840A CN 110348840 A CN110348840 A CN 110348840A CN 201910467491 A CN201910467491 A CN 201910467491A CN 110348840 A CN110348840 A CN 110348840A
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- G06Q20/00—Payment architectures, schemes or protocols
- G06Q20/22—Payment schemes or models
- G06Q20/29—Payment schemes or models characterised by micropayments
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q20/00—Payment architectures, schemes or protocols
- G06Q20/38—Payment protocols; Details thereof
- G06Q20/40—Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check credit lines or negative lists
- G06Q20/401—Transaction verification
- G06Q20/4014—Identity check for transactions
- G06Q20/40145—Biometric identity checks
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Abstract
A kind of use improved small amount of biological identification technology of the invention exempts from close payment system, the biometric information of biometric information acquisition module acquisition is transferred to microprocessor after feature extraction, microprocessor carries out quality of evaluation analysis to biometric information, when meeting Mass accuracy, then financial services transactions center is sent to by transmission module.Each region gradient amplitude that the present invention is calculated separately out using gradient operator is simultaneously normalized, and the weight in four regions is respectively set, the quantization average value XP for calculating the boundary of four zonules, when meeting precision, same method calculates the quantized value X of four main featuresZL, when meeting precision, quality evaluation R is calculated, meets precision and determines that quality evaluation meets precision, improve the precision of quality evaluation judgement, wherein quantization average value XP, quantized value XZ, any one of quality evaluation R is when being unsatisfactory for precision, so that microprocessor is resurveyed biometric information immediately, improve the efficiency of quality evaluation judgement.
Description
Technical field
The present invention relates to bio-identification payment technology fields, especially a kind of to be exempted from using the improved small amount of biological identification technology
Close payment system.
Background technique
With the development of intellectualization times, using online payment, the mode of mobile-phone payment is more and more common, needs user defeated
Enter the payment cipher of 6 bit digitals or show two dimensional code to complete payment flow, paid in public using mobile phone, is easy
By stealing passwords such as pedestrians or brush two dimensional code is stolen, there are security risks.
In order to simplify user's operation, improve payment safety, there is the means of payment based on biological identification technology, such as refer to
Line identification, recognition of face, iris recognition, hand vein recognition etc. acquire biometric information by biometric information acquisition module
It is transferred to microprocessor, microprocessor is sent to financial services transactions center by transmission module, and financial services transactions will be biological
The biometric information prestored in identification information and database is matched, successful match, namely by verifying, by matching result
Microprocessor is re-send to through transmission module, microprocessor control allows payment terminals to carry out exempting from close transaction, if the biology of acquisition is known
There are major defects for other information quality, and when can make to be matched with the biometric information prestored in database, matching is not achieved
Requirement, feedback reminds again to microprocessor and resurveys, directly affect subsequent matched with biometric information in database
Efficiency, accuracy, therefore quality evaluation need to be carried out to the biometric information of acquisition, make to meet the biometric information of quality again
Financial services transactions center is sent to by transmission module.
Therefore the present invention provides a kind of new scheme to solve the problems, such as this.
Summary of the invention
In view of the deficienciess of the prior art, using biological identification technology improved purpose of the present invention is to provide a kind of
Small amount exempts from close payment system, can carry out quality evaluation to the biometric information of acquisition, make the biometric information for meeting quality
Financial services transactions center is sent to by transmission module again.
To achieve the goals above, the present invention is to realize by the following technical solutions: being adopted including biometric information
Collect module, microprocessor, transmission module, financial services transactions center, payment terminals, which is characterized in that the biometric information
The biometric information of acquisition module acquisition is transferred to microprocessor after feature extraction, microprocessor to biometric information into
The analysis of row quality of evaluation, when meeting Mass accuracy, is sent to financial services transactions center, financial services transactions by transmission module
The biometric information prestored in center and database is matched, and is exported matching result through transmission module and is sent to micro process
Device, microprocessor control whether that payment terminals is allowed to carry out exempting from close transaction:
Steps are as follows for the specific method to biometric information progress quality of evaluation analysis:
S1, the biometric information after obtaining feature extraction, and it is further divided into four zonule X1, X2, X3, X4, four
A zonule constitutes matrix form, forms eight edges between the zonule adjacent on column direction of being expert at；
S2 extracts eight edges of four zonules, the boundary of main feature using edge detection method；
S3 calculates the gradient magnitude of step 2 extraction using gradient operator, calculates separately out quantization average value XP, quantization
Value XZL；
S4, calculates quality evaluation R, and formula is R=XP × 40%+XZL× 60%；
S5 is converted into 1 or 0 expression and meets precision or be unsatisfactory for by quality evaluation R compared with threshold value 0.8, binaryzation；
S6, when meeting, microprocessor control is sent to financial services transactions center by transmission module, when being unsatisfactory for, makes
Microprocessor resurveys biometric information.
Due to the use of above technical scheme, the invention has the following advantages over the prior art:
1, four zonules, edge detection method are divided into eight of four zonules to the biometric information of acquisition
Edge, main feature boundary extract, for improving the accuracy of quality evaluation；
2, each region gradient amplitude X calculated separately out using gradient operator1A, A2A, A3A, A4A, and place is normalized
Reason, makes gradient magnitude between 0-1, and the weight that four regions are respectively set is 28%, 35%, 22%, 15%, calculates four
The quantization average value XP on the boundary of a zonule, compared with threshold value 0.3, when meeting precision, same method calculates four masters
Want the quantized value X of featureZL, compared with threshold value 0.5, when meeting precision, quality evaluation R is calculated, it is same full compared with threshold value 0.8
When sufficient precision, microprocessor controls again is sent to financial services transactions center by transmission module, improves quality evaluation judgement
Precision, wherein quantization average value XP, quantized value XZ, any one of quality evaluation R is when being unsatisfactory for precision, think highly of micro process immediately
New acquisition biometric information, improves the efficiency of quality evaluation judgement.
Detailed description of the invention
Fig. 1 is entire block diagram of the present invention.
Fig. 2 is overall step flow chart of the present invention.
Fig. 3 is that the present invention calculates quantization average value XP, quantized value XZLStep flow chart.
Specific embodiment
For the present invention aforementioned and other technology contents, feature and effect, in following cooperation with reference to figures 1 through attached drawing 3
To in the detailed description of embodiment, can clearly present.The structure content being previously mentioned in following embodiment is with specification
Attached drawing is reference.
In order to verify the feasibility of this method and the effect of actual use, analyzing examples are carried out below and verify this method.
Embodiment one, it is a kind of to exempt from close payment system, the biometric information using the improved small amount of biological identification technology
(fingerprint recognition, recognition of face, iris recognition, specifically can be by identifying accordingly for the biometric information of acquisition module acquisition
Module is identified) be transferred to after feature extraction microprocessor (can be single-chip microcontroller), microprocessor to biometric information into
The analysis of row quality of evaluation when meeting Mass accuracy, then passes through transmission module (can be GPRS module, 3G/4G communication network) and is sent to
Financial services transactions center (is such as moved) in financial services transactions center, the biology prestored in financial services transactions center and database
Identification information is matched, and is exported matching result through transmission module and is sent to microprocessor, microprocessor controls whether to allow to prop up
End is paid to carry out exempting from close transaction:
Steps are as follows for the specific method to biometric information progress quality of evaluation analysis:
S1, the biometric information after obtaining feature extraction, according to the characteristic variable of biometric information, (bio-identification is believed
Breath: structure type-circularity of fingerprint, face, iris, such as fingerprint are arch, account arch, left its shape, right dustpan, spiral shell shape, face
For standard, O shape, rectangular, triangle, diamond shape, cycle of sixty years type, elongated, iris be almond-eyed, slim eye, get deeply stuck in eye, thick convex eye, under
Hang eye, hypertropia, the size of fingerprint, face, iris, such as standard, large and small) classify, and it is further divided into four
A zonule X1, X2, X3, X4 (can be divided, face water by the primary biological feature of biometric information, fingerprint, iris cross
It is flat to divide), four zonules constitute matrix form, form eight edges between the zonule adjacent on column direction of being expert at；
S2 extracts eight edges of four zonules, the boundary of main feature using edge detection method (optional
It takes and is not susceptible to noise jamming in edge detection method, be able to detect that the Canny method at real weak edge extracts, have
The extraction process of body is the prior art, and this will not be detailed here)；
S3, eight edges of step 2 extraction are calculated using gradient operator, the gradient magnitude on boundary of main feature (can
Calculated using Roberts operator, specific calculating process is the prior art, and this will not be detailed here), it calculates separately out and quantifies
Average value XP, quantized value XZL；
S4 will quantify average value XP, quantized value XZLIt substitutes into quality evaluation R formula and calculates quality evaluation R, R=XP × 40%+
XZL× 60%；
S5 is converted into 1 or 0 expression and meets precision or be unsatisfactory for by quality evaluation R compared with threshold value 0.8, binaryzation；
S6, when meeting, microprocessor control is sent to financial services transactions center by transmission module, when being unsatisfactory for, makes
Microprocessor resurveys biometric information, and quality evaluation R is more than that microprocessor, which is reminded, to carry out when being still unsatisfactory for precision three times
Other kinds of biometric information acquisition, assessment.
Embodiment two, it is on the basis of example 1, described that gradient magnitude specific steps are calculated using gradient operator are as follows:
S31, using gradient operator calculate separately out each region gradient magnitude (Roberts operator can be used to be calculated,
Specific calculating process is the prior art, and this will not be detailed here) X1A, A2A, A3A, A4A, and be normalized, make gradient width
Value is between 0-1, and according to the importance of the influence quality evaluation precision in four regions, the weight that four regions are respectively set is
28%, 35%, 22%, 15%, calculate the quantization average value on the boundary of four zonules
XP=(X1L+X2L+X3L+X4L)/4, wherein X1L=X1A× 28%, X2L=A2A× 35%, X3L=A3A× 22%,
X4L=A4A× 15%；
S32 is converted into 1 or 0 expression and meets precision or discontented by quantization average value XP compared with threshold value 0.3, binaryzation
Foot executes step 6 when being unsatisfactory for；
When S33, S32 meet, the gradient magnitude X of main feature in four zonules is calculated separately out using gradient operator1B,
A2B, A3B, A4B, and be normalized, make gradient magnitude be respectively set between 0-1 four regions weight 20%,
40%, 20%, 20%, calculate the quantized value of four main features
XZL=(X1B× 20%+A2B× 40%+A3B× 20%+A4B× 20%)/100；
S34 is converted into 1 or 0 expression and meets precision or discontented by quantization average value XP compared with threshold value 0.5, binaryzation
Foot executes step 4 when meeting, step 6 is executed when being unsatisfactory for.
The present invention carry out using when, biometric information acquisition module acquisition biometric information mentioned through feature
Microprocessor is transferred to after taking, microprocessor carries out quality of evaluation analysis to biometric information, and specific quality of evaluation, which is analyzed, is,
Classified according to the characteristic variable of biometric information, and is further divided into four zonules X1, X2, X3, X4, four small
Region constitutes matrix form, forms eight edges between the zonule adjacent on column direction of being expert at；Using side edge detection
Method extracts eight edges of four zonules, the boundary of main feature；Eight sides extracted are calculated using gradient operator
The gradient magnitude X of edge1A, A2A, A3A, A4A, and it is normalized, makes gradient magnitude between 0-1, according to four region
The importance for influencing quality evaluation precision, the weight that four regions are respectively set is 28%, 35%, 22%, 15%, calculates four
The quantization average value XP=(X on the boundary of a zonule1L+X2L+X3L+X4L)/4, wherein X1L=X1A× 28%, X2L=A2A×
35%, X3L=A3A× 22%, X4L=A4A× 15%；By quantization average value XP compared with threshold value 0.3, binaryzation, it is converted into 1 or 0
Expression meets precision or is unsatisfactory for, and when being unsatisfactory for, so that microprocessor is resurveyed biometric information immediately；When meeting, use
Gradient operator calculates separately out the gradient magnitude X of main feature in four zonules1B, A2B, A3B, A4B, and place is normalized
Reason, makes gradient magnitude that the weight 20%, 40%, 20%, 20% in four regions be respectively set between 0-1, calculates four
The quantized value X of main featureZL=(X1B× 20%+A2B× 40%+A3B× 20%+A4B× 20%)/100；It will quantify average value
XP is compared with threshold value 0.5, binaryzation, when being unsatisfactory for, so that microprocessor is resurveyed biometric information immediately；It, will when meeting
Quantify average value XP, quantized value XZLIt substitutes into quality evaluation R formula and calculates quality evaluation R, R=XP × 40%+XZL× 60%；It will
Quality evaluation R is compared with threshold value 0.8, binaryzation, and when meeting, microprocessor control is sent to financial service by transmission module and hands over
Easy center when being unsatisfactory for, makes microprocessor resurvey biometric information immediately, and quality evaluation R is more than still to be unsatisfactory for three times
When precision, microprocessor, which is reminded, carries out other kinds of biometric information acquisition, assessment.
Claims (4)
1. a kind of exempt from close payment system using the improved small amount of biological identification technology, including it is biometric information acquisition module, micro-
Processor, transmission module, financial services transactions center, payment terminals, which is characterized in that the biometric information acquisition module is adopted
The biometric information of collection is transferred to microprocessor after feature extraction, and microprocessor carries out quality of evaluation to biometric information
Analysis, when meeting Mass accuracy, is sent to financial services transactions center, financial services transactions center and data by transmission module
The biometric information prestored in library is matched, and is exported matching result through transmission module and is sent to microprocessor, microprocessor
It controls whether that payment terminals is allowed to carry out exempting from close transaction:
Steps are as follows for the specific method to biometric information progress quality of evaluation analysis:
S1, the biometric information after obtaining feature extraction, and it is further divided into four zonule X1, X2, X3, X4, four small
Region constitutes matrix form, forms eight edges between the zonule adjacent on column direction of being expert at；
S2 extracts eight edges of four zonules, the boundary of main feature using edge detection method；
S3 calculates the gradient magnitude of step 2 extraction using gradient operator, calculates separately out quantization average value XP, quantized value XZL；
S4, calculates quality evaluation R, and formula is R=XP × 40%+XZL× 60%；
S5 is converted into 1 or 0 expression and meets precision or be unsatisfactory for by quality evaluation R compared with threshold value 0.8, binaryzation；
S6, when meeting, microprocessor control is sent to financial services transactions center by transmission module, when being unsatisfactory for, makes micro- place
Reason device resurveys biometric information.
2. a kind of use improved small amount of biological identification technology according to claim 1 exempts from close payment system, feature exists
In the step 3 calculates the gradient magnitude of step 2 extraction using gradient operator specifically:
S31 calculates separately out the gradient magnitude X in each region using gradient operator1A, A2A, A3A, A4A, and be normalized,
Make gradient magnitude between 0-1, the weight that four regions are respectively set is 28%, 35%, 22%, 15%, is calculated four small
The quantization average value on the boundary in region
XP=(X1L+X2L+X3L+X4L)/4, wherein X1L=X1A× 28%, X2L=A2A× 35%, X3L=A3A× 22%, X4L=
A4A× 15%；
S32 is converted into 1 or 0 expression and meets precision or be unsatisfactory for, no by quantization average value XP compared with threshold value 0.3, binaryzation
Step 6 is executed when meeting；
When S33, S32 meet, the gradient magnitude X of main feature in four zonules is calculated separately out using gradient operator1B, A2B,
A3B, A4B, and be normalized, make gradient magnitude be respectively set between 0-1 four regions weight 20%, 40%,
20%, 20%, calculate the quantized value of four main features
XZL=(X1B× 20%+A2B× 40%+A3B× 20%+A4B× 20%)/100；
S34 is converted into 1 or 0 expression and meets precision or be unsatisfactory for by quantization average value XP compared with threshold value 0.5, binaryzation, full
Step 4 is executed when sufficient, and step 6 is executed when being unsatisfactory for.
3. a kind of use improved small amount of biological identification technology according to claim 1 exempts from close payment system, feature exists
In quality evaluation R is more than when being still unsatisfactory for precision three times in the step S5, and microprocessor, which is reminded, carries out other kinds of biology
Identification information acquisition, assessment.
4. according to claim 1, a kind of described in 3 exempt from close payment system, feature using the improved small amount of biological identification technology
It is, the biometric information includes fingerprint recognition, recognition of face, iris recognition.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1543908A (en) * | 2003-11-18 | 2004-11-10 | 清华大学 | Iris image collecting method |
| US20100246973A1 (en) * | 2009-03-26 | 2010-09-30 | Canon Kabushiki Kaisha | Image processing apparatus and image processing method |
| CN105354835A (en) * | 2015-10-16 | 2016-02-24 | 浙江工业大学 | Method for evaluating medical image quality in combination with phase consistency, gradient magnitude and structural prominence |
| CN107392607A (en) * | 2017-07-05 | 2017-11-24 | 佛山杰致信息科技有限公司 | Payment system |
| CN107507166A (en) * | 2017-07-21 | 2017-12-22 | 华侨大学 | It is a kind of based on support vector regression without refer to screen image quality measure method |
-
2019
- 2019-05-30 CN CN201910467491.3A patent/CN110348840B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1543908A (en) * | 2003-11-18 | 2004-11-10 | 清华大学 | Iris image collecting method |
| US20100246973A1 (en) * | 2009-03-26 | 2010-09-30 | Canon Kabushiki Kaisha | Image processing apparatus and image processing method |
| CN105354835A (en) * | 2015-10-16 | 2016-02-24 | 浙江工业大学 | Method for evaluating medical image quality in combination with phase consistency, gradient magnitude and structural prominence |
| CN107392607A (en) * | 2017-07-05 | 2017-11-24 | 佛山杰致信息科技有限公司 | Payment system |
| CN107507166A (en) * | 2017-07-21 | 2017-12-22 | 华侨大学 | It is a kind of based on support vector regression without refer to screen image quality measure method |
Non-Patent Citations (1)
| Title |
|---|
| 王虹等: "一种基于梯度幅度值的图像质量客观评价方法", 《光学与光电技术》 * |
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